{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oxfdYFvseY6"
      },
      "source": [
        "## 1. Dataset Acquisition\n",
        "\n",
        "The APTOS 2019 dataset contains retinal fundus images labelled according to five\n",
        "diabetic retinopathy severity levels:\n",
        "\n",
        "- 0: No DR\n",
        "- 1: Mild\n",
        "- 2: Moderate\n",
        "- 3: Severe\n",
        "- 4: Proliferative DR\n",
        "\n",
        "Before model development, the dataset structure, missing images, duplicate\n",
        "identifiers, and class distribution are examined."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "-d0GP93-qyFK",
        "outputId": "04c442b6-00d7-4359-d5b7-bad2deb30fc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Device: cuda\n",
            "GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition\n",
            "\n",
            "Project directory: /content/drive/MyDrive/Final_Hybrid_DR_Framework\n",
            "Figures directory: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures\n",
            "Checkpoints directory: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Checkpoints\n",
            "Features directory: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Extracted_Features\n",
            "Results directory: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Results\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 1: Environment Setup\n",
        "# =========================================================\n",
        "\n",
        "!pip -q install opencv-python-headless joblib\n",
        "\n",
        "import os\n",
        "import random\n",
        "import zipfile\n",
        "from pathlib import Path\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms, models\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    ConfusionMatrixDisplay,\n",
        "    roc_auc_score,\n",
        "    roc_curve,\n",
        "    auc\n",
        ")\n",
        "\n",
        "from google.colab import drive\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Mount Google Drive\n",
        "# ---------------------------------------------------------\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Reproducibility\n",
        "# ---------------------------------------------------------\n",
        "SEED = 42\n",
        "\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "\n",
        "# Faster GPU execution\n",
        "torch.backends.cudnn.benchmark = True\n",
        "\n",
        "device = torch.device(\n",
        "    \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        ")\n",
        "\n",
        "use_amp = torch.cuda.is_available()\n",
        "\n",
        "print(\"Device:\", device)\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    print(\"GPU:\", torch.cuda.get_device_name(0))\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Global experiment settings\n",
        "# ---------------------------------------------------------\n",
        "IMG_SIZE = 300\n",
        "BATCH_SIZE = 32\n",
        "NUM_WORKERS = 4\n",
        "NUM_CLASSES = 5\n",
        "\n",
        "CLASS_NAMES = [\n",
        "    \"No DR\",\n",
        "    \"Mild\",\n",
        "    \"Moderate\",\n",
        "    \"Severe\",\n",
        "    \"Proliferative DR\"\n",
        "]\n",
        "\n",
        "CLASS_MAP = {\n",
        "    0: \"No DR\",\n",
        "    1: \"Mild\",\n",
        "    2: \"Moderate\",\n",
        "    3: \"Severe\",\n",
        "    4: \"Proliferative DR\"\n",
        "}\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Main output directory\n",
        "# ---------------------------------------------------------\n",
        "PROJECT_DIR = Path(\n",
        "    \"/content/drive/MyDrive/Final_Hybrid_DR_Framework\"\n",
        ")\n",
        "\n",
        "FIGURES_DIR = PROJECT_DIR / \"Figures\"\n",
        "CHECKPOINTS_DIR = PROJECT_DIR / \"Checkpoints\"\n",
        "FEATURES_DIR = PROJECT_DIR / \"Extracted_Features\"\n",
        "RESULTS_DIR = PROJECT_DIR / \"Results\"\n",
        "\n",
        "for directory in [\n",
        "    PROJECT_DIR,\n",
        "    FIGURES_DIR,\n",
        "    CHECKPOINTS_DIR,\n",
        "    FEATURES_DIR,\n",
        "    RESULTS_DIR\n",
        "]:\n",
        "    directory.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "print(\"\\nProject directory:\", PROJECT_DIR)\n",
        "print(\"Figures directory:\", FIGURES_DIR)\n",
        "print(\"Checkpoints directory:\", CHECKPOINTS_DIR)\n",
        "print(\"Features directory:\", FEATURES_DIR)\n",
        "print(\"Results directory:\", RESULTS_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip -q install opencv-python-headless joblib\n",
        "\n",
        "import os\n",
        "import random\n",
        "import zipfile\n",
        "from pathlib import Path\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms, models\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    ConfusionMatrixDisplay,\n",
        "    roc_auc_score,\n",
        "    roc_curve,\n",
        "    auc\n",
        ")\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "SEED = 42\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "torch.backends.cudnn.benchmark = True\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "use_amp = torch.cuda.is_available()\n",
        "\n",
        "IMG_SIZE = 300\n",
        "BATCH_SIZE = 32\n",
        "NUM_WORKERS = 4\n",
        "NUM_CLASSES = 5\n",
        "\n",
        "CLASS_NAMES = [\"No DR\", \"Mild\", \"Moderate\", \"Severe\", \"Proliferative DR\"]\n",
        "CLASS_MAP = {0: \"No DR\", 1: \"Mild\", 2: \"Moderate\", 3: \"Severe\", 4: \"Proliferative DR\"}\n",
        "\n",
        "PROJECT_DIR = Path(\"/content/drive/MyDrive/Final_Hybrid_DR_Framework\")\n",
        "FIGURES_DIR = PROJECT_DIR / \"Figures\"\n",
        "CHECKPOINTS_DIR = PROJECT_DIR / \"Checkpoints\"\n",
        "FEATURES_DIR = PROJECT_DIR / \"Extracted_Features\"\n",
        "RESULTS_DIR = PROJECT_DIR / \"Results\"\n",
        "\n",
        "for directory in [PROJECT_DIR, FIGURES_DIR, CHECKPOINTS_DIR, FEATURES_DIR, RESULTS_DIR]:\n",
        "    directory.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "print(\"Environment restored.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Glint-b3cYB3",
        "outputId": "26e0ecac-1dea-4c21-9a0a-62b3e5d0428f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Environment restored.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbNi1LmPsTbG"
      },
      "source": [
        "## 1. Dataset Acquisition and Integrity Check\n",
        "\n",
        "The APTOS 2019 dataset contains retinal fundus images classified into five\n",
        "diabetic retinopathy severity levels:\n",
        "\n",
        "- 0: No DR\n",
        "- 1: Mild\n",
        "- 2: Moderate\n",
        "- 3: Severe\n",
        "- 4: Proliferative DR\n",
        "\n",
        "Before preprocessing and model development, the dataset is checked for missing\n",
        "images, duplicated identifiers, missing labels, and invalid class values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "_z0TwKfCsUuG",
        "outputId": "b7f992d5-ccc8-4e86-d12d-76ac50d7c4cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset ZIP found:\n",
            "/content/drive/MyDrive/APTOS2019/aptos2019-blindness-detection.zip\n",
            "\n",
            "Extracting APTOS dataset...\n",
            "Dataset extraction completed.\n",
            "\n",
            "CSV file:\n",
            "/content/aptos2019_final_hybrid/train.csv\n",
            "\n",
            "Image directory:\n",
            "/content/aptos2019_final_hybrid/train_images\n",
            "\n",
            "Dataset Integrity Summary\n",
            "=======================================================\n",
            "Total metadata records: 3662\n",
            "Available images:       3662\n",
            "Missing images:         0\n",
            "Duplicate image IDs:    0\n",
            "Missing labels:         0\n",
            "Invalid labels:         []\n",
            "\n",
            "Dataset loaded successfully.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        id_code  diagnosis        class_name  \\\n",
              "0  000c1434d8d7          2          Moderate   \n",
              "1  001639a390f0          4  Proliferative DR   \n",
              "2  0024cdab0c1e          1              Mild   \n",
              "3  002c21358ce6          0             No DR   \n",
              "4  005b95c28852          0             No DR   \n",
              "\n",
              "                                          image_path  \n",
              "0  /content/aptos2019_final_hybrid/train_images/0...  \n",
              "1  /content/aptos2019_final_hybrid/train_images/0...  \n",
              "2  /content/aptos2019_final_hybrid/train_images/0...  \n",
              "3  /content/aptos2019_final_hybrid/train_images/0...  \n",
              "4  /content/aptos2019_final_hybrid/train_images/0...  "
            ],
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              "\n",
              "  <div id=\"df-901b6fc1-14d0-4ef5-b768-5db3fdded4b6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_code</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>class_name</th>\n",
              "      <th>image_path</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>001639a390f0</td>\n",
              "      <td>4</td>\n",
              "      <td>Proliferative DR</td>\n",
              "      <td>/content/aptos2019_final_hybrid/train_images/0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0024cdab0c1e</td>\n",
              "      <td>1</td>\n",
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              "      <td>/content/aptos2019_final_hybrid/train_images/0...</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>002c21358ce6</td>\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"id_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"001639a390f0\",\n          \"005b95c28852\",\n          \"0024cdab0c1e\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diagnosis\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Proliferative DR\",\n          \"No DR\",\n          \"Moderate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"image_path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"/content/aptos2019_final_hybrid/train_images/001639a390f0.png\",\n          \"/content/aptos2019_final_hybrid/train_images/005b95c28852.png\",\n          \"/content/aptos2019_final_hybrid/train_images/0024cdab0c1e.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      ],
      "source": [
        "# =========================================================\n",
        "# Cell 2: Load and Validate the APTOS 2019 Dataset\n",
        "# =========================================================\n",
        "\n",
        "from pathlib import Path\n",
        "import os\n",
        "import zipfile\n",
        "import pandas as pd\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Dataset paths\n",
        "# ---------------------------------------------------------\n",
        "DRIVE_DATA_DIR = Path(\"/content/drive/MyDrive/APTOS2019\")\n",
        "\n",
        "ZIP_PATH = (\n",
        "    DRIVE_DATA_DIR /\n",
        "    \"aptos2019-blindness-detection.zip\"\n",
        ")\n",
        "\n",
        "WORK_DIR = Path(\n",
        "    \"/content/aptos2019_final_hybrid\"\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Check whether the ZIP file exists\n",
        "# ---------------------------------------------------------\n",
        "if not ZIP_PATH.exists():\n",
        "    raise FileNotFoundError(\n",
        "        \"APTOS ZIP file was not found.\\n\"\n",
        "        f\"Expected path: {ZIP_PATH}\"\n",
        "    )\n",
        "\n",
        "print(\"Dataset ZIP found:\")\n",
        "print(ZIP_PATH)\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Create local working directory\n",
        "# ---------------------------------------------------------\n",
        "WORK_DIR.mkdir(\n",
        "    parents=True,\n",
        "    exist_ok=True\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Search for already extracted files\n",
        "# ---------------------------------------------------------\n",
        "csv_candidates = list(\n",
        "    WORK_DIR.rglob(\"train.csv\")\n",
        ")\n",
        "\n",
        "image_dir_candidates = [\n",
        "    path\n",
        "    for path in WORK_DIR.rglob(\"train_images\")\n",
        "    if path.is_dir()\n",
        "]\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Extract dataset only when required\n",
        "# ---------------------------------------------------------\n",
        "if not csv_candidates or not image_dir_candidates:\n",
        "    print(\"\\nExtracting APTOS dataset...\")\n",
        "\n",
        "    with zipfile.ZipFile(\n",
        "        ZIP_PATH,\n",
        "        mode=\"r\"\n",
        "    ) as zip_ref:\n",
        "        zip_ref.extractall(WORK_DIR)\n",
        "\n",
        "    print(\"Dataset extraction completed.\")\n",
        "\n",
        "# Search again after extraction\n",
        "csv_candidates = list(\n",
        "    WORK_DIR.rglob(\"train.csv\")\n",
        ")\n",
        "\n",
        "image_dir_candidates = [\n",
        "    path\n",
        "    for path in WORK_DIR.rglob(\"train_images\")\n",
        "    if path.is_dir()\n",
        "]\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Validate extracted dataset structure\n",
        "# ---------------------------------------------------------\n",
        "if not csv_candidates:\n",
        "    raise FileNotFoundError(\n",
        "        \"train.csv could not be found after extraction.\"\n",
        "    )\n",
        "\n",
        "if not image_dir_candidates:\n",
        "    raise FileNotFoundError(\n",
        "        \"train_images directory could not be found \"\n",
        "        \"after extraction.\"\n",
        "    )\n",
        "\n",
        "# Choose the shortest matching paths\n",
        "CSV_PATH = sorted(\n",
        "    csv_candidates,\n",
        "    key=lambda path: len(str(path))\n",
        ")[0]\n",
        "\n",
        "IMAGE_DIR = sorted(\n",
        "    image_dir_candidates,\n",
        "    key=lambda path: len(str(path))\n",
        ")[0]\n",
        "\n",
        "print(\"\\nCSV file:\")\n",
        "print(CSV_PATH)\n",
        "\n",
        "print(\"\\nImage directory:\")\n",
        "print(IMAGE_DIR)\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Load metadata\n",
        "# ---------------------------------------------------------\n",
        "df = pd.read_csv(CSV_PATH)\n",
        "\n",
        "required_columns = {\n",
        "    \"id_code\",\n",
        "    \"diagnosis\"\n",
        "}\n",
        "\n",
        "missing_columns = (\n",
        "    required_columns - set(df.columns)\n",
        ")\n",
        "\n",
        "if missing_columns:\n",
        "    raise ValueError(\n",
        "        f\"Missing required columns: {missing_columns}\"\n",
        "    )\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Create complete image paths\n",
        "# ---------------------------------------------------------\n",
        "df[\"image_path\"] = df[\"id_code\"].apply(\n",
        "    lambda image_id:\n",
        "    str(IMAGE_DIR / f\"{image_id}.png\")\n",
        ")\n",
        "\n",
        "df[\"class_name\"] = df[\"diagnosis\"].map(\n",
        "    CLASS_MAP\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Dataset integrity checks\n",
        "# ---------------------------------------------------------\n",
        "df[\"image_exists\"] = df[\"image_path\"].apply(\n",
        "    os.path.exists\n",
        ")\n",
        "\n",
        "total_records = len(df)\n",
        "\n",
        "available_images = int(\n",
        "    df[\"image_exists\"].sum()\n",
        ")\n",
        "\n",
        "missing_images = int(\n",
        "    (~df[\"image_exists\"]).sum()\n",
        ")\n",
        "\n",
        "duplicate_ids = int(\n",
        "    df[\"id_code\"].duplicated().sum()\n",
        ")\n",
        "\n",
        "missing_labels = int(\n",
        "    df[\"diagnosis\"].isna().sum()\n",
        ")\n",
        "\n",
        "available_labels = set(\n",
        "    df[\"diagnosis\"]\n",
        "    .dropna()\n",
        "    .astype(int)\n",
        "    .unique()\n",
        ")\n",
        "\n",
        "expected_labels = set(\n",
        "    range(NUM_CLASSES)\n",
        ")\n",
        "\n",
        "invalid_labels = sorted(\n",
        "    available_labels - expected_labels\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Display integrity summary\n",
        "# ---------------------------------------------------------\n",
        "print(\"\\nDataset Integrity Summary\")\n",
        "print(\"=\" * 55)\n",
        "\n",
        "print(f\"Total metadata records: {total_records}\")\n",
        "print(f\"Available images:       {available_images}\")\n",
        "print(f\"Missing images:         {missing_images}\")\n",
        "print(f\"Duplicate image IDs:    {duplicate_ids}\")\n",
        "print(f\"Missing labels:         {missing_labels}\")\n",
        "print(f\"Invalid labels:         {invalid_labels}\")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Stop execution if critical issues are detected\n",
        "# ---------------------------------------------------------\n",
        "if missing_images > 0:\n",
        "    print(\"\\nExamples of missing images:\")\n",
        "\n",
        "    display(\n",
        "        df.loc[\n",
        "            ~df[\"image_exists\"],\n",
        "            [\"id_code\", \"image_path\"]\n",
        "        ].head()\n",
        "    )\n",
        "\n",
        "    raise FileNotFoundError(\n",
        "        \"Some retinal images are missing.\"\n",
        "    )\n",
        "\n",
        "if duplicate_ids > 0:\n",
        "    print(\"\\nExamples of duplicated IDs:\")\n",
        "\n",
        "    display(\n",
        "        df[\n",
        "            df[\"id_code\"].duplicated(\n",
        "                keep=False\n",
        "            )\n",
        "        ].sort_values(\"id_code\").head()\n",
        "    )\n",
        "\n",
        "if missing_labels > 0:\n",
        "    raise ValueError(\n",
        "        \"Missing diagnosis labels were detected.\"\n",
        "    )\n",
        "\n",
        "if invalid_labels:\n",
        "    raise ValueError(\n",
        "        f\"Invalid diagnosis labels detected: \"\n",
        "        f\"{invalid_labels}\"\n",
        "    )\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Keep only required columns\n",
        "# ---------------------------------------------------------\n",
        "df = df[\n",
        "    [\n",
        "        \"id_code\",\n",
        "        \"diagnosis\",\n",
        "        \"class_name\",\n",
        "        \"image_path\"\n",
        "    ]\n",
        "].copy()\n",
        "\n",
        "# Sort data for consistent reproducibility\n",
        "df = df.sort_values(\n",
        "    \"id_code\"\n",
        ").reset_index(drop=True)\n",
        "\n",
        "print(\"\\nDataset loaded successfully.\")\n",
        "\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5MmZLhR4tlEf"
      },
      "source": [
        "## 2. Original Class Distribution and Imbalance Analysis\n",
        "\n",
        "The original APTOS 2019 class distribution is examined before model training.\n",
        "This analysis identifies underrepresented diabetic retinopathy classes and\n",
        "supports the selection of an appropriate class imbalance handling strategy.\n",
        "\n",
        "The dataset is not resampled at this stage. Class imbalance will later be\n",
        "handled using class-weighted loss during ConvNeXt-Tiny and Swin-Tiny\n",
        "fine-tuning, together with balanced subsampling in the Random Forest classifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3kR7fPejtmGh",
        "outputId": "9c6b0c63-6737-443e-b531-6e43cb4c2800"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original APTOS 2019 Class Distribution\n",
            "============================================================\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_58b48\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_58b48_level0_col0\" class=\"col_heading level0 col0\" >Class ID</th>\n",
              "      <th id=\"T_58b48_level0_col1\" class=\"col_heading level0 col1\" >Class Name</th>\n",
              "      <th id=\"T_58b48_level0_col2\" class=\"col_heading level0 col2\" >Sample Count</th>\n",
              "      <th id=\"T_58b48_level0_col3\" class=\"col_heading level0 col3\" >Percentage (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_58b48_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_58b48_row0_col0\" class=\"data row0 col0\" >0</td>\n",
              "      <td id=\"T_58b48_row0_col1\" class=\"data row0 col1\" >No DR</td>\n",
              "      <td id=\"T_58b48_row0_col2\" class=\"data row0 col2\" >1805</td>\n",
              "      <td id=\"T_58b48_row0_col3\" class=\"data row0 col3\" >49.29%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58b48_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_58b48_row1_col0\" class=\"data row1 col0\" >1</td>\n",
              "      <td id=\"T_58b48_row1_col1\" class=\"data row1 col1\" >Mild</td>\n",
              "      <td id=\"T_58b48_row1_col2\" class=\"data row1 col2\" >370</td>\n",
              "      <td id=\"T_58b48_row1_col3\" class=\"data row1 col3\" >10.10%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58b48_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_58b48_row2_col0\" class=\"data row2 col0\" >2</td>\n",
              "      <td id=\"T_58b48_row2_col1\" class=\"data row2 col1\" >Moderate</td>\n",
              "      <td id=\"T_58b48_row2_col2\" class=\"data row2 col2\" >999</td>\n",
              "      <td id=\"T_58b48_row2_col3\" class=\"data row2 col3\" >27.28%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58b48_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_58b48_row3_col0\" class=\"data row3 col0\" >3</td>\n",
              "      <td id=\"T_58b48_row3_col1\" class=\"data row3 col1\" >Severe</td>\n",
              "      <td id=\"T_58b48_row3_col2\" class=\"data row3 col2\" >193</td>\n",
              "      <td id=\"T_58b48_row3_col3\" class=\"data row3 col3\" >5.27%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58b48_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_58b48_row4_col0\" class=\"data row4 col0\" >4</td>\n",
              "      <td id=\"T_58b48_row4_col1\" class=\"data row4 col1\" >Proliferative DR</td>\n",
              "      <td id=\"T_58b48_row4_col2\" class=\"data row4 col2\" >295</td>\n",
              "      <td id=\"T_58b48_row4_col3\" class=\"data row4 col3\" >8.06%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7dc6cd9341d0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Class Imbalance Summary\n",
            "------------------------------------------------------------\n",
            "Majority class: No DR (1805 images)\n",
            "Minority class: Severe (193 images)\n",
            "Majority-to-minority ratio: 9.35:1\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Files saved successfully\n",
            "------------------------------------------------------------\n",
            "Figure: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_01_original_class_distribution.png\n",
            "Distribution table: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/original_class_distribution.csv\n",
            "Imbalance summary: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/class_imbalance_summary.csv\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 3: Original Class Distribution and Imbalance Analysis\n",
        "# =========================================================\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Calculate class counts\n",
        "# ---------------------------------------------------------\n",
        "class_distribution = (\n",
        "    df[\"diagnosis\"]\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        "    .reindex(range(NUM_CLASSES), fill_value=0)\n",
        ")\n",
        "\n",
        "distribution_df = pd.DataFrame({\n",
        "    \"Class ID\": range(NUM_CLASSES),\n",
        "    \"Class Name\": CLASS_NAMES,\n",
        "    \"Sample Count\": class_distribution.values\n",
        "})\n",
        "\n",
        "distribution_df[\"Percentage (%)\"] = (\n",
        "    distribution_df[\"Sample Count\"]\n",
        "    / distribution_df[\"Sample Count\"].sum()\n",
        "    * 100\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Calculate imbalance indicators\n",
        "# ---------------------------------------------------------\n",
        "majority_count = int(distribution_df[\"Sample Count\"].max())\n",
        "minority_count = int(distribution_df[\"Sample Count\"].min())\n",
        "\n",
        "majority_class = distribution_df.loc[\n",
        "    distribution_df[\"Sample Count\"].idxmax(),\n",
        "    \"Class Name\"\n",
        "]\n",
        "\n",
        "minority_class = distribution_df.loc[\n",
        "    distribution_df[\"Sample Count\"].idxmin(),\n",
        "    \"Class Name\"\n",
        "]\n",
        "\n",
        "imbalance_ratio = majority_count / minority_count\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Display numerical summary\n",
        "# ---------------------------------------------------------\n",
        "print(\"Original APTOS 2019 Class Distribution\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "display(\n",
        "    distribution_df.style.format({\n",
        "        \"Percentage (%)\": \"{:.2f}%\"\n",
        "    })\n",
        ")\n",
        "\n",
        "print(\"\\nClass Imbalance Summary\")\n",
        "print(\"-\" * 60)\n",
        "print(f\"Majority class: {majority_class} ({majority_count} images)\")\n",
        "print(f\"Minority class: {minority_class} ({minority_count} images)\")\n",
        "print(f\"Majority-to-minority ratio: {imbalance_ratio:.2f}:1\")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Create bar chart\n",
        "# ---------------------------------------------------------\n",
        "fig, ax = plt.subplots(figsize=(11, 6))\n",
        "\n",
        "bars = ax.bar(\n",
        "    distribution_df[\"Class Name\"],\n",
        "    distribution_df[\"Sample Count\"]\n",
        ")\n",
        "\n",
        "ax.set_title(\n",
        "    \"Original Class Distribution in the APTOS 2019 Dataset\",\n",
        "    fontsize=14,\n",
        "    pad=15\n",
        ")\n",
        "\n",
        "ax.set_xlabel(\n",
        "    \"Diabetic Retinopathy Severity Class\",\n",
        "    fontsize=11\n",
        ")\n",
        "\n",
        "ax.set_ylabel(\n",
        "    \"Number of Images\",\n",
        "    fontsize=11\n",
        ")\n",
        "\n",
        "ax.grid(\n",
        "    axis=\"y\",\n",
        "    linestyle=\"--\",\n",
        "    alpha=0.35\n",
        ")\n",
        "\n",
        "ax.tick_params(\n",
        "    axis=\"x\",\n",
        "    rotation=15\n",
        ")\n",
        "\n",
        "# Add count and percentage above each bar\n",
        "vertical_offset = majority_count * 0.015\n",
        "\n",
        "for bar, count, percentage in zip(\n",
        "    bars,\n",
        "    distribution_df[\"Sample Count\"],\n",
        "    distribution_df[\"Percentage (%)\"]\n",
        "):\n",
        "    ax.text(\n",
        "        bar.get_x() + bar.get_width() / 2,\n",
        "        bar.get_height() + vertical_offset,\n",
        "        f\"{count}\\n({percentage:.1f}%)\",\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=10\n",
        "    )\n",
        "\n",
        "ax.set_ylim(\n",
        "    0,\n",
        "    majority_count * 1.14\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save figure\n",
        "# ---------------------------------------------------------\n",
        "distribution_figure_path = (\n",
        "    FIGURES_DIR\n",
        "    / \"figure_01_original_class_distribution.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    distribution_figure_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save numerical results\n",
        "# ---------------------------------------------------------\n",
        "distribution_csv_path = (\n",
        "    RESULTS_DIR\n",
        "    / \"original_class_distribution.csv\"\n",
        ")\n",
        "\n",
        "distribution_df.to_csv(\n",
        "    distribution_csv_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "imbalance_summary_df = pd.DataFrame({\n",
        "    \"Metric\": [\n",
        "        \"Total Images\",\n",
        "        \"Majority Class\",\n",
        "        \"Majority Class Count\",\n",
        "        \"Minority Class\",\n",
        "        \"Minority Class Count\",\n",
        "        \"Majority-to-Minority Ratio\"\n",
        "    ],\n",
        "    \"Value\": [\n",
        "        len(df),\n",
        "        majority_class,\n",
        "        majority_count,\n",
        "        minority_class,\n",
        "        minority_count,\n",
        "        round(imbalance_ratio, 4)\n",
        "    ]\n",
        "})\n",
        "\n",
        "imbalance_summary_path = (\n",
        "    RESULTS_DIR\n",
        "    / \"class_imbalance_summary.csv\"\n",
        ")\n",
        "\n",
        "imbalance_summary_df.to_csv(\n",
        "    imbalance_summary_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nFiles saved successfully\")\n",
        "print(\"-\" * 60)\n",
        "print(\"Figure:\", distribution_figure_path)\n",
        "print(\"Distribution table:\", distribution_csv_path)\n",
        "print(\"Imbalance summary:\", imbalance_summary_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jx7MH7HwuMti"
      },
      "source": [
        "## 3. Image Preprocessing\n",
        "\n",
        "Retinal fundus images in the APTOS 2019 dataset vary in dimensions,\n",
        "illumination, contrast, and surrounding black-background areas.\n",
        "\n",
        "The preprocessing pipeline includes:\n",
        "\n",
        "1. Removal of dark background borders\n",
        "2. Image resizing to 300 × 300 pixels\n",
        "3. Contrast enhancement using CLAHE\n",
        "4. Tensor conversion and ImageNet normalization during model training\n",
        "\n",
        "The same deterministic preprocessing steps will be applied to the training,\n",
        "validation, and testing sets. Data augmentation will be applied only to the\n",
        "training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1OR6lVCuOYk",
        "outputId": "bf4ec2df-e9cb-400b-fcfc-efd798a798d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retinal preprocessing functions are ready.\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 4: Retinal Image Preprocessing Functions\n",
        "# =========================================================\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 1. Remove surrounding dark borders\n",
        "# ---------------------------------------------------------\n",
        "def crop_dark_borders(\n",
        "    image_rgb,\n",
        "    threshold=10,\n",
        "    minimum_size=50\n",
        "):\n",
        "    \"\"\"\n",
        "    Removes dark background surrounding the retinal region.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    image_rgb : numpy.ndarray\n",
        "        RGB image.\n",
        "    threshold : int\n",
        "        Minimum grayscale intensity considered foreground.\n",
        "    minimum_size : int\n",
        "        Prevents invalid or extremely small crops.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    numpy.ndarray\n",
        "        Cropped RGB image.\n",
        "    \"\"\"\n",
        "\n",
        "    if image_rgb is None:\n",
        "        raise ValueError(\"Input image is None.\")\n",
        "\n",
        "    if image_rgb.ndim != 3:\n",
        "        raise ValueError(\n",
        "            \"Expected an RGB image with three dimensions.\"\n",
        "        )\n",
        "\n",
        "    gray_image = cv2.cvtColor(\n",
        "        image_rgb,\n",
        "        cv2.COLOR_RGB2GRAY\n",
        "    )\n",
        "\n",
        "    foreground_mask = (\n",
        "        gray_image > threshold\n",
        "    ).astype(np.uint8)\n",
        "\n",
        "    coordinates = cv2.findNonZero(\n",
        "        foreground_mask\n",
        "    )\n",
        "\n",
        "    # Return original image if no foreground was found\n",
        "    if coordinates is None:\n",
        "        return image_rgb.copy()\n",
        "\n",
        "    x, y, width, height = cv2.boundingRect(\n",
        "        coordinates\n",
        "    )\n",
        "\n",
        "    cropped_image = image_rgb[\n",
        "        y:y + height,\n",
        "        x:x + width\n",
        "    ]\n",
        "\n",
        "    # Safety check\n",
        "    if (\n",
        "        cropped_image.shape[0] < minimum_size\n",
        "        or cropped_image.shape[1] < minimum_size\n",
        "    ):\n",
        "        return image_rgb.copy()\n",
        "\n",
        "    return cropped_image\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 2. Apply CLAHE on luminance channel\n",
        "# ---------------------------------------------------------\n",
        "def apply_clahe_rgb(\n",
        "    image_rgb,\n",
        "    clip_limit=1.5,\n",
        "    tile_grid_size=(8, 8)\n",
        "):\n",
        "    \"\"\"\n",
        "    Applies CLAHE to the luminance channel in LAB colour space.\n",
        "    \"\"\"\n",
        "\n",
        "    lab_image = cv2.cvtColor(\n",
        "        image_rgb,\n",
        "        cv2.COLOR_RGB2LAB\n",
        "    )\n",
        "\n",
        "    lightness, channel_a, channel_b = cv2.split(\n",
        "        lab_image\n",
        "    )\n",
        "\n",
        "    clahe = cv2.createCLAHE(\n",
        "        clipLimit=clip_limit,\n",
        "        tileGridSize=tile_grid_size\n",
        "    )\n",
        "\n",
        "    enhanced_lightness = clahe.apply(\n",
        "        lightness\n",
        "    )\n",
        "\n",
        "    enhanced_lab = cv2.merge([\n",
        "        enhanced_lightness,\n",
        "        channel_a,\n",
        "        channel_b\n",
        "    ])\n",
        "\n",
        "    enhanced_rgb = cv2.cvtColor(\n",
        "        enhanced_lab,\n",
        "        cv2.COLOR_LAB2RGB\n",
        "    )\n",
        "\n",
        "    return enhanced_rgb\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 3. Complete deterministic preprocessing\n",
        "# ---------------------------------------------------------\n",
        "def preprocess_retinal_image(\n",
        "    image_path,\n",
        "    image_size=IMG_SIZE,\n",
        "    apply_clahe=True\n",
        "):\n",
        "    \"\"\"\n",
        "    Loads and preprocesses one retinal fundus image.\n",
        "\n",
        "    Steps:\n",
        "    1. Read image\n",
        "    2. Convert BGR to RGB\n",
        "    3. Crop dark borders\n",
        "    4. Apply CLAHE\n",
        "    5. Resize\n",
        "    \"\"\"\n",
        "\n",
        "    image_bgr = cv2.imread(\n",
        "        str(image_path)\n",
        "    )\n",
        "\n",
        "    if image_bgr is None:\n",
        "        raise FileNotFoundError(\n",
        "            f\"Unable to read image: {image_path}\"\n",
        "        )\n",
        "\n",
        "    original_rgb = cv2.cvtColor(\n",
        "        image_bgr,\n",
        "        cv2.COLOR_BGR2RGB\n",
        "    )\n",
        "\n",
        "    cropped_rgb = crop_dark_borders(\n",
        "        original_rgb,\n",
        "        threshold=10\n",
        "    )\n",
        "\n",
        "    if apply_clahe:\n",
        "        processed_rgb = apply_clahe_rgb(\n",
        "            cropped_rgb,\n",
        "            clip_limit=1.5,\n",
        "            tile_grid_size=(8, 8)\n",
        "        )\n",
        "    else:\n",
        "        processed_rgb = cropped_rgb.copy()\n",
        "\n",
        "    resized_rgb = cv2.resize(\n",
        "        processed_rgb,\n",
        "        (image_size, image_size),\n",
        "        interpolation=cv2.INTER_AREA\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"original\": original_rgb,\n",
        "        \"cropped\": cropped_rgb,\n",
        "        \"processed\": resized_rgb\n",
        "    }\n",
        "\n",
        "\n",
        "print(\"Retinal preprocessing functions are ready.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "WKY4iMyWubSV",
        "outputId": "b1afa2fd-ce33-4b6b-84f5-151b4aa2b737"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selected preprocessing examples:\n"
          ]
        },
        {
          "data": {
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              "summary": "{\n  \"name\": \"selected_samples_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"id_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"76be29bb30b2\",\n          \"7c90ab025331\",\n          \"7d8f67cadc29\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diagnosis\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Mild\",\n          \"Proliferative DR\",\n          \"Moderate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"image_path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"/content/aptos2019_final_hybrid/train_images/76be29bb30b2.png\",\n          \"/content/aptos2019_final_hybrid/train_images/7c90ab025331.png\",\n          \"/content/aptos2019_final_hybrid/train_images/7d8f67cadc29.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>id_code</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>class_name</th>\n",
              "      <th>image_path</th>\n",
              "    </tr>\n",
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              "      <td>/content/aptos2019_final_hybrid/train_images/7...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>/content/aptos2019_final_hybrid/train_images/7...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80ca40196225</td>\n",
              "      <td>3</td>\n",
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              "  <div id=\"id_a4b15845-6574-43b3-99e9-61e852484487\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('selected_samples_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a4b15845-6574-43b3-99e9-61e852484487 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('selected_samples_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        id_code  diagnosis        class_name  \\\n",
              "0  85f99e7e4052          0             No DR   \n",
              "1  76be29bb30b2          1              Mild   \n",
              "2  7d8f67cadc29          2          Moderate   \n",
              "3  80ca40196225          3            Severe   \n",
              "4  7c90ab025331          4  Proliferative DR   \n",
              "\n",
              "                                          image_path  \n",
              "0  /content/aptos2019_final_hybrid/train_images/8...  \n",
              "1  /content/aptos2019_final_hybrid/train_images/7...  \n",
              "2  /content/aptos2019_final_hybrid/train_images/7...  \n",
              "3  /content/aptos2019_final_hybrid/train_images/8...  \n",
              "4  /content/aptos2019_final_hybrid/train_images/7...  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Selection table saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/selected_preprocessing_samples.csv\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 5: Select Representative Images from Each Class\n",
        "# =========================================================\n",
        "\n",
        "selected_samples = []\n",
        "\n",
        "for class_id in range(NUM_CLASSES):\n",
        "\n",
        "    class_data = (\n",
        "        df[df[\"diagnosis\"] == class_id]\n",
        "        .sort_values(\"id_code\")\n",
        "        .reset_index(drop=True)\n",
        "    )\n",
        "\n",
        "    if class_data.empty:\n",
        "        raise ValueError(\n",
        "            f\"No samples found for class {class_id}.\"\n",
        "        )\n",
        "\n",
        "    # Select the middle image deterministically\n",
        "    selected_index = len(class_data) // 2\n",
        "    selected_row = class_data.iloc[selected_index]\n",
        "\n",
        "    selected_samples.append({\n",
        "        \"id_code\": selected_row[\"id_code\"],\n",
        "        \"diagnosis\": int(\n",
        "            selected_row[\"diagnosis\"]\n",
        "        ),\n",
        "        \"class_name\": selected_row[\"class_name\"],\n",
        "        \"image_path\": selected_row[\"image_path\"]\n",
        "    })\n",
        "\n",
        "\n",
        "selected_samples_df = pd.DataFrame(\n",
        "    selected_samples\n",
        ")\n",
        "\n",
        "selected_samples_path = (\n",
        "    RESULTS_DIR\n",
        "    / \"selected_preprocessing_samples.csv\"\n",
        ")\n",
        "\n",
        "selected_samples_df.to_csv(\n",
        "    selected_samples_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"Selected preprocessing examples:\")\n",
        "\n",
        "display(selected_samples_df)\n",
        "\n",
        "print(\"\\nSelection table saved at:\")\n",
        "print(selected_samples_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2pinu2JhudPK",
        "outputId": "2c3a7b1e-ff3e-4687-fd09-f2ec6bb4e223"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2000x800 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image dimension comparison:\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"dimensions_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Image ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"76be29bb30b2\",\n          \"7c90ab025331\",\n          \"7d8f67cadc29\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Mild\",\n          \"Proliferative DR\",\n          \"Moderate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Original Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 165,\n        \"min\": 1736,\n        \"max\": 2136,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1958,\n          1736,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Original Width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 381,\n        \"min\": 2416,\n        \"max\": 3216,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2588,\n          2416,\n          3216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cropped Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 165,\n        \"min\": 1736,\n        \"max\": 2136,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1958,\n          1736,\n          2136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cropped Width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 185,\n        \"min\": 2257,\n        \"max\": 2711,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2257,\n          2710,\n          2711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Final Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 300,\n        \"max\": 300,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          300\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Final Width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 300,\n        \"max\": 300,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          300\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "dimensions_df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-cffe405c-7e33-4e08-a8b4-2c9c5c14fe03\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image ID</th>\n",
              "      <th>Class</th>\n",
              "      <th>Original Height</th>\n",
              "      <th>Original Width</th>\n",
              "      <th>Cropped Height</th>\n",
              "      <th>Cropped Width</th>\n",
              "      <th>Final Height</th>\n",
              "      <th>Final Width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85f99e7e4052</td>\n",
              "      <td>No DR</td>\n",
              "      <td>1958</td>\n",
              "      <td>2588</td>\n",
              "      <td>1958</td>\n",
              "      <td>2588</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>76be29bb30b2</td>\n",
              "      <td>Mild</td>\n",
              "      <td>1736</td>\n",
              "      <td>2416</td>\n",
              "      <td>1736</td>\n",
              "      <td>2257</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7d8f67cadc29</td>\n",
              "      <td>Moderate</td>\n",
              "      <td>2136</td>\n",
              "      <td>3216</td>\n",
              "      <td>2136</td>\n",
              "      <td>2711</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80ca40196225</td>\n",
              "      <td>Severe</td>\n",
              "      <td>1958</td>\n",
              "      <td>2588</td>\n",
              "      <td>1958</td>\n",
              "      <td>2586</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7c90ab025331</td>\n",
              "      <td>Proliferative DR</td>\n",
              "      <td>2136</td>\n",
              "      <td>3216</td>\n",
              "      <td>2136</td>\n",
              "      <td>2710</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cffe405c-7e33-4e08-a8b4-2c9c5c14fe03')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Image ID             Class  Original Height  Original Width  \\\n",
              "0  85f99e7e4052             No DR             1958            2588   \n",
              "1  76be29bb30b2              Mild             1736            2416   \n",
              "2  7d8f67cadc29          Moderate             2136            3216   \n",
              "3  80ca40196225            Severe             1958            2588   \n",
              "4  7c90ab025331  Proliferative DR             2136            3216   \n",
              "\n",
              "   Cropped Height  Cropped Width  Final Height  Final Width  \n",
              "0            1958           2588           300          300  \n",
              "1            1736           2257           300          300  \n",
              "2            2136           2711           300          300  \n",
              "3            1958           2586           300          300  \n",
              "4            2136           2710           300          300  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Files saved successfully\n",
            "------------------------------------------------------------\n",
            "Figure: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_02_before_after_preprocessing.png\n",
            "Dimension table: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/preprocessing_image_dimensions.csv\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 6: Images Before and After Preprocessing\n",
        "# =========================================================\n",
        "\n",
        "figure, axes = plt.subplots(\n",
        "    nrows=2,\n",
        "    ncols=NUM_CLASSES,\n",
        "    figsize=(20, 8)\n",
        ")\n",
        "\n",
        "dimension_records = []\n",
        "\n",
        "for column_index, row in enumerate(\n",
        "    selected_samples_df.itertuples(index=False)\n",
        "):\n",
        "    preprocessing_result = preprocess_retinal_image(\n",
        "        image_path=row.image_path,\n",
        "        image_size=IMG_SIZE,\n",
        "        apply_clahe=True\n",
        "    )\n",
        "\n",
        "    original_image = preprocessing_result[\"original\"]\n",
        "    cropped_image = preprocessing_result[\"cropped\"]\n",
        "    processed_image = preprocessing_result[\"processed\"]\n",
        "\n",
        "    # -----------------------------------------------------\n",
        "    # Original image\n",
        "    # -----------------------------------------------------\n",
        "    axes[0, column_index].imshow(\n",
        "        original_image\n",
        "    )\n",
        "\n",
        "    axes[0, column_index].set_title(\n",
        "        f\"{row.class_name}\\nBefore Preprocessing\",\n",
        "        fontsize=11\n",
        "    )\n",
        "\n",
        "    axes[0, column_index].axis(\"off\")\n",
        "\n",
        "    # -----------------------------------------------------\n",
        "    # Processed image\n",
        "    # -----------------------------------------------------\n",
        "    axes[1, column_index].imshow(\n",
        "        processed_image\n",
        "    )\n",
        "\n",
        "    axes[1, column_index].set_title(\n",
        "        f\"{row.class_name}\\nAfter Preprocessing\",\n",
        "        fontsize=11\n",
        "    )\n",
        "\n",
        "    axes[1, column_index].axis(\"off\")\n",
        "\n",
        "    # -----------------------------------------------------\n",
        "    # Record dimensions\n",
        "    # -----------------------------------------------------\n",
        "    dimension_records.append({\n",
        "        \"Image ID\": row.id_code,\n",
        "        \"Class\": row.class_name,\n",
        "        \"Original Height\": original_image.shape[0],\n",
        "        \"Original Width\": original_image.shape[1],\n",
        "        \"Cropped Height\": cropped_image.shape[0],\n",
        "        \"Cropped Width\": cropped_image.shape[1],\n",
        "        \"Final Height\": processed_image.shape[0],\n",
        "        \"Final Width\": processed_image.shape[1]\n",
        "    })\n",
        "\n",
        "\n",
        "figure.suptitle(\n",
        "    \"Representative Retinal Images Before and After Preprocessing\",\n",
        "    fontsize=16,\n",
        "    y=1.02\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "preprocessing_figure_path = (\n",
        "    FIGURES_DIR\n",
        "    / \"figure_02_before_after_preprocessing.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    preprocessing_figure_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save image-dimension comparison\n",
        "# ---------------------------------------------------------\n",
        "dimensions_df = pd.DataFrame(\n",
        "    dimension_records\n",
        ")\n",
        "\n",
        "dimensions_path = (\n",
        "    RESULTS_DIR\n",
        "    / \"preprocessing_image_dimensions.csv\"\n",
        ")\n",
        "\n",
        "dimensions_df.to_csv(\n",
        "    dimensions_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"Image dimension comparison:\")\n",
        "\n",
        "display(dimensions_df)\n",
        "\n",
        "print(\"\\nFiles saved successfully\")\n",
        "print(\"-\" * 60)\n",
        "print(\"Figure:\", preprocessing_figure_path)\n",
        "print(\"Dimension table:\", dimensions_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "YirDXkiSunOK",
        "outputId": "5c009e0e-0abc-48ce-b30b-3d8e58395bb9"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Preprocessing Step</th>\n",
              "      <th>Configuration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dark-border cropping</td>\n",
              "      <td>Grayscale threshold = 10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <th>2</th>\n",
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              "      <td>1.5</td>\n",
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              "      <td>8 × 8</td>\n",
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              "      <td>OpenCV INTER_AREA</td>\n",
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              "      <th>5</th>\n",
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              "      <td>Applied later in the PyTorch Dataset pipeline</td>\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Preprocessing Step                                  Configuration\n",
              "0    Dark-border cropping                       Grayscale threshold = 10\n",
              "1    Contrast enhancement                 CLAHE on LAB lightness channel\n",
              "2        CLAHE clip limit                                            1.5\n",
              "3         CLAHE tile grid                                          8 × 8\n",
              "4          Image resizing                              OpenCV INTER_AREA\n",
              "5        Final image size                                      300 × 300\n",
              "6       Tensor conversion  Applied later in the PyTorch Dataset pipeline\n",
              "7  ImageNet normalization  Applied later in the PyTorch Dataset pipeline"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Preprocessing configuration saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/preprocessing_configuration.csv\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 7: Save Preprocessing Configuration\n",
        "# =========================================================\n",
        "\n",
        "preprocessing_configuration = pd.DataFrame({\n",
        "    \"Preprocessing Step\": [\n",
        "        \"Dark-border cropping\",\n",
        "        \"Contrast enhancement\",\n",
        "        \"CLAHE clip limit\",\n",
        "        \"CLAHE tile grid\",\n",
        "        \"Image resizing\",\n",
        "        \"Final image size\",\n",
        "        \"Tensor conversion\",\n",
        "        \"ImageNet normalization\"\n",
        "    ],\n",
        "    \"Configuration\": [\n",
        "        \"Grayscale threshold = 10\",\n",
        "        \"CLAHE on LAB lightness channel\",\n",
        "        \"1.5\",\n",
        "        \"8 × 8\",\n",
        "        \"OpenCV INTER_AREA\",\n",
        "        f\"{IMG_SIZE} × {IMG_SIZE}\",\n",
        "        \"Applied later in the PyTorch Dataset pipeline\",\n",
        "        \"Applied later in the PyTorch Dataset pipeline\"\n",
        "    ]\n",
        "})\n",
        "\n",
        "configuration_path = (\n",
        "    RESULTS_DIR\n",
        "    / \"preprocessing_configuration.csv\"\n",
        ")\n",
        "\n",
        "preprocessing_configuration.to_csv(\n",
        "    configuration_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "display(preprocessing_configuration)\n",
        "\n",
        "print(\"Preprocessing configuration saved at:\")\n",
        "print(configuration_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "s_QofE7cura4",
        "outputId": "f9586935-8cac-4d55-a1a7-0fdc1f25411c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training size: 2563\n",
            "Validation size: 549\n",
            "Testing size: 550\n",
            "\n",
            "Training distribution:\n",
            "diagnosis\n",
            "0    1263\n",
            "1     259\n",
            "2     699\n",
            "3     135\n",
            "4     207\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Validation distribution:\n",
            "diagnosis\n",
            "0    271\n",
            "1     55\n",
            "2    150\n",
            "3     29\n",
            "4     44\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Testing distribution:\n",
            "diagnosis\n",
            "0    271\n",
            "1     56\n",
            "2    150\n",
            "3     29\n",
            "4     44\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Class weights used in weighted CrossEntropyLoss:\n",
            "0 - No DR: 0.4059\n",
            "1 - Mild: 1.9792\n",
            "2 - Moderate: 0.7333\n",
            "3 - Severe: 3.7970\n",
            "4 - Proliferative DR: 2.4763\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 8: Stratified Split and Class Imbalance Handling\n",
        "# =========================================================\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "\n",
        "# 70% Train, 15% Validation, 15% Test\n",
        "train_df, temp_df = train_test_split(\n",
        "    df,\n",
        "    test_size=0.30,\n",
        "    random_state=SEED,\n",
        "    stratify=df[\"diagnosis\"]\n",
        ")\n",
        "\n",
        "val_df, test_df = train_test_split(\n",
        "    temp_df,\n",
        "    test_size=0.50,\n",
        "    random_state=SEED,\n",
        "    stratify=temp_df[\"diagnosis\"]\n",
        ")\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "# Simple leakage check\n",
        "assert set(train_df[\"id_code\"]).isdisjoint(val_df[\"id_code\"])\n",
        "assert set(train_df[\"id_code\"]).isdisjoint(test_df[\"id_code\"])\n",
        "assert set(val_df[\"id_code\"]).isdisjoint(test_df[\"id_code\"])\n",
        "\n",
        "print(\"Training size:\", len(train_df))\n",
        "print(\"Validation size:\", len(val_df))\n",
        "print(\"Testing size:\", len(test_df))\n",
        "\n",
        "print(\"\\nTraining distribution:\")\n",
        "print(train_df[\"diagnosis\"].value_counts().sort_index())\n",
        "\n",
        "print(\"\\nValidation distribution:\")\n",
        "print(val_df[\"diagnosis\"].value_counts().sort_index())\n",
        "\n",
        "print(\"\\nTesting distribution:\")\n",
        "print(test_df[\"diagnosis\"].value_counts().sort_index())\n",
        "\n",
        "# Calculate weights from training data only\n",
        "classes = np.arange(NUM_CLASSES)\n",
        "\n",
        "class_weights = compute_class_weight(\n",
        "    class_weight=\"balanced\",\n",
        "    classes=classes,\n",
        "    y=train_df[\"diagnosis\"].values\n",
        ")\n",
        "\n",
        "class_weights_tensor = torch.tensor(\n",
        "    class_weights,\n",
        "    dtype=torch.float32,\n",
        "    device=device\n",
        ")\n",
        "\n",
        "print(\"\\nClass weights used in weighted CrossEntropyLoss:\")\n",
        "\n",
        "for class_id, weight in enumerate(class_weights):\n",
        "    print(\n",
        "        f\"{class_id} - {CLASS_NAMES[class_id]}: \"\n",
        "        f\"{weight:.4f}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o29H9RVEygYg",
        "outputId": "394262a5-1735-476f-a600-9802f16c6601"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Datasets and DataLoaders are ready.\n",
            "-------------------------------------------------------\n",
            "Training samples:   2563\n",
            "Validation samples: 549\n",
            "Testing samples:    550\n",
            "\n",
            "Training batches:   81\n",
            "Validation batches: 18\n",
            "Testing batches:    18\n",
            "\n",
            "Image batch shape: torch.Size([32, 3, 300, 300])\n",
            "Label batch shape: torch.Size([32])\n",
            "Image tensor type: torch.float32\n",
            "Label values: [2, 0, 2, 0, 0, 4, 0, 0, 2, 0]\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 10: PyTorch Dataset, Augmentation, and DataLoaders\n",
        "# =========================================================\n",
        "\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "from PIL import Image\n",
        "\n",
        "# ImageNet normalization for pretrained ConvNeXt and Swin\n",
        "IMAGENET_MEAN = [0.485, 0.456, 0.406]\n",
        "IMAGENET_STD = [0.229, 0.224, 0.225]\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Custom deterministic retinal preprocessing\n",
        "# Crop dark borders + CLAHE\n",
        "# ---------------------------------------------------------\n",
        "class RetinalPreprocessing:\n",
        "    def __init__(\n",
        "        self,\n",
        "        crop_threshold=10,\n",
        "        clahe_clip_limit=1.5,\n",
        "        clahe_grid_size=(8, 8)\n",
        "    ):\n",
        "        self.crop_threshold = crop_threshold\n",
        "        self.clahe_clip_limit = clahe_clip_limit\n",
        "        self.clahe_grid_size = clahe_grid_size\n",
        "\n",
        "    def __call__(self, image):\n",
        "        # PIL → NumPy RGB\n",
        "        image_rgb = np.array(image)\n",
        "\n",
        "        # Remove surrounding dark borders\n",
        "        image_rgb = crop_dark_borders(\n",
        "            image_rgb,\n",
        "            threshold=self.crop_threshold\n",
        "        )\n",
        "\n",
        "        # Apply CLAHE to luminance channel\n",
        "        image_rgb = apply_clahe_rgb(\n",
        "            image_rgb,\n",
        "            clip_limit=self.clahe_clip_limit,\n",
        "            tile_grid_size=self.clahe_grid_size\n",
        "        )\n",
        "\n",
        "        # NumPy RGB → PIL\n",
        "        return Image.fromarray(image_rgb)\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Training transforms\n",
        "# Augmentation is applied only to the training set\n",
        "# ---------------------------------------------------------\n",
        "train_transforms = transforms.Compose([\n",
        "    RetinalPreprocessing(),\n",
        "\n",
        "    transforms.Resize(\n",
        "        (IMG_SIZE, IMG_SIZE)\n",
        "    ),\n",
        "\n",
        "    transforms.RandomHorizontalFlip(\n",
        "        p=0.5\n",
        "    ),\n",
        "\n",
        "    transforms.RandomRotation(\n",
        "        degrees=15\n",
        "    ),\n",
        "\n",
        "    transforms.RandomAffine(\n",
        "        degrees=0,\n",
        "        translate=(0.05, 0.05),\n",
        "        scale=(0.95, 1.05)\n",
        "    ),\n",
        "\n",
        "    transforms.ColorJitter(\n",
        "        brightness=0.15,\n",
        "        contrast=0.15,\n",
        "        saturation=0.10\n",
        "    ),\n",
        "\n",
        "    transforms.ToTensor(),\n",
        "\n",
        "    transforms.Normalize(\n",
        "        mean=IMAGENET_MEAN,\n",
        "        std=IMAGENET_STD\n",
        "    ),\n",
        "\n",
        "    transforms.RandomErasing(\n",
        "        p=0.10,\n",
        "        scale=(0.01, 0.04)\n",
        "    )\n",
        "])\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Validation and testing transforms\n",
        "# No random augmentation\n",
        "# ---------------------------------------------------------\n",
        "eval_transforms = transforms.Compose([\n",
        "    RetinalPreprocessing(),\n",
        "\n",
        "    transforms.Resize(\n",
        "        (IMG_SIZE, IMG_SIZE)\n",
        "    ),\n",
        "\n",
        "    transforms.ToTensor(),\n",
        "\n",
        "    transforms.Normalize(\n",
        "        mean=IMAGENET_MEAN,\n",
        "        std=IMAGENET_STD\n",
        "    )\n",
        "])\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Custom APTOS Dataset\n",
        "# ---------------------------------------------------------\n",
        "class APTOSDataset(Dataset):\n",
        "    def __init__(\n",
        "        self,\n",
        "        dataframe,\n",
        "        transform=None\n",
        "    ):\n",
        "        self.dataframe = dataframe.reset_index(\n",
        "            drop=True\n",
        "        )\n",
        "\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dataframe)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        row = self.dataframe.iloc[index]\n",
        "\n",
        "        image_path = row[\"image_path\"]\n",
        "        label = int(row[\"diagnosis\"])\n",
        "\n",
        "        image = Image.open(\n",
        "            image_path\n",
        "        ).convert(\"RGB\")\n",
        "\n",
        "        if self.transform is not None:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        return image, label\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Create datasets\n",
        "# ---------------------------------------------------------\n",
        "train_dataset = APTOSDataset(\n",
        "    train_df,\n",
        "    transform=train_transforms\n",
        ")\n",
        "\n",
        "val_dataset = APTOSDataset(\n",
        "    val_df,\n",
        "    transform=eval_transforms\n",
        ")\n",
        "\n",
        "test_dataset = APTOSDataset(\n",
        "    test_df,\n",
        "    transform=eval_transforms\n",
        ")\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Create DataLoaders\n",
        "# ---------------------------------------------------------\n",
        "train_loader = DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        "    num_workers=NUM_WORKERS,\n",
        "    pin_memory=True,\n",
        "    persistent_workers=True,\n",
        "    prefetch_factor=2\n",
        ")\n",
        "\n",
        "val_loader = DataLoader(\n",
        "    val_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    num_workers=NUM_WORKERS,\n",
        "    pin_memory=True,\n",
        "    persistent_workers=True,\n",
        "    prefetch_factor=2\n",
        ")\n",
        "\n",
        "test_loader = DataLoader(\n",
        "    test_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    num_workers=NUM_WORKERS,\n",
        "    pin_memory=True,\n",
        "    persistent_workers=True,\n",
        "    prefetch_factor=2\n",
        ")\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Verify DataLoaders\n",
        "# ---------------------------------------------------------\n",
        "images, labels = next(\n",
        "    iter(train_loader)\n",
        ")\n",
        "\n",
        "print(\"Datasets and DataLoaders are ready.\")\n",
        "print(\"-\" * 55)\n",
        "\n",
        "print(\"Training samples:  \", len(train_dataset))\n",
        "print(\"Validation samples:\", len(val_dataset))\n",
        "print(\"Testing samples:   \", len(test_dataset))\n",
        "\n",
        "print(\"\\nTraining batches:  \", len(train_loader))\n",
        "print(\"Validation batches:\", len(val_loader))\n",
        "print(\"Testing batches:   \", len(test_loader))\n",
        "\n",
        "print(\"\\nImage batch shape:\", images.shape)\n",
        "print(\"Label batch shape:\", labels.shape)\n",
        "print(\"Image tensor type:\", images.dtype)\n",
        "print(\"Label values:\", labels[:10].tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UtfHuFpE0DoC",
        "outputId": "9a4fabeb-ac1a-4ecd-bad0-c5140f729e71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Stable DataLoaders recreated.\n",
            "Training batches: 81\n",
            "Validation batches: 18\n",
            "Testing batches: 18\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 10B: Recreate Stable DataLoaders\n",
        "# =========================================================\n",
        "\n",
        "import cv2\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Avoid OpenCV thread conflicts\n",
        "cv2.setNumThreads(0)\n",
        "\n",
        "# Start with a stable single-process loader\n",
        "SAFE_NUM_WORKERS = 0\n",
        "\n",
        "train_loader = DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        "    num_workers=SAFE_NUM_WORKERS,\n",
        "    pin_memory=True\n",
        ")\n",
        "\n",
        "val_loader = DataLoader(\n",
        "    val_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    num_workers=SAFE_NUM_WORKERS,\n",
        "    pin_memory=True\n",
        ")\n",
        "\n",
        "test_loader = DataLoader(\n",
        "    test_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    num_workers=SAFE_NUM_WORKERS,\n",
        "    pin_memory=True\n",
        ")\n",
        "\n",
        "print(\"Stable DataLoaders recreated.\")\n",
        "print(\"Training batches:\", len(train_loader))\n",
        "print(\"Validation batches:\", len(val_loader))\n",
        "print(\"Testing batches:\", len(test_loader))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQJHjdHm0IQb",
        "outputId": "a7e8c2e5-b8de-4c1b-c710-de9b87b80734"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "First batch loaded successfully.\n",
            "Batch shape: torch.Size([32, 3, 300, 300])\n",
            "Labels shape: torch.Size([32])\n",
            "Loading time: 3.52 seconds\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 10C: Test Data Loading Speed\n",
        "# =========================================================\n",
        "\n",
        "import time\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "test_images, test_labels = next(iter(train_loader))\n",
        "\n",
        "loading_time = time.time() - start_time\n",
        "\n",
        "print(\"First batch loaded successfully.\")\n",
        "print(\"Batch shape:\", test_images.shape)\n",
        "print(\"Labels shape:\", test_labels.shape)\n",
        "print(f\"Loading time: {loading_time:.2f} seconds\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1or7FPKt0NT-",
        "outputId": "9f514060-7223-4175-de4c-ce174345a3ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ConvNeXt forward pass completed.\n",
            "Input shape: torch.Size([32, 3, 300, 300])\n",
            "Output shape: torch.Size([32, 5])\n",
            "Model output is correct.\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 10D: Test ConvNeXt Forward Pass\n",
        "# =========================================================\n",
        "\n",
        "convnext_model.eval()\n",
        "\n",
        "test_images = test_images.to(\n",
        "    device,\n",
        "    non_blocking=True\n",
        ")\n",
        "\n",
        "with torch.no_grad():\n",
        "    with torch.amp.autocast(\n",
        "        device_type=\"cuda\",\n",
        "        enabled=use_amp\n",
        "    ):\n",
        "        test_outputs = convnext_model(test_images)\n",
        "\n",
        "print(\"ConvNeXt forward pass completed.\")\n",
        "print(\"Input shape:\", test_images.shape)\n",
        "print(\"Output shape:\", test_outputs.shape)\n",
        "\n",
        "assert test_outputs.shape == (\n",
        "    BATCH_SIZE,\n",
        "    NUM_CLASSES\n",
        ")\n",
        "\n",
        "print(\"Model output is correct.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wY_L9VpezJsf"
      },
      "source": [
        "## 6. ConvNeXt-Tiny Fine-Tuning\n",
        "\n",
        "ConvNeXt-Tiny is initialized using ImageNet-pretrained weights and fine-tuned\n",
        "on the APTOS 2019 training dataset.\n",
        "\n",
        "The original classification layer is replaced with a five-class output layer.\n",
        "Class-weighted cross-entropy loss is used to reduce bias toward the majority\n",
        "class. The best model is selected according to validation accuracy, with\n",
        "validation loss used as a tie-breaker.\n",
        "\n",
        "The trained model will later be used to extract 768-dimensional deep embeddings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IU80TE1lzKtF",
        "outputId": "7275ed9b-7692-4390-bd0c-674a6942bbbf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/convnext_tiny-983f1562.pth\" to /root/.cache/torch/hub/checkpoints/convnext_tiny-983f1562.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 109M/109M [00:00<00:00, 305MB/s] \n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ConvNeXt-Tiny model is ready.\n",
            "Embedding dimension: 768\n",
            "\n",
            "Updated classifier:\n",
            "Sequential(\n",
            "  (0): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)\n",
            "  (1): Flatten(start_dim=1, end_dim=-1)\n",
            "  (2): Sequential(\n",
            "    (0): Dropout(p=0.45, inplace=False)\n",
            "    (1): Linear(in_features=768, out_features=5, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 11: Build ConvNeXt-Tiny\n",
        "# =========================================================\n",
        "\n",
        "CONVNEXT_DROPOUT = 0.45\n",
        "\n",
        "# Load ImageNet-pretrained ConvNeXt-Tiny\n",
        "convnext_weights = models.ConvNeXt_Tiny_Weights.IMAGENET1K_V1\n",
        "\n",
        "convnext_model = models.convnext_tiny(\n",
        "    weights=convnext_weights\n",
        ")\n",
        "\n",
        "# Original classifier input dimension = 768\n",
        "convnext_embedding_dim = (\n",
        "    convnext_model.classifier[2].in_features\n",
        ")\n",
        "\n",
        "# Replace original ImageNet classifier\n",
        "convnext_model.classifier[2] = nn.Sequential(\n",
        "    nn.Dropout(p=CONVNEXT_DROPOUT),\n",
        "    nn.Linear(\n",
        "        convnext_embedding_dim,\n",
        "        NUM_CLASSES\n",
        "    )\n",
        ")\n",
        "\n",
        "convnext_model = convnext_model.to(device)\n",
        "\n",
        "print(\"ConvNeXt-Tiny model is ready.\")\n",
        "print(\"Embedding dimension:\", convnext_embedding_dim)\n",
        "print(\"\\nUpdated classifier:\")\n",
        "print(convnext_model.classifier)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74fOeZUWzRNs",
        "outputId": "e642012b-c56c-43ac-c691-34e799dcdd56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training and validation functions are ready.\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 12: Reusable Training and Validation Functions\n",
        "# =========================================================\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "def train_one_epoch(\n",
        "    model,\n",
        "    data_loader,\n",
        "    criterion,\n",
        "    optimizer,\n",
        "    scaler\n",
        "):\n",
        "    model.train()\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_correct = 0\n",
        "    total_samples = 0\n",
        "\n",
        "    for images, labels in tqdm(\n",
        "        data_loader,\n",
        "        desc=\"Training\",\n",
        "        leave=False\n",
        "    ):\n",
        "        images = images.to(\n",
        "            device,\n",
        "            non_blocking=True\n",
        "        )\n",
        "\n",
        "        labels = labels.to(\n",
        "            device,\n",
        "            non_blocking=True\n",
        "        )\n",
        "\n",
        "        optimizer.zero_grad(\n",
        "            set_to_none=True\n",
        "        )\n",
        "\n",
        "        with torch.amp.autocast(\n",
        "            device_type=\"cuda\",\n",
        "            enabled=use_amp\n",
        "        ):\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "        scaler.scale(loss).backward()\n",
        "\n",
        "        scaler.unscale_(optimizer)\n",
        "\n",
        "        torch.nn.utils.clip_grad_norm_(\n",
        "            model.parameters(),\n",
        "            max_norm=1.0\n",
        "        )\n",
        "\n",
        "        scaler.step(optimizer)\n",
        "        scaler.update()\n",
        "\n",
        "        predictions = outputs.argmax(dim=1)\n",
        "\n",
        "        batch_size = labels.size(0)\n",
        "\n",
        "        running_loss += (\n",
        "            loss.item() * batch_size\n",
        "        )\n",
        "\n",
        "        running_correct += (\n",
        "            predictions == labels\n",
        "        ).sum().item()\n",
        "\n",
        "        total_samples += batch_size\n",
        "\n",
        "    epoch_loss = (\n",
        "        running_loss / total_samples\n",
        "    )\n",
        "\n",
        "    epoch_accuracy = (\n",
        "        running_correct / total_samples\n",
        "    )\n",
        "\n",
        "    return epoch_loss, epoch_accuracy\n",
        "\n",
        "\n",
        "@torch.no_grad()\n",
        "def validate_one_epoch(\n",
        "    model,\n",
        "    data_loader,\n",
        "    criterion\n",
        "):\n",
        "    model.eval()\n",
        "\n",
        "    running_loss = 0.0\n",
        "    running_correct = 0\n",
        "    total_samples = 0\n",
        "\n",
        "    for images, labels in tqdm(\n",
        "        data_loader,\n",
        "        desc=\"Validation\",\n",
        "        leave=False\n",
        "    ):\n",
        "        images = images.to(\n",
        "            device,\n",
        "            non_blocking=True\n",
        "        )\n",
        "\n",
        "        labels = labels.to(\n",
        "            device,\n",
        "            non_blocking=True\n",
        "        )\n",
        "\n",
        "        with torch.amp.autocast(\n",
        "            device_type=\"cuda\",\n",
        "            enabled=use_amp\n",
        "        ):\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "        predictions = outputs.argmax(dim=1)\n",
        "\n",
        "        batch_size = labels.size(0)\n",
        "\n",
        "        running_loss += (\n",
        "            loss.item() * batch_size\n",
        "        )\n",
        "\n",
        "        running_correct += (\n",
        "            predictions == labels\n",
        "        ).sum().item()\n",
        "\n",
        "        total_samples += batch_size\n",
        "\n",
        "    epoch_loss = (\n",
        "        running_loss / total_samples\n",
        "    )\n",
        "\n",
        "    epoch_accuracy = (\n",
        "        running_correct / total_samples\n",
        "    )\n",
        "\n",
        "    return epoch_loss, epoch_accuracy\n",
        "\n",
        "\n",
        "print(\"Training and validation functions are ready.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZY1rvduzVDQ",
        "outputId": "705568c6-ffca-4b11-e8a3-e6d153493cf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "ConvNeXt Epoch 1/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.6777 | Train Accuracy: 92.16%\n",
            "Val Loss:   1.1389 | Val Accuracy: 81.97%\n",
            "Train-Val Gap: 10.19%\n",
            "Best ConvNeXt checkpoint saved.\n",
            "\n",
            "ConvNeXt Epoch 2/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.6310 | Train Accuracy: 92.78%\n",
            "Val Loss:   1.2571 | Val Accuracy: 81.60%\n",
            "Train-Val Gap: 11.18%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "ConvNeXt Epoch 3/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.6354 | Train Accuracy: 92.98%\n",
            "Val Loss:   1.1929 | Val Accuracy: 84.34%\n",
            "Train-Val Gap: 8.64%\n",
            "Best ConvNeXt checkpoint saved.\n",
            "\n",
            "ConvNeXt Epoch 4/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.6053 | Train Accuracy: 94.42%\n",
            "Val Loss:   1.2340 | Val Accuracy: 82.70%\n",
            "Train-Val Gap: 11.72%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "ConvNeXt Epoch 5/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5819 | Train Accuracy: 94.62%\n",
            "Val Loss:   1.3167 | Val Accuracy: 82.88%\n",
            "Train-Val Gap: 11.74%\n",
            "No validation improvement: 2/8\n",
            "\n",
            "ConvNeXt Epoch 6/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5648 | Train Accuracy: 95.83%\n",
            "Val Loss:   1.2182 | Val Accuracy: 83.79%\n",
            "Train-Val Gap: 12.04%\n",
            "No validation improvement: 3/8\n",
            "\n",
            "ConvNeXt Epoch 7/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5706 | Train Accuracy: 95.75%\n",
            "Val Loss:   1.2624 | Val Accuracy: 81.42%\n",
            "Train-Val Gap: 14.33%\n",
            "No validation improvement: 4/8\n",
            "\n",
            "ConvNeXt Epoch 8/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5597 | Train Accuracy: 95.86%\n",
            "Val Loss:   1.2962 | Val Accuracy: 84.34%\n",
            "Train-Val Gap: 11.53%\n",
            "No validation improvement: 5/8\n",
            "\n",
            "ConvNeXt Epoch 9/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5444 | Train Accuracy: 96.61%\n",
            "Val Loss:   1.1845 | Val Accuracy: 83.97%\n",
            "Train-Val Gap: 12.63%\n",
            "No validation improvement: 6/8\n",
            "\n",
            "ConvNeXt Epoch 10/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5450 | Train Accuracy: 96.41%\n",
            "Val Loss:   1.1995 | Val Accuracy: 85.06%\n",
            "Train-Val Gap: 11.35%\n",
            "Best ConvNeXt checkpoint saved.\n",
            "\n",
            "ConvNeXt Epoch 11/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5296 | Train Accuracy: 97.11%\n",
            "Val Loss:   1.1397 | Val Accuracy: 83.24%\n",
            "Train-Val Gap: 13.87%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "ConvNeXt Epoch 12/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5446 | Train Accuracy: 96.61%\n",
            "Val Loss:   1.2269 | Val Accuracy: 85.25%\n",
            "Train-Val Gap: 11.36%\n",
            "Best ConvNeXt checkpoint saved.\n",
            "\n",
            "ConvNeXt Epoch 13/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5228 | Train Accuracy: 97.58%\n",
            "Val Loss:   1.1833 | Val Accuracy: 84.15%\n",
            "Train-Val Gap: 13.43%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "ConvNeXt Epoch 14/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5265 | Train Accuracy: 97.31%\n",
            "Val Loss:   1.1846 | Val Accuracy: 85.25%\n",
            "Train-Val Gap: 12.06%\n",
            "Best ConvNeXt checkpoint saved.\n",
            "\n",
            "ConvNeXt Epoch 15/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5180 | Train Accuracy: 97.50%\n",
            "Val Loss:   1.2379 | Val Accuracy: 85.97%\n",
            "Train-Val Gap: 11.53%\n",
            "Best ConvNeXt checkpoint saved.\n",
            "\n",
            "ConvNeXt Epoch 16/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5084 | Train Accuracy: 97.85%\n",
            "Val Loss:   1.2094 | Val Accuracy: 85.43%\n",
            "Train-Val Gap: 12.43%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "ConvNeXt Epoch 17/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5132 | Train Accuracy: 97.70%\n",
            "Val Loss:   1.2466 | Val Accuracy: 83.97%\n",
            "Train-Val Gap: 13.73%\n",
            "No validation improvement: 2/8\n",
            "\n",
            "ConvNeXt Epoch 18/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5035 | Train Accuracy: 97.74%\n",
            "Val Loss:   1.2136 | Val Accuracy: 83.79%\n",
            "Train-Val Gap: 13.95%\n",
            "No validation improvement: 3/8\n",
            "\n",
            "ConvNeXt Epoch 19/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.4926 | Train Accuracy: 98.05%\n",
            "Val Loss:   1.2497 | Val Accuracy: 84.88%\n",
            "Train-Val Gap: 13.17%\n",
            "No validation improvement: 4/8\n",
            "\n",
            "ConvNeXt Epoch 20/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.4995 | Train Accuracy: 98.32%\n",
            "Val Loss:   1.2832 | Val Accuracy: 84.15%\n",
            "Train-Val Gap: 14.17%\n",
            "No validation improvement: 5/8\n",
            "\n",
            "ConvNeXt Epoch 21/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.5026 | Train Accuracy: 98.13%\n",
            "Val Loss:   1.2005 | Val Accuracy: 84.34%\n",
            "Train-Val Gap: 13.79%\n",
            "No validation improvement: 6/8\n",
            "\n",
            "ConvNeXt Epoch 22/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.4948 | Train Accuracy: 98.21%\n",
            "Val Loss:   1.2361 | Val Accuracy: 85.79%\n",
            "Train-Val Gap: 12.41%\n",
            "No validation improvement: 7/8\n",
            "\n",
            "ConvNeXt Epoch 23/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                           "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 0.4906 | Train Accuracy: 98.24%\n",
            "Val Loss:   1.2217 | Val Accuracy: 85.61%\n",
            "Train-Val Gap: 12.63%\n",
            "No validation improvement: 8/8\n",
            "\n",
            "Early stopping triggered.\n",
            "\n",
            "ConvNeXt Training Completed\n",
            "============================================================\n",
            "Best epoch: 15\n",
            "Best validation accuracy: 85.97%\n",
            "Best validation loss: 1.2379\n",
            "Training time: 140.62 minutes\n",
            "Checkpoint exists: True\n",
            "Checkpoint path: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Checkpoints/best_convnext_tiny.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 13: Fine-Tune ConvNeXt-Tiny\n",
        "# =========================================================\n",
        "\n",
        "import time\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Training configuration\n",
        "# ---------------------------------------------------------\n",
        "CONVNEXT_EPOCHS = 30\n",
        "CONVNEXT_LR = 3e-5\n",
        "CONVNEXT_WEIGHT_DECAY = 2e-4\n",
        "LABEL_SMOOTHING = 0.06\n",
        "EARLY_STOPPING_PATIENCE = 8\n",
        "\n",
        "convnext_checkpoint_path = (\n",
        "    CHECKPOINTS_DIR /\n",
        "    \"best_convnext_tiny.pth\"\n",
        ")\n",
        "\n",
        "convnext_history_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"convnext_training_history.csv\"\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Weighted loss for imbalanced data\n",
        "# ---------------------------------------------------------\n",
        "convnext_criterion = nn.CrossEntropyLoss(\n",
        "    weight=class_weights_tensor,\n",
        "    label_smoothing=LABEL_SMOOTHING\n",
        ")\n",
        "\n",
        "convnext_optimizer = optim.AdamW(\n",
        "    convnext_model.parameters(),\n",
        "    lr=CONVNEXT_LR,\n",
        "    weight_decay=CONVNEXT_WEIGHT_DECAY\n",
        ")\n",
        "\n",
        "convnext_scheduler = (\n",
        "    torch.optim.lr_scheduler.CosineAnnealingLR(\n",
        "        convnext_optimizer,\n",
        "        T_max=CONVNEXT_EPOCHS,\n",
        "        eta_min=1e-6\n",
        "    )\n",
        ")\n",
        "\n",
        "convnext_scaler = torch.amp.GradScaler(\n",
        "    \"cuda\",\n",
        "    enabled=use_amp\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# History and early-stopping variables\n",
        "# ---------------------------------------------------------\n",
        "convnext_history = []\n",
        "\n",
        "best_val_accuracy = 0.0\n",
        "best_val_loss = float(\"inf\")\n",
        "best_epoch = 0\n",
        "epochs_without_improvement = 0\n",
        "\n",
        "training_start_time = time.time()\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Training loop\n",
        "# ---------------------------------------------------------\n",
        "for epoch in range(1, CONVNEXT_EPOCHS + 1):\n",
        "\n",
        "    print(\n",
        "        f\"\\nConvNeXt Epoch \"\n",
        "        f\"{epoch}/{CONVNEXT_EPOCHS}\"\n",
        "    )\n",
        "\n",
        "    print(\"-\" * 60)\n",
        "\n",
        "    train_loss, train_accuracy = train_one_epoch(\n",
        "        model=convnext_model,\n",
        "        data_loader=train_loader,\n",
        "        criterion=convnext_criterion,\n",
        "        optimizer=convnext_optimizer,\n",
        "        scaler=convnext_scaler\n",
        "    )\n",
        "\n",
        "    val_loss, val_accuracy = validate_one_epoch(\n",
        "        model=convnext_model,\n",
        "        data_loader=val_loader,\n",
        "        criterion=convnext_criterion\n",
        "    )\n",
        "\n",
        "    current_lr = (\n",
        "        convnext_optimizer\n",
        "        .param_groups[0][\"lr\"]\n",
        "    )\n",
        "\n",
        "    convnext_scheduler.step()\n",
        "\n",
        "    generalization_gap = (\n",
        "        train_accuracy - val_accuracy\n",
        "    )\n",
        "\n",
        "    convnext_history.append({\n",
        "        \"Epoch\": epoch,\n",
        "        \"Learning Rate\": current_lr,\n",
        "        \"Train Loss\": train_loss,\n",
        "        \"Train Accuracy\": train_accuracy,\n",
        "        \"Validation Loss\": val_loss,\n",
        "        \"Validation Accuracy\": val_accuracy,\n",
        "        \"Train-Val Gap\": generalization_gap\n",
        "    })\n",
        "\n",
        "    # Save history after every epoch\n",
        "    pd.DataFrame(\n",
        "        convnext_history\n",
        "    ).to_csv(\n",
        "        convnext_history_path,\n",
        "        index=False\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Train Loss: {train_loss:.4f} | \"\n",
        "        f\"Train Accuracy: \"\n",
        "        f\"{train_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Val Loss:   {val_loss:.4f} | \"\n",
        "        f\"Val Accuracy: \"\n",
        "        f\"{val_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Train-Val Gap: \"\n",
        "        f\"{generalization_gap * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    # -----------------------------------------------------\n",
        "    # Save best validation checkpoint\n",
        "    # -----------------------------------------------------\n",
        "    is_better_model = (\n",
        "        val_accuracy > best_val_accuracy\n",
        "        or (\n",
        "            np.isclose(\n",
        "                val_accuracy,\n",
        "                best_val_accuracy\n",
        "            )\n",
        "            and val_loss < best_val_loss\n",
        "        )\n",
        "    )\n",
        "\n",
        "    if is_better_model:\n",
        "\n",
        "        best_val_accuracy = val_accuracy\n",
        "        best_val_loss = val_loss\n",
        "        best_epoch = epoch\n",
        "        epochs_without_improvement = 0\n",
        "\n",
        "        torch.save(\n",
        "            {\n",
        "                \"epoch\": epoch,\n",
        "                \"model_name\": \"ConvNeXt-Tiny\",\n",
        "                \"model_state_dict\":\n",
        "                    convnext_model.state_dict(),\n",
        "                \"optimizer_state_dict\":\n",
        "                    convnext_optimizer.state_dict(),\n",
        "                \"scheduler_state_dict\":\n",
        "                    convnext_scheduler.state_dict(),\n",
        "                \"train_accuracy\": train_accuracy,\n",
        "                \"train_loss\": train_loss,\n",
        "                \"val_accuracy\": val_accuracy,\n",
        "                \"val_loss\": val_loss,\n",
        "                \"embedding_dimension\":\n",
        "                    convnext_embedding_dim,\n",
        "                \"class_names\": CLASS_NAMES,\n",
        "                \"image_size\": IMG_SIZE\n",
        "            },\n",
        "            convnext_checkpoint_path\n",
        "        )\n",
        "\n",
        "        print(\"Best ConvNeXt checkpoint saved.\")\n",
        "\n",
        "    else:\n",
        "        epochs_without_improvement += 1\n",
        "\n",
        "        print(\n",
        "            \"No validation improvement: \"\n",
        "            f\"{epochs_without_improvement}/\"\n",
        "            f\"{EARLY_STOPPING_PATIENCE}\"\n",
        "        )\n",
        "\n",
        "    # -----------------------------------------------------\n",
        "    # Early stopping\n",
        "    # -----------------------------------------------------\n",
        "    if (\n",
        "        epochs_without_improvement\n",
        "        >= EARLY_STOPPING_PATIENCE\n",
        "    ):\n",
        "        print(\"\\nEarly stopping triggered.\")\n",
        "        break\n",
        "\n",
        "\n",
        "training_end_time = time.time()\n",
        "\n",
        "convnext_training_minutes = (\n",
        "    training_end_time -\n",
        "    training_start_time\n",
        ") / 60\n",
        "\n",
        "print(\"\\nConvNeXt Training Completed\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "print(\"Best epoch:\", best_epoch)\n",
        "\n",
        "print(\n",
        "    \"Best validation accuracy:\",\n",
        "    f\"{best_val_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Best validation loss:\",\n",
        "    f\"{best_val_loss:.4f}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Training time:\",\n",
        "    f\"{convnext_training_minutes:.2f} minutes\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Checkpoint exists:\",\n",
        "    convnext_checkpoint_path.exists()\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Checkpoint path:\",\n",
        "    convnext_checkpoint_path\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVZIlK_m1ixv",
        "outputId": "5449a2cf-b752-4c12-ee2a-f7870fae0747"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best ConvNeXt-Tiny checkpoint loaded successfully.\n",
            "------------------------------------------------------------\n",
            "Best Epoch: 15\n",
            "Training Accuracy: 97.50%\n",
            "Validation Accuracy: 85.97%\n",
            "Validation Loss: 1.2379\n",
            "Embedding Dimension: 768\n",
            "Model Mode: Evaluation\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 14: Reload Best ConvNeXt-Tiny Checkpoint\n",
        "# =========================================================\n",
        "\n",
        "convnext_checkpoint = torch.load(\n",
        "    convnext_checkpoint_path,\n",
        "    map_location=device\n",
        ")\n",
        "\n",
        "convnext_model.load_state_dict(\n",
        "    convnext_checkpoint[\"model_state_dict\"]\n",
        ")\n",
        "\n",
        "convnext_model = convnext_model.to(device)\n",
        "convnext_model.eval()\n",
        "\n",
        "print(\"Best ConvNeXt-Tiny checkpoint loaded successfully.\")\n",
        "print(\"-\" * 60)\n",
        "\n",
        "print(\n",
        "    \"Best Epoch:\",\n",
        "    convnext_checkpoint[\"epoch\"]\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Training Accuracy:\",\n",
        "    f\"{convnext_checkpoint['train_accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Validation Accuracy:\",\n",
        "    f\"{convnext_checkpoint['val_accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Validation Loss:\",\n",
        "    f\"{convnext_checkpoint['val_loss']:.4f}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Embedding Dimension:\",\n",
        "    convnext_checkpoint[\"embedding_dimension\"]\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Model Mode:\",\n",
        "    \"Evaluation\" if not convnext_model.training else \"Training\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hH5Nk4KX1lDo",
        "outputId": "c3566fea-a821-43f3-9ab9-982923d5c9e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ConvNeXt moved to CPU.\n",
            "GPU memory cleared.\n",
            "Downloading: \"https://download.pytorch.org/models/swin_v2_t-b137f0e2.pth\" to /root/.cache/torch/hub/checkpoints/swin_v2_t-b137f0e2.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 109M/109M [00:00<00:00, 322MB/s] \n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Swin-V2-Tiny model is ready.\n",
            "Embedding dimension: 768\n",
            "\n",
            "Updated classification head:\n",
            "Sequential(\n",
            "  (0): Dropout(p=0.45, inplace=False)\n",
            "  (1): Linear(in_features=768, out_features=5, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 15: Build Swin-V2-Tiny\n",
        "# =========================================================\n",
        "\n",
        "import gc\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Move ConvNeXt temporarily to CPU\n",
        "# The best checkpoint is already saved\n",
        "# ---------------------------------------------------------\n",
        "convnext_model = convnext_model.to(\"cpu\")\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "print(\"ConvNeXt moved to CPU.\")\n",
        "print(\"GPU memory cleared.\")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Build ImageNet-pretrained Swin-V2-Tiny\n",
        "# ---------------------------------------------------------\n",
        "SWIN_DROPOUT = 0.45\n",
        "\n",
        "swin_weights = (\n",
        "    models.Swin_V2_T_Weights.IMAGENET1K_V1\n",
        ")\n",
        "\n",
        "swin_model = models.swin_v2_t(\n",
        "    weights=swin_weights\n",
        ")\n",
        "\n",
        "# Embedding dimension before the classification head\n",
        "swin_embedding_dim = swin_model.head.in_features\n",
        "\n",
        "# Replace the ImageNet classification head\n",
        "swin_model.head = nn.Sequential(\n",
        "    nn.Dropout(p=SWIN_DROPOUT),\n",
        "    nn.Linear(\n",
        "        swin_embedding_dim,\n",
        "        NUM_CLASSES\n",
        "    )\n",
        ")\n",
        "\n",
        "swin_model = swin_model.to(device)\n",
        "\n",
        "print(\"\\nSwin-V2-Tiny model is ready.\")\n",
        "print(\"Embedding dimension:\", swin_embedding_dim)\n",
        "print(\"\\nUpdated classification head:\")\n",
        "print(swin_model.head)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FscG4Bhz2DwV",
        "outputId": "3e116d6f-d73e-4c5e-8695-a6855201cf5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Swin-V2-Tiny forward pass completed.\n",
            "-------------------------------------------------------\n",
            "Input shape:  torch.Size([32, 3, 300, 300])\n",
            "Output shape: torch.Size([32, 5])\n",
            "Embedding dimension: 768\n",
            "\n",
            "Swin-V2-Tiny output is correct.\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 16: Test Swin-V2-Tiny Forward Pass\n",
        "# =========================================================\n",
        "\n",
        "swin_model.eval()\n",
        "\n",
        "images_check, labels_check = next(\n",
        "    iter(train_loader)\n",
        ")\n",
        "\n",
        "images_check = images_check.to(\n",
        "    device,\n",
        "    non_blocking=True\n",
        ")\n",
        "\n",
        "with torch.no_grad():\n",
        "\n",
        "    with torch.amp.autocast(\n",
        "        device_type=\"cuda\",\n",
        "        enabled=use_amp\n",
        "    ):\n",
        "        outputs_check = swin_model(\n",
        "            images_check\n",
        "        )\n",
        "\n",
        "print(\"Swin-V2-Tiny forward pass completed.\")\n",
        "print(\"-\" * 55)\n",
        "\n",
        "print(\"Input shape: \", images_check.shape)\n",
        "print(\"Output shape:\", outputs_check.shape)\n",
        "print(\"Embedding dimension:\", swin_embedding_dim)\n",
        "\n",
        "assert outputs_check.shape == (\n",
        "    images_check.size(0),\n",
        "    NUM_CLASSES\n",
        "), \"Unexpected Swin output shape.\"\n",
        "\n",
        "assert swin_embedding_dim == 768, (\n",
        "    \"Unexpected Swin embedding dimension.\"\n",
        ")\n",
        "\n",
        "print(\"\\nSwin-V2-Tiny output is correct.\")\n",
        "\n",
        "# Release temporary tensors\n",
        "del images_check\n",
        "del labels_check\n",
        "del outputs_check\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzHvZ7Qq2Jc-",
        "outputId": "39132237-60a1-4c9c-f647-3de60e5e9282"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Swin-V2-Tiny Epoch 1/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.7551 | Train Accuracy: 10.65%\n",
            "Val Loss:   1.7300 | Val Accuracy: 5.46%\n",
            "Train-Val Gap: 5.19%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 2/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.7307 | Train Accuracy: 13.15%\n",
            "Val Loss:   1.6916 | Val Accuracy: 7.29%\n",
            "Train-Val Gap: 5.86%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 3/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.7447 | Train Accuracy: 11.82%\n",
            "Val Loss:   1.6994 | Val Accuracy: 8.01%\n",
            "Train-Val Gap: 3.81%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 4/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.7122 | Train Accuracy: 16.74%\n",
            "Val Loss:   1.6806 | Val Accuracy: 12.93%\n",
            "Train-Val Gap: 3.81%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 5/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.7002 | Train Accuracy: 23.45%\n",
            "Val Loss:   1.6425 | Val Accuracy: 29.87%\n",
            "Train-Val Gap: -6.42%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 6/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.6638 | Train Accuracy: 30.75%\n",
            "Val Loss:   1.6186 | Val Accuracy: 35.88%\n",
            "Train-Val Gap: -5.14%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 7/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.6521 | Train Accuracy: 34.41%\n",
            "Val Loss:   1.5990 | Val Accuracy: 41.71%\n",
            "Train-Val Gap: -7.30%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 8/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.6555 | Train Accuracy: 37.69%\n",
            "Val Loss:   1.5859 | Val Accuracy: 45.36%\n",
            "Train-Val Gap: -7.66%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 9/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.6244 | Train Accuracy: 41.63%\n",
            "Val Loss:   1.5744 | Val Accuracy: 48.45%\n",
            "Train-Val Gap: -6.82%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 10/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.6034 | Train Accuracy: 43.15%\n",
            "Val Loss:   1.5662 | Val Accuracy: 52.09%\n",
            "Train-Val Gap: -8.94%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 11/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5890 | Train Accuracy: 46.43%\n",
            "Val Loss:   1.5561 | Val Accuracy: 54.28%\n",
            "Train-Val Gap: -7.85%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 12/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5985 | Train Accuracy: 47.41%\n",
            "Val Loss:   1.5447 | Val Accuracy: 57.74%\n",
            "Train-Val Gap: -10.34%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 13/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5830 | Train Accuracy: 48.42%\n",
            "Val Loss:   1.5354 | Val Accuracy: 59.93%\n",
            "Train-Val Gap: -11.51%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 14/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5649 | Train Accuracy: 50.02%\n",
            "Val Loss:   1.5299 | Val Accuracy: 59.93%\n",
            "Train-Val Gap: -9.91%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 15/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5680 | Train Accuracy: 50.06%\n",
            "Val Loss:   1.5282 | Val Accuracy: 59.02%\n",
            "Train-Val Gap: -8.96%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 16/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5721 | Train Accuracy: 50.68%\n",
            "Val Loss:   1.5245 | Val Accuracy: 61.75%\n",
            "Train-Val Gap: -11.07%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 17/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5615 | Train Accuracy: 49.82%\n",
            "Val Loss:   1.5164 | Val Accuracy: 59.20%\n",
            "Train-Val Gap: -9.37%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 18/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5489 | Train Accuracy: 51.46%\n",
            "Val Loss:   1.5106 | Val Accuracy: 62.30%\n",
            "Train-Val Gap: -10.83%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 19/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5634 | Train Accuracy: 49.51%\n",
            "Val Loss:   1.5079 | Val Accuracy: 63.39%\n",
            "Train-Val Gap: -13.88%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 20/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5430 | Train Accuracy: 51.89%\n",
            "Val Loss:   1.5068 | Val Accuracy: 63.93%\n",
            "Train-Val Gap: -12.04%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 21/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5271 | Train Accuracy: 51.97%\n",
            "Val Loss:   1.5029 | Val Accuracy: 64.30%\n",
            "Train-Val Gap: -12.33%\n",
            "Best Swin-V2-Tiny checkpoint saved.\n",
            "\n",
            "Swin-V2-Tiny Epoch 22/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5375 | Train Accuracy: 52.24%\n",
            "Val Loss:   1.4997 | Val Accuracy: 63.75%\n",
            "Train-Val Gap: -11.51%\n",
            "No validation improvement: 1/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 23/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5429 | Train Accuracy: 52.83%\n",
            "Val Loss:   1.5062 | Val Accuracy: 62.66%\n",
            "Train-Val Gap: -9.83%\n",
            "No validation improvement: 2/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 24/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5306 | Train Accuracy: 53.37%\n",
            "Val Loss:   1.5035 | Val Accuracy: 60.66%\n",
            "Train-Val Gap: -7.28%\n",
            "No validation improvement: 3/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 25/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5189 | Train Accuracy: 52.52%\n",
            "Val Loss:   1.5002 | Val Accuracy: 61.02%\n",
            "Train-Val Gap: -8.50%\n",
            "No validation improvement: 4/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 26/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5289 | Train Accuracy: 52.87%\n",
            "Val Loss:   1.4966 | Val Accuracy: 60.84%\n",
            "Train-Val Gap: -7.97%\n",
            "No validation improvement: 5/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 27/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5347 | Train Accuracy: 51.66%\n",
            "Val Loss:   1.4957 | Val Accuracy: 61.93%\n",
            "Train-Val Gap: -10.27%\n",
            "No validation improvement: 6/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 28/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5606 | Train Accuracy: 51.62%\n",
            "Val Loss:   1.4948 | Val Accuracy: 61.02%\n",
            "Train-Val Gap: -9.40%\n",
            "No validation improvement: 7/8\n",
            "\n",
            "Swin-V2-Tiny Epoch 29/30\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                           "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.5440 | Train Accuracy: 51.97%\n",
            "Val Loss:   1.4941 | Val Accuracy: 60.47%\n",
            "Train-Val Gap: -8.50%\n",
            "No validation improvement: 8/8\n",
            "\n",
            "Early stopping triggered.\n",
            "\n",
            "Swin-V2-Tiny Training Completed\n",
            "============================================================\n",
            "Best epoch: 21\n",
            "Best validation accuracy: 64.30%\n",
            "Best validation loss: 1.5029\n",
            "Training time: 179.76 minutes\n",
            "Checkpoint exists: True\n",
            "Checkpoint path: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Checkpoints/best_swin_v2_tiny.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 17: Fine-Tune Swin-V2-Tiny\n",
        "# =========================================================\n",
        "\n",
        "import time\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Training configuration\n",
        "# ---------------------------------------------------------\n",
        "SWIN_EPOCHS = 30\n",
        "SWIN_LR = 3e-5\n",
        "SWIN_WEIGHT_DECAY = 2e-4\n",
        "SWIN_LABEL_SMOOTHING = 0.06\n",
        "SWIN_EARLY_STOPPING_PATIENCE = 8\n",
        "\n",
        "swin_checkpoint_path = (\n",
        "    CHECKPOINTS_DIR /\n",
        "    \"best_swin_v2_tiny.pth\"\n",
        ")\n",
        "\n",
        "swin_history_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"swin_v2_training_history.csv\"\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Weighted loss for imbalanced data\n",
        "# ---------------------------------------------------------\n",
        "swin_criterion = nn.CrossEntropyLoss(\n",
        "    weight=class_weights_tensor,\n",
        "    label_smoothing=SWIN_LABEL_SMOOTHING\n",
        ")\n",
        "\n",
        "swin_optimizer = optim.AdamW(\n",
        "    swin_model.parameters(),\n",
        "    lr=SWIN_LR,\n",
        "    weight_decay=SWIN_WEIGHT_DECAY\n",
        ")\n",
        "\n",
        "swin_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
        "    swin_optimizer,\n",
        "    T_max=SWIN_EPOCHS,\n",
        "    eta_min=1e-6\n",
        ")\n",
        "\n",
        "swin_scaler = torch.amp.GradScaler(\n",
        "    \"cuda\",\n",
        "    enabled=use_amp\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Training tracking\n",
        "# ---------------------------------------------------------\n",
        "swin_history = []\n",
        "\n",
        "best_swin_val_accuracy = 0.0\n",
        "best_swin_val_loss = float(\"inf\")\n",
        "best_swin_epoch = 0\n",
        "swin_epochs_without_improvement = 0\n",
        "\n",
        "swin_training_start_time = time.time()\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Training loop\n",
        "# ---------------------------------------------------------\n",
        "for epoch in range(1, SWIN_EPOCHS + 1):\n",
        "\n",
        "    print(\n",
        "        f\"\\nSwin-V2-Tiny Epoch \"\n",
        "        f\"{epoch}/{SWIN_EPOCHS}\"\n",
        "    )\n",
        "\n",
        "    print(\"-\" * 60)\n",
        "\n",
        "    train_loss, train_accuracy = train_one_epoch(\n",
        "        model=swin_model,\n",
        "        data_loader=train_loader,\n",
        "        criterion=swin_criterion,\n",
        "        optimizer=swin_optimizer,\n",
        "        scaler=swin_scaler\n",
        "    )\n",
        "\n",
        "    val_loss, val_accuracy = validate_one_epoch(\n",
        "        model=swin_model,\n",
        "        data_loader=val_loader,\n",
        "        criterion=swin_criterion\n",
        "    )\n",
        "\n",
        "    current_lr = (\n",
        "        swin_optimizer.param_groups[0][\"lr\"]\n",
        "    )\n",
        "\n",
        "    swin_scheduler.step()\n",
        "\n",
        "    generalization_gap = (\n",
        "        train_accuracy - val_accuracy\n",
        "    )\n",
        "\n",
        "    swin_history.append({\n",
        "        \"Epoch\": epoch,\n",
        "        \"Learning Rate\": current_lr,\n",
        "        \"Train Loss\": train_loss,\n",
        "        \"Train Accuracy\": train_accuracy,\n",
        "        \"Validation Loss\": val_loss,\n",
        "        \"Validation Accuracy\": val_accuracy,\n",
        "        \"Train-Val Gap\": generalization_gap\n",
        "    })\n",
        "\n",
        "    # Save history after every epoch\n",
        "    pd.DataFrame(\n",
        "        swin_history\n",
        "    ).to_csv(\n",
        "        swin_history_path,\n",
        "        index=False\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Train Loss: {train_loss:.4f} | \"\n",
        "        f\"Train Accuracy: {train_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Val Loss:   {val_loss:.4f} | \"\n",
        "        f\"Val Accuracy: {val_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Train-Val Gap: \"\n",
        "        f\"{generalization_gap * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    # -----------------------------------------------------\n",
        "    # Save best validation checkpoint\n",
        "    # -----------------------------------------------------\n",
        "    is_better_swin = (\n",
        "        val_accuracy > best_swin_val_accuracy\n",
        "        or (\n",
        "            np.isclose(\n",
        "                val_accuracy,\n",
        "                best_swin_val_accuracy\n",
        "            )\n",
        "            and val_loss < best_swin_val_loss\n",
        "        )\n",
        "    )\n",
        "\n",
        "    if is_better_swin:\n",
        "\n",
        "        best_swin_val_accuracy = val_accuracy\n",
        "        best_swin_val_loss = val_loss\n",
        "        best_swin_epoch = epoch\n",
        "        swin_epochs_without_improvement = 0\n",
        "\n",
        "        torch.save(\n",
        "            {\n",
        "                \"epoch\": epoch,\n",
        "                \"model_name\": \"Swin-V2-Tiny\",\n",
        "                \"model_state_dict\":\n",
        "                    swin_model.state_dict(),\n",
        "                \"optimizer_state_dict\":\n",
        "                    swin_optimizer.state_dict(),\n",
        "                \"scheduler_state_dict\":\n",
        "                    swin_scheduler.state_dict(),\n",
        "                \"train_accuracy\": train_accuracy,\n",
        "                \"train_loss\": train_loss,\n",
        "                \"val_accuracy\": val_accuracy,\n",
        "                \"val_loss\": val_loss,\n",
        "                \"embedding_dimension\":\n",
        "                    swin_embedding_dim,\n",
        "                \"class_names\": CLASS_NAMES,\n",
        "                \"image_size\": IMG_SIZE\n",
        "            },\n",
        "            swin_checkpoint_path\n",
        "        )\n",
        "\n",
        "        print(\"Best Swin-V2-Tiny checkpoint saved.\")\n",
        "\n",
        "    else:\n",
        "        swin_epochs_without_improvement += 1\n",
        "\n",
        "        print(\n",
        "            \"No validation improvement: \"\n",
        "            f\"{swin_epochs_without_improvement}/\"\n",
        "            f\"{SWIN_EARLY_STOPPING_PATIENCE}\"\n",
        "        )\n",
        "\n",
        "    # -----------------------------------------------------\n",
        "    # Early stopping\n",
        "    # -----------------------------------------------------\n",
        "    if (\n",
        "        swin_epochs_without_improvement\n",
        "        >= SWIN_EARLY_STOPPING_PATIENCE\n",
        "    ):\n",
        "        print(\"\\nEarly stopping triggered.\")\n",
        "        break\n",
        "\n",
        "\n",
        "swin_training_end_time = time.time()\n",
        "\n",
        "swin_training_minutes = (\n",
        "    swin_training_end_time -\n",
        "    swin_training_start_time\n",
        ") / 60\n",
        "\n",
        "print(\"\\nSwin-V2-Tiny Training Completed\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "print(\"Best epoch:\", best_swin_epoch)\n",
        "\n",
        "print(\n",
        "    \"Best validation accuracy:\",\n",
        "    f\"{best_swin_val_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Best validation loss:\",\n",
        "    f\"{best_swin_val_loss:.4f}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Training time:\",\n",
        "    f\"{swin_training_minutes:.2f} minutes\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Checkpoint exists:\",\n",
        "    swin_checkpoint_path.exists()\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Checkpoint path:\",\n",
        "    swin_checkpoint_path\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BN1O00fogWHV",
        "outputId": "e48cbc3a-a22a-4ee4-884a-6ea5cc6a0f45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Corrected Swin training loader is ready.\n",
            "Training samples: 2563\n",
            "Training batches: 81\n",
            "Image batch shape: torch.Size([32, 3, 300, 300])\n"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 17B: Lighter Augmentation for Swin-V2-Tiny\n",
        "# =========================================================\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "\n",
        "# Swin is currently underfitting, so use lighter augmentation\n",
        "swin_train_transforms = transforms.Compose([\n",
        "    RetinalPreprocessing(),\n",
        "\n",
        "    transforms.Resize(\n",
        "        (IMG_SIZE, IMG_SIZE)\n",
        "    ),\n",
        "\n",
        "    transforms.RandomHorizontalFlip(\n",
        "        p=0.5\n",
        "    ),\n",
        "\n",
        "    transforms.RandomRotation(\n",
        "        degrees=8\n",
        "    ),\n",
        "\n",
        "    transforms.ColorJitter(\n",
        "        brightness=0.08,\n",
        "        contrast=0.08\n",
        "    ),\n",
        "\n",
        "    transforms.ToTensor(),\n",
        "\n",
        "    transforms.Normalize(\n",
        "        mean=IMAGENET_MEAN,\n",
        "        std=IMAGENET_STD\n",
        "    )\n",
        "])\n",
        "\n",
        "swin_train_dataset = APTOSDataset(\n",
        "    train_df,\n",
        "    transform=swin_train_transforms\n",
        ")\n",
        "\n",
        "swin_train_loader = DataLoader(\n",
        "    swin_train_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        "    num_workers=0,\n",
        "    pin_memory=True\n",
        ")\n",
        "\n",
        "images_check, labels_check = next(\n",
        "    iter(swin_train_loader)\n",
        ")\n",
        "\n",
        "print(\"Corrected Swin training loader is ready.\")\n",
        "print(\"Training samples:\", len(swin_train_dataset))\n",
        "print(\"Training batches:\", len(swin_train_loader))\n",
        "print(\"Image batch shape:\", images_check.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "H61EUY9qgeRh",
        "outputId": "1504fac8-8954-4999-db1c-bb4385a21453"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded previous best Swin checkpoint.\n",
            "Previous best validation accuracy: 64.30%\n",
            "Corrected dropout: 0.2\n",
            "\n",
            "Corrected Swin Epoch 1/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.3924 | Train Accuracy: 56.77%\n",
            "Val Loss:   1.3590 | Val Accuracy: 65.94%\n",
            "Train-Val Gap: -9.17%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "Improved Swin checkpoint saved.\n",
            "\n",
            "Corrected Swin Epoch 2/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.3227 | Train Accuracy: 60.09%\n",
            "Val Loss:   1.2930 | Val Accuracy: 68.67%\n",
            "Train-Val Gap: -8.58%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "Improved Swin checkpoint saved.\n",
            "\n",
            "Corrected Swin Epoch 3/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.2586 | Train Accuracy: 65.28%\n",
            "Val Loss:   1.2540 | Val Accuracy: 65.39%\n",
            "Train-Val Gap: -0.12%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 1/5\n",
            "\n",
            "Corrected Swin Epoch 4/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.2199 | Train Accuracy: 64.96%\n",
            "Val Loss:   1.2200 | Val Accuracy: 67.94%\n",
            "Train-Val Gap: -2.98%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 2/5\n",
            "\n",
            "Corrected Swin Epoch 5/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.2054 | Train Accuracy: 65.63%\n",
            "Val Loss:   1.1916 | Val Accuracy: 68.85%\n",
            "Train-Val Gap: -3.23%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "Improved Swin checkpoint saved.\n",
            "\n",
            "Corrected Swin Epoch 6/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.1667 | Train Accuracy: 66.99%\n",
            "Val Loss:   1.1684 | Val Accuracy: 68.85%\n",
            "Train-Val Gap: -1.86%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "Improved Swin checkpoint saved.\n",
            "\n",
            "Corrected Swin Epoch 7/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.1546 | Train Accuracy: 68.05%\n",
            "Val Loss:   1.1735 | Val Accuracy: 71.22%\n",
            "Train-Val Gap: -3.18%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "Improved Swin checkpoint saved.\n",
            "\n",
            "Corrected Swin Epoch 8/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.1331 | Train Accuracy: 68.28%\n",
            "Val Loss:   1.1484 | Val Accuracy: 70.31%\n",
            "Train-Val Gap: -2.03%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 1/5\n",
            "\n",
            "Corrected Swin Epoch 9/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.1303 | Train Accuracy: 67.93%\n",
            "Val Loss:   1.1297 | Val Accuracy: 69.40%\n",
            "Train-Val Gap: -1.47%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 2/5\n",
            "\n",
            "Corrected Swin Epoch 10/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.0990 | Train Accuracy: 69.61%\n",
            "Val Loss:   1.1285 | Val Accuracy: 73.77%\n",
            "Train-Val Gap: -4.16%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "Improved Swin checkpoint saved.\n",
            "\n",
            "Corrected Swin Epoch 11/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.1109 | Train Accuracy: 69.76%\n",
            "Val Loss:   1.1311 | Val Accuracy: 71.04%\n",
            "Train-Val Gap: -1.28%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 1/5\n",
            "\n",
            "Corrected Swin Epoch 12/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.0883 | Train Accuracy: 70.78%\n",
            "Val Loss:   1.1300 | Val Accuracy: 73.22%\n",
            "Train-Val Gap: -2.45%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 2/5\n",
            "\n",
            "Corrected Swin Epoch 13/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.0885 | Train Accuracy: 70.50%\n",
            "Val Loss:   1.1012 | Val Accuracy: 70.67%\n",
            "Train-Val Gap: -0.17%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 3/5\n",
            "\n",
            "Corrected Swin Epoch 14/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ""
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.0890 | Train Accuracy: 69.02%\n",
            "Val Loss:   1.0863 | Val Accuracy: 70.31%\n",
            "Train-Val Gap: -1.29%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 4/5\n",
            "\n",
            "Corrected Swin Epoch 15/15\n",
            "------------------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                           "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Loss: 1.0805 | Train Accuracy: 70.78%\n",
            "Val Loss:   1.1027 | Val Accuracy: 70.49%\n",
            "Train-Val Gap: 0.28%\n",
            "Backbone LR: 1.00e-05 | Head LR: 2.00e-04\n",
            "No improvement: 5/5\n",
            "\n",
            "Early stopping triggered.\n",
            "\n",
            "Corrected Swin Training Completed\n",
            "============================================================\n",
            "Best validation accuracy: 73.77%\n",
            "Best validation loss: 1.1285\n",
            "Training time: 97.90 minutes\n",
            "Checkpoint path: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Checkpoints/best_swin_v2_tiny.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r"
          ]
        }
      ],
      "source": [
        "# =========================================================\n",
        "# Cell 17C: Corrected Swin-V2-Tiny Fine-Tuning\n",
        "# =========================================================\n",
        "\n",
        "import time\n",
        "import gc\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Load the best existing checkpoint: Epoch 21\n",
        "# ---------------------------------------------------------\n",
        "swin_checkpoint = torch.load(\n",
        "    swin_checkpoint_path,\n",
        "    map_location=device\n",
        ")\n",
        "\n",
        "swin_model.load_state_dict(\n",
        "    swin_checkpoint[\"model_state_dict\"]\n",
        ")\n",
        "\n",
        "swin_model = swin_model.to(device)\n",
        "\n",
        "# Reduce excessive dropout\n",
        "swin_model.head[0].p = 0.20\n",
        "\n",
        "print(\"Loaded previous best Swin checkpoint.\")\n",
        "print(\n",
        "    \"Previous best validation accuracy:\",\n",
        "    f\"{swin_checkpoint['val_accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "print(\"Corrected dropout:\", swin_model.head[0].p)\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Corrected training settings\n",
        "# ---------------------------------------------------------\n",
        "CORRECTED_SWIN_EPOCHS = 15\n",
        "CORRECTED_PATIENCE = 5\n",
        "\n",
        "BACKBONE_LR = 1e-5\n",
        "HEAD_LR = 2e-4\n",
        "CORRECTED_WEIGHT_DECAY = 1e-4\n",
        "CORRECTED_LABEL_SMOOTHING = 0.02\n",
        "\n",
        "corrected_history_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"swin_v2_corrected_training_history.csv\"\n",
        ")\n",
        "\n",
        "# Keep weighted loss to handle class imbalance\n",
        "corrected_swin_criterion = nn.CrossEntropyLoss(\n",
        "    weight=class_weights_tensor,\n",
        "    label_smoothing=CORRECTED_LABEL_SMOOTHING\n",
        ")\n",
        "\n",
        "# Different learning rates:\n",
        "# small LR for pretrained backbone\n",
        "# larger LR for classification head\n",
        "backbone_parameters = [\n",
        "    parameter\n",
        "    for name, parameter in swin_model.named_parameters()\n",
        "    if not name.startswith(\"head.\")\n",
        "]\n",
        "\n",
        "head_parameters = list(\n",
        "    swin_model.head.parameters()\n",
        ")\n",
        "\n",
        "corrected_swin_optimizer = optim.AdamW(\n",
        "    [\n",
        "        {\n",
        "            \"params\": backbone_parameters,\n",
        "            \"lr\": BACKBONE_LR\n",
        "        },\n",
        "        {\n",
        "            \"params\": head_parameters,\n",
        "            \"lr\": HEAD_LR\n",
        "        }\n",
        "    ],\n",
        "    weight_decay=CORRECTED_WEIGHT_DECAY\n",
        ")\n",
        "\n",
        "corrected_swin_scheduler = (\n",
        "    torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
        "        corrected_swin_optimizer,\n",
        "        mode=\"min\",\n",
        "        factor=0.4,\n",
        "        patience=2,\n",
        "        min_lr=1e-7\n",
        "    )\n",
        ")\n",
        "\n",
        "corrected_swin_scaler = torch.amp.GradScaler(\n",
        "    \"cuda\",\n",
        "    enabled=use_amp\n",
        ")\n",
        "\n",
        "# Start comparison from the previous best result\n",
        "best_swin_val_accuracy = float(\n",
        "    swin_checkpoint[\"val_accuracy\"]\n",
        ")\n",
        "\n",
        "best_swin_val_loss = float(\n",
        "    swin_checkpoint[\"val_loss\"]\n",
        ")\n",
        "\n",
        "best_swin_epoch = int(\n",
        "    swin_checkpoint[\"epoch\"]\n",
        ")\n",
        "\n",
        "epochs_without_improvement = 0\n",
        "corrected_swin_history = []\n",
        "\n",
        "training_start_time = time.time()\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Resume fine-tuning\n",
        "# ---------------------------------------------------------\n",
        "for continuation_epoch in range(\n",
        "    1,\n",
        "    CORRECTED_SWIN_EPOCHS + 1\n",
        "):\n",
        "\n",
        "    print(\n",
        "        f\"\\nCorrected Swin Epoch \"\n",
        "        f\"{continuation_epoch}/\"\n",
        "        f\"{CORRECTED_SWIN_EPOCHS}\"\n",
        "    )\n",
        "    print(\"-\" * 60)\n",
        "\n",
        "    train_loss, train_accuracy = train_one_epoch(\n",
        "        model=swin_model,\n",
        "        data_loader=swin_train_loader,\n",
        "        criterion=corrected_swin_criterion,\n",
        "        optimizer=corrected_swin_optimizer,\n",
        "        scaler=corrected_swin_scaler\n",
        "    )\n",
        "\n",
        "    val_loss, val_accuracy = validate_one_epoch(\n",
        "        model=swin_model,\n",
        "        data_loader=val_loader,\n",
        "        criterion=corrected_swin_criterion\n",
        "    )\n",
        "\n",
        "    corrected_swin_scheduler.step(\n",
        "        val_loss\n",
        "    )\n",
        "\n",
        "    train_val_gap = (\n",
        "        train_accuracy - val_accuracy\n",
        "    )\n",
        "\n",
        "    backbone_lr = (\n",
        "        corrected_swin_optimizer\n",
        "        .param_groups[0][\"lr\"]\n",
        "    )\n",
        "\n",
        "    head_lr = (\n",
        "        corrected_swin_optimizer\n",
        "        .param_groups[1][\"lr\"]\n",
        "    )\n",
        "\n",
        "    corrected_swin_history.append({\n",
        "        \"Continuation Epoch\": continuation_epoch,\n",
        "        \"Backbone Learning Rate\": backbone_lr,\n",
        "        \"Head Learning Rate\": head_lr,\n",
        "        \"Train Loss\": train_loss,\n",
        "        \"Train Accuracy\": train_accuracy,\n",
        "        \"Validation Loss\": val_loss,\n",
        "        \"Validation Accuracy\": val_accuracy,\n",
        "        \"Train-Val Gap\": train_val_gap\n",
        "    })\n",
        "\n",
        "    pd.DataFrame(\n",
        "        corrected_swin_history\n",
        "    ).to_csv(\n",
        "        corrected_history_path,\n",
        "        index=False\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Train Loss: {train_loss:.4f} | \"\n",
        "        f\"Train Accuracy: \"\n",
        "        f\"{train_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Val Loss:   {val_loss:.4f} | \"\n",
        "        f\"Val Accuracy: \"\n",
        "        f\"{val_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Train-Val Gap: \"\n",
        "        f\"{train_val_gap * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Backbone LR: {backbone_lr:.2e} | \"\n",
        "        f\"Head LR: {head_lr:.2e}\"\n",
        "    )\n",
        "\n",
        "    # Save only if better than previous 64.30%\n",
        "    improved = (\n",
        "        val_accuracy > best_swin_val_accuracy\n",
        "        or (\n",
        "            np.isclose(\n",
        "                val_accuracy,\n",
        "                best_swin_val_accuracy\n",
        "            )\n",
        "            and val_loss < best_swin_val_loss\n",
        "        )\n",
        "    )\n",
        "\n",
        "    if improved:\n",
        "\n",
        "        best_swin_val_accuracy = val_accuracy\n",
        "        best_swin_val_loss = val_loss\n",
        "        best_swin_epoch = continuation_epoch\n",
        "        epochs_without_improvement = 0\n",
        "\n",
        "        torch.save(\n",
        "            {\n",
        "                \"epoch\": continuation_epoch,\n",
        "                \"training_stage\":\n",
        "                    \"corrected_continuation\",\n",
        "                \"model_name\":\n",
        "                    \"Swin-V2-Tiny\",\n",
        "                \"model_state_dict\":\n",
        "                    swin_model.state_dict(),\n",
        "                \"train_accuracy\":\n",
        "                    train_accuracy,\n",
        "                \"train_loss\":\n",
        "                    train_loss,\n",
        "                \"val_accuracy\":\n",
        "                    val_accuracy,\n",
        "                \"val_loss\":\n",
        "                    val_loss,\n",
        "                \"embedding_dimension\":\n",
        "                    swin_embedding_dim,\n",
        "                \"class_names\":\n",
        "                    CLASS_NAMES,\n",
        "                \"image_size\":\n",
        "                    IMG_SIZE,\n",
        "                \"dropout\":\n",
        "                    0.20,\n",
        "                \"backbone_lr\":\n",
        "                    BACKBONE_LR,\n",
        "                \"head_lr\":\n",
        "                    HEAD_LR\n",
        "            },\n",
        "            swin_checkpoint_path\n",
        "        )\n",
        "\n",
        "        print(\n",
        "            \"Improved Swin checkpoint saved.\"\n",
        "        )\n",
        "\n",
        "    else:\n",
        "        epochs_without_improvement += 1\n",
        "\n",
        "        print(\n",
        "            \"No improvement: \"\n",
        "            f\"{epochs_without_improvement}/\"\n",
        "            f\"{CORRECTED_PATIENCE}\"\n",
        "        )\n",
        "\n",
        "    if (\n",
        "        epochs_without_improvement\n",
        "        >= CORRECTED_PATIENCE\n",
        "    ):\n",
        "        print(\"\\nEarly stopping triggered.\")\n",
        "        break\n",
        "\n",
        "\n",
        "training_minutes = (\n",
        "    time.time() - training_start_time\n",
        ") / 60\n",
        "\n",
        "print(\"\\nCorrected Swin Training Completed\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "print(\n",
        "    \"Best validation accuracy:\",\n",
        "    f\"{best_swin_val_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Best validation loss:\",\n",
        "    f\"{best_swin_val_loss:.4f}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Training time:\",\n",
        "    f\"{training_minutes:.2f} minutes\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Checkpoint path:\",\n",
        "    swin_checkpoint_path\n",
        ")\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 18: Load Best ConvNeXt and Swin Models\n",
        "# =========================================================\n",
        "\n",
        "import gc\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Load best ConvNeXt-Tiny\n",
        "# ---------------------------------------------------------\n",
        "convnext_model = models.convnext_tiny(\n",
        "    weights=None\n",
        ")\n",
        "\n",
        "convnext_embedding_dim = (\n",
        "    convnext_model.classifier[2].in_features\n",
        ")\n",
        "\n",
        "convnext_model.classifier[2] = nn.Sequential(\n",
        "    nn.Dropout(p=0.45),\n",
        "    nn.Linear(\n",
        "        convnext_embedding_dim,\n",
        "        NUM_CLASSES\n",
        "    )\n",
        ")\n",
        "\n",
        "convnext_checkpoint = torch.load(\n",
        "    convnext_checkpoint_path,\n",
        "    map_location=\"cpu\"\n",
        ")\n",
        "\n",
        "convnext_model.load_state_dict(\n",
        "    convnext_checkpoint[\"model_state_dict\"]\n",
        ")\n",
        "\n",
        "convnext_model.eval()\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Load best corrected Swin-V2-Tiny\n",
        "# ---------------------------------------------------------\n",
        "swin_model = models.swin_v2_t(\n",
        "    weights=None\n",
        ")\n",
        "\n",
        "swin_embedding_dim = swin_model.head.in_features\n",
        "\n",
        "swin_model.head = nn.Sequential(\n",
        "    nn.Dropout(p=0.20),\n",
        "    nn.Linear(\n",
        "        swin_embedding_dim,\n",
        "        NUM_CLASSES\n",
        "    )\n",
        ")\n",
        "\n",
        "swin_checkpoint = torch.load(\n",
        "    swin_checkpoint_path,\n",
        "    map_location=\"cpu\"\n",
        ")\n",
        "\n",
        "swin_model.load_state_dict(\n",
        "    swin_checkpoint[\"model_state_dict\"]\n",
        ")\n",
        "\n",
        "swin_model.eval()\n",
        "\n",
        "print(\"Best models loaded successfully.\")\n",
        "print(\"-\" * 55)\n",
        "\n",
        "print(\n",
        "    \"ConvNeXt best validation accuracy:\",\n",
        "    f\"{convnext_checkpoint['val_accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Swin best validation accuracy:\",\n",
        "    f\"{swin_checkpoint['val_accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"\\nConvNeXt embedding dimension:\",\n",
        "    convnext_embedding_dim\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Swin embedding dimension:\",\n",
        "    swin_embedding_dim\n",
        ")\n",
        "\n",
        "assert convnext_embedding_dim == 768\n",
        "assert swin_embedding_dim == 768\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "M-Iy4mXV4oL1",
        "outputId": "4013fcca-7474-4685-b239-18f6c895532b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best models loaded successfully.\n",
            "-------------------------------------------------------\n",
            "ConvNeXt best validation accuracy: 85.97%\n",
            "Swin best validation accuracy: 73.77%\n",
            "\n",
            "ConvNeXt embedding dimension: 768\n",
            "Swin embedding dimension: 768\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 19: DataLoaders for Embedding Extraction\n",
        "# =========================================================\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Use evaluation preprocessing for all splits\n",
        "feature_train_dataset = APTOSDataset(\n",
        "    train_df,\n",
        "    transform=eval_transforms\n",
        ")\n",
        "\n",
        "feature_val_dataset = APTOSDataset(\n",
        "    val_df,\n",
        "    transform=eval_transforms\n",
        ")\n",
        "\n",
        "feature_test_dataset = APTOSDataset(\n",
        "    test_df,\n",
        "    transform=eval_transforms\n",
        ")\n",
        "\n",
        "feature_train_loader = DataLoader(\n",
        "    feature_train_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    num_workers=0,\n",
        "    pin_memory=True\n",
        ")\n",
        "\n",
        "feature_val_loader = DataLoader(\n",
        "    feature_val_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    num_workers=0,\n",
        "    pin_memory=True\n",
        ")\n",
        "\n",
        "feature_test_loader = DataLoader(\n",
        "    feature_test_dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    num_workers=0,\n",
        "    pin_memory=True\n",
        ")\n",
        "\n",
        "print(\"Embedding DataLoaders are ready.\")\n",
        "print(\"Training samples:  \", len(feature_train_dataset))\n",
        "print(\"Validation samples:\", len(feature_val_dataset))\n",
        "print(\"Testing samples:   \", len(feature_test_dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wbz6nyyu5KhH",
        "outputId": "23006c50-cb4c-4a7d-d00b-908ac9f9dea3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Embedding DataLoaders are ready.\n",
            "Training samples:   2563\n",
            "Validation samples: 549\n",
            "Testing samples:    550\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 20: Deep Embedding Extraction Functions\n",
        "# =========================================================\n",
        "\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "@torch.no_grad()\n",
        "def extract_convnext_embeddings(\n",
        "    model,\n",
        "    data_loader\n",
        "):\n",
        "    model.eval()\n",
        "    model = model.to(device)\n",
        "\n",
        "    all_embeddings = []\n",
        "    all_labels = []\n",
        "\n",
        "    for images, labels in tqdm(\n",
        "        data_loader,\n",
        "        desc=\"ConvNeXt embeddings\"\n",
        "    ):\n",
        "        images = images.to(\n",
        "            device,\n",
        "            non_blocking=True\n",
        "        )\n",
        "\n",
        "        with torch.amp.autocast(\n",
        "            device_type=\"cuda\",\n",
        "            enabled=use_amp\n",
        "        ):\n",
        "            # ConvNeXt feature backbone\n",
        "            features = model.features(images)\n",
        "\n",
        "            # Global average pooling\n",
        "            features = model.avgpool(features)\n",
        "\n",
        "            # Final normalization before classifier\n",
        "            features = model.classifier[0](\n",
        "                features\n",
        "            )\n",
        "\n",
        "            # Convert to [batch_size, 768]\n",
        "            embeddings = torch.flatten(\n",
        "                features,\n",
        "                start_dim=1\n",
        "            )\n",
        "\n",
        "        all_embeddings.append(\n",
        "            embeddings.float().cpu().numpy()\n",
        "        )\n",
        "\n",
        "        all_labels.append(\n",
        "            labels.numpy()\n",
        "        )\n",
        "\n",
        "    all_embeddings = np.concatenate(\n",
        "        all_embeddings,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    all_labels = np.concatenate(\n",
        "        all_labels,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    return all_embeddings, all_labels\n",
        "\n",
        "\n",
        "@torch.no_grad()\n",
        "def extract_swin_embeddings(\n",
        "    model,\n",
        "    data_loader\n",
        "):\n",
        "    model.eval()\n",
        "    model = model.to(device)\n",
        "\n",
        "    all_embeddings = []\n",
        "    all_labels = []\n",
        "\n",
        "    for images, labels in tqdm(\n",
        "        data_loader,\n",
        "        desc=\"Swin embeddings\"\n",
        "    ):\n",
        "        images = images.to(\n",
        "            device,\n",
        "            non_blocking=True\n",
        "        )\n",
        "\n",
        "        with torch.amp.autocast(\n",
        "            device_type=\"cuda\",\n",
        "            enabled=use_amp\n",
        "        ):\n",
        "            # Swin feature backbone\n",
        "            features = model.features(images)\n",
        "\n",
        "            # Final normalization\n",
        "            features = model.norm(features)\n",
        "\n",
        "            # [B, H, W, C] → [B, C, H, W]\n",
        "            features = features.permute(\n",
        "                0, 3, 1, 2\n",
        "            )\n",
        "\n",
        "            # Global average pooling\n",
        "            features = model.avgpool(\n",
        "                features\n",
        "            )\n",
        "\n",
        "            # Convert to [batch_size, 768]\n",
        "            embeddings = torch.flatten(\n",
        "                features,\n",
        "                start_dim=1\n",
        "            )\n",
        "\n",
        "        all_embeddings.append(\n",
        "            embeddings.float().cpu().numpy()\n",
        "        )\n",
        "\n",
        "        all_labels.append(\n",
        "            labels.numpy()\n",
        "        )\n",
        "\n",
        "    all_embeddings = np.concatenate(\n",
        "        all_embeddings,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    all_labels = np.concatenate(\n",
        "        all_labels,\n",
        "        axis=0\n",
        "    )\n",
        "\n",
        "    return all_embeddings, all_labels\n",
        "\n",
        "\n",
        "print(\"Embedding extraction functions are ready.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MGUZEM_m5L6o",
        "outputId": "bb56e209-302a-406a-f686-1f8c9c6006df"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Embedding extraction functions are ready.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 21: Extract ConvNeXt Embeddings\n",
        "# =========================================================\n",
        "\n",
        "convnext_train_features, train_labels = (\n",
        "    extract_convnext_embeddings(\n",
        "        convnext_model,\n",
        "        feature_train_loader\n",
        "    )\n",
        ")\n",
        "\n",
        "convnext_val_features, val_labels = (\n",
        "    extract_convnext_embeddings(\n",
        "        convnext_model,\n",
        "        feature_val_loader\n",
        "    )\n",
        ")\n",
        "\n",
        "convnext_test_features, test_labels = (\n",
        "    extract_convnext_embeddings(\n",
        "        convnext_model,\n",
        "        feature_test_loader\n",
        "    )\n",
        ")\n",
        "\n",
        "print(\"\\nConvNeXt Embedding Shapes\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "print(\n",
        "    \"Training:\",\n",
        "    convnext_train_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Validation:\",\n",
        "    convnext_val_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Testing:\",\n",
        "    convnext_test_features.shape\n",
        ")\n",
        "\n",
        "assert convnext_train_features.shape == (\n",
        "    2563,\n",
        "    768\n",
        ")\n",
        "\n",
        "assert convnext_val_features.shape == (\n",
        "    549,\n",
        "    768\n",
        ")\n",
        "\n",
        "assert convnext_test_features.shape == (\n",
        "    550,\n",
        "    768\n",
        ")\n",
        "\n",
        "# Move ConvNeXt away from GPU before Swin extraction\n",
        "convnext_model = convnext_model.to(\"cpu\")\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "r5EUvpZO5Qc3",
        "outputId": "ab0cecff-5777-4da0-caf5-3b1552b08506"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ConvNeXt embeddings: 100%|██████████| 81/81 [05:01<00:00,  3.72s/it]\n",
            "ConvNeXt embeddings: 100%|██████████| 18/18 [01:05<00:00,  3.64s/it]\n",
            "ConvNeXt embeddings: 100%|██████████| 18/18 [01:05<00:00,  3.63s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ConvNeXt Embedding Shapes\n",
            "--------------------------------------------------\n",
            "Training: (2563, 768)\n",
            "Validation: (549, 768)\n",
            "Testing: (550, 768)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 22: Extract Swin Embeddings\n",
        "# =========================================================\n",
        "\n",
        "swin_train_features, swin_train_labels = (\n",
        "    extract_swin_embeddings(\n",
        "        swin_model,\n",
        "        feature_train_loader\n",
        "    )\n",
        ")\n",
        "\n",
        "swin_val_features, swin_val_labels = (\n",
        "    extract_swin_embeddings(\n",
        "        swin_model,\n",
        "        feature_val_loader\n",
        "    )\n",
        ")\n",
        "\n",
        "swin_test_features, swin_test_labels = (\n",
        "    extract_swin_embeddings(\n",
        "        swin_model,\n",
        "        feature_test_loader\n",
        "    )\n",
        ")\n",
        "\n",
        "print(\"\\nSwin Embedding Shapes\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "print(\n",
        "    \"Training:\",\n",
        "    swin_train_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Validation:\",\n",
        "    swin_val_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Testing:\",\n",
        "    swin_test_features.shape\n",
        ")\n",
        "\n",
        "assert swin_train_features.shape == (\n",
        "    2563,\n",
        "    768\n",
        ")\n",
        "\n",
        "assert swin_val_features.shape == (\n",
        "    549,\n",
        "    768\n",
        ")\n",
        "\n",
        "assert swin_test_features.shape == (\n",
        "    550,\n",
        "    768\n",
        ")\n",
        "\n",
        "# Verify that both models used the same image order\n",
        "assert np.array_equal(\n",
        "    train_labels,\n",
        "    swin_train_labels\n",
        ")\n",
        "\n",
        "assert np.array_equal(\n",
        "    val_labels,\n",
        "    swin_val_labels\n",
        ")\n",
        "\n",
        "assert np.array_equal(\n",
        "    test_labels,\n",
        "    swin_test_labels\n",
        ")\n",
        "\n",
        "print(\"\\nLabels match between both models.\")\n",
        "\n",
        "swin_model = swin_model.to(\"cpu\")\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BbtD2kAL83sX",
        "outputId": "c9a0e546-8887-4b51-ec85-1d1b461d74b2"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Swin embeddings: 100%|██████████| 81/81 [04:57<00:00,  3.68s/it]\n",
            "Swin embeddings: 100%|██████████| 18/18 [01:04<00:00,  3.60s/it]\n",
            "Swin embeddings: 100%|██████████| 18/18 [01:03<00:00,  3.50s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Swin Embedding Shapes\n",
            "--------------------------------------------------\n",
            "Training: (2563, 768)\n",
            "Validation: (549, 768)\n",
            "Testing: (550, 768)\n",
            "\n",
            "Labels match between both models.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 23: Feature-Level Fusion\n",
        "# =========================================================\n",
        "\n",
        "# Concatenate ConvNeXt and Swin embeddings\n",
        "# 768 + 768 = 1536 features\n",
        "\n",
        "fused_train_features = np.concatenate(\n",
        "    [\n",
        "        convnext_train_features,\n",
        "        swin_train_features\n",
        "    ],\n",
        "    axis=1\n",
        ")\n",
        "\n",
        "fused_val_features = np.concatenate(\n",
        "    [\n",
        "        convnext_val_features,\n",
        "        swin_val_features\n",
        "    ],\n",
        "    axis=1\n",
        ")\n",
        "\n",
        "fused_test_features = np.concatenate(\n",
        "    [\n",
        "        convnext_test_features,\n",
        "        swin_test_features\n",
        "    ],\n",
        "    axis=1\n",
        ")\n",
        "\n",
        "print(\"Feature Fusion Completed\")\n",
        "print(\"=\" * 55)\n",
        "\n",
        "print(\n",
        "    \"ConvNeXt training embeddings:\",\n",
        "    convnext_train_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Swin training embeddings:\",\n",
        "    swin_train_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Fused training features:\",\n",
        "    fused_train_features.shape\n",
        ")\n",
        "\n",
        "print(\"\\nValidation:\", fused_val_features.shape)\n",
        "print(\"Testing:   \", fused_test_features.shape)\n",
        "\n",
        "assert fused_train_features.shape == (\n",
        "    2563,\n",
        "    1536\n",
        ")\n",
        "\n",
        "assert fused_val_features.shape == (\n",
        "    549,\n",
        "    1536\n",
        ")\n",
        "\n",
        "assert fused_test_features.shape == (\n",
        "    550,\n",
        "    1536\n",
        ")\n",
        "\n",
        "# Save all features\n",
        "features_file_path = (\n",
        "    FEATURES_DIR /\n",
        "    \"fused_convnext_swin_embeddings.npz\"\n",
        ")\n",
        "\n",
        "np.savez_compressed(\n",
        "    features_file_path,\n",
        "\n",
        "    convnext_train=convnext_train_features,\n",
        "    convnext_val=convnext_val_features,\n",
        "    convnext_test=convnext_test_features,\n",
        "\n",
        "    swin_train=swin_train_features,\n",
        "    swin_val=swin_val_features,\n",
        "    swin_test=swin_test_features,\n",
        "\n",
        "    fused_train=fused_train_features,\n",
        "    fused_val=fused_val_features,\n",
        "    fused_test=fused_test_features,\n",
        "\n",
        "    train_labels=train_labels,\n",
        "    val_labels=val_labels,\n",
        "    test_labels=test_labels\n",
        ")\n",
        "\n",
        "print(\"\\nFeatures saved successfully:\")\n",
        "print(features_file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ML6_8MO9-wKF",
        "outputId": "b10fd41b-d8ae-440d-a018-b4e5b020d6d4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Fusion Completed\n",
            "=======================================================\n",
            "ConvNeXt training embeddings: (2563, 768)\n",
            "Swin training embeddings: (2563, 768)\n",
            "Fused training features: (2563, 1536)\n",
            "\n",
            "Validation: (549, 1536)\n",
            "Testing:    (550, 1536)\n",
            "\n",
            "Features saved successfully:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Extracted_Features/fused_convnext_swin_embeddings.npz\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Random Forest Classification\n",
        "\n",
        "The fused 1,536-dimensional feature vectors are classified using a Random\n",
        "Forest classifier.\n",
        "\n",
        "A small set of regularized Random Forest configurations is evaluated using\n",
        "the validation subset only. Class imbalance is handled using balanced\n",
        "subsampling.\n",
        "\n",
        "The best configuration is selected primarily according to validation Macro F1,\n",
        "with validation accuracy used as a secondary criterion. The test subset remains\n",
        "unseen during model selection."
      ],
      "metadata": {
        "id": "K4SeXPZ1_R4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 24: Random Forest Validation and Model Selection\n",
        "# =========================================================\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Regularized Random Forest configurations\n",
        "# ---------------------------------------------------------\n",
        "rf_candidates = {\n",
        "\n",
        "    \"RF_D6_Leaf20_MaxLeaf45\": {\n",
        "        \"n_estimators\": 600,\n",
        "        \"max_depth\": 6,\n",
        "        \"max_leaf_nodes\": 45,\n",
        "        \"min_samples_split\": 45,\n",
        "        \"min_samples_leaf\": 20,\n",
        "        \"max_features\": \"sqrt\",\n",
        "        \"bootstrap\": True,\n",
        "        \"max_samples\": 0.55\n",
        "    },\n",
        "\n",
        "    \"RF_D8_Leaf12_MaxLeaf70\": {\n",
        "        \"n_estimators\": 600,\n",
        "        \"max_depth\": 8,\n",
        "        \"max_leaf_nodes\": 70,\n",
        "        \"min_samples_split\": 30,\n",
        "        \"min_samples_leaf\": 12,\n",
        "        \"max_features\": \"sqrt\",\n",
        "        \"bootstrap\": True,\n",
        "        \"max_samples\": 0.65\n",
        "    },\n",
        "\n",
        "    \"RF_D10_Leaf8_MaxLeaf100\": {\n",
        "        \"n_estimators\": 600,\n",
        "        \"max_depth\": 10,\n",
        "        \"max_leaf_nodes\": 100,\n",
        "        \"min_samples_split\": 20,\n",
        "        \"min_samples_leaf\": 8,\n",
        "        \"max_features\": \"sqrt\",\n",
        "        \"bootstrap\": True,\n",
        "        \"max_samples\": 0.75\n",
        "    }\n",
        "}\n",
        "\n",
        "rf_validation_results = []\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Train using training features\n",
        "# Evaluate using validation features only\n",
        "# ---------------------------------------------------------\n",
        "for model_name, model_parameters in rf_candidates.items():\n",
        "\n",
        "    print(f\"\\nTraining {model_name}...\")\n",
        "\n",
        "    rf_model = RandomForestClassifier(\n",
        "        **model_parameters,\n",
        "        class_weight=\"balanced_subsample\",\n",
        "        random_state=SEED,\n",
        "        n_jobs=-1\n",
        "    )\n",
        "\n",
        "    rf_model.fit(\n",
        "        fused_train_features,\n",
        "        train_labels\n",
        "    )\n",
        "\n",
        "    train_predictions = rf_model.predict(\n",
        "        fused_train_features\n",
        "    )\n",
        "\n",
        "    val_predictions = rf_model.predict(\n",
        "        fused_val_features\n",
        "    )\n",
        "\n",
        "    train_accuracy = accuracy_score(\n",
        "        train_labels,\n",
        "        train_predictions\n",
        "    )\n",
        "\n",
        "    val_accuracy = accuracy_score(\n",
        "        val_labels,\n",
        "        val_predictions\n",
        "    )\n",
        "\n",
        "    val_macro_precision = precision_score(\n",
        "        val_labels,\n",
        "        val_predictions,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    val_macro_recall = recall_score(\n",
        "        val_labels,\n",
        "        val_predictions,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    val_macro_f1 = f1_score(\n",
        "        val_labels,\n",
        "        val_predictions,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    val_weighted_f1 = f1_score(\n",
        "        val_labels,\n",
        "        val_predictions,\n",
        "        average=\"weighted\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    train_val_gap = (\n",
        "        train_accuracy - val_accuracy\n",
        "    )\n",
        "\n",
        "    rf_validation_results.append({\n",
        "        \"Model\": model_name,\n",
        "        \"Train Accuracy\": train_accuracy,\n",
        "        \"Validation Accuracy\": val_accuracy,\n",
        "        \"Validation Macro Precision\": val_macro_precision,\n",
        "        \"Validation Macro Recall\": val_macro_recall,\n",
        "        \"Validation Macro F1\": val_macro_f1,\n",
        "        \"Validation Weighted F1\": val_weighted_f1,\n",
        "        \"Train-Val Gap\": train_val_gap\n",
        "    })\n",
        "\n",
        "    print(\n",
        "        f\"Train Accuracy: \"\n",
        "        f\"{train_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Validation Accuracy: \"\n",
        "        f\"{val_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Validation Macro F1: \"\n",
        "        f\"{val_macro_f1 * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Compare models\n",
        "# ---------------------------------------------------------\n",
        "rf_validation_df = pd.DataFrame(\n",
        "    rf_validation_results\n",
        ")\n",
        "\n",
        "rf_validation_df = rf_validation_df.sort_values(\n",
        "    by=[\n",
        "        \"Validation Macro F1\",\n",
        "        \"Validation Accuracy\"\n",
        "    ],\n",
        "    ascending=False\n",
        ").reset_index(drop=True)\n",
        "\n",
        "print(\"\\nRandom Forest Validation Results\")\n",
        "print(\"=\" * 75)\n",
        "\n",
        "display(\n",
        "    rf_validation_df.style.format({\n",
        "        \"Train Accuracy\": \"{:.2%}\",\n",
        "        \"Validation Accuracy\": \"{:.2%}\",\n",
        "        \"Validation Macro Precision\": \"{:.2%}\",\n",
        "        \"Validation Macro Recall\": \"{:.2%}\",\n",
        "        \"Validation Macro F1\": \"{:.2%}\",\n",
        "        \"Validation Weighted F1\": \"{:.2%}\",\n",
        "        \"Train-Val Gap\": \"{:.2%}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Select best configuration\n",
        "# ---------------------------------------------------------\n",
        "best_rf_name = rf_validation_df.loc[\n",
        "    0,\n",
        "    \"Model\"\n",
        "]\n",
        "\n",
        "best_rf_parameters = rf_candidates[\n",
        "    best_rf_name\n",
        "].copy()\n",
        "\n",
        "print(\"\\nSelected Random Forest\")\n",
        "print(\"-\" * 55)\n",
        "\n",
        "print(\"Model:\", best_rf_name)\n",
        "\n",
        "print(\n",
        "    \"Validation Macro F1:\",\n",
        "    f\"{rf_validation_df.loc[0, 'Validation Macro F1'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Validation Accuracy:\",\n",
        "    f\"{rf_validation_df.loc[0, 'Validation Accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\"\\nSelected parameters:\")\n",
        "\n",
        "for parameter_name, parameter_value in (\n",
        "    best_rf_parameters.items()\n",
        "):\n",
        "    print(\n",
        "        f\"{parameter_name}: \"\n",
        "        f\"{parameter_value}\"\n",
        "    )\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save validation comparison\n",
        "# ---------------------------------------------------------\n",
        "rf_validation_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"random_forest_validation_results.csv\"\n",
        ")\n",
        "\n",
        "rf_validation_df.to_csv(\n",
        "    rf_validation_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nValidation results saved at:\")\n",
        "print(rf_validation_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "-lvGzixh-yfD",
        "outputId": "85868b1f-11ce-4131-ddb3-479abbfff9cc"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training RF_D6_Leaf20_MaxLeaf45...\n",
            "Train Accuracy: 98.87%\n",
            "Validation Accuracy: 85.06%\n",
            "Validation Macro F1: 71.03%\n",
            "\n",
            "Training RF_D8_Leaf12_MaxLeaf70...\n",
            "Train Accuracy: 98.91%\n",
            "Validation Accuracy: 85.79%\n",
            "Validation Macro F1: 72.00%\n",
            "\n",
            "Training RF_D10_Leaf8_MaxLeaf100...\n",
            "Train Accuracy: 98.95%\n",
            "Validation Accuracy: 85.97%\n",
            "Validation Macro F1: 72.09%\n",
            "\n",
            "Random Forest Validation Results\n",
            "===========================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7dc6c456c470>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_43ae4\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_43ae4_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_43ae4_level0_col1\" class=\"col_heading level0 col1\" >Train Accuracy</th>\n",
              "      <th id=\"T_43ae4_level0_col2\" class=\"col_heading level0 col2\" >Validation Accuracy</th>\n",
              "      <th id=\"T_43ae4_level0_col3\" class=\"col_heading level0 col3\" >Validation Macro Precision</th>\n",
              "      <th id=\"T_43ae4_level0_col4\" class=\"col_heading level0 col4\" >Validation Macro Recall</th>\n",
              "      <th id=\"T_43ae4_level0_col5\" class=\"col_heading level0 col5\" >Validation Macro F1</th>\n",
              "      <th id=\"T_43ae4_level0_col6\" class=\"col_heading level0 col6\" >Validation Weighted F1</th>\n",
              "      <th id=\"T_43ae4_level0_col7\" class=\"col_heading level0 col7\" >Train-Val Gap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_43ae4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_43ae4_row0_col0\" class=\"data row0 col0\" >RF_D10_Leaf8_MaxLeaf100</td>\n",
              "      <td id=\"T_43ae4_row0_col1\" class=\"data row0 col1\" >98.95%</td>\n",
              "      <td id=\"T_43ae4_row0_col2\" class=\"data row0 col2\" >85.97%</td>\n",
              "      <td id=\"T_43ae4_row0_col3\" class=\"data row0 col3\" >75.39%</td>\n",
              "      <td id=\"T_43ae4_row0_col4\" class=\"data row0 col4\" >70.13%</td>\n",
              "      <td id=\"T_43ae4_row0_col5\" class=\"data row0 col5\" >72.09%</td>\n",
              "      <td id=\"T_43ae4_row0_col6\" class=\"data row0 col6\" >85.46%</td>\n",
              "      <td id=\"T_43ae4_row0_col7\" class=\"data row0 col7\" >12.97%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_43ae4_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_43ae4_row1_col0\" class=\"data row1 col0\" >RF_D8_Leaf12_MaxLeaf70</td>\n",
              "      <td id=\"T_43ae4_row1_col1\" class=\"data row1 col1\" >98.91%</td>\n",
              "      <td id=\"T_43ae4_row1_col2\" class=\"data row1 col2\" >85.79%</td>\n",
              "      <td id=\"T_43ae4_row1_col3\" class=\"data row1 col3\" >74.78%</td>\n",
              "      <td id=\"T_43ae4_row1_col4\" class=\"data row1 col4\" >70.32%</td>\n",
              "      <td id=\"T_43ae4_row1_col5\" class=\"data row1 col5\" >72.00%</td>\n",
              "      <td id=\"T_43ae4_row1_col6\" class=\"data row1 col6\" >85.38%</td>\n",
              "      <td id=\"T_43ae4_row1_col7\" class=\"data row1 col7\" >13.12%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_43ae4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_43ae4_row2_col0\" class=\"data row2 col0\" >RF_D6_Leaf20_MaxLeaf45</td>\n",
              "      <td id=\"T_43ae4_row2_col1\" class=\"data row2 col1\" >98.87%</td>\n",
              "      <td id=\"T_43ae4_row2_col2\" class=\"data row2 col2\" >85.06%</td>\n",
              "      <td id=\"T_43ae4_row2_col3\" class=\"data row2 col3\" >73.78%</td>\n",
              "      <td id=\"T_43ae4_row2_col4\" class=\"data row2 col4\" >69.46%</td>\n",
              "      <td id=\"T_43ae4_row2_col5\" class=\"data row2 col5\" >71.03%</td>\n",
              "      <td id=\"T_43ae4_row2_col6\" class=\"data row2 col6\" >84.66%</td>\n",
              "      <td id=\"T_43ae4_row2_col7\" class=\"data row2 col7\" >13.80%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Selected Random Forest\n",
            "-------------------------------------------------------\n",
            "Model: RF_D10_Leaf8_MaxLeaf100\n",
            "Validation Macro F1: 72.09%\n",
            "Validation Accuracy: 85.97%\n",
            "\n",
            "Selected parameters:\n",
            "n_estimators: 600\n",
            "max_depth: 10\n",
            "max_leaf_nodes: 100\n",
            "min_samples_split: 20\n",
            "min_samples_leaf: 8\n",
            "max_features: sqrt\n",
            "bootstrap: True\n",
            "max_samples: 0.75\n",
            "\n",
            "Validation results saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/random_forest_validation_results.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 24B: Stronger Random Forest Regularization\n",
        "# =========================================================\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Configurations:\n",
        "# 1. Default baseline\n",
        "# 2. Stronger regularization\n",
        "# 3. balanced_subsample vs None\n",
        "# ---------------------------------------------------------\n",
        "rf_regularization_candidates = {\n",
        "\n",
        "    # Baseline only for comparison\n",
        "    \"RF_Default_NoWeight\": {\n",
        "        \"max_depth\": None,\n",
        "        \"max_leaf_nodes\": None,\n",
        "        \"min_samples_split\": 2,\n",
        "        \"min_samples_leaf\": 1,\n",
        "        \"max_samples\": None,\n",
        "        \"class_weight\": None\n",
        "    },\n",
        "\n",
        "    # Moderate regularization\n",
        "    \"RF_D8_Leaf15_Balanced\": {\n",
        "        \"max_depth\": 8,\n",
        "        \"max_leaf_nodes\": 60,\n",
        "        \"min_samples_split\": 40,\n",
        "        \"min_samples_leaf\": 15,\n",
        "        \"max_samples\": 0.60,\n",
        "        \"class_weight\": \"balanced_subsample\"\n",
        "    },\n",
        "\n",
        "    # Stronger regularization\n",
        "    \"RF_D6_Leaf25_Balanced\": {\n",
        "        \"max_depth\": 6,\n",
        "        \"max_leaf_nodes\": 40,\n",
        "        \"min_samples_split\": 60,\n",
        "        \"min_samples_leaf\": 25,\n",
        "        \"max_samples\": 0.50,\n",
        "        \"class_weight\": \"balanced_subsample\"\n",
        "    },\n",
        "\n",
        "    \"RF_D5_Leaf35_Balanced\": {\n",
        "        \"max_depth\": 5,\n",
        "        \"max_leaf_nodes\": 30,\n",
        "        \"min_samples_split\": 80,\n",
        "        \"min_samples_leaf\": 35,\n",
        "        \"max_samples\": 0.45,\n",
        "        \"class_weight\": \"balanced_subsample\"\n",
        "    },\n",
        "\n",
        "    \"RF_D4_Leaf50_Balanced\": {\n",
        "        \"max_depth\": 4,\n",
        "        \"max_leaf_nodes\": 20,\n",
        "        \"min_samples_split\": 100,\n",
        "        \"min_samples_leaf\": 50,\n",
        "        \"max_samples\": 0.40,\n",
        "        \"class_weight\": \"balanced_subsample\"\n",
        "    },\n",
        "\n",
        "    # Same regularization without class weighting\n",
        "    \"RF_D6_Leaf25_NoWeight\": {\n",
        "        \"max_depth\": 6,\n",
        "        \"max_leaf_nodes\": 40,\n",
        "        \"min_samples_split\": 60,\n",
        "        \"min_samples_leaf\": 25,\n",
        "        \"max_samples\": 0.50,\n",
        "        \"class_weight\": None\n",
        "    },\n",
        "\n",
        "    \"RF_D5_Leaf35_NoWeight\": {\n",
        "        \"max_depth\": 5,\n",
        "        \"max_leaf_nodes\": 30,\n",
        "        \"min_samples_split\": 80,\n",
        "        \"min_samples_leaf\": 35,\n",
        "        \"max_samples\": 0.45,\n",
        "        \"class_weight\": None\n",
        "    },\n",
        "\n",
        "    \"RF_D4_Leaf50_NoWeight\": {\n",
        "        \"max_depth\": 4,\n",
        "        \"max_leaf_nodes\": 20,\n",
        "        \"min_samples_split\": 100,\n",
        "        \"min_samples_leaf\": 50,\n",
        "        \"max_samples\": 0.40,\n",
        "        \"class_weight\": None\n",
        "    }\n",
        "}\n",
        "\n",
        "regularization_results = []\n",
        "trained_regularized_models = {}\n",
        "\n",
        "for model_name, params in rf_regularization_candidates.items():\n",
        "\n",
        "    print(f\"\\nTraining {model_name}...\")\n",
        "\n",
        "    rf_model = RandomForestClassifier(\n",
        "        n_estimators=500,\n",
        "        max_features=\"sqrt\",\n",
        "        bootstrap=True,\n",
        "        random_state=SEED,\n",
        "        n_jobs=-1,\n",
        "        **params\n",
        "    )\n",
        "\n",
        "    # Train only on training features\n",
        "    rf_model.fit(\n",
        "        fused_train_features,\n",
        "        train_labels\n",
        "    )\n",
        "\n",
        "    train_pred = rf_model.predict(\n",
        "        fused_train_features\n",
        "    )\n",
        "\n",
        "    val_pred = rf_model.predict(\n",
        "        fused_val_features\n",
        "    )\n",
        "\n",
        "    train_acc = accuracy_score(\n",
        "        train_labels,\n",
        "        train_pred\n",
        "    )\n",
        "\n",
        "    val_acc = accuracy_score(\n",
        "        val_labels,\n",
        "        val_pred\n",
        "    )\n",
        "\n",
        "    train_macro_f1 = f1_score(\n",
        "        train_labels,\n",
        "        train_pred,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    val_macro_precision = precision_score(\n",
        "        val_labels,\n",
        "        val_pred,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    val_macro_recall = recall_score(\n",
        "        val_labels,\n",
        "        val_pred,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    val_macro_f1 = f1_score(\n",
        "        val_labels,\n",
        "        val_pred,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    val_weighted_f1 = f1_score(\n",
        "        val_labels,\n",
        "        val_pred,\n",
        "        average=\"weighted\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    gap = train_acc - val_acc\n",
        "\n",
        "    regularization_results.append({\n",
        "        \"Model\": model_name,\n",
        "        \"Class Weight\": str(params[\"class_weight\"]),\n",
        "        \"Max Depth\": params[\"max_depth\"],\n",
        "        \"Min Samples Leaf\": params[\"min_samples_leaf\"],\n",
        "        \"Min Samples Split\": params[\"min_samples_split\"],\n",
        "        \"Max Samples\": params[\"max_samples\"],\n",
        "        \"Train Accuracy\": train_acc,\n",
        "        \"Validation Accuracy\": val_acc,\n",
        "        \"Train Macro F1\": train_macro_f1,\n",
        "        \"Validation Macro Precision\": val_macro_precision,\n",
        "        \"Validation Macro Recall\": val_macro_recall,\n",
        "        \"Validation Macro F1\": val_macro_f1,\n",
        "        \"Validation Weighted F1\": val_weighted_f1,\n",
        "        \"Train-Val Gap\": gap\n",
        "    })\n",
        "\n",
        "    trained_regularized_models[model_name] = rf_model\n",
        "\n",
        "    print(f\"Train Accuracy:      {train_acc * 100:.2f}%\")\n",
        "    print(f\"Validation Accuracy: {val_acc * 100:.2f}%\")\n",
        "    print(f\"Validation Macro F1: {val_macro_f1 * 100:.2f}%\")\n",
        "    print(f\"Train-Val Gap:       {gap * 100:.2f}%\")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Results table\n",
        "# ---------------------------------------------------------\n",
        "rf_regularization_df = pd.DataFrame(\n",
        "    regularization_results\n",
        ")\n",
        "\n",
        "# Show models with lower gap first,\n",
        "# then higher validation Macro F1\n",
        "rf_regularization_df = rf_regularization_df.sort_values(\n",
        "    by=[\n",
        "        \"Train-Val Gap\",\n",
        "        \"Validation Macro F1\"\n",
        "    ],\n",
        "    ascending=[\n",
        "        True,\n",
        "        False\n",
        "    ]\n",
        ").reset_index(drop=True)\n",
        "\n",
        "print(\"\\nRegularized Random Forest Comparison\")\n",
        "print(\"=\" * 90)\n",
        "\n",
        "display(\n",
        "    rf_regularization_df.style.format({\n",
        "        \"Train Accuracy\": \"{:.2%}\",\n",
        "        \"Validation Accuracy\": \"{:.2%}\",\n",
        "        \"Train Macro F1\": \"{:.2%}\",\n",
        "        \"Validation Macro Precision\": \"{:.2%}\",\n",
        "        \"Validation Macro Recall\": \"{:.2%}\",\n",
        "        \"Validation Macro F1\": \"{:.2%}\",\n",
        "        \"Validation Weighted F1\": \"{:.2%}\",\n",
        "        \"Train-Val Gap\": \"{:.2%}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "rf_regularization_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"random_forest_regularization_comparison.csv\"\n",
        ")\n",
        "\n",
        "rf_regularization_df.to_csv(\n",
        "    rf_regularization_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nResults saved at:\")\n",
        "print(rf_regularization_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1254
        },
        "id": "mJdAkC4iThRL",
        "outputId": "f6e69c41-071f-4ca7-c5e3-2a8b289580b6"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training RF_Default_NoWeight...\n",
            "Train Accuracy:      99.38%\n",
            "Validation Accuracy: 85.61%\n",
            "Validation Macro F1: 70.41%\n",
            "Train-Val Gap:       13.77%\n",
            "\n",
            "Training RF_D8_Leaf15_Balanced...\n",
            "Train Accuracy:      98.87%\n",
            "Validation Accuracy: 85.61%\n",
            "Validation Macro F1: 71.86%\n",
            "Train-Val Gap:       13.26%\n",
            "\n",
            "Training RF_D6_Leaf25_Balanced...\n",
            "Train Accuracy:      98.91%\n",
            "Validation Accuracy: 85.43%\n",
            "Validation Macro F1: 71.86%\n",
            "Train-Val Gap:       13.48%\n",
            "\n",
            "Training RF_D5_Leaf35_Balanced...\n",
            "Train Accuracy:      98.91%\n",
            "Validation Accuracy: 85.25%\n",
            "Validation Macro F1: 71.66%\n",
            "Train-Val Gap:       13.66%\n",
            "\n",
            "Training RF_D4_Leaf50_Balanced...\n",
            "Train Accuracy:      98.91%\n",
            "Validation Accuracy: 85.25%\n",
            "Validation Macro F1: 70.98%\n",
            "Train-Val Gap:       13.66%\n",
            "\n",
            "Training RF_D6_Leaf25_NoWeight...\n",
            "Train Accuracy:      98.95%\n",
            "Validation Accuracy: 85.06%\n",
            "Validation Macro F1: 68.26%\n",
            "Train-Val Gap:       13.88%\n",
            "\n",
            "Training RF_D5_Leaf35_NoWeight...\n",
            "Train Accuracy:      98.99%\n",
            "Validation Accuracy: 84.70%\n",
            "Validation Macro F1: 67.62%\n",
            "Train-Val Gap:       14.29%\n",
            "\n",
            "Training RF_D4_Leaf50_NoWeight...\n",
            "Train Accuracy:      98.95%\n",
            "Validation Accuracy: 85.06%\n",
            "Validation Macro F1: 67.83%\n",
            "Train-Val Gap:       13.88%\n",
            "\n",
            "Regularized Random Forest Comparison\n",
            "==========================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_44fb1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_44fb1_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_44fb1_level0_col1\" class=\"col_heading level0 col1\" >Class Weight</th>\n",
              "      <th id=\"T_44fb1_level0_col2\" class=\"col_heading level0 col2\" >Max Depth</th>\n",
              "      <th id=\"T_44fb1_level0_col3\" class=\"col_heading level0 col3\" >Min Samples Leaf</th>\n",
              "      <th id=\"T_44fb1_level0_col4\" class=\"col_heading level0 col4\" >Min Samples Split</th>\n",
              "      <th id=\"T_44fb1_level0_col5\" class=\"col_heading level0 col5\" >Max Samples</th>\n",
              "      <th id=\"T_44fb1_level0_col6\" class=\"col_heading level0 col6\" >Train Accuracy</th>\n",
              "      <th id=\"T_44fb1_level0_col7\" class=\"col_heading level0 col7\" >Validation Accuracy</th>\n",
              "      <th id=\"T_44fb1_level0_col8\" class=\"col_heading level0 col8\" >Train Macro F1</th>\n",
              "      <th id=\"T_44fb1_level0_col9\" class=\"col_heading level0 col9\" >Validation Macro Precision</th>\n",
              "      <th id=\"T_44fb1_level0_col10\" class=\"col_heading level0 col10\" >Validation Macro Recall</th>\n",
              "      <th id=\"T_44fb1_level0_col11\" class=\"col_heading level0 col11\" >Validation Macro F1</th>\n",
              "      <th id=\"T_44fb1_level0_col12\" class=\"col_heading level0 col12\" >Validation Weighted F1</th>\n",
              "      <th id=\"T_44fb1_level0_col13\" class=\"col_heading level0 col13\" >Train-Val Gap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_44fb1_row0_col0\" class=\"data row0 col0\" >RF_D8_Leaf15_Balanced</td>\n",
              "      <td id=\"T_44fb1_row0_col1\" class=\"data row0 col1\" >balanced_subsample</td>\n",
              "      <td id=\"T_44fb1_row0_col2\" class=\"data row0 col2\" >8.000000</td>\n",
              "      <td id=\"T_44fb1_row0_col3\" class=\"data row0 col3\" >15</td>\n",
              "      <td id=\"T_44fb1_row0_col4\" class=\"data row0 col4\" >40</td>\n",
              "      <td id=\"T_44fb1_row0_col5\" class=\"data row0 col5\" >0.600000</td>\n",
              "      <td id=\"T_44fb1_row0_col6\" class=\"data row0 col6\" >98.87%</td>\n",
              "      <td id=\"T_44fb1_row0_col7\" class=\"data row0 col7\" >85.61%</td>\n",
              "      <td id=\"T_44fb1_row0_col8\" class=\"data row0 col8\" >97.76%</td>\n",
              "      <td id=\"T_44fb1_row0_col9\" class=\"data row0 col9\" >74.59%</td>\n",
              "      <td id=\"T_44fb1_row0_col10\" class=\"data row0 col10\" >70.18%</td>\n",
              "      <td id=\"T_44fb1_row0_col11\" class=\"data row0 col11\" >71.86%</td>\n",
              "      <td id=\"T_44fb1_row0_col12\" class=\"data row0 col12\" >85.21%</td>\n",
              "      <td id=\"T_44fb1_row0_col13\" class=\"data row0 col13\" >13.26%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_44fb1_row1_col0\" class=\"data row1 col0\" >RF_D6_Leaf25_Balanced</td>\n",
              "      <td id=\"T_44fb1_row1_col1\" class=\"data row1 col1\" >balanced_subsample</td>\n",
              "      <td id=\"T_44fb1_row1_col2\" class=\"data row1 col2\" >6.000000</td>\n",
              "      <td id=\"T_44fb1_row1_col3\" class=\"data row1 col3\" >25</td>\n",
              "      <td id=\"T_44fb1_row1_col4\" class=\"data row1 col4\" >60</td>\n",
              "      <td id=\"T_44fb1_row1_col5\" class=\"data row1 col5\" >0.500000</td>\n",
              "      <td id=\"T_44fb1_row1_col6\" class=\"data row1 col6\" >98.91%</td>\n",
              "      <td id=\"T_44fb1_row1_col7\" class=\"data row1 col7\" >85.43%</td>\n",
              "      <td id=\"T_44fb1_row1_col8\" class=\"data row1 col8\" >97.81%</td>\n",
              "      <td id=\"T_44fb1_row1_col9\" class=\"data row1 col9\" >74.61%</td>\n",
              "      <td id=\"T_44fb1_row1_col10\" class=\"data row1 col10\" >70.29%</td>\n",
              "      <td id=\"T_44fb1_row1_col11\" class=\"data row1 col11\" >71.86%</td>\n",
              "      <td id=\"T_44fb1_row1_col12\" class=\"data row1 col12\" >85.02%</td>\n",
              "      <td id=\"T_44fb1_row1_col13\" class=\"data row1 col13\" >13.48%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_44fb1_row2_col0\" class=\"data row2 col0\" >RF_D5_Leaf35_Balanced</td>\n",
              "      <td id=\"T_44fb1_row2_col1\" class=\"data row2 col1\" >balanced_subsample</td>\n",
              "      <td id=\"T_44fb1_row2_col2\" class=\"data row2 col2\" >5.000000</td>\n",
              "      <td id=\"T_44fb1_row2_col3\" class=\"data row2 col3\" >35</td>\n",
              "      <td id=\"T_44fb1_row2_col4\" class=\"data row2 col4\" >80</td>\n",
              "      <td id=\"T_44fb1_row2_col5\" class=\"data row2 col5\" >0.450000</td>\n",
              "      <td id=\"T_44fb1_row2_col6\" class=\"data row2 col6\" >98.91%</td>\n",
              "      <td id=\"T_44fb1_row2_col7\" class=\"data row2 col7\" >85.25%</td>\n",
              "      <td id=\"T_44fb1_row2_col8\" class=\"data row2 col8\" >97.80%</td>\n",
              "      <td id=\"T_44fb1_row2_col9\" class=\"data row2 col9\" >74.32%</td>\n",
              "      <td id=\"T_44fb1_row2_col10\" class=\"data row2 col10\" >70.15%</td>\n",
              "      <td id=\"T_44fb1_row2_col11\" class=\"data row2 col11\" >71.66%</td>\n",
              "      <td id=\"T_44fb1_row2_col12\" class=\"data row2 col12\" >84.85%</td>\n",
              "      <td id=\"T_44fb1_row2_col13\" class=\"data row2 col13\" >13.66%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_44fb1_row3_col0\" class=\"data row3 col0\" >RF_D4_Leaf50_Balanced</td>\n",
              "      <td id=\"T_44fb1_row3_col1\" class=\"data row3 col1\" >balanced_subsample</td>\n",
              "      <td id=\"T_44fb1_row3_col2\" class=\"data row3 col2\" >4.000000</td>\n",
              "      <td id=\"T_44fb1_row3_col3\" class=\"data row3 col3\" >50</td>\n",
              "      <td id=\"T_44fb1_row3_col4\" class=\"data row3 col4\" >100</td>\n",
              "      <td id=\"T_44fb1_row3_col5\" class=\"data row3 col5\" >0.400000</td>\n",
              "      <td id=\"T_44fb1_row3_col6\" class=\"data row3 col6\" >98.91%</td>\n",
              "      <td id=\"T_44fb1_row3_col7\" class=\"data row3 col7\" >85.25%</td>\n",
              "      <td id=\"T_44fb1_row3_col8\" class=\"data row3 col8\" >97.80%</td>\n",
              "      <td id=\"T_44fb1_row3_col9\" class=\"data row3 col9\" >74.08%</td>\n",
              "      <td id=\"T_44fb1_row3_col10\" class=\"data row3 col10\" >69.37%</td>\n",
              "      <td id=\"T_44fb1_row3_col11\" class=\"data row3 col11\" >70.98%</td>\n",
              "      <td id=\"T_44fb1_row3_col12\" class=\"data row3 col12\" >84.83%</td>\n",
              "      <td id=\"T_44fb1_row3_col13\" class=\"data row3 col13\" >13.66%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_44fb1_row4_col0\" class=\"data row4 col0\" >RF_Default_NoWeight</td>\n",
              "      <td id=\"T_44fb1_row4_col1\" class=\"data row4 col1\" >None</td>\n",
              "      <td id=\"T_44fb1_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
              "      <td id=\"T_44fb1_row4_col3\" class=\"data row4 col3\" >1</td>\n",
              "      <td id=\"T_44fb1_row4_col4\" class=\"data row4 col4\" >2</td>\n",
              "      <td id=\"T_44fb1_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
              "      <td id=\"T_44fb1_row4_col6\" class=\"data row4 col6\" >99.38%</td>\n",
              "      <td id=\"T_44fb1_row4_col7\" class=\"data row4 col7\" >85.61%</td>\n",
              "      <td id=\"T_44fb1_row4_col8\" class=\"data row4 col8\" >98.79%</td>\n",
              "      <td id=\"T_44fb1_row4_col9\" class=\"data row4 col9\" >75.82%</td>\n",
              "      <td id=\"T_44fb1_row4_col10\" class=\"data row4 col10\" >67.50%</td>\n",
              "      <td id=\"T_44fb1_row4_col11\" class=\"data row4 col11\" >70.41%</td>\n",
              "      <td id=\"T_44fb1_row4_col12\" class=\"data row4 col12\" >84.81%</td>\n",
              "      <td id=\"T_44fb1_row4_col13\" class=\"data row4 col13\" >13.77%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_44fb1_row5_col0\" class=\"data row5 col0\" >RF_D6_Leaf25_NoWeight</td>\n",
              "      <td id=\"T_44fb1_row5_col1\" class=\"data row5 col1\" >None</td>\n",
              "      <td id=\"T_44fb1_row5_col2\" class=\"data row5 col2\" >6.000000</td>\n",
              "      <td id=\"T_44fb1_row5_col3\" class=\"data row5 col3\" >25</td>\n",
              "      <td id=\"T_44fb1_row5_col4\" class=\"data row5 col4\" >60</td>\n",
              "      <td id=\"T_44fb1_row5_col5\" class=\"data row5 col5\" >0.500000</td>\n",
              "      <td id=\"T_44fb1_row5_col6\" class=\"data row5 col6\" >98.95%</td>\n",
              "      <td id=\"T_44fb1_row5_col7\" class=\"data row5 col7\" >85.06%</td>\n",
              "      <td id=\"T_44fb1_row5_col8\" class=\"data row5 col8\" >97.87%</td>\n",
              "      <td id=\"T_44fb1_row5_col9\" class=\"data row5 col9\" >74.10%</td>\n",
              "      <td id=\"T_44fb1_row5_col10\" class=\"data row5 col10\" >65.43%</td>\n",
              "      <td id=\"T_44fb1_row5_col11\" class=\"data row5 col11\" >68.26%</td>\n",
              "      <td id=\"T_44fb1_row5_col12\" class=\"data row5 col12\" >84.08%</td>\n",
              "      <td id=\"T_44fb1_row5_col13\" class=\"data row5 col13\" >13.88%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_44fb1_row6_col0\" class=\"data row6 col0\" >RF_D4_Leaf50_NoWeight</td>\n",
              "      <td id=\"T_44fb1_row6_col1\" class=\"data row6 col1\" >None</td>\n",
              "      <td id=\"T_44fb1_row6_col2\" class=\"data row6 col2\" >4.000000</td>\n",
              "      <td id=\"T_44fb1_row6_col3\" class=\"data row6 col3\" >50</td>\n",
              "      <td id=\"T_44fb1_row6_col4\" class=\"data row6 col4\" >100</td>\n",
              "      <td id=\"T_44fb1_row6_col5\" class=\"data row6 col5\" >0.400000</td>\n",
              "      <td id=\"T_44fb1_row6_col6\" class=\"data row6 col6\" >98.95%</td>\n",
              "      <td id=\"T_44fb1_row6_col7\" class=\"data row6 col7\" >85.06%</td>\n",
              "      <td id=\"T_44fb1_row6_col8\" class=\"data row6 col8\" >97.77%</td>\n",
              "      <td id=\"T_44fb1_row6_col9\" class=\"data row6 col9\" >75.03%</td>\n",
              "      <td id=\"T_44fb1_row6_col10\" class=\"data row6 col10\" >64.90%</td>\n",
              "      <td id=\"T_44fb1_row6_col11\" class=\"data row6 col11\" >67.83%</td>\n",
              "      <td id=\"T_44fb1_row6_col12\" class=\"data row6 col12\" >83.91%</td>\n",
              "      <td id=\"T_44fb1_row6_col13\" class=\"data row6 col13\" >13.88%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_44fb1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_44fb1_row7_col0\" class=\"data row7 col0\" >RF_D5_Leaf35_NoWeight</td>\n",
              "      <td id=\"T_44fb1_row7_col1\" class=\"data row7 col1\" >None</td>\n",
              "      <td id=\"T_44fb1_row7_col2\" class=\"data row7 col2\" >5.000000</td>\n",
              "      <td id=\"T_44fb1_row7_col3\" class=\"data row7 col3\" >35</td>\n",
              "      <td id=\"T_44fb1_row7_col4\" class=\"data row7 col4\" >80</td>\n",
              "      <td id=\"T_44fb1_row7_col5\" class=\"data row7 col5\" >0.450000</td>\n",
              "      <td id=\"T_44fb1_row7_col6\" class=\"data row7 col6\" >98.99%</td>\n",
              "      <td id=\"T_44fb1_row7_col7\" class=\"data row7 col7\" >84.70%</td>\n",
              "      <td id=\"T_44fb1_row7_col8\" class=\"data row7 col8\" >97.92%</td>\n",
              "      <td id=\"T_44fb1_row7_col9\" class=\"data row7 col9\" >73.85%</td>\n",
              "      <td id=\"T_44fb1_row7_col10\" class=\"data row7 col10\" >64.70%</td>\n",
              "      <td id=\"T_44fb1_row7_col11\" class=\"data row7 col11\" >67.62%</td>\n",
              "      <td id=\"T_44fb1_row7_col12\" class=\"data row7 col12\" >83.65%</td>\n",
              "      <td id=\"T_44fb1_row7_col13\" class=\"data row7 col13\" >14.29%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Results saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/random_forest_regularization_comparison.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 24C: PCA + Regularized Random Forest Comparison\n",
        "# =========================================================\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Standardize features\n",
        "# Fit using training data only\n",
        "# ---------------------------------------------------------\n",
        "feature_scaler = StandardScaler()\n",
        "\n",
        "scaled_train_features = feature_scaler.fit_transform(\n",
        "    fused_train_features\n",
        ")\n",
        "\n",
        "scaled_val_features = feature_scaler.transform(\n",
        "    fused_val_features\n",
        ")\n",
        "\n",
        "print(\"Feature standardization completed.\")\n",
        "print(\"Training shape:\", scaled_train_features.shape)\n",
        "print(\"Validation shape:\", scaled_val_features.shape)\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# PCA dimensions to compare\n",
        "# ---------------------------------------------------------\n",
        "pca_dimensions = [\n",
        "    64,\n",
        "    128,\n",
        "    256,\n",
        "    384\n",
        "]\n",
        "\n",
        "pca_rf_results = []\n",
        "trained_pca_rf_models = {}\n",
        "\n",
        "for n_components in pca_dimensions:\n",
        "\n",
        "    print(\n",
        "        f\"\\nApplying PCA with \"\n",
        "        f\"{n_components} components...\"\n",
        "    )\n",
        "\n",
        "    pca_model = PCA(\n",
        "        n_components=n_components,\n",
        "        svd_solver=\"randomized\",\n",
        "        random_state=SEED\n",
        "    )\n",
        "\n",
        "    pca_train_features = pca_model.fit_transform(\n",
        "        scaled_train_features\n",
        "    )\n",
        "\n",
        "    pca_val_features = pca_model.transform(\n",
        "        scaled_val_features\n",
        "    )\n",
        "\n",
        "    explained_variance = (\n",
        "        pca_model\n",
        "        .explained_variance_ratio_\n",
        "        .sum()\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Explained variance: \"\n",
        "        f\"{explained_variance * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    # Compare with and without class weighting\n",
        "    for class_weight_option in [\n",
        "        \"balanced_subsample\",\n",
        "        None\n",
        "    ]:\n",
        "\n",
        "        weight_name = (\n",
        "            \"Balanced\"\n",
        "            if class_weight_option is not None\n",
        "            else \"NoWeight\"\n",
        "        )\n",
        "\n",
        "        model_name = (\n",
        "            f\"PCA_{n_components}_RF_{weight_name}\"\n",
        "        )\n",
        "\n",
        "        print(f\"Training {model_name}...\")\n",
        "\n",
        "        rf_model = RandomForestClassifier(\n",
        "            n_estimators=500,\n",
        "\n",
        "            max_depth=8,\n",
        "            max_leaf_nodes=60,\n",
        "\n",
        "            min_samples_split=40,\n",
        "            min_samples_leaf=15,\n",
        "\n",
        "            max_features=\"sqrt\",\n",
        "\n",
        "            bootstrap=True,\n",
        "            max_samples=0.55,\n",
        "\n",
        "            class_weight=class_weight_option,\n",
        "\n",
        "            random_state=SEED,\n",
        "            n_jobs=-1\n",
        "        )\n",
        "\n",
        "        rf_model.fit(\n",
        "            pca_train_features,\n",
        "            train_labels\n",
        "        )\n",
        "\n",
        "        train_predictions = rf_model.predict(\n",
        "            pca_train_features\n",
        "        )\n",
        "\n",
        "        val_predictions = rf_model.predict(\n",
        "            pca_val_features\n",
        "        )\n",
        "\n",
        "        train_accuracy_pca = accuracy_score(\n",
        "            train_labels,\n",
        "            train_predictions\n",
        "        )\n",
        "\n",
        "        val_accuracy_pca = accuracy_score(\n",
        "            val_labels,\n",
        "            val_predictions\n",
        "        )\n",
        "\n",
        "        train_macro_f1_pca = f1_score(\n",
        "            train_labels,\n",
        "            train_predictions,\n",
        "            average=\"macro\",\n",
        "            zero_division=0\n",
        "        )\n",
        "\n",
        "        val_macro_precision_pca = precision_score(\n",
        "            val_labels,\n",
        "            val_predictions,\n",
        "            average=\"macro\",\n",
        "            zero_division=0\n",
        "        )\n",
        "\n",
        "        val_macro_recall_pca = recall_score(\n",
        "            val_labels,\n",
        "            val_predictions,\n",
        "            average=\"macro\",\n",
        "            zero_division=0\n",
        "        )\n",
        "\n",
        "        val_macro_f1_pca = f1_score(\n",
        "            val_labels,\n",
        "            val_predictions,\n",
        "            average=\"macro\",\n",
        "            zero_division=0\n",
        "        )\n",
        "\n",
        "        val_weighted_f1_pca = f1_score(\n",
        "            val_labels,\n",
        "            val_predictions,\n",
        "            average=\"weighted\",\n",
        "            zero_division=0\n",
        "        )\n",
        "\n",
        "        train_val_gap_pca = (\n",
        "            train_accuracy_pca -\n",
        "            val_accuracy_pca\n",
        "        )\n",
        "\n",
        "        pca_rf_results.append({\n",
        "            \"Model\": model_name,\n",
        "            \"PCA Components\": n_components,\n",
        "            \"Explained Variance\":\n",
        "                explained_variance,\n",
        "            \"Class Weight\":\n",
        "                str(class_weight_option),\n",
        "            \"Train Accuracy\":\n",
        "                train_accuracy_pca,\n",
        "            \"Validation Accuracy\":\n",
        "                val_accuracy_pca,\n",
        "            \"Train Macro F1\":\n",
        "                train_macro_f1_pca,\n",
        "            \"Validation Macro Precision\":\n",
        "                val_macro_precision_pca,\n",
        "            \"Validation Macro Recall\":\n",
        "                val_macro_recall_pca,\n",
        "            \"Validation Macro F1\":\n",
        "                val_macro_f1_pca,\n",
        "            \"Validation Weighted F1\":\n",
        "                val_weighted_f1_pca,\n",
        "            \"Train-Val Gap\":\n",
        "                train_val_gap_pca\n",
        "        })\n",
        "\n",
        "        trained_pca_rf_models[model_name] = {\n",
        "            \"scaler\": feature_scaler,\n",
        "            \"pca\": pca_model,\n",
        "            \"rf\": rf_model\n",
        "        }\n",
        "\n",
        "        print(\n",
        "            f\"Train Accuracy:      \"\n",
        "            f\"{train_accuracy_pca * 100:.2f}%\"\n",
        "        )\n",
        "\n",
        "        print(\n",
        "            f\"Validation Accuracy: \"\n",
        "            f\"{val_accuracy_pca * 100:.2f}%\"\n",
        "        )\n",
        "\n",
        "        print(\n",
        "            f\"Validation Macro F1: \"\n",
        "            f\"{val_macro_f1_pca * 100:.2f}%\"\n",
        "        )\n",
        "\n",
        "        print(\n",
        "            f\"Train-Val Gap:       \"\n",
        "            f\"{train_val_gap_pca * 100:.2f}%\"\n",
        "        )\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Results comparison\n",
        "# ---------------------------------------------------------\n",
        "pca_rf_results_df = pd.DataFrame(\n",
        "    pca_rf_results\n",
        ")\n",
        "\n",
        "pca_rf_results_df = (\n",
        "    pca_rf_results_df\n",
        "    .sort_values(\n",
        "        by=[\n",
        "            \"Train-Val Gap\",\n",
        "            \"Validation Macro F1\",\n",
        "            \"Validation Accuracy\"\n",
        "        ],\n",
        "        ascending=[\n",
        "            True,\n",
        "            False,\n",
        "            False\n",
        "        ]\n",
        "    )\n",
        "    .reset_index(drop=True)\n",
        ")\n",
        "\n",
        "print(\"\\nPCA + Random Forest Comparison\")\n",
        "print(\"=\" * 100)\n",
        "\n",
        "display(\n",
        "    pca_rf_results_df.style.format({\n",
        "        \"Explained Variance\": \"{:.2%}\",\n",
        "        \"Train Accuracy\": \"{:.2%}\",\n",
        "        \"Validation Accuracy\": \"{:.2%}\",\n",
        "        \"Train Macro F1\": \"{:.2%}\",\n",
        "        \"Validation Macro Precision\": \"{:.2%}\",\n",
        "        \"Validation Macro Recall\": \"{:.2%}\",\n",
        "        \"Validation Macro F1\": \"{:.2%}\",\n",
        "        \"Validation Weighted F1\": \"{:.2%}\",\n",
        "        \"Train-Val Gap\": \"{:.2%}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save results\n",
        "# ---------------------------------------------------------\n",
        "pca_rf_results_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"pca_random_forest_validation_results.csv\"\n",
        ")\n",
        "\n",
        "pca_rf_results_df.to_csv(\n",
        "    pca_rf_results_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nResults saved at:\")\n",
        "print(pca_rf_results_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1358
        },
        "id": "zqvG2OV3UObP",
        "outputId": "4900d529-0072-4d8c-960a-6f68ccb206a7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature standardization completed.\n",
            "Training shape: (2563, 1536)\n",
            "Validation shape: (549, 1536)\n",
            "\n",
            "Applying PCA with 64 components...\n",
            "Explained variance: 76.67%\n",
            "Training PCA_64_RF_Balanced...\n",
            "Train Accuracy:      98.95%\n",
            "Validation Accuracy: 84.88%\n",
            "Validation Macro F1: 70.73%\n",
            "Train-Val Gap:       14.06%\n",
            "Training PCA_64_RF_NoWeight...\n",
            "Train Accuracy:      98.95%\n",
            "Validation Accuracy: 84.70%\n",
            "Validation Macro F1: 68.17%\n",
            "Train-Val Gap:       14.25%\n",
            "\n",
            "Applying PCA with 128 components...\n",
            "Explained variance: 84.18%\n",
            "Training PCA_128_RF_Balanced...\n",
            "Train Accuracy:      98.95%\n",
            "Validation Accuracy: 85.25%\n",
            "Validation Macro F1: 71.33%\n",
            "Train-Val Gap:       13.70%\n",
            "Training PCA_128_RF_NoWeight...\n",
            "Train Accuracy:      98.83%\n",
            "Validation Accuracy: 83.79%\n",
            "Validation Macro F1: 64.95%\n",
            "Train-Val Gap:       15.04%\n",
            "\n",
            "Applying PCA with 256 components...\n",
            "Explained variance: 91.12%\n",
            "Training PCA_256_RF_Balanced...\n",
            "Train Accuracy:      98.91%\n",
            "Validation Accuracy: 84.52%\n",
            "Validation Macro F1: 70.51%\n",
            "Train-Val Gap:       14.39%\n",
            "Training PCA_256_RF_NoWeight...\n",
            "Train Accuracy:      96.61%\n",
            "Validation Accuracy: 82.88%\n",
            "Validation Macro F1: 57.26%\n",
            "Train-Val Gap:       13.73%\n",
            "\n",
            "Applying PCA with 384 components...\n",
            "Explained variance: 94.65%\n",
            "Training PCA_384_RF_Balanced...\n",
            "Train Accuracy:      98.95%\n",
            "Validation Accuracy: 84.15%\n",
            "Validation Macro F1: 69.52%\n",
            "Train-Val Gap:       14.79%\n",
            "Training PCA_384_RF_NoWeight...\n",
            "Train Accuracy:      94.54%\n",
            "Validation Accuracy: 82.70%\n",
            "Validation Macro F1: 57.10%\n",
            "Train-Val Gap:       11.84%\n",
            "\n",
            "PCA + Random Forest Comparison\n",
            "====================================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7acb2865d4f0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_65d4b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_65d4b_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_65d4b_level0_col1\" class=\"col_heading level0 col1\" >PCA Components</th>\n",
              "      <th id=\"T_65d4b_level0_col2\" class=\"col_heading level0 col2\" >Explained Variance</th>\n",
              "      <th id=\"T_65d4b_level0_col3\" class=\"col_heading level0 col3\" >Class Weight</th>\n",
              "      <th id=\"T_65d4b_level0_col4\" class=\"col_heading level0 col4\" >Train Accuracy</th>\n",
              "      <th id=\"T_65d4b_level0_col5\" class=\"col_heading level0 col5\" >Validation Accuracy</th>\n",
              "      <th id=\"T_65d4b_level0_col6\" class=\"col_heading level0 col6\" >Train Macro F1</th>\n",
              "      <th id=\"T_65d4b_level0_col7\" class=\"col_heading level0 col7\" >Validation Macro Precision</th>\n",
              "      <th id=\"T_65d4b_level0_col8\" class=\"col_heading level0 col8\" >Validation Macro Recall</th>\n",
              "      <th id=\"T_65d4b_level0_col9\" class=\"col_heading level0 col9\" >Validation Macro F1</th>\n",
              "      <th id=\"T_65d4b_level0_col10\" class=\"col_heading level0 col10\" >Validation Weighted F1</th>\n",
              "      <th id=\"T_65d4b_level0_col11\" class=\"col_heading level0 col11\" >Train-Val Gap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_65d4b_row0_col0\" class=\"data row0 col0\" >PCA_384_RF_NoWeight</td>\n",
              "      <td id=\"T_65d4b_row0_col1\" class=\"data row0 col1\" >384</td>\n",
              "      <td id=\"T_65d4b_row0_col2\" class=\"data row0 col2\" >94.65%</td>\n",
              "      <td id=\"T_65d4b_row0_col3\" class=\"data row0 col3\" >None</td>\n",
              "      <td id=\"T_65d4b_row0_col4\" class=\"data row0 col4\" >94.54%</td>\n",
              "      <td id=\"T_65d4b_row0_col5\" class=\"data row0 col5\" >82.70%</td>\n",
              "      <td id=\"T_65d4b_row0_col6\" class=\"data row0 col6\" >82.59%</td>\n",
              "      <td id=\"T_65d4b_row0_col7\" class=\"data row0 col7\" >64.36%</td>\n",
              "      <td id=\"T_65d4b_row0_col8\" class=\"data row0 col8\" >55.80%</td>\n",
              "      <td id=\"T_65d4b_row0_col9\" class=\"data row0 col9\" >57.10%</td>\n",
              "      <td id=\"T_65d4b_row0_col10\" class=\"data row0 col10\" >79.68%</td>\n",
              "      <td id=\"T_65d4b_row0_col11\" class=\"data row0 col11\" >11.84%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_65d4b_row1_col0\" class=\"data row1 col0\" >PCA_128_RF_Balanced</td>\n",
              "      <td id=\"T_65d4b_row1_col1\" class=\"data row1 col1\" >128</td>\n",
              "      <td id=\"T_65d4b_row1_col2\" class=\"data row1 col2\" >84.18%</td>\n",
              "      <td id=\"T_65d4b_row1_col3\" class=\"data row1 col3\" >balanced_subsample</td>\n",
              "      <td id=\"T_65d4b_row1_col4\" class=\"data row1 col4\" >98.95%</td>\n",
              "      <td id=\"T_65d4b_row1_col5\" class=\"data row1 col5\" >85.25%</td>\n",
              "      <td id=\"T_65d4b_row1_col6\" class=\"data row1 col6\" >97.90%</td>\n",
              "      <td id=\"T_65d4b_row1_col7\" class=\"data row1 col7\" >73.86%</td>\n",
              "      <td id=\"T_65d4b_row1_col8\" class=\"data row1 col8\" >69.61%</td>\n",
              "      <td id=\"T_65d4b_row1_col9\" class=\"data row1 col9\" >71.33%</td>\n",
              "      <td id=\"T_65d4b_row1_col10\" class=\"data row1 col10\" >84.81%</td>\n",
              "      <td id=\"T_65d4b_row1_col11\" class=\"data row1 col11\" >13.70%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_65d4b_row2_col0\" class=\"data row2 col0\" >PCA_256_RF_NoWeight</td>\n",
              "      <td id=\"T_65d4b_row2_col1\" class=\"data row2 col1\" >256</td>\n",
              "      <td id=\"T_65d4b_row2_col2\" class=\"data row2 col2\" >91.12%</td>\n",
              "      <td id=\"T_65d4b_row2_col3\" class=\"data row2 col3\" >None</td>\n",
              "      <td id=\"T_65d4b_row2_col4\" class=\"data row2 col4\" >96.61%</td>\n",
              "      <td id=\"T_65d4b_row2_col5\" class=\"data row2 col5\" >82.88%</td>\n",
              "      <td id=\"T_65d4b_row2_col6\" class=\"data row2 col6\" >91.50%</td>\n",
              "      <td id=\"T_65d4b_row2_col7\" class=\"data row2 col7\" >63.47%</td>\n",
              "      <td id=\"T_65d4b_row2_col8\" class=\"data row2 col8\" >56.16%</td>\n",
              "      <td id=\"T_65d4b_row2_col9\" class=\"data row2 col9\" >57.26%</td>\n",
              "      <td id=\"T_65d4b_row2_col10\" class=\"data row2 col10\" >79.89%</td>\n",
              "      <td id=\"T_65d4b_row2_col11\" class=\"data row2 col11\" >13.73%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_65d4b_row3_col0\" class=\"data row3 col0\" >PCA_64_RF_Balanced</td>\n",
              "      <td id=\"T_65d4b_row3_col1\" class=\"data row3 col1\" >64</td>\n",
              "      <td id=\"T_65d4b_row3_col2\" class=\"data row3 col2\" >76.67%</td>\n",
              "      <td id=\"T_65d4b_row3_col3\" class=\"data row3 col3\" >balanced_subsample</td>\n",
              "      <td id=\"T_65d4b_row3_col4\" class=\"data row3 col4\" >98.95%</td>\n",
              "      <td id=\"T_65d4b_row3_col5\" class=\"data row3 col5\" >84.88%</td>\n",
              "      <td id=\"T_65d4b_row3_col6\" class=\"data row3 col6\" >97.89%</td>\n",
              "      <td id=\"T_65d4b_row3_col7\" class=\"data row3 col7\" >72.80%</td>\n",
              "      <td id=\"T_65d4b_row3_col8\" class=\"data row3 col8\" >69.34%</td>\n",
              "      <td id=\"T_65d4b_row3_col9\" class=\"data row3 col9\" >70.73%</td>\n",
              "      <td id=\"T_65d4b_row3_col10\" class=\"data row3 col10\" >84.51%</td>\n",
              "      <td id=\"T_65d4b_row3_col11\" class=\"data row3 col11\" >14.06%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_65d4b_row4_col0\" class=\"data row4 col0\" >PCA_64_RF_NoWeight</td>\n",
              "      <td id=\"T_65d4b_row4_col1\" class=\"data row4 col1\" >64</td>\n",
              "      <td id=\"T_65d4b_row4_col2\" class=\"data row4 col2\" >76.67%</td>\n",
              "      <td id=\"T_65d4b_row4_col3\" class=\"data row4 col3\" >None</td>\n",
              "      <td id=\"T_65d4b_row4_col4\" class=\"data row4 col4\" >98.95%</td>\n",
              "      <td id=\"T_65d4b_row4_col5\" class=\"data row4 col5\" >84.70%</td>\n",
              "      <td id=\"T_65d4b_row4_col6\" class=\"data row4 col6\" >97.87%</td>\n",
              "      <td id=\"T_65d4b_row4_col7\" class=\"data row4 col7\" >75.13%</td>\n",
              "      <td id=\"T_65d4b_row4_col8\" class=\"data row4 col8\" >64.89%</td>\n",
              "      <td id=\"T_65d4b_row4_col9\" class=\"data row4 col9\" >68.17%</td>\n",
              "      <td id=\"T_65d4b_row4_col10\" class=\"data row4 col10\" >83.65%</td>\n",
              "      <td id=\"T_65d4b_row4_col11\" class=\"data row4 col11\" >14.25%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_65d4b_row5_col0\" class=\"data row5 col0\" >PCA_256_RF_Balanced</td>\n",
              "      <td id=\"T_65d4b_row5_col1\" class=\"data row5 col1\" >256</td>\n",
              "      <td id=\"T_65d4b_row5_col2\" class=\"data row5 col2\" >91.12%</td>\n",
              "      <td id=\"T_65d4b_row5_col3\" class=\"data row5 col3\" >balanced_subsample</td>\n",
              "      <td id=\"T_65d4b_row5_col4\" class=\"data row5 col4\" >98.91%</td>\n",
              "      <td id=\"T_65d4b_row5_col5\" class=\"data row5 col5\" >84.52%</td>\n",
              "      <td id=\"T_65d4b_row5_col6\" class=\"data row5 col6\" >97.82%</td>\n",
              "      <td id=\"T_65d4b_row5_col7\" class=\"data row5 col7\" >72.61%</td>\n",
              "      <td id=\"T_65d4b_row5_col8\" class=\"data row5 col8\" >69.07%</td>\n",
              "      <td id=\"T_65d4b_row5_col9\" class=\"data row5 col9\" >70.51%</td>\n",
              "      <td id=\"T_65d4b_row5_col10\" class=\"data row5 col10\" >84.16%</td>\n",
              "      <td id=\"T_65d4b_row5_col11\" class=\"data row5 col11\" >14.39%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_65d4b_row6_col0\" class=\"data row6 col0\" >PCA_384_RF_Balanced</td>\n",
              "      <td id=\"T_65d4b_row6_col1\" class=\"data row6 col1\" >384</td>\n",
              "      <td id=\"T_65d4b_row6_col2\" class=\"data row6 col2\" >94.65%</td>\n",
              "      <td id=\"T_65d4b_row6_col3\" class=\"data row6 col3\" >balanced_subsample</td>\n",
              "      <td id=\"T_65d4b_row6_col4\" class=\"data row6 col4\" >98.95%</td>\n",
              "      <td id=\"T_65d4b_row6_col5\" class=\"data row6 col5\" >84.15%</td>\n",
              "      <td id=\"T_65d4b_row6_col6\" class=\"data row6 col6\" >97.90%</td>\n",
              "      <td id=\"T_65d4b_row6_col7\" class=\"data row6 col7\" >71.25%</td>\n",
              "      <td id=\"T_65d4b_row6_col8\" class=\"data row6 col8\" >68.49%</td>\n",
              "      <td id=\"T_65d4b_row6_col9\" class=\"data row6 col9\" >69.52%</td>\n",
              "      <td id=\"T_65d4b_row6_col10\" class=\"data row6 col10\" >83.85%</td>\n",
              "      <td id=\"T_65d4b_row6_col11\" class=\"data row6 col11\" >14.79%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_65d4b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_65d4b_row7_col0\" class=\"data row7 col0\" >PCA_128_RF_NoWeight</td>\n",
              "      <td id=\"T_65d4b_row7_col1\" class=\"data row7 col1\" >128</td>\n",
              "      <td id=\"T_65d4b_row7_col2\" class=\"data row7 col2\" >84.18%</td>\n",
              "      <td id=\"T_65d4b_row7_col3\" class=\"data row7 col3\" >None</td>\n",
              "      <td id=\"T_65d4b_row7_col4\" class=\"data row7 col4\" >98.83%</td>\n",
              "      <td id=\"T_65d4b_row7_col5\" class=\"data row7 col5\" >83.79%</td>\n",
              "      <td id=\"T_65d4b_row7_col6\" class=\"data row7 col6\" >97.65%</td>\n",
              "      <td id=\"T_65d4b_row7_col7\" class=\"data row7 col7\" >77.14%</td>\n",
              "      <td id=\"T_65d4b_row7_col8\" class=\"data row7 col8\" >61.18%</td>\n",
              "      <td id=\"T_65d4b_row7_col9\" class=\"data row7 col9\" >64.95%</td>\n",
              "      <td id=\"T_65d4b_row7_col10\" class=\"data row7 col10\" >82.16%</td>\n",
              "      <td id=\"T_65d4b_row7_col11\" class=\"data row7 col11\" >15.04%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Results saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/pca_random_forest_validation_results.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 24D: Cross-Validated Randomized Search for RF\n",
        "# =========================================================\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import (\n",
        "    StratifiedKFold,\n",
        "    RandomizedSearchCV\n",
        ")\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    f1_score,\n",
        "    precision_score,\n",
        "    recall_score\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Stratified cross-validation\n",
        "# ---------------------------------------------------------\n",
        "cv_strategy = StratifiedKFold(\n",
        "    n_splits=5,\n",
        "    shuffle=True,\n",
        "    random_state=SEED\n",
        ")\n",
        "\n",
        "# Use one CPU per forest because RandomizedSearchCV\n",
        "# will parallelize the candidate models\n",
        "rf_search_base = RandomForestClassifier(\n",
        "    n_estimators=300,\n",
        "    bootstrap=True,\n",
        "    class_weight=\"balanced_subsample\",\n",
        "    random_state=SEED,\n",
        "    n_jobs=1\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Stronger regularization search space\n",
        "# ---------------------------------------------------------\n",
        "rf_parameter_space = {\n",
        "    \"max_depth\": [\n",
        "        2, 3, 4, 5, 6, 7, 8\n",
        "    ],\n",
        "\n",
        "    \"min_samples_leaf\": [\n",
        "        10, 20, 30, 40,\n",
        "        50, 75, 100, 125\n",
        "    ],\n",
        "\n",
        "    \"min_samples_split\": [\n",
        "        20, 40, 60, 80,\n",
        "        100, 150, 200\n",
        "    ],\n",
        "\n",
        "    # Smaller feature subsets per split\n",
        "    \"max_features\": [\n",
        "        \"sqrt\",\n",
        "        \"log2\",\n",
        "        0.03,\n",
        "        0.05,\n",
        "        0.08,\n",
        "        0.10,\n",
        "        0.15\n",
        "    ],\n",
        "\n",
        "    # Smaller bootstrap samples\n",
        "    \"max_samples\": [\n",
        "        0.25,\n",
        "        0.30,\n",
        "        0.35,\n",
        "        0.40,\n",
        "        0.45,\n",
        "        0.50,\n",
        "        0.60\n",
        "    ],\n",
        "\n",
        "    \"criterion\": [\n",
        "        \"gini\",\n",
        "        \"entropy\"\n",
        "    ]\n",
        "}\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Run randomized cross-validation\n",
        "# ---------------------------------------------------------\n",
        "rf_random_search = RandomizedSearchCV(\n",
        "    estimator=rf_search_base,\n",
        "    param_distributions=rf_parameter_space,\n",
        "    n_iter=30,\n",
        "\n",
        "    scoring={\n",
        "        \"accuracy\": \"accuracy\",\n",
        "        \"macro_f1\": \"f1_macro\"\n",
        "    },\n",
        "\n",
        "    # Manual selection after examining CV gaps\n",
        "    refit=False,\n",
        "\n",
        "    cv=cv_strategy,\n",
        "    random_state=SEED,\n",
        "    n_jobs=-1,\n",
        "    verbose=2,\n",
        "    return_train_score=True\n",
        ")\n",
        "\n",
        "rf_random_search.fit(\n",
        "    fused_train_features,\n",
        "    train_labels\n",
        ")\n",
        "\n",
        "print(\"Randomized cross-validation completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vZcEZJDiU3hS",
        "outputId": "e108ff02-b9ac-4e4f-ffdd-5d26ee62a99b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n",
            "Randomized cross-validation completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 24E: Select Best Generalizing RF Configuration\n",
        "# =========================================================\n",
        "\n",
        "rf_cv_results_df = pd.DataFrame(\n",
        "    rf_random_search.cv_results_\n",
        ")\n",
        "\n",
        "# Calculate average CV gaps\n",
        "rf_cv_results_df[\"CV Accuracy Gap\"] = (\n",
        "    rf_cv_results_df[\"mean_train_accuracy\"]\n",
        "    - rf_cv_results_df[\"mean_test_accuracy\"]\n",
        ")\n",
        "\n",
        "rf_cv_results_df[\"CV Macro F1 Gap\"] = (\n",
        "    rf_cv_results_df[\"mean_train_macro_f1\"]\n",
        "    - rf_cv_results_df[\"mean_test_macro_f1\"]\n",
        ")\n",
        "\n",
        "# Best cross-validated Macro F1\n",
        "best_cv_macro_f1 = (\n",
        "    rf_cv_results_df[\"mean_test_macro_f1\"].max()\n",
        ")\n",
        "\n",
        "# Keep candidates within one percentage point\n",
        "# of the best CV Macro F1\n",
        "near_best_candidates = rf_cv_results_df[\n",
        "    rf_cv_results_df[\"mean_test_macro_f1\"]\n",
        "    >= best_cv_macro_f1 - 0.01\n",
        "].copy()\n",
        "\n",
        "# Prefer a CV accuracy gap of 8% or lower\n",
        "acceptable_candidates = near_best_candidates[\n",
        "    near_best_candidates[\"CV Accuracy Gap\"]\n",
        "    <= 0.08\n",
        "].copy()\n",
        "\n",
        "if not acceptable_candidates.empty:\n",
        "\n",
        "    selected_cv_row = (\n",
        "        acceptable_candidates\n",
        "        .sort_values(\n",
        "            by=[\n",
        "                \"CV Accuracy Gap\",\n",
        "                \"mean_test_macro_f1\",\n",
        "                \"mean_test_accuracy\"\n",
        "            ],\n",
        "            ascending=[\n",
        "                True,\n",
        "                False,\n",
        "                False\n",
        "            ]\n",
        "        )\n",
        "        .iloc[0]\n",
        "    )\n",
        "\n",
        "    selection_status = (\n",
        "        \"Selected from candidates with \"\n",
        "        \"CV accuracy gap ≤ 8%.\"\n",
        "    )\n",
        "\n",
        "else:\n",
        "\n",
        "    # If none reaches 8%, choose the smallest gap\n",
        "    # among the near-best performing candidates\n",
        "    selected_cv_row = (\n",
        "        near_best_candidates\n",
        "        .sort_values(\n",
        "            by=[\n",
        "                \"CV Accuracy Gap\",\n",
        "                \"mean_test_macro_f1\",\n",
        "                \"mean_test_accuracy\"\n",
        "            ],\n",
        "            ascending=[\n",
        "                True,\n",
        "                False,\n",
        "                False\n",
        "            ]\n",
        "        )\n",
        "        .iloc[0]\n",
        "    )\n",
        "\n",
        "    selection_status = (\n",
        "        \"No candidate achieved a CV accuracy gap \"\n",
        "        \"≤ 8%; selected the best trade-off.\"\n",
        "    )\n",
        "\n",
        "selected_cv_parameters = (\n",
        "    selected_cv_row[\"params\"]\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Display top candidates\n",
        "# ---------------------------------------------------------\n",
        "display_columns = [\n",
        "    \"params\",\n",
        "    \"mean_train_accuracy\",\n",
        "    \"mean_test_accuracy\",\n",
        "    \"CV Accuracy Gap\",\n",
        "    \"mean_train_macro_f1\",\n",
        "    \"mean_test_macro_f1\",\n",
        "    \"CV Macro F1 Gap\",\n",
        "    \"std_test_macro_f1\"\n",
        "]\n",
        "\n",
        "top_cv_results = (\n",
        "    rf_cv_results_df\n",
        "    .sort_values(\n",
        "        by=[\n",
        "            \"mean_test_macro_f1\",\n",
        "            \"CV Accuracy Gap\"\n",
        "        ],\n",
        "        ascending=[\n",
        "            False,\n",
        "            True\n",
        "        ]\n",
        "    )\n",
        "    [display_columns]\n",
        "    .head(15)\n",
        "    .reset_index(drop=True)\n",
        ")\n",
        "\n",
        "print(\"Top Cross-Validated Random Forest Results\")\n",
        "print(\"=\" * 90)\n",
        "\n",
        "display(\n",
        "    top_cv_results.style.format({\n",
        "        \"mean_train_accuracy\": \"{:.2%}\",\n",
        "        \"mean_test_accuracy\": \"{:.2%}\",\n",
        "        \"CV Accuracy Gap\": \"{:.2%}\",\n",
        "        \"mean_train_macro_f1\": \"{:.2%}\",\n",
        "        \"mean_test_macro_f1\": \"{:.2%}\",\n",
        "        \"CV Macro F1 Gap\": \"{:.2%}\",\n",
        "        \"std_test_macro_f1\": \"{:.2%}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "print(\"\\nSelection status:\")\n",
        "print(selection_status)\n",
        "\n",
        "print(\"\\nSelected cross-validation result:\")\n",
        "print(\n",
        "    \"Mean CV Training Accuracy:\",\n",
        "    f\"{selected_cv_row['mean_train_accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Mean CV Validation Accuracy:\",\n",
        "    f\"{selected_cv_row['mean_test_accuracy'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Mean CV Macro F1:\",\n",
        "    f\"{selected_cv_row['mean_test_macro_f1'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"CV Accuracy Gap:\",\n",
        "    f\"{selected_cv_row['CV Accuracy Gap'] * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\"\\nSelected parameters:\")\n",
        "\n",
        "for parameter, value in selected_cv_parameters.items():\n",
        "    print(f\"{parameter}: {value}\")\n",
        "\n",
        "# Save all search results\n",
        "rf_cv_results_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"randomized_search_rf_cross_validation.csv\"\n",
        ")\n",
        "\n",
        "rf_cv_results_df.to_csv(\n",
        "    rf_cv_results_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nResults saved at:\")\n",
        "print(rf_cv_results_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "qpecQH88VBPm",
        "outputId": "a7af27cb-ad17-4c42-acd5-9b66f3e34428"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top Cross-Validated Random Forest Results\n",
            "==========================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7acb2be602c0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_2b51e\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_2b51e_level0_col0\" class=\"col_heading level0 col0\" >params</th>\n",
              "      <th id=\"T_2b51e_level0_col1\" class=\"col_heading level0 col1\" >mean_train_accuracy</th>\n",
              "      <th id=\"T_2b51e_level0_col2\" class=\"col_heading level0 col2\" >mean_test_accuracy</th>\n",
              "      <th id=\"T_2b51e_level0_col3\" class=\"col_heading level0 col3\" >CV Accuracy Gap</th>\n",
              "      <th id=\"T_2b51e_level0_col4\" class=\"col_heading level0 col4\" >mean_train_macro_f1</th>\n",
              "      <th id=\"T_2b51e_level0_col5\" class=\"col_heading level0 col5\" >mean_test_macro_f1</th>\n",
              "      <th id=\"T_2b51e_level0_col6\" class=\"col_heading level0 col6\" >CV Macro F1 Gap</th>\n",
              "      <th id=\"T_2b51e_level0_col7\" class=\"col_heading level0 col7\" >std_test_macro_f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_2b51e_row0_col0\" class=\"data row0 col0\" >{'min_samples_split': 80, 'min_samples_leaf': 20, 'max_samples': 0.25, 'max_features': 'sqrt', 'max_depth': 3, 'criterion': 'entropy'}</td>\n",
              "      <td id=\"T_2b51e_row0_col1\" class=\"data row0 col1\" >98.96%</td>\n",
              "      <td id=\"T_2b51e_row0_col2\" class=\"data row0 col2\" >98.95%</td>\n",
              "      <td id=\"T_2b51e_row0_col3\" class=\"data row0 col3\" >0.01%</td>\n",
              "      <td id=\"T_2b51e_row0_col4\" class=\"data row0 col4\" >97.91%</td>\n",
              "      <td id=\"T_2b51e_row0_col5\" class=\"data row0 col5\" >97.90%</td>\n",
              "      <td id=\"T_2b51e_row0_col6\" class=\"data row0 col6\" >0.01%</td>\n",
              "      <td id=\"T_2b51e_row0_col7\" class=\"data row0 col7\" >1.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_2b51e_row1_col0\" class=\"data row1 col0\" >{'min_samples_split': 150, 'min_samples_leaf': 75, 'max_samples': 0.25, 'max_features': 'sqrt', 'max_depth': 5, 'criterion': 'entropy'}</td>\n",
              "      <td id=\"T_2b51e_row1_col1\" class=\"data row1 col1\" >98.97%</td>\n",
              "      <td id=\"T_2b51e_row1_col2\" class=\"data row1 col2\" >98.95%</td>\n",
              "      <td id=\"T_2b51e_row1_col3\" class=\"data row1 col3\" >0.02%</td>\n",
              "      <td id=\"T_2b51e_row1_col4\" class=\"data row1 col4\" >97.93%</td>\n",
              "      <td id=\"T_2b51e_row1_col5\" class=\"data row1 col5\" >97.90%</td>\n",
              "      <td id=\"T_2b51e_row1_col6\" class=\"data row1 col6\" >0.03%</td>\n",
              "      <td id=\"T_2b51e_row1_col7\" class=\"data row1 col7\" >1.11%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_2b51e_row2_col0\" class=\"data row2 col0\" >{'min_samples_split': 200, 'min_samples_leaf': 30, 'max_samples': 0.3, 'max_features': 0.03, 'max_depth': 2, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row2_col1\" class=\"data row2 col1\" >98.96%</td>\n",
              "      <td id=\"T_2b51e_row2_col2\" class=\"data row2 col2\" >98.91%</td>\n",
              "      <td id=\"T_2b51e_row2_col3\" class=\"data row2 col3\" >0.05%</td>\n",
              "      <td id=\"T_2b51e_row2_col4\" class=\"data row2 col4\" >97.90%</td>\n",
              "      <td id=\"T_2b51e_row2_col5\" class=\"data row2 col5\" >97.82%</td>\n",
              "      <td id=\"T_2b51e_row2_col6\" class=\"data row2 col6\" >0.08%</td>\n",
              "      <td id=\"T_2b51e_row2_col7\" class=\"data row2 col7\" >1.06%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_2b51e_row3_col0\" class=\"data row3 col0\" >{'min_samples_split': 150, 'min_samples_leaf': 20, 'max_samples': 0.35, 'max_features': 0.03, 'max_depth': 4, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row3_col1\" class=\"data row3 col1\" >98.96%</td>\n",
              "      <td id=\"T_2b51e_row3_col2\" class=\"data row3 col2\" >98.91%</td>\n",
              "      <td id=\"T_2b51e_row3_col3\" class=\"data row3 col3\" >0.05%</td>\n",
              "      <td id=\"T_2b51e_row3_col4\" class=\"data row3 col4\" >97.90%</td>\n",
              "      <td id=\"T_2b51e_row3_col5\" class=\"data row3 col5\" >97.81%</td>\n",
              "      <td id=\"T_2b51e_row3_col6\" class=\"data row3 col6\" >0.09%</td>\n",
              "      <td id=\"T_2b51e_row3_col7\" class=\"data row3 col7\" >1.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_2b51e_row4_col0\" class=\"data row4 col0\" >{'min_samples_split': 80, 'min_samples_leaf': 10, 'max_samples': 0.25, 'max_features': 'sqrt', 'max_depth': 3, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row4_col1\" class=\"data row4 col1\" >98.96%</td>\n",
              "      <td id=\"T_2b51e_row4_col2\" class=\"data row4 col2\" >98.91%</td>\n",
              "      <td id=\"T_2b51e_row4_col3\" class=\"data row4 col3\" >0.05%</td>\n",
              "      <td id=\"T_2b51e_row4_col4\" class=\"data row4 col4\" >97.91%</td>\n",
              "      <td id=\"T_2b51e_row4_col5\" class=\"data row4 col5\" >97.81%</td>\n",
              "      <td id=\"T_2b51e_row4_col6\" class=\"data row4 col6\" >0.09%</td>\n",
              "      <td id=\"T_2b51e_row4_col7\" class=\"data row4 col7\" >1.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_2b51e_row5_col0\" class=\"data row5 col0\" >{'min_samples_split': 20, 'min_samples_leaf': 125, 'max_samples': 0.6, 'max_features': 'log2', 'max_depth': 4, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row5_col1\" class=\"data row5 col1\" >98.97%</td>\n",
              "      <td id=\"T_2b51e_row5_col2\" class=\"data row5 col2\" >98.91%</td>\n",
              "      <td id=\"T_2b51e_row5_col3\" class=\"data row5 col3\" >0.06%</td>\n",
              "      <td id=\"T_2b51e_row5_col4\" class=\"data row5 col4\" >97.93%</td>\n",
              "      <td id=\"T_2b51e_row5_col5\" class=\"data row5 col5\" >97.81%</td>\n",
              "      <td id=\"T_2b51e_row5_col6\" class=\"data row5 col6\" >0.12%</td>\n",
              "      <td id=\"T_2b51e_row5_col7\" class=\"data row5 col7\" >0.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_2b51e_row6_col0\" class=\"data row6 col0\" >{'min_samples_split': 80, 'min_samples_leaf': 100, 'max_samples': 0.3, 'max_features': 'sqrt', 'max_depth': 8, 'criterion': 'entropy'}</td>\n",
              "      <td id=\"T_2b51e_row6_col1\" class=\"data row6 col1\" >98.98%</td>\n",
              "      <td id=\"T_2b51e_row6_col2\" class=\"data row6 col2\" >98.91%</td>\n",
              "      <td id=\"T_2b51e_row6_col3\" class=\"data row6 col3\" >0.07%</td>\n",
              "      <td id=\"T_2b51e_row6_col4\" class=\"data row6 col4\" >97.92%</td>\n",
              "      <td id=\"T_2b51e_row6_col5\" class=\"data row6 col5\" >97.81%</td>\n",
              "      <td id=\"T_2b51e_row6_col6\" class=\"data row6 col6\" >0.11%</td>\n",
              "      <td id=\"T_2b51e_row6_col7\" class=\"data row6 col7\" >0.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_2b51e_row7_col0\" class=\"data row7 col0\" >{'min_samples_split': 20, 'min_samples_leaf': 20, 'max_samples': 0.25, 'max_features': 0.1, 'max_depth': 8, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row7_col1\" class=\"data row7 col1\" >98.92%</td>\n",
              "      <td id=\"T_2b51e_row7_col2\" class=\"data row7 col2\" >98.87%</td>\n",
              "      <td id=\"T_2b51e_row7_col3\" class=\"data row7 col3\" >0.05%</td>\n",
              "      <td id=\"T_2b51e_row7_col4\" class=\"data row7 col4\" >97.86%</td>\n",
              "      <td id=\"T_2b51e_row7_col5\" class=\"data row7 col5\" >97.79%</td>\n",
              "      <td id=\"T_2b51e_row7_col6\" class=\"data row7 col6\" >0.08%</td>\n",
              "      <td id=\"T_2b51e_row7_col7\" class=\"data row7 col7\" >1.15%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_2b51e_row8_col0\" class=\"data row8 col0\" >{'min_samples_split': 40, 'min_samples_leaf': 125, 'max_samples': 0.6, 'max_features': 'sqrt', 'max_depth': 8, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row8_col1\" class=\"data row8 col1\" >98.96%</td>\n",
              "      <td id=\"T_2b51e_row8_col2\" class=\"data row8 col2\" >98.91%</td>\n",
              "      <td id=\"T_2b51e_row8_col3\" class=\"data row8 col3\" >0.05%</td>\n",
              "      <td id=\"T_2b51e_row8_col4\" class=\"data row8 col4\" >97.90%</td>\n",
              "      <td id=\"T_2b51e_row8_col5\" class=\"data row8 col5\" >97.78%</td>\n",
              "      <td id=\"T_2b51e_row8_col6\" class=\"data row8 col6\" >0.13%</td>\n",
              "      <td id=\"T_2b51e_row8_col7\" class=\"data row8 col7\" >1.30%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_2b51e_row9_col0\" class=\"data row9 col0\" >{'min_samples_split': 80, 'min_samples_leaf': 20, 'max_samples': 0.4, 'max_features': 0.03, 'max_depth': 4, 'criterion': 'entropy'}</td>\n",
              "      <td id=\"T_2b51e_row9_col1\" class=\"data row9 col1\" >98.95%</td>\n",
              "      <td id=\"T_2b51e_row9_col2\" class=\"data row9 col2\" >98.87%</td>\n",
              "      <td id=\"T_2b51e_row9_col3\" class=\"data row9 col3\" >0.08%</td>\n",
              "      <td id=\"T_2b51e_row9_col4\" class=\"data row9 col4\" >97.89%</td>\n",
              "      <td id=\"T_2b51e_row9_col5\" class=\"data row9 col5\" >97.74%</td>\n",
              "      <td id=\"T_2b51e_row9_col6\" class=\"data row9 col6\" >0.15%</td>\n",
              "      <td id=\"T_2b51e_row9_col7\" class=\"data row9 col7\" >1.09%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_2b51e_row10_col0\" class=\"data row10 col0\" >{'min_samples_split': 20, 'min_samples_leaf': 40, 'max_samples': 0.3, 'max_features': 0.05, 'max_depth': 5, 'criterion': 'entropy'}</td>\n",
              "      <td id=\"T_2b51e_row10_col1\" class=\"data row10 col1\" >98.94%</td>\n",
              "      <td id=\"T_2b51e_row10_col2\" class=\"data row10 col2\" >98.87%</td>\n",
              "      <td id=\"T_2b51e_row10_col3\" class=\"data row10 col3\" >0.07%</td>\n",
              "      <td id=\"T_2b51e_row10_col4\" class=\"data row10 col4\" >97.88%</td>\n",
              "      <td id=\"T_2b51e_row10_col5\" class=\"data row10 col5\" >97.70%</td>\n",
              "      <td id=\"T_2b51e_row10_col6\" class=\"data row10 col6\" >0.18%</td>\n",
              "      <td id=\"T_2b51e_row10_col7\" class=\"data row10 col7\" >1.12%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_2b51e_row11_col0\" class=\"data row11 col0\" >{'min_samples_split': 20, 'min_samples_leaf': 40, 'max_samples': 0.4, 'max_features': 'sqrt', 'max_depth': 2, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row11_col1\" class=\"data row11 col1\" >98.96%</td>\n",
              "      <td id=\"T_2b51e_row11_col2\" class=\"data row11 col2\" >98.87%</td>\n",
              "      <td id=\"T_2b51e_row11_col3\" class=\"data row11 col3\" >0.09%</td>\n",
              "      <td id=\"T_2b51e_row11_col4\" class=\"data row11 col4\" >97.92%</td>\n",
              "      <td id=\"T_2b51e_row11_col5\" class=\"data row11 col5\" >97.70%</td>\n",
              "      <td id=\"T_2b51e_row11_col6\" class=\"data row11 col6\" >0.21%</td>\n",
              "      <td id=\"T_2b51e_row11_col7\" class=\"data row11 col7\" >1.12%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_2b51e_row12_col0\" class=\"data row12 col0\" >{'min_samples_split': 150, 'min_samples_leaf': 40, 'max_samples': 0.25, 'max_features': 'log2', 'max_depth': 6, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row12_col1\" class=\"data row12 col1\" >98.97%</td>\n",
              "      <td id=\"T_2b51e_row12_col2\" class=\"data row12 col2\" >98.87%</td>\n",
              "      <td id=\"T_2b51e_row12_col3\" class=\"data row12 col3\" >0.10%</td>\n",
              "      <td id=\"T_2b51e_row12_col4\" class=\"data row12 col4\" >97.93%</td>\n",
              "      <td id=\"T_2b51e_row12_col5\" class=\"data row12 col5\" >97.69%</td>\n",
              "      <td id=\"T_2b51e_row12_col6\" class=\"data row12 col6\" >0.24%</td>\n",
              "      <td id=\"T_2b51e_row12_col7\" class=\"data row12 col7\" >0.95%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_2b51e_row13_col0\" class=\"data row13 col0\" >{'min_samples_split': 20, 'min_samples_leaf': 30, 'max_samples': 0.35, 'max_features': 0.15, 'max_depth': 8, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row13_col1\" class=\"data row13 col1\" >98.91%</td>\n",
              "      <td id=\"T_2b51e_row13_col2\" class=\"data row13 col2\" >98.79%</td>\n",
              "      <td id=\"T_2b51e_row13_col3\" class=\"data row13 col3\" >0.12%</td>\n",
              "      <td id=\"T_2b51e_row13_col4\" class=\"data row13 col4\" >97.83%</td>\n",
              "      <td id=\"T_2b51e_row13_col5\" class=\"data row13 col5\" >97.65%</td>\n",
              "      <td id=\"T_2b51e_row13_col6\" class=\"data row13 col6\" >0.18%</td>\n",
              "      <td id=\"T_2b51e_row13_col7\" class=\"data row13 col7\" >1.28%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2b51e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_2b51e_row14_col0\" class=\"data row14 col0\" >{'min_samples_split': 40, 'min_samples_leaf': 40, 'max_samples': 0.5, 'max_features': 0.15, 'max_depth': 8, 'criterion': 'gini'}</td>\n",
              "      <td id=\"T_2b51e_row14_col1\" class=\"data row14 col1\" >98.92%</td>\n",
              "      <td id=\"T_2b51e_row14_col2\" class=\"data row14 col2\" >98.79%</td>\n",
              "      <td id=\"T_2b51e_row14_col3\" class=\"data row14 col3\" >0.13%</td>\n",
              "      <td id=\"T_2b51e_row14_col4\" class=\"data row14 col4\" >97.86%</td>\n",
              "      <td id=\"T_2b51e_row14_col5\" class=\"data row14 col5\" >97.65%</td>\n",
              "      <td id=\"T_2b51e_row14_col6\" class=\"data row14 col6\" >0.20%</td>\n",
              "      <td id=\"T_2b51e_row14_col7\" class=\"data row14 col7\" >1.28%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Selection status:\n",
            "Selected from candidates with CV accuracy gap ≤ 8%.\n",
            "\n",
            "Selected cross-validation result:\n",
            "Mean CV Training Accuracy: 98.96%\n",
            "Mean CV Validation Accuracy: 98.95%\n",
            "Mean CV Macro F1: 97.90%\n",
            "CV Accuracy Gap: 0.01%\n",
            "\n",
            "Selected parameters:\n",
            "min_samples_split: 80\n",
            "min_samples_leaf: 20\n",
            "max_samples: 0.25\n",
            "max_features: sqrt\n",
            "max_depth: 3\n",
            "criterion: entropy\n",
            "\n",
            "Results saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/randomized_search_rf_cross_validation.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 24F: Validation Confirmation\n",
        "# =========================================================\n",
        "\n",
        "selected_regularized_rf = RandomForestClassifier(\n",
        "    n_estimators=700,\n",
        "    bootstrap=True,\n",
        "    class_weight=\"balanced_subsample\",\n",
        "    random_state=SEED,\n",
        "    n_jobs=-1,\n",
        "    **selected_cv_parameters\n",
        ")\n",
        "\n",
        "selected_regularized_rf.fit(\n",
        "    fused_train_features,\n",
        "    train_labels\n",
        ")\n",
        "\n",
        "selected_train_predictions = (\n",
        "    selected_regularized_rf.predict(\n",
        "        fused_train_features\n",
        "    )\n",
        ")\n",
        "\n",
        "selected_val_predictions = (\n",
        "    selected_regularized_rf.predict(\n",
        "        fused_val_features\n",
        "    )\n",
        ")\n",
        "\n",
        "selected_train_accuracy = accuracy_score(\n",
        "    train_labels,\n",
        "    selected_train_predictions\n",
        ")\n",
        "\n",
        "selected_val_accuracy = accuracy_score(\n",
        "    val_labels,\n",
        "    selected_val_predictions\n",
        ")\n",
        "\n",
        "selected_val_precision = precision_score(\n",
        "    val_labels,\n",
        "    selected_val_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "selected_val_recall = recall_score(\n",
        "    val_labels,\n",
        "    selected_val_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "selected_val_macro_f1 = f1_score(\n",
        "    val_labels,\n",
        "    selected_val_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "selected_gap = (\n",
        "    selected_train_accuracy\n",
        "    - selected_val_accuracy\n",
        ")\n",
        "\n",
        "print(\"Selected RF Validation Confirmation\")\n",
        "print(\"=\" * 65)\n",
        "\n",
        "print(\n",
        "    f\"Training Accuracy:   \"\n",
        "    f\"{selected_train_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Validation Accuracy: \"\n",
        "    f\"{selected_val_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Validation Macro Precision: \"\n",
        "    f\"{selected_val_precision * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Validation Macro Recall:    \"\n",
        "    f\"{selected_val_recall * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Validation Macro F1:        \"\n",
        "    f\"{selected_val_macro_f1 * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Train–Validation Gap:       \"\n",
        "    f\"{selected_gap * 100:.2f}%\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TAAsn_K9VMkl",
        "outputId": "1236f232-6795-44da-8032-d1c58bf5ee37"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected RF Validation Confirmation\n",
            "=================================================================\n",
            "Training Accuracy:   98.95%\n",
            "Validation Accuracy: 86.16%\n",
            "Validation Macro Precision: 75.32%\n",
            "Validation Macro Recall:    70.58%\n",
            "Validation Macro F1:        72.38%\n",
            "Train–Validation Gap:       12.79%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 24G: Out-of-Fold Overfitting Evaluation\n",
        "# =========================================================\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score\n",
        ")\n",
        "\n",
        "# Make sure the selected parameters are available\n",
        "if \"selected_cv_parameters\" not in globals():\n",
        "    raise NameError(\n",
        "        \"selected_cv_parameters is missing. \"\n",
        "        \"Run Cell 24E first.\"\n",
        "    )\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 5-fold stratified cross-validation\n",
        "# ---------------------------------------------------------\n",
        "cv = StratifiedKFold(\n",
        "    n_splits=5,\n",
        "    shuffle=True,\n",
        "    random_state=SEED\n",
        ")\n",
        "\n",
        "oof_predictions = np.zeros_like(\n",
        "    train_labels\n",
        ")\n",
        "\n",
        "fold_results = []\n",
        "\n",
        "for fold_number, (fold_train_idx, fold_val_idx) in enumerate(\n",
        "    cv.split(\n",
        "        fused_train_features,\n",
        "        train_labels\n",
        "    ),\n",
        "    start=1\n",
        "):\n",
        "\n",
        "    print(f\"\\nFold {fold_number}/5\")\n",
        "    print(\"-\" * 50)\n",
        "\n",
        "    X_fold_train = fused_train_features[\n",
        "        fold_train_idx\n",
        "    ]\n",
        "\n",
        "    y_fold_train = train_labels[\n",
        "        fold_train_idx\n",
        "    ]\n",
        "\n",
        "    X_fold_val = fused_train_features[\n",
        "        fold_val_idx\n",
        "    ]\n",
        "\n",
        "    y_fold_val = train_labels[\n",
        "        fold_val_idx\n",
        "    ]\n",
        "\n",
        "    fold_rf = RandomForestClassifier(\n",
        "        n_estimators=700,\n",
        "        bootstrap=True,\n",
        "        oob_score=True,\n",
        "        class_weight=\"balanced_subsample\",\n",
        "        random_state=SEED + fold_number,\n",
        "        n_jobs=-1,\n",
        "        **selected_cv_parameters\n",
        "    )\n",
        "\n",
        "    fold_rf.fit(\n",
        "        X_fold_train,\n",
        "        y_fold_train\n",
        "    )\n",
        "\n",
        "    fold_train_predictions = fold_rf.predict(\n",
        "        X_fold_train\n",
        "    )\n",
        "\n",
        "    fold_val_predictions = fold_rf.predict(\n",
        "        X_fold_val\n",
        "    )\n",
        "\n",
        "    oof_predictions[\n",
        "        fold_val_idx\n",
        "    ] = fold_val_predictions\n",
        "\n",
        "    fold_train_accuracy = accuracy_score(\n",
        "        y_fold_train,\n",
        "        fold_train_predictions\n",
        "    )\n",
        "\n",
        "    fold_val_accuracy = accuracy_score(\n",
        "        y_fold_val,\n",
        "        fold_val_predictions\n",
        "    )\n",
        "\n",
        "    fold_val_macro_f1 = f1_score(\n",
        "        y_fold_val,\n",
        "        fold_val_predictions,\n",
        "        average=\"macro\",\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    fold_gap = (\n",
        "        fold_train_accuracy -\n",
        "        fold_val_accuracy\n",
        "    )\n",
        "\n",
        "    fold_results.append({\n",
        "        \"Fold\": fold_number,\n",
        "        \"Training Accuracy\":\n",
        "            fold_train_accuracy,\n",
        "        \"Validation Accuracy\":\n",
        "            fold_val_accuracy,\n",
        "        \"Validation Macro F1\":\n",
        "            fold_val_macro_f1,\n",
        "        \"Train-Val Gap\":\n",
        "            fold_gap,\n",
        "        \"OOB Accuracy\":\n",
        "            fold_rf.oob_score_\n",
        "    })\n",
        "\n",
        "    print(\n",
        "        f\"Training Accuracy:   \"\n",
        "        f\"{fold_train_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Validation Accuracy: \"\n",
        "        f\"{fold_val_accuracy * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Validation Macro F1: \"\n",
        "        f\"{fold_val_macro_f1 * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"Train-Val Gap:       \"\n",
        "        f\"{fold_gap * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"OOB Accuracy:        \"\n",
        "        f\"{fold_rf.oob_score_ * 100:.2f}%\"\n",
        "    )\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Cross-validation summary\n",
        "# ---------------------------------------------------------\n",
        "fold_results_df = pd.DataFrame(\n",
        "    fold_results\n",
        ")\n",
        "\n",
        "mean_train_accuracy = (\n",
        "    fold_results_df[\n",
        "        \"Training Accuracy\"\n",
        "    ].mean()\n",
        ")\n",
        "\n",
        "mean_val_accuracy = (\n",
        "    fold_results_df[\n",
        "        \"Validation Accuracy\"\n",
        "    ].mean()\n",
        ")\n",
        "\n",
        "mean_val_macro_f1 = (\n",
        "    fold_results_df[\n",
        "        \"Validation Macro F1\"\n",
        "    ].mean()\n",
        ")\n",
        "\n",
        "mean_cv_gap = (\n",
        "    fold_results_df[\n",
        "        \"Train-Val Gap\"\n",
        "    ].mean()\n",
        ")\n",
        "\n",
        "mean_oob_accuracy = (\n",
        "    fold_results_df[\n",
        "        \"OOB Accuracy\"\n",
        "    ].mean()\n",
        ")\n",
        "\n",
        "# OOF metrics: each image predicted by an unseen fold model\n",
        "oof_accuracy = accuracy_score(\n",
        "    train_labels,\n",
        "    oof_predictions\n",
        ")\n",
        "\n",
        "oof_macro_precision = precision_score(\n",
        "    train_labels,\n",
        "    oof_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "oof_macro_recall = recall_score(\n",
        "    train_labels,\n",
        "    oof_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "oof_macro_f1 = f1_score(\n",
        "    train_labels,\n",
        "    oof_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "print(\"\\nCross-Validated Generalization Results\")\n",
        "print(\"=\" * 65)\n",
        "\n",
        "display(\n",
        "    fold_results_df.style.format({\n",
        "        \"Training Accuracy\": \"{:.2%}\",\n",
        "        \"Validation Accuracy\": \"{:.2%}\",\n",
        "        \"Validation Macro F1\": \"{:.2%}\",\n",
        "        \"Train-Val Gap\": \"{:.2%}\",\n",
        "        \"OOB Accuracy\": \"{:.2%}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Mean Fold Training Accuracy:   \"\n",
        "    f\"{mean_train_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Mean Fold Validation Accuracy: \"\n",
        "    f\"{mean_val_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Mean Validation Macro F1:      \"\n",
        "    f\"{mean_val_macro_f1 * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Mean CV Train-Val Gap:         \"\n",
        "    f\"{mean_cv_gap * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Mean OOB Accuracy:             \"\n",
        "    f\"{mean_oob_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\"\\nOut-of-Fold Performance\")\n",
        "print(\"-\" * 65)\n",
        "\n",
        "print(\n",
        "    f\"OOF Accuracy:        \"\n",
        "    f\"{oof_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"OOF Macro Precision: \"\n",
        "    f\"{oof_macro_precision * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"OOF Macro Recall:    \"\n",
        "    f\"{oof_macro_recall * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"OOF Macro F1:        \"\n",
        "    f\"{oof_macro_f1 * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save results\n",
        "# ---------------------------------------------------------\n",
        "cv_overfitting_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"final_rf_cross_validated_overfitting_check.csv\"\n",
        ")\n",
        "\n",
        "fold_results_df.to_csv(\n",
        "    cv_overfitting_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nResults saved at:\")\n",
        "print(cv_overfitting_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1212
        },
        "id": "5ST0fw36Vp_X",
        "outputId": "28319262-d62a-47fc-f7dc-d4606dfedb0c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Fold 1/5\n",
            "--------------------------------------------------\n",
            "Training Accuracy:   98.98%\n",
            "Validation Accuracy: 98.83%\n",
            "Validation Macro F1: 97.39%\n",
            "Train-Val Gap:       0.15%\n",
            "OOB Accuracy:        98.93%\n",
            "\n",
            "Fold 2/5\n",
            "--------------------------------------------------\n",
            "Training Accuracy:   99.17%\n",
            "Validation Accuracy: 98.05%\n",
            "Validation Macro F1: 96.20%\n",
            "Train-Val Gap:       1.12%\n",
            "OOB Accuracy:        99.12%\n",
            "\n",
            "Fold 3/5\n",
            "--------------------------------------------------\n",
            "Training Accuracy:   98.98%\n",
            "Validation Accuracy: 98.83%\n",
            "Validation Macro F1: 97.69%\n",
            "Train-Val Gap:       0.15%\n",
            "OOB Accuracy:        98.93%\n",
            "\n",
            "Fold 4/5\n",
            "--------------------------------------------------\n",
            "Training Accuracy:   98.93%\n",
            "Validation Accuracy: 99.02%\n",
            "Validation Macro F1: 98.20%\n",
            "Train-Val Gap:       -0.10%\n",
            "OOB Accuracy:        98.83%\n",
            "\n",
            "Fold 5/5\n",
            "--------------------------------------------------\n",
            "Training Accuracy:   98.78%\n",
            "Validation Accuracy: 99.61%\n",
            "Validation Macro F1: 99.15%\n",
            "Train-Val Gap:       -0.83%\n",
            "OOB Accuracy:        98.73%\n",
            "\n",
            "Cross-Validated Generalization Results\n",
            "=================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7acb2edb12b0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_abfcd\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_abfcd_level0_col0\" class=\"col_heading level0 col0\" >Fold</th>\n",
              "      <th id=\"T_abfcd_level0_col1\" class=\"col_heading level0 col1\" >Training Accuracy</th>\n",
              "      <th id=\"T_abfcd_level0_col2\" class=\"col_heading level0 col2\" >Validation Accuracy</th>\n",
              "      <th id=\"T_abfcd_level0_col3\" class=\"col_heading level0 col3\" >Validation Macro F1</th>\n",
              "      <th id=\"T_abfcd_level0_col4\" class=\"col_heading level0 col4\" >Train-Val Gap</th>\n",
              "      <th id=\"T_abfcd_level0_col5\" class=\"col_heading level0 col5\" >OOB Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_abfcd_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_abfcd_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "      <td id=\"T_abfcd_row0_col1\" class=\"data row0 col1\" >98.98%</td>\n",
              "      <td id=\"T_abfcd_row0_col2\" class=\"data row0 col2\" >98.83%</td>\n",
              "      <td id=\"T_abfcd_row0_col3\" class=\"data row0 col3\" >97.39%</td>\n",
              "      <td id=\"T_abfcd_row0_col4\" class=\"data row0 col4\" >0.15%</td>\n",
              "      <td id=\"T_abfcd_row0_col5\" class=\"data row0 col5\" >98.93%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_abfcd_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_abfcd_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_abfcd_row1_col1\" class=\"data row1 col1\" >99.17%</td>\n",
              "      <td id=\"T_abfcd_row1_col2\" class=\"data row1 col2\" >98.05%</td>\n",
              "      <td id=\"T_abfcd_row1_col3\" class=\"data row1 col3\" >96.20%</td>\n",
              "      <td id=\"T_abfcd_row1_col4\" class=\"data row1 col4\" >1.12%</td>\n",
              "      <td id=\"T_abfcd_row1_col5\" class=\"data row1 col5\" >99.12%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_abfcd_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_abfcd_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "      <td id=\"T_abfcd_row2_col1\" class=\"data row2 col1\" >98.98%</td>\n",
              "      <td id=\"T_abfcd_row2_col2\" class=\"data row2 col2\" >98.83%</td>\n",
              "      <td id=\"T_abfcd_row2_col3\" class=\"data row2 col3\" >97.69%</td>\n",
              "      <td id=\"T_abfcd_row2_col4\" class=\"data row2 col4\" >0.15%</td>\n",
              "      <td id=\"T_abfcd_row2_col5\" class=\"data row2 col5\" >98.93%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_abfcd_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_abfcd_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "      <td id=\"T_abfcd_row3_col1\" class=\"data row3 col1\" >98.93%</td>\n",
              "      <td id=\"T_abfcd_row3_col2\" class=\"data row3 col2\" >99.02%</td>\n",
              "      <td id=\"T_abfcd_row3_col3\" class=\"data row3 col3\" >98.20%</td>\n",
              "      <td id=\"T_abfcd_row3_col4\" class=\"data row3 col4\" >-0.10%</td>\n",
              "      <td id=\"T_abfcd_row3_col5\" class=\"data row3 col5\" >98.83%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_abfcd_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_abfcd_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "      <td id=\"T_abfcd_row4_col1\" class=\"data row4 col1\" >98.78%</td>\n",
              "      <td id=\"T_abfcd_row4_col2\" class=\"data row4 col2\" >99.61%</td>\n",
              "      <td id=\"T_abfcd_row4_col3\" class=\"data row4 col3\" >99.15%</td>\n",
              "      <td id=\"T_abfcd_row4_col4\" class=\"data row4 col4\" >-0.83%</td>\n",
              "      <td id=\"T_abfcd_row4_col5\" class=\"data row4 col5\" >98.73%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Fold Training Accuracy:   98.97%\n",
            "Mean Fold Validation Accuracy: 98.87%\n",
            "Mean Validation Macro F1:      97.73%\n",
            "Mean CV Train-Val Gap:         0.10%\n",
            "Mean OOB Accuracy:             98.91%\n",
            "\n",
            "Out-of-Fold Performance\n",
            "-----------------------------------------------------------------\n",
            "OOF Accuracy:        98.87%\n",
            "OOF Macro Precision: 97.34%\n",
            "OOF Macro Recall:    98.09%\n",
            "OOF Macro F1:        97.70%\n",
            "\n",
            "Results saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/final_rf_cross_validated_overfitting_check.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# FIX: Wire the properly cross-validated, gap-aware RF\n",
        "# configuration (from Cell 24E) into the final evaluation.\n",
        "#\n",
        "# Without this cell, Cell 25 silently falls back to the\n",
        "# unregularized \"best_rf_parameters\" chosen in Cell 24 by\n",
        "# validation Macro F1 alone (no gap penalty), which is why\n",
        "# the reported gap was 14.04% despite the extensive\n",
        "# regularization work done in Cells 24B-24G.\n",
        "# =========================================================\n",
        "\n",
        "if \"selected_cv_parameters\" not in globals():\n",
        "    raise NameError(\n",
        "        \"selected_cv_parameters is missing. \"\n",
        "        \"Run Cell 24D and 24E first before this fix.\"\n",
        "    )\n",
        "\n",
        "best_rf_parameters = dict(selected_cv_parameters)\n",
        "best_rf_parameters[\"n_estimators\"] = 700\n",
        "best_rf_parameters[\"bootstrap\"] = True\n",
        "\n",
        "best_rf_name = \"CV_Selected_RF\"\n",
        "\n",
        "print(\"Final model will now use the CV-selected configuration:\")\n",
        "print(\"=\" * 65)\n",
        "for key, value in best_rf_parameters.items():\n",
        "    print(f\"{key}: {value}\")\n",
        "\n",
        "print(\"\\nSelection rationale (from Cell 24E):\")\n",
        "print(selection_status)\n",
        "\n",
        "print(\"\\nExpected cross-validated Train-Val gap for this config:\")\n",
        "print(f\"{selected_row_gap * 100:.2f}%\" if \"selected_row_gap\" in globals()\n",
        "      else \"(re-check Cell 24E output table for this row's 'CV Accuracy Gap')\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tz4Zsur9ZUOv",
        "outputId": "647cbd37-83f3-461c-c167-9580e768eda3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final model will now use the CV-selected configuration:\n",
            "=================================================================\n",
            "min_samples_split: 80\n",
            "min_samples_leaf: 20\n",
            "max_samples: 0.25\n",
            "max_features: sqrt\n",
            "max_depth: 3\n",
            "criterion: entropy\n",
            "n_estimators: 700\n",
            "bootstrap: True\n",
            "\n",
            "Selection rationale (from Cell 24E):\n",
            "Selected from candidates with CV accuracy gap ≤ 8%.\n",
            "\n",
            "Expected cross-validated Train-Val gap for this config:\n",
            "(re-check Cell 24E output table for this row's 'CV Accuracy Gap')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# إعادة بناء test_df بنفس الـ split تمامًا (نفس SEED = نتيجة مطابقة 100%)\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_df, temp_df = train_test_split(\n",
        "    df, test_size=0.30, random_state=SEED, stratify=df[\"diagnosis\"]\n",
        ")\n",
        "val_df, test_df = train_test_split(\n",
        "    temp_df, test_size=0.50, random_state=SEED, stratify=temp_df[\"diagnosis\"]\n",
        ")\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "print(\"test_df restored:\", test_df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "B7kGEUvuaoOH",
        "outputId": "936df9ca-1133-47b1-e872-89657e527d5e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_df restored: (550, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# تجاوز الـ CV selection، وفرض تنظيم قوي فعليًا\n",
        "best_rf_parameters = {\n",
        "    \"n_estimators\": 700,\n",
        "    \"max_depth\": 4,\n",
        "    \"max_leaf_nodes\": 20,\n",
        "    \"min_samples_split\": 100,\n",
        "    \"min_samples_leaf\": 50,\n",
        "    \"max_samples\": 0.40,\n",
        "    \"max_features\": \"sqrt\",\n",
        "    \"bootstrap\": True\n",
        "}\n",
        "best_rf_name = \"RF_D4_Leaf50_Manual_Aggressive\"\n",
        "\n",
        "print(\"Testing aggressive manual regularization:\")\n",
        "print(best_rf_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6iLHK8uPePaM",
        "outputId": "e58e7f09-85dc-4076-d509-d8c04d532aee"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing aggressive manual regularization:\n",
            "{'n_estimators': 700, 'max_depth': 4, 'max_leaf_nodes': 20, 'min_samples_split': 100, 'min_samples_leaf': 50, 'max_samples': 0.4, 'max_features': 'sqrt', 'bootstrap': True}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Self-Contained Diagnostic: Frozen ImageNet Baseline\n",
        "# (يحتاج فقط df من Cell 2 — كل شي تاني معرّف هون)\n",
        "# =========================================================\n",
        "import cv2\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from PIL import Image\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms, models\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from tqdm import tqdm\n",
        "\n",
        "IMAGENET_MEAN = [0.485, 0.456, 0.406]\n",
        "IMAGENET_STD = [0.229, 0.224, 0.225]\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 1. دوال المعالجة الأصلية (نفسها بالضبط)\n",
        "# ---------------------------------------------------------\n",
        "def crop_dark_borders(image_rgb, threshold=10, minimum_size=50):\n",
        "    gray_image = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)\n",
        "    foreground_mask = (gray_image > threshold).astype(np.uint8)\n",
        "    coordinates = cv2.findNonZero(foreground_mask)\n",
        "    if coordinates is None:\n",
        "        return image_rgb.copy()\n",
        "    x, y, width, height = cv2.boundingRect(coordinates)\n",
        "    cropped_image = image_rgb[y:y + height, x:x + width]\n",
        "    if cropped_image.shape[0] < minimum_size or cropped_image.shape[1] < minimum_size:\n",
        "        return image_rgb.copy()\n",
        "    return cropped_image\n",
        "\n",
        "def apply_clahe_rgb(image_rgb, clip_limit=1.5, tile_grid_size=(8, 8)):\n",
        "    lab_image = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2LAB)\n",
        "    lightness, channel_a, channel_b = cv2.split(lab_image)\n",
        "    clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)\n",
        "    enhanced_lightness = clahe.apply(lightness)\n",
        "    enhanced_lab = cv2.merge([enhanced_lightness, channel_a, channel_b])\n",
        "    return cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2RGB)\n",
        "\n",
        "class RetinalPreprocessing:\n",
        "    def __call__(self, image):\n",
        "        image_rgb = np.array(image)\n",
        "        image_rgb = crop_dark_borders(image_rgb, threshold=10)\n",
        "        image_rgb = apply_clahe_rgb(image_rgb, clip_limit=1.5, tile_grid_size=(8, 8))\n",
        "        return Image.fromarray(image_rgb)\n",
        "\n",
        "eval_transforms = transforms.Compose([\n",
        "    RetinalPreprocessing(),\n",
        "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)\n",
        "])\n",
        "\n",
        "class APTOSDataset(Dataset):\n",
        "    def __init__(self, dataframe, transform=None):\n",
        "        self.dataframe = dataframe.reset_index(drop=True)\n",
        "        self.transform = transform\n",
        "    def __len__(self):\n",
        "        return len(self.dataframe)\n",
        "    def __getitem__(self, index):\n",
        "        row = self.dataframe.iloc[index]\n",
        "        image = Image.open(row[\"image_path\"]).convert(\"RGB\")\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "        return image, int(row[\"diagnosis\"])\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 2. إعادة بناء نفس الـ split تمامًا (نفس SEED)\n",
        "# ---------------------------------------------------------\n",
        "SEED = 42\n",
        "train_df, temp_df = train_test_split(df, test_size=0.30, random_state=SEED, stratify=df[\"diagnosis\"])\n",
        "val_df, test_df = train_test_split(temp_df, test_size=0.50, random_state=SEED, stratify=temp_df[\"diagnosis\"])\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "feature_train_loader = DataLoader(APTOSDataset(train_df, eval_transforms), batch_size=BATCH_SIZE, shuffle=False)\n",
        "feature_val_loader   = DataLoader(APTOSDataset(val_df, eval_transforms),   batch_size=BATCH_SIZE, shuffle=False)\n",
        "feature_test_loader  = DataLoader(APTOSDataset(test_df, eval_transforms),  batch_size=BATCH_SIZE, shuffle=False)\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 3. دوال الاستخراج\n",
        "# ---------------------------------------------------------\n",
        "@torch.no_grad()\n",
        "def extract_convnext(model, loader):\n",
        "    model.eval().to(device)\n",
        "    feats, labels = [], []\n",
        "    for images, lbls in tqdm(loader, desc=\"ConvNeXt\"):\n",
        "        images = images.to(device)\n",
        "        f = model.avgpool(model.features(images))\n",
        "        f = torch.flatten(f, start_dim=1)\n",
        "        feats.append(f.cpu().numpy())\n",
        "        labels.append(lbls.numpy())\n",
        "    return np.concatenate(feats), np.concatenate(labels)\n",
        "\n",
        "@torch.no_grad()\n",
        "def extract_swin(model, loader):\n",
        "    model.eval().to(device)\n",
        "    feats, labels = [], []\n",
        "    for images, lbls in tqdm(loader, desc=\"Swin\"):\n",
        "        images = images.to(device)\n",
        "        f = model.norm(model.features(images))\n",
        "        f = f.permute(0, 3, 1, 2)\n",
        "        f = torch.nn.functional.adaptive_avg_pool2d(f, 1)\n",
        "        f = torch.flatten(f, start_dim=1)\n",
        "        feats.append(f.cpu().numpy())\n",
        "        labels.append(lbls.numpy())\n",
        "    return np.concatenate(feats), np.concatenate(labels)\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 4. تحميل موديلات ImageNet الأصلية (بدون fine-tuning إطلاقًا)\n",
        "# ---------------------------------------------------------\n",
        "convnext_frozen = models.convnext_tiny(weights=models.ConvNeXt_Tiny_Weights.IMAGENET1K_V1)\n",
        "swin_frozen = models.swin_v2_t(weights=models.Swin_V2_T_Weights.IMAGENET1K_V1)\n",
        "print(\"Frozen ImageNet backbones loaded.\")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 5. الاستخراج والدمج\n",
        "# ---------------------------------------------------------\n",
        "conv_train, train_labels = extract_convnext(convnext_frozen, feature_train_loader)\n",
        "conv_val, val_labels     = extract_convnext(convnext_frozen, feature_val_loader)\n",
        "conv_test, test_labels   = extract_convnext(convnext_frozen, feature_test_loader)\n",
        "\n",
        "swin_train, _ = extract_swin(swin_frozen, feature_train_loader)\n",
        "swin_val, _   = extract_swin(swin_frozen, feature_val_loader)\n",
        "swin_test, _  = extract_swin(swin_frozen, feature_test_loader)\n",
        "\n",
        "fused_train_frozen = np.concatenate([conv_train, swin_train], axis=1)\n",
        "fused_val_frozen   = np.concatenate([conv_val, swin_val], axis=1)\n",
        "fused_test_frozen  = np.concatenate([conv_test, swin_test], axis=1)\n",
        "\n",
        "print(\"Fused frozen features:\", fused_train_frozen.shape)\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# 6. تدريب RF وطباعة النتيجة\n",
        "# ---------------------------------------------------------\n",
        "rf_frozen = RandomForestClassifier(\n",
        "    n_estimators=700, max_depth=8, min_samples_leaf=15, min_samples_split=30,\n",
        "    max_samples=0.60, max_features=\"sqrt\", bootstrap=True,\n",
        "    class_weight=\"balanced_subsample\", random_state=SEED, n_jobs=-1\n",
        ")\n",
        "rf_frozen.fit(fused_train_frozen, train_labels)\n",
        "\n",
        "train_acc = accuracy_score(train_labels, rf_frozen.predict(fused_train_frozen))\n",
        "val_acc = accuracy_score(val_labels, rf_frozen.predict(fused_val_frozen))\n",
        "test_acc = accuracy_score(test_labels, rf_frozen.predict(fused_test_frozen))\n",
        "\n",
        "print(f\"\\nTrain: {train_acc*100:.2f}% | Val: {val_acc*100:.2f}% | Test: {test_acc*100:.2f}%\")\n",
        "print(f\"Gap: {(train_acc-test_acc)*100:.2f} percentage points\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "DLTjwMoDiuZK",
        "outputId": "ba688df8-f891-4f7a-abc3-e010a3a048cb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frozen ImageNet backbones loaded.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ConvNeXt: 100%|██████████| 81/81 [03:59<00:00,  2.96s/it]\n",
            "ConvNeXt: 100%|██████████| 18/18 [00:54<00:00,  3.01s/it]\n",
            "ConvNeXt: 100%|██████████| 18/18 [00:52<00:00,  2.92s/it]\n",
            "Swin: 100%|██████████| 81/81 [03:49<00:00,  2.84s/it]\n",
            "Swin: 100%|██████████| 18/18 [00:49<00:00,  2.73s/it]\n",
            "Swin: 100%|██████████| 18/18 [00:47<00:00,  2.65s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fused frozen features: (2563, 1536)\n",
            "\n",
            "Train: 91.42% | Val: 77.60% | Test: 76.55%\n",
            "Gap: 14.87 percentage points\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(fused_train_features)\n",
        "X_test_scaled = scaler.transform(fused_test_features)\n",
        "\n",
        "# --- Logistic Regression ---\n",
        "lr = LogisticRegression(max_iter=5000, C=0.1, class_weight=\"balanced\", random_state=SEED)\n",
        "lr.fit(X_train_scaled, train_labels)\n",
        "lr_train_acc = accuracy_score(train_labels, lr.predict(X_train_scaled))\n",
        "lr_test_acc = accuracy_score(test_labels, lr.predict(X_test_scaled))\n",
        "print(f\"Logistic Regression | Train: {lr_train_acc*100:.2f}% | Test: {lr_test_acc*100:.2f}% | Gap: {(lr_train_acc-lr_test_acc)*100:.2f}%\")\n",
        "\n",
        "# --- SVM ---\n",
        "svm = SVC(kernel=\"rbf\", C=1, gamma=\"scale\", class_weight=\"balanced\", random_state=SEED)\n",
        "svm.fit(X_train_scaled, train_labels)\n",
        "svm_train_acc = accuracy_score(train_labels, svm.predict(X_train_scaled))\n",
        "svm_test_acc = accuracy_score(test_labels, svm.predict(X_test_scaled))\n",
        "print(f\"SVM (RBF)            | Train: {svm_train_acc*100:.2f}% | Test: {svm_test_acc*100:.2f}% | Gap: {(svm_train_acc-svm_test_acc)*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "55j9uSiJl_i0",
        "outputId": "8a508909-0415-4ef8-e2d8-d8bf660a902f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression | Train: 99.30% | Test: 84.73% | Gap: 14.57%\n",
            "SVM (RBF)            | Train: 99.06% | Test: 84.91% | Gap: 14.15%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "X_all = np.concatenate([fused_train_features, fused_val_features], axis=0)\n",
        "y_all = np.concatenate([train_labels, val_labels], axis=0)\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)\n",
        "\n",
        "rf_final = RandomForestClassifier(\n",
        "    n_estimators=700, max_depth=8, min_samples_leaf=15, min_samples_split=30,\n",
        "    max_samples=0.60, max_features=\"sqrt\", bootstrap=True,\n",
        "    class_weight=\"balanced_subsample\", random_state=SEED, n_jobs=-1\n",
        ")\n",
        "\n",
        "cv_scores = cross_val_score(rf_final, X_all, y_all, cv=cv, scoring=\"accuracy\", n_jobs=-1)\n",
        "\n",
        "print(f\"5-Fold CV Accuracy: {cv_scores.mean()*100:.2f}% ± {cv_scores.std()*100:.2f}%\")\n",
        "print(f\"Individual folds: {[f'{s*100:.2f}%' for s in cv_scores]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "x_n4WCGemnRy",
        "outputId": "0df606af-ee45-4f14-e88d-2294f710829e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5-Fold CV Accuracy: 96.34% ± 0.29%\n",
            "Individual folds: ['95.83%', '96.31%', '96.62%', '96.62%', '96.30%']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# =========================================================\n",
        "# Cell 25: Final Random Forest Test Evaluation\n",
        "# =========================================================\n",
        "\n",
        "import joblib\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score,\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    roc_auc_score\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Rebuild the selected Random Forest configuration\n",
        "# ---------------------------------------------------------\n",
        "best_rf_model = RandomForestClassifier(\n",
        "    **best_rf_parameters,\n",
        "    class_weight=\"balanced_subsample\",\n",
        "    random_state=SEED,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "# Train only on the training features\n",
        "best_rf_model.fit(\n",
        "    fused_train_features,\n",
        "    train_labels\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Predictions\n",
        "# ---------------------------------------------------------\n",
        "train_predictions = best_rf_model.predict(\n",
        "    fused_train_features\n",
        ")\n",
        "\n",
        "val_predictions = best_rf_model.predict(\n",
        "    fused_val_features\n",
        ")\n",
        "\n",
        "test_predictions = best_rf_model.predict(\n",
        "    fused_test_features\n",
        ")\n",
        "\n",
        "test_probabilities = best_rf_model.predict_proba(\n",
        "    fused_test_features\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Main metrics\n",
        "# ---------------------------------------------------------\n",
        "train_accuracy = accuracy_score(\n",
        "    train_labels,\n",
        "    train_predictions\n",
        ")\n",
        "\n",
        "val_accuracy = accuracy_score(\n",
        "    val_labels,\n",
        "    val_predictions\n",
        ")\n",
        "\n",
        "test_accuracy = accuracy_score(\n",
        "    test_labels,\n",
        "    test_predictions\n",
        ")\n",
        "\n",
        "test_macro_precision = precision_score(\n",
        "    test_labels,\n",
        "    test_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "test_macro_recall = recall_score(\n",
        "    test_labels,\n",
        "    test_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "test_macro_f1 = f1_score(\n",
        "    test_labels,\n",
        "    test_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "test_weighted_f1 = f1_score(\n",
        "    test_labels,\n",
        "    test_predictions,\n",
        "    average=\"weighted\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "train_test_gap = (\n",
        "    train_accuracy - test_accuracy\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Display final result\n",
        "# ---------------------------------------------------------\n",
        "print(\"Final Hybrid Model Results\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "print(\"Selected Model:\", best_rf_name)\n",
        "\n",
        "print(\n",
        "    f\"Training Accuracy:   \"\n",
        "    f\"{train_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Validation Accuracy: \"\n",
        "    f\"{val_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Testing Accuracy:    \"\n",
        "    f\"{test_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Macro Precision: \"\n",
        "    f\"{test_macro_precision * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Macro Recall:    \"\n",
        "    f\"{test_macro_recall * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Macro F1:        \"\n",
        "    f\"{test_macro_f1 * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Weighted F1:     \"\n",
        "    f\"{test_weighted_f1 * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Train-Test Gap:       \"\n",
        "    f\"{train_test_gap * 100:.2f} percentage points\"\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save the final model\n",
        "# ---------------------------------------------------------\n",
        "final_rf_model_path = (\n",
        "    CHECKPOINTS_DIR /\n",
        "    \"final_hybrid_convnext_swin_random_forest.pkl\"\n",
        ")\n",
        "\n",
        "joblib.dump(\n",
        "    {\n",
        "        \"model\": best_rf_model,\n",
        "        \"model_name\": best_rf_name,\n",
        "        \"parameters\": best_rf_parameters,\n",
        "        \"class_names\": CLASS_NAMES,\n",
        "        \"convnext_embedding_dimension\": 768,\n",
        "        \"swin_embedding_dimension\": 768,\n",
        "        \"fused_feature_dimension\": 1536,\n",
        "        \"test_accuracy\": test_accuracy,\n",
        "        \"test_macro_f1\": test_macro_f1,\n",
        "        \"test_weighted_f1\": test_weighted_f1,\n",
        "        \"train_test_gap\": train_test_gap\n",
        "    },\n",
        "    final_rf_model_path\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Save test predictions\n",
        "# ---------------------------------------------------------\n",
        "test_predictions_df = pd.DataFrame({\n",
        "    \"Image ID\": test_df[\"id_code\"].values,\n",
        "    \"True Class ID\": test_labels,\n",
        "    \"True Class\": [\n",
        "        CLASS_NAMES[label]\n",
        "        for label in test_labels\n",
        "    ],\n",
        "    \"Predicted Class ID\": test_predictions,\n",
        "    \"Predicted Class\": [\n",
        "        CLASS_NAMES[label]\n",
        "        for label in test_predictions\n",
        "    ]\n",
        "})\n",
        "\n",
        "for class_id, class_name in enumerate(CLASS_NAMES):\n",
        "    probability_column = (\n",
        "        \"Probability_\" +\n",
        "        class_name.replace(\" \", \"_\")\n",
        "    )\n",
        "\n",
        "    test_predictions_df[probability_column] = (\n",
        "        test_probabilities[:, class_id]\n",
        "    )\n",
        "\n",
        "test_predictions_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"final_hybrid_test_predictions.csv\"\n",
        ")\n",
        "\n",
        "test_predictions_df.to_csv(\n",
        "    test_predictions_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nFinal model saved at:\")\n",
        "print(final_rf_model_path)\n",
        "\n",
        "print(\"\\nTest predictions saved at:\")\n",
        "print(test_predictions_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vGv4PwY8_0nI",
        "outputId": "5a4210b4-dc21-4d42-ef4b-7fba25dcf670"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Hybrid Model Results\n",
            "============================================================\n",
            "Selected Model: RF_D4_Leaf50_Manual_Aggressive\n",
            "Training Accuracy:   98.91%\n",
            "Validation Accuracy: 85.06%\n",
            "Testing Accuracy:    85.45%\n",
            "Test Macro Precision: 75.01%\n",
            "Test Macro Recall:    70.23%\n",
            "Test Macro F1:        72.13%\n",
            "Test Weighted F1:     85.05%\n",
            "Train-Test Gap:       13.45 percentage points\n",
            "\n",
            "Final model saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Checkpoints/final_hybrid_convnext_swin_random_forest.pkl\n",
            "\n",
            "Test predictions saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/final_hybrid_test_predictions.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# اختبار سريع: هل تنويع بسيط بالـ features بيأثر على Train Accuracy؟\n",
        "# (لا يلمس Cell 25 ولا يحفظ أي ملف - بس تشخيص)\n",
        "# =========================================================\n",
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "def add_feature_noise(X, noise_std=0.05, n_copies=3, seed=42):\n",
        "    rng = np.random.RandomState(seed)\n",
        "    X_list = [X]\n",
        "    for _ in range(n_copies - 1):\n",
        "        noise = rng.normal(0, noise_std * X.std(axis=0), X.shape)\n",
        "        X_list.append(X + noise)\n",
        "    return np.concatenate(X_list, axis=0)\n",
        "\n",
        "fused_train_noisy = add_feature_noise(fused_train_features, noise_std=0.05, n_copies=3)\n",
        "train_labels_noisy = np.tile(train_labels, 3)\n",
        "\n",
        "quick_rf = RandomForestClassifier(\n",
        "    n_estimators=700, max_depth=8, min_samples_leaf=15,\n",
        "    min_samples_split=30, max_samples=0.60, max_features=\"sqrt\",\n",
        "    bootstrap=True, class_weight=\"balanced_subsample\",\n",
        "    random_state=SEED, n_jobs=-1\n",
        ")\n",
        "quick_rf.fit(fused_train_noisy, train_labels_noisy)\n",
        "\n",
        "train_acc = accuracy_score(train_labels_noisy, quick_rf.predict(fused_train_noisy))\n",
        "test_acc = accuracy_score(test_labels, quick_rf.predict(fused_test_features))\n",
        "\n",
        "print(f\"Train Accuracy (with noise): {train_acc*100:.2f}%\")\n",
        "print(f\"Test Accuracy:               {test_acc*100:.2f}%\")\n",
        "print(f\"Gap:                         {(train_acc-test_acc)*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WUgepY5VgHqj",
        "outputId": "8493c60d-6d30-4bfd-d4d7-97737698135a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy (with noise): 99.02%\n",
            "Test Accuracy:               85.27%\n",
            "Gap:                         13.75%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# اختبار حاسم: تقليل الأبعاد بشكل جذري (يكسر ظاهرة الفصل التلقائي)\n",
        "# =========================================================\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaled_train = scaler.fit_transform(fused_train_features)\n",
        "scaled_test = scaler.transform(fused_test_features)\n",
        "\n",
        "for n_comp in [8, 16, 32, 64, 128]:\n",
        "    pca = PCA(n_components=n_comp, random_state=SEED)\n",
        "    pca_train = pca.fit_transform(scaled_train)\n",
        "    pca_test = pca.transform(scaled_test)\n",
        "\n",
        "    rf = RandomForestClassifier(\n",
        "        n_estimators=700, max_depth=6, min_samples_leaf=15,\n",
        "        min_samples_split=30, max_features=\"sqrt\", bootstrap=True,\n",
        "        max_samples=0.60, class_weight=\"balanced_subsample\",\n",
        "        random_state=SEED, n_jobs=-1\n",
        "    )\n",
        "    rf.fit(pca_train, train_labels)\n",
        "\n",
        "    train_acc = accuracy_score(train_labels, rf.predict(pca_train))\n",
        "    test_acc = accuracy_score(test_labels, rf.predict(pca_test))\n",
        "    variance = pca.explained_variance_ratio_.sum()\n",
        "\n",
        "    print(f\"PCA={n_comp:3d} | Variance={variance*100:5.1f}% | \"\n",
        "          f\"Train={train_acc*100:5.2f}% | Test={test_acc*100:5.2f}% | \"\n",
        "          f\"Gap={((train_acc-test_acc)*100):5.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VZcPvoLugfU6",
        "outputId": "66e5b7d5-b870-4cd5-9ec0-734ac719be01"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PCA=  8 | Variance= 49.4% | Train=98.95% | Test=84.36% | Gap=14.58%\n",
            "PCA= 16 | Variance= 59.7% | Train=98.95% | Test=84.91% | Gap=14.04%\n",
            "PCA= 32 | Variance= 68.7% | Train=98.95% | Test=84.36% | Gap=14.58%\n",
            "PCA= 64 | Variance= 76.7% | Train=98.95% | Test=84.73% | Gap=14.22%\n",
            "PCA=128 | Variance= 84.2% | Train=98.95% | Test=84.55% | Gap=14.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# تجربة سريعة: binary classification (No DR vs Any DR)\n",
        "binary_train_labels = (train_labels > 0).astype(int)\n",
        "binary_test_labels = (test_labels > 0).astype(int)\n",
        "\n",
        "binary_rf = RandomForestClassifier(\n",
        "    n_estimators=700, max_depth=8, min_samples_leaf=15,\n",
        "    class_weight=\"balanced_subsample\", random_state=SEED, n_jobs=-1\n",
        ")\n",
        "binary_rf.fit(fused_train_features, binary_train_labels)\n",
        "\n",
        "binary_train_acc = accuracy_score(binary_train_labels, binary_rf.predict(fused_train_features))\n",
        "binary_test_acc = accuracy_score(binary_test_labels, binary_rf.predict(fused_test_features))\n",
        "\n",
        "print(f\"Binary — Train: {binary_train_acc*100:.2f}% | Test: {binary_test_acc*100:.2f}%\")\n",
        "print(f\"Binary Gap: {(binary_train_acc-binary_test_acc)*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lJCq5mYQvSQo",
        "outputId": "ab8312d8-0319-4967-b221-7ed7a5a8fdcc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Binary — Train: 99.73% | Test: 98.18%\n",
            "Binary Gap: 1.55%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 26: Classification Report and Confusion Matrix\n",
        "# =========================================================\n",
        "\n",
        "from sklearn.metrics import (\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    ConfusionMatrixDisplay\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Classification report\n",
        "# ---------------------------------------------------------\n",
        "classification_report_dict = classification_report(\n",
        "    test_labels,\n",
        "    test_predictions,\n",
        "    labels=np.arange(NUM_CLASSES),\n",
        "    target_names=CLASS_NAMES,\n",
        "    output_dict=True,\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "classification_report_df = pd.DataFrame(\n",
        "    classification_report_dict\n",
        ").transpose()\n",
        "\n",
        "print(\"Final Classification Report\")\n",
        "print(\"=\" * 80)\n",
        "\n",
        "display(\n",
        "    classification_report_df.style.format({\n",
        "        \"precision\": \"{:.4f}\",\n",
        "        \"recall\": \"{:.4f}\",\n",
        "        \"f1-score\": \"{:.4f}\",\n",
        "        \"support\": \"{:.0f}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "classification_report_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"final_hybrid_classification_report.csv\"\n",
        ")\n",
        "\n",
        "classification_report_df.to_csv(\n",
        "    classification_report_path\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Confusion matrix\n",
        "# ---------------------------------------------------------\n",
        "cm = confusion_matrix(\n",
        "    test_labels,\n",
        "    test_predictions,\n",
        "    labels=np.arange(NUM_CLASSES)\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Calculate specificity for each class\n",
        "# ---------------------------------------------------------\n",
        "specificity_records = []\n",
        "\n",
        "for class_id in range(NUM_CLASSES):\n",
        "\n",
        "    true_positive = cm[class_id, class_id]\n",
        "\n",
        "    false_negative = (\n",
        "        cm[class_id, :].sum() -\n",
        "        true_positive\n",
        "    )\n",
        "\n",
        "    false_positive = (\n",
        "        cm[:, class_id].sum() -\n",
        "        true_positive\n",
        "    )\n",
        "\n",
        "    true_negative = (\n",
        "        cm.sum() -\n",
        "        true_positive -\n",
        "        false_negative -\n",
        "        false_positive\n",
        "    )\n",
        "\n",
        "    specificity = (\n",
        "        true_negative /\n",
        "        (true_negative + false_positive)\n",
        "        if (true_negative + false_positive) > 0\n",
        "        else 0\n",
        "    )\n",
        "\n",
        "    specificity_records.append({\n",
        "        \"Class ID\": class_id,\n",
        "        \"Class Name\": CLASS_NAMES[class_id],\n",
        "        \"Specificity\": specificity\n",
        "    })\n",
        "\n",
        "specificity_df = pd.DataFrame(\n",
        "    specificity_records\n",
        ")\n",
        "\n",
        "macro_specificity = (\n",
        "    specificity_df[\"Specificity\"].mean()\n",
        ")\n",
        "\n",
        "print(\"\\nSpecificity by Class\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "display(\n",
        "    specificity_df.style.format({\n",
        "        \"Specificity\": \"{:.4f}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Macro Average Specificity:\",\n",
        "    f\"{macro_specificity * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "specificity_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"final_hybrid_specificity.csv\"\n",
        ")\n",
        "\n",
        "specificity_df.to_csv(\n",
        "    specificity_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Plot confusion matrix\n",
        "# ---------------------------------------------------------\n",
        "fig, ax = plt.subplots(\n",
        "    figsize=(9, 8)\n",
        ")\n",
        "\n",
        "display_matrix = ConfusionMatrixDisplay(\n",
        "    confusion_matrix=cm,\n",
        "    display_labels=CLASS_NAMES\n",
        ")\n",
        "\n",
        "display_matrix.plot(\n",
        "    ax=ax,\n",
        "    values_format=\"d\"\n",
        ")\n",
        "\n",
        "ax.set_title(\n",
        "    \"Confusion Matrix of the Final Hybrid Model\",\n",
        "    fontsize=14\n",
        ")\n",
        "\n",
        "plt.xticks(\n",
        "    rotation=20,\n",
        "    ha=\"right\"\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "confusion_matrix_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_05_final_hybrid_confusion_matrix.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    confusion_matrix_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nClassification report saved at:\")\n",
        "print(classification_report_path)\n",
        "\n",
        "print(\"\\nSpecificity results saved at:\")\n",
        "print(specificity_path)\n",
        "\n",
        "print(\"\\nConfusion matrix saved at:\")\n",
        "print(confusion_matrix_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1300
        },
        "id": "hf7rkyGF_a95",
        "outputId": "f4323162-8aef-4d40-f3da-7d91d31779ea"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Classification Report\n",
            "================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7dcab12dc230>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_4021a\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_4021a_level0_col0\" class=\"col_heading level0 col0\" >precision</th>\n",
              "      <th id=\"T_4021a_level0_col1\" class=\"col_heading level0 col1\" >recall</th>\n",
              "      <th id=\"T_4021a_level0_col2\" class=\"col_heading level0 col2\" >f1-score</th>\n",
              "      <th id=\"T_4021a_level0_col3\" class=\"col_heading level0 col3\" >support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row0\" class=\"row_heading level0 row0\" >No DR</th>\n",
              "      <td id=\"T_4021a_row0_col0\" class=\"data row0 col0\" >0.9851</td>\n",
              "      <td id=\"T_4021a_row0_col1\" class=\"data row0 col1\" >0.9779</td>\n",
              "      <td id=\"T_4021a_row0_col2\" class=\"data row0 col2\" >0.9815</td>\n",
              "      <td id=\"T_4021a_row0_col3\" class=\"data row0 col3\" >271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row1\" class=\"row_heading level0 row1\" >Mild</th>\n",
              "      <td id=\"T_4021a_row1_col0\" class=\"data row1 col0\" >0.7083</td>\n",
              "      <td id=\"T_4021a_row1_col1\" class=\"data row1 col1\" >0.6071</td>\n",
              "      <td id=\"T_4021a_row1_col2\" class=\"data row1 col2\" >0.6538</td>\n",
              "      <td id=\"T_4021a_row1_col3\" class=\"data row1 col3\" >56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row2\" class=\"row_heading level0 row2\" >Moderate</th>\n",
              "      <td id=\"T_4021a_row2_col0\" class=\"data row2 col0\" >0.7529</td>\n",
              "      <td id=\"T_4021a_row2_col1\" class=\"data row2 col1\" >0.8733</td>\n",
              "      <td id=\"T_4021a_row2_col2\" class=\"data row2 col2\" >0.8086</td>\n",
              "      <td id=\"T_4021a_row2_col3\" class=\"data row2 col3\" >150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row3\" class=\"row_heading level0 row3\" >Severe</th>\n",
              "      <td id=\"T_4021a_row3_col0\" class=\"data row3 col0\" >0.5833</td>\n",
              "      <td id=\"T_4021a_row3_col1\" class=\"data row3 col1\" >0.4828</td>\n",
              "      <td id=\"T_4021a_row3_col2\" class=\"data row3 col2\" >0.5283</td>\n",
              "      <td id=\"T_4021a_row3_col3\" class=\"data row3 col3\" >29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row4\" class=\"row_heading level0 row4\" >Proliferative DR</th>\n",
              "      <td id=\"T_4021a_row4_col0\" class=\"data row4 col0\" >0.6571</td>\n",
              "      <td id=\"T_4021a_row4_col1\" class=\"data row4 col1\" >0.5227</td>\n",
              "      <td id=\"T_4021a_row4_col2\" class=\"data row4 col2\" >0.5823</td>\n",
              "      <td id=\"T_4021a_row4_col3\" class=\"data row4 col3\" >44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row5\" class=\"row_heading level0 row5\" >accuracy</th>\n",
              "      <td id=\"T_4021a_row5_col0\" class=\"data row5 col0\" >0.8491</td>\n",
              "      <td id=\"T_4021a_row5_col1\" class=\"data row5 col1\" >0.8491</td>\n",
              "      <td id=\"T_4021a_row5_col2\" class=\"data row5 col2\" >0.8491</td>\n",
              "      <td id=\"T_4021a_row5_col3\" class=\"data row5 col3\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row6\" class=\"row_heading level0 row6\" >macro avg</th>\n",
              "      <td id=\"T_4021a_row6_col0\" class=\"data row6 col0\" >0.7374</td>\n",
              "      <td id=\"T_4021a_row6_col1\" class=\"data row6 col1\" >0.6928</td>\n",
              "      <td id=\"T_4021a_row6_col2\" class=\"data row6 col2\" >0.7109</td>\n",
              "      <td id=\"T_4021a_row6_col3\" class=\"data row6 col3\" >550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4021a_level0_row7\" class=\"row_heading level0 row7\" >weighted avg</th>\n",
              "      <td id=\"T_4021a_row7_col0\" class=\"data row7 col0\" >0.8462</td>\n",
              "      <td id=\"T_4021a_row7_col1\" class=\"data row7 col1\" >0.8491</td>\n",
              "      <td id=\"T_4021a_row7_col2\" class=\"data row7 col2\" >0.8452</td>\n",
              "      <td id=\"T_4021a_row7_col3\" class=\"data row7 col3\" >550</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Specificity by Class\n",
            "============================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7dcaae338fb0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_ad826\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_ad826_level0_col0\" class=\"col_heading level0 col0\" >Class ID</th>\n",
              "      <th id=\"T_ad826_level0_col1\" class=\"col_heading level0 col1\" >Class Name</th>\n",
              "      <th id=\"T_ad826_level0_col2\" class=\"col_heading level0 col2\" >Specificity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_ad826_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_ad826_row0_col0\" class=\"data row0 col0\" >0</td>\n",
              "      <td id=\"T_ad826_row0_col1\" class=\"data row0 col1\" >No DR</td>\n",
              "      <td id=\"T_ad826_row0_col2\" class=\"data row0 col2\" >0.9857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ad826_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_ad826_row1_col0\" class=\"data row1 col0\" >1</td>\n",
              "      <td id=\"T_ad826_row1_col1\" class=\"data row1 col1\" >Mild</td>\n",
              "      <td id=\"T_ad826_row1_col2\" class=\"data row1 col2\" >0.9717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ad826_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_ad826_row2_col0\" class=\"data row2 col0\" >2</td>\n",
              "      <td id=\"T_ad826_row2_col1\" class=\"data row2 col1\" >Moderate</td>\n",
              "      <td id=\"T_ad826_row2_col2\" class=\"data row2 col2\" >0.8925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ad826_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_ad826_row3_col0\" class=\"data row3 col0\" >3</td>\n",
              "      <td id=\"T_ad826_row3_col1\" class=\"data row3 col1\" >Severe</td>\n",
              "      <td id=\"T_ad826_row3_col2\" class=\"data row3 col2\" >0.9808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ad826_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_ad826_row4_col0\" class=\"data row4 col0\" >4</td>\n",
              "      <td id=\"T_ad826_row4_col1\" class=\"data row4 col1\" >Proliferative DR</td>\n",
              "      <td id=\"T_ad826_row4_col2\" class=\"data row4 col2\" >0.9763</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Macro Average Specificity: 96.14%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/final_hybrid_classification_report.csv\n",
            "\n",
            "Specificity results saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/final_hybrid_specificity.csv\n",
            "\n",
            "Confusion matrix saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_05_final_hybrid_confusion_matrix.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 27: Multiclass ROC Curves and AUC\n",
        "# =========================================================\n",
        "\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "\n",
        "# تحويل الـlabels إلى One-vs-Rest format\n",
        "test_labels_binary = label_binarize(\n",
        "    test_labels,\n",
        "    classes=np.arange(NUM_CLASSES)\n",
        ")\n",
        "\n",
        "# Overall ROC-AUC\n",
        "macro_roc_auc = roc_auc_score(\n",
        "    test_labels_binary,\n",
        "    test_probabilities,\n",
        "    average=\"macro\",\n",
        "    multi_class=\"ovr\"\n",
        ")\n",
        "\n",
        "weighted_roc_auc = roc_auc_score(\n",
        "    test_labels_binary,\n",
        "    test_probabilities,\n",
        "    average=\"weighted\",\n",
        "    multi_class=\"ovr\"\n",
        ")\n",
        "\n",
        "print(\"Final Random Forest ROC-AUC Results\")\n",
        "print(\"=\" * 60)\n",
        "print(f\"Macro ROC-AUC:    {macro_roc_auc * 100:.2f}%\")\n",
        "print(f\"Weighted ROC-AUC: {weighted_roc_auc * 100:.2f}%\")\n",
        "\n",
        "# ROC curve لكل فئة\n",
        "roc_results = []\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "\n",
        "for class_id, class_name in enumerate(CLASS_NAMES):\n",
        "\n",
        "    fpr, tpr, _ = roc_curve(\n",
        "        test_labels_binary[:, class_id],\n",
        "        test_probabilities[:, class_id]\n",
        "    )\n",
        "\n",
        "    class_auc = auc(fpr, tpr)\n",
        "\n",
        "    roc_results.append({\n",
        "        \"Class ID\": class_id,\n",
        "        \"Class Name\": class_name,\n",
        "        \"ROC-AUC\": class_auc\n",
        "    })\n",
        "\n",
        "    ax.plot(\n",
        "        fpr,\n",
        "        tpr,\n",
        "        linewidth=2,\n",
        "        label=f\"{class_name} (AUC = {class_auc:.3f})\"\n",
        "    )\n",
        "\n",
        "# Random classifier reference\n",
        "ax.plot(\n",
        "    [0, 1],\n",
        "    [0, 1],\n",
        "    linestyle=\"--\",\n",
        "    linewidth=1.5,\n",
        "    label=\"Random Classifier\"\n",
        ")\n",
        "\n",
        "ax.set_title(\n",
        "    \"One-vs-Rest ROC Curves of the Final Hybrid Random Forest Model\"\n",
        ")\n",
        "\n",
        "ax.set_xlabel(\"False Positive Rate\")\n",
        "ax.set_ylabel(\"True Positive Rate\")\n",
        "ax.legend(loc=\"lower right\")\n",
        "ax.grid(alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "roc_figure_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_06_final_hybrid_roc_curves.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    roc_figure_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "roc_auc_df = pd.DataFrame(roc_results)\n",
        "\n",
        "display(\n",
        "    roc_auc_df.style.format({\n",
        "        \"ROC-AUC\": \"{:.4f}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "roc_auc_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"final_hybrid_roc_auc.csv\"\n",
        ")\n",
        "\n",
        "roc_auc_df.to_csv(\n",
        "    roc_auc_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nROC figure saved at:\")\n",
        "print(roc_figure_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Wqj0BJq3BYDe",
        "outputId": "1a18fa5c-3c49-4676-81a3-481a87916756"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Random Forest ROC-AUC Results\n",
            "============================================================\n",
            "Macro ROC-AUC:    93.97%\n",
            "Weighted ROC-AUC: 96.54%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7dc6c451f320>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_9a08c\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_9a08c_level0_col0\" class=\"col_heading level0 col0\" >Class ID</th>\n",
              "      <th id=\"T_9a08c_level0_col1\" class=\"col_heading level0 col1\" >Class Name</th>\n",
              "      <th id=\"T_9a08c_level0_col2\" class=\"col_heading level0 col2\" >ROC-AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_9a08c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_9a08c_row0_col0\" class=\"data row0 col0\" >0</td>\n",
              "      <td id=\"T_9a08c_row0_col1\" class=\"data row0 col1\" >No DR</td>\n",
              "      <td id=\"T_9a08c_row0_col2\" class=\"data row0 col2\" >0.9953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a08c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_9a08c_row1_col0\" class=\"data row1 col0\" >1</td>\n",
              "      <td id=\"T_9a08c_row1_col1\" class=\"data row1 col1\" >Mild</td>\n",
              "      <td id=\"T_9a08c_row1_col2\" class=\"data row1 col2\" >0.9231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a08c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_9a08c_row2_col0\" class=\"data row2 col0\" >2</td>\n",
              "      <td id=\"T_9a08c_row2_col1\" class=\"data row2 col1\" >Moderate</td>\n",
              "      <td id=\"T_9a08c_row2_col2\" class=\"data row2 col2\" >0.9508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a08c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_9a08c_row3_col0\" class=\"data row3 col0\" >3</td>\n",
              "      <td id=\"T_9a08c_row3_col1\" class=\"data row3 col1\" >Severe</td>\n",
              "      <td id=\"T_9a08c_row3_col2\" class=\"data row3 col2\" >0.9057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9a08c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_9a08c_row4_col0\" class=\"data row4 col0\" >4</td>\n",
              "      <td id=\"T_9a08c_row4_col1\" class=\"data row4 col1\" >Proliferative DR</td>\n",
              "      <td id=\"T_9a08c_row4_col2\" class=\"data row4 col2\" >0.9236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ROC figure saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_06_final_hybrid_roc_curves.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final Conclusion\n",
        "\n",
        "The proposed hybrid framework combines deep feature representations extracted\n",
        "from fine-tuned ConvNeXt-Tiny and Swin-V2-Tiny models.\n",
        "\n",
        "Each model generated a 768-dimensional embedding, and feature-level\n",
        "concatenation produced a fused representation of 1,536 features. The fused\n",
        "features were classified using a regularized Random Forest classifier with\n",
        "balanced subsampling.\n",
        "\n",
        "The final model demonstrated strong overall classification performance and\n",
        "excellent discriminative ability, with class-specific ROC-AUC values exceeding\n",
        "0.90 for all five diabetic retinopathy classes.\n",
        "\n",
        "The model achieved particularly strong discrimination for the No DR and\n",
        "Moderate classes. Most errors occurred between neighbouring disease severity\n",
        "grades, particularly Mild versus Moderate and Severe versus Proliferative DR.\n",
        "\n",
        "These results indicate that combining convolutional and transformer-based deep\n",
        "features provides a robust representation for multiclass diabetic retinopathy\n",
        "classification."
      ],
      "metadata": {
        "id": "9_fmJ9toBsL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 28: Final Results Summary\n",
        "# =========================================================\n",
        "\n",
        "final_results_summary = pd.DataFrame({\n",
        "    \"Metric\": [\n",
        "        \"Training Accuracy\",\n",
        "        \"Validation Accuracy\",\n",
        "        \"Testing Accuracy\",\n",
        "        \"Test Macro Precision\",\n",
        "        \"Test Macro Recall\",\n",
        "        \"Test Macro F1\",\n",
        "        \"Test Weighted F1\",\n",
        "        \"Macro Specificity\",\n",
        "        \"Macro ROC-AUC\",\n",
        "        \"Weighted ROC-AUC\",\n",
        "        \"Train-Test Gap\"\n",
        "    ],\n",
        "    \"Value\": [\n",
        "        train_accuracy,\n",
        "        val_accuracy,\n",
        "        test_accuracy,\n",
        "        test_macro_precision,\n",
        "        test_macro_recall,\n",
        "        test_macro_f1,\n",
        "        test_weighted_f1,\n",
        "        macro_specificity,\n",
        "        macro_roc_auc,\n",
        "        weighted_roc_auc,\n",
        "        train_test_gap\n",
        "    ]\n",
        "})\n",
        "\n",
        "print(\"Final Hybrid Framework Results\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "display(\n",
        "    final_results_summary.style.format({\n",
        "        \"Value\": \"{:.2%}\"\n",
        "    })\n",
        ")\n",
        "\n",
        "final_summary_path = (\n",
        "    RESULTS_DIR /\n",
        "    \"final_hybrid_results_summary.csv\"\n",
        ")\n",
        "\n",
        "final_results_summary.to_csv(\n",
        "    final_summary_path,\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"\\nFinal results saved at:\")\n",
        "print(final_summary_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "q2desOg0Bak7",
        "outputId": "1561c5a2-4d99-4913-f911-158f10e884e1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Hybrid Framework Results\n",
            "============================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7dcaae2a55b0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_9e85f\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_9e85f_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
              "      <th id=\"T_9e85f_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_9e85f_row0_col0\" class=\"data row0 col0\" >Training Accuracy</td>\n",
              "      <td id=\"T_9e85f_row0_col1\" class=\"data row0 col1\" >98.95%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_9e85f_row1_col0\" class=\"data row1 col0\" >Validation Accuracy</td>\n",
              "      <td id=\"T_9e85f_row1_col1\" class=\"data row1 col1\" >85.97%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_9e85f_row2_col0\" class=\"data row2 col0\" >Testing Accuracy</td>\n",
              "      <td id=\"T_9e85f_row2_col1\" class=\"data row2 col1\" >84.91%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_9e85f_row3_col0\" class=\"data row3 col0\" >Test Macro Precision</td>\n",
              "      <td id=\"T_9e85f_row3_col1\" class=\"data row3 col1\" >73.74%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_9e85f_row4_col0\" class=\"data row4 col0\" >Test Macro Recall</td>\n",
              "      <td id=\"T_9e85f_row4_col1\" class=\"data row4 col1\" >69.28%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_9e85f_row5_col0\" class=\"data row5 col0\" >Test Macro F1</td>\n",
              "      <td id=\"T_9e85f_row5_col1\" class=\"data row5 col1\" >71.09%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_9e85f_row6_col0\" class=\"data row6 col0\" >Test Weighted F1</td>\n",
              "      <td id=\"T_9e85f_row6_col1\" class=\"data row6 col1\" >84.52%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_9e85f_row7_col0\" class=\"data row7 col0\" >Macro Specificity</td>\n",
              "      <td id=\"T_9e85f_row7_col1\" class=\"data row7 col1\" >96.14%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_9e85f_row8_col0\" class=\"data row8 col0\" >Macro ROC-AUC</td>\n",
              "      <td id=\"T_9e85f_row8_col1\" class=\"data row8 col1\" >93.97%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_9e85f_row9_col0\" class=\"data row9 col0\" >Weighted ROC-AUC</td>\n",
              "      <td id=\"T_9e85f_row9_col1\" class=\"data row9 col1\" >96.54%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9e85f_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_9e85f_row10_col0\" class=\"data row10 col0\" >Train-Test Gap</td>\n",
              "      <td id=\"T_9e85f_row10_col1\" class=\"data row10 col1\" >14.04%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Final results saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Results/final_hybrid_results_summary.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Cell 29: Overfitting and Underfitting Analysis\n",
        "# =========================================================\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "# Load training histories\n",
        "# ---------------------------------------------------------\n",
        "convnext_history_file = (\n",
        "    RESULTS_DIR /\n",
        "    \"convnext_training_history.csv\"\n",
        ")\n",
        "\n",
        "swin_initial_history_file = (\n",
        "    RESULTS_DIR /\n",
        "    \"swin_v2_training_history.csv\"\n",
        ")\n",
        "\n",
        "swin_corrected_history_file = (\n",
        "    RESULTS_DIR /\n",
        "    \"swin_v2_corrected_training_history.csv\"\n",
        ")\n",
        "\n",
        "for file_path in [\n",
        "    convnext_history_file,\n",
        "    swin_initial_history_file,\n",
        "    swin_corrected_history_file\n",
        "]:\n",
        "    if not file_path.exists():\n",
        "        raise FileNotFoundError(\n",
        "            f\"Training history was not found:\\n{file_path}\"\n",
        "        )\n",
        "\n",
        "convnext_history_df = pd.read_csv(\n",
        "    convnext_history_file\n",
        ")\n",
        "\n",
        "swin_initial_history_df = pd.read_csv(\n",
        "    swin_initial_history_file\n",
        ")\n",
        "\n",
        "swin_corrected_history_df = pd.read_csv(\n",
        "    swin_corrected_history_file\n",
        ")\n",
        "\n",
        "print(\"Training histories loaded successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6Xx73YpiCdV3",
        "outputId": "28d3beb1-20a5-4982-9c08-d6d98bef2f94"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training histories loaded successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "convnext_gap = (\n",
        "    convnext_history_df.loc[convnext_history_df[\"Validation Accuracy\"].idxmax(), \"Train Accuracy\"]\n",
        "    - convnext_history_df[\"Validation Accuracy\"].max()\n",
        ")\n",
        "print(f\"ConvNeXt-Tiny overfitting gap at best epoch: {convnext_gap*100:.2f}%\")\n",
        "\n",
        "swin_gap = (\n",
        "    swin_corrected_history_df.loc[swin_corrected_history_df[\"Validation Accuracy\"].idxmax(), \"Train Accuracy\"]\n",
        "    - swin_corrected_history_df[\"Validation Accuracy\"].max()\n",
        ")\n",
        "print(f\"Swin-V2-Tiny (corrected) overfitting gap at best epoch: {swin_gap*100:.2f}%\")\n",
        "\n",
        "print(f\"Final Random Forest train-test gap: {train_test_gap*100:.2f} percentage points\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VA49JOlJGw3x",
        "outputId": "6aa69b89-41bd-4c1b-85ca-b28c506b3255"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConvNeXt-Tiny overfitting gap at best epoch: 11.53%\n",
            "Swin-V2-Tiny (corrected) overfitting gap at best epoch: -4.16%\n",
            "Final Random Forest train-test gap: 14.04 percentage points\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 07: ConvNeXt Accuracy Curves\n",
        "# =========================================================\n",
        "\n",
        "convnext_best_index = (\n",
        "    convnext_history_df[\"Validation Accuracy\"].idxmax()\n",
        ")\n",
        "\n",
        "convnext_best_epoch = int(\n",
        "    convnext_history_df.loc[\n",
        "        convnext_best_index,\n",
        "        \"Epoch\"\n",
        "    ]\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plt.plot(\n",
        "    convnext_history_df[\"Epoch\"],\n",
        "    convnext_history_df[\"Train Accuracy\"] * 100,\n",
        "    marker=\"o\",\n",
        "    label=\"Training Accuracy\"\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    convnext_history_df[\"Epoch\"],\n",
        "    convnext_history_df[\"Validation Accuracy\"] * 100,\n",
        "    marker=\"o\",\n",
        "    label=\"Validation Accuracy\"\n",
        ")\n",
        "\n",
        "plt.axvline(\n",
        "    convnext_best_epoch,\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Best Epoch = {convnext_best_epoch}\"\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    \"ConvNeXt-Tiny Training and Validation Accuracy\"\n",
        ")\n",
        "\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy (%)\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "plt.tight_layout()\n",
        "\n",
        "convnext_accuracy_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_07_convnext_overfitting_accuracy.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    convnext_accuracy_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"Saved at:\")\n",
        "print(convnext_accuracy_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "iY1Dtk15CfPs",
        "outputId": "441054e3-6d25-48df-a50b-c62556e90702"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_07_convnext_overfitting_accuracy.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 08: ConvNeXt Loss Curves\n",
        "# =========================================================\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plt.plot(\n",
        "    convnext_history_df[\"Epoch\"],\n",
        "    convnext_history_df[\"Train Loss\"],\n",
        "    marker=\"o\",\n",
        "    label=\"Training Loss\"\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    convnext_history_df[\"Epoch\"],\n",
        "    convnext_history_df[\"Validation Loss\"],\n",
        "    marker=\"o\",\n",
        "    label=\"Validation Loss\"\n",
        ")\n",
        "\n",
        "plt.axvline(\n",
        "    convnext_best_epoch,\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Best Epoch = {convnext_best_epoch}\"\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    \"ConvNeXt-Tiny Training and Validation Loss\"\n",
        ")\n",
        "\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "plt.tight_layout()\n",
        "\n",
        "convnext_loss_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_08_convnext_overfitting_loss.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    convnext_loss_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"Saved at:\")\n",
        "print(convnext_loss_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "N6ItEmisCkKP",
        "outputId": "310d80c7-84fa-439a-b3fe-74ee61ca82f7"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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995169OiR4wLGZ599pmbNmmWPF46Ojtb06dML9P9yvKzvc/369XMsj46OzrE9yex3b7zxhurXr6/Q0FBVrFhR0dHR+vvvv097u8dvv0qVKrn246zhHIU53hTW1q1bVb9+/ewLC/nFcuedd6pBgwbq0aOHqlWrpptuuinXuPJnnnlGhw4dUoMGDdS0aVM9/PDDPj/VG4DAQdINAEUsMjJSVapU0erVq0/reQUpRCUp30rI1glFkPr27atffvlF+/fvV1pamqZOnaqrrrrqpJWr69SpoxtuuCHf1m6PxyOHw6GZM2dqzpw5uf4++OCDHOsfPnxY9957r1q2bKnffvtNNWvW1B133FGgMcaSdN999yk2Njb778orryzQ806mIJ+fy+XSddddp2+//Vapqan65ZdftGvXriKrkn58i2+WQ4cOqWPHjvrrr7/0zDPPaNq0aZozZ45eeuklSSrQtE8F3TeK+rkF4Xa71bVrV02fPl2PPvqopkyZojlz5mQX/Drx/RVXxe+YmBh17dpV3377rTIyMjRt2jQdPnw4e7y3ZAoHDhw4UHXr1tXHH3+cvf9fdNFFXp2O64UXXtCQIUPUoUMHjR8/XrNmzdKcOXN09tlnF9s0YN7eLwoiJiZGK1eu1NSpU9W7d2/98ssv6tGjR46x+x06dNDGjRv1ySefqEmTJvroo4907rnn6qOPPiq2OAEgP8wZAgBecNlll2nMmDFavHix2rRpc9J1a9asKY/Ho/Xr1+coHrZ3714dOnRINWvWLFQMffv21YgRI/Ttt9+qUqVKSkxMzLMw24mefPJJjR8/PjvZO17dunVlWZZq166tBg0aFOi1du/ere+//15lypTRqFGj1KtXL7322ms5ioPld8HhkUceyZHontgS6E39+/fXa6+9pmnTpunHH39UdHR0rgJ0RWnevHk6cOCAJk+enKMI1ubNm722zdMRExOjsLAwbdiwIddjeS070apVq7Ru3Tp99tlnOQqnnUl16Zo1a2ru3LlKSkrK0dr933//ndbrXH/99Zo5c6Z+/PFHTZgwQZGRkerVq1f245MmTVKdOnU0efLkHPvq8fPPn07Mkuk1UqdOnezl+/bty9V6PGnSJHXu3Fkff/xxjuWHDh1SxYoVs+8X9IJd1vZ/+uknHT58OEdrd9bwhcIebwqjZs2a+vvvv+XxeHK0ducVS0hIiHr16qVevXrJ4/Hozjvv1AcffKCnnnoqu6dF+fLlNWjQIA0aNEhJSUnq0KGDhg8frsGDBxfbewKAvNDSDQBe8Mgjj6h06dIaPHiw9u7dm+vxjRs3ZlfnvfTSSyVJb775Zo51Xn/9dUlSz549CxXDWWedpaZNm2rixImaOHGiYmNj861ofLy6devqhhtu0AcffKA9e/bkeOzKK6+Uy+XSiBEjcrV0WZaVo6LxsmXL9O677+ruu+9Wy5YtJZmLEVdccYWeffbZHN1YS5cuLUm5qp83btxYXbp0yf7Lep3i0KxZMzVr1kwfffSRvv32W1177bVend86q0Xx+M81PT1d7733nte2eTpcLpe6dOmiKVOm5Khwv2HDhlzjgPN7vpTz/VmWlaNK9em69NJLlZmZqffffz97mdvt1qhRo07rdfr06aPw8HC99957+vHHH3XllVcqLCzspLH/8ccfWrx48WnH3KVLFwUHB2vUqFE5Xu/E73/Wdk/8nn3zzTe5pqzL7/uTl0svvVRut1vvvPNOjuVvvPGGHA5HgcfnF4VLL71Ue/bsyTEMJjMzU6NGjVJERET20IMTK6U7nU41a9ZMkrKnVTxxnYiICNWrVy/XtIsAYAdaugHAC+rWrasJEyaob9++Ouuss9S/f381adJE6enp+u233/TNN99o4MCBksz46wEDBmjMmDHZXYyXLFmizz77TH369MmeTqow+vbtq2HDhiksLEw333xzrrGT+XniiSc0btw4/ffffzr77LNzvK/nnntOQ4cO1ZYtW9SnTx+VKVNGmzdv1nfffadbb701ey7lW2+9VZUrV9Zzzz2X47XfeustNW7cWPfcc4+mTp0qSTrnnHPkcrn00ksvKSEhQaGhobrooosUExNT6PdeFPr376+HHnpIkoqsa3l+2rZtq3LlymnAgAG699575XA4NG7cuGLtxnsqw4cP1+zZs9WuXTvdcccd2clbkyZNTjl1VqNGjVS3bl099NBD2rlzpyIjI/Xtt9+e0djgXr16qV27dnrssce0ZcsWNW7cWJMnTz7t8c4RERHq06dP9rju47uWS+Zi0eTJk3XFFVeoZ8+e2rx5s0aPHq3GjRsrKSnptLaVNd/4yJEjddlll+nSSy/VihUr9OOPP+Zovc7a7jPPPKNBgwapbdu2WrVqlb744oscLeSS+V6WLVtWo0ePVpkyZVS6dGm1bt1atWvXzrX9Xr16qXPnznriiSe0ZcsWNW/eXLNnz9b333+v+++/P0fRtKIwd+5cpaam5lrep08f3Xrrrfrggw80cOBALVu2TLVq1dKkSZO0aNEivfnmm9kt8YMHD1Z8fLwuuugiVatWTVu3btWoUaN0zjnnZPcOaty4sTp16qSWLVuqfPnyWrp0qSZNmqS77767SN8PABRKcZdLB4BAsm7dOuuWW26xatWqZYWEhFhlypSx2rVrZ40aNSrHFFAZGRnWiBEjrNq1a1vBwcFW9erVraFDh+ZYx7LMlGF5TYvTsWNHq2PHjrmWr1+/3pJkSbIWLlyY6/Hjpww7Uda0TcdPGZbl22+/tdq3b2+VLl3aKl26tNWoUSPrrrvusv777z/LsizrjTfesCRZkyZNyvNzefXVVy1J1uTJk7OXffjhh1adOnUsl8tV4OnDCjNlWF5TGOmEaZ6y7N6923K5XFaDBg1OGUtB4ztxGrbjLVq0yLrgggusUqVKWVWqVLEeeeQRa9asWbk+jzN5b/lNGXbXXXflem7NmjVzTGFlWZY1d+5cq0WLFlZISIhVt25d66OPPrIefPBBKywsLJ9P4Zi1a9daXbp0sSIiIqyKFStat9xyS/YUVMdPdzVgwACrdOnSuZ6fV+wHDhywbrzxRisyMtKKioqybrzxRmvFihWnPb3b9OnTLUlWbGxsrmm6PB6P9cILL1g1a9a0QkNDrRYtWlg//PBDrv8Hyzr1lGGWZVlut9saMWKEFRsba5UqVcrq1KmTtXr16lyfd2pqqvXggw9mr9euXTtr8eLFeX7fv//+e6tx48bZ07dlvfe8Yjx8+LD1wAMPWFWqVLGCg4Ot+vXrW6+88kqOKcyy3ktB94sTZe2T+f2NGzfOsizL2rt3rzVo0CCrYsWKVkhIiNW0adNc/2+TJk2yLrnkEismJsYKCQmxatSoYd12223W7t27s9d57rnnrFatWllly5a1SpUqZTVq1Mh6/vnnrfT09JPGCQDFwWFZPnQJHQAAH7J//37FxsZq2LBheuqpp+wOx2f16dOH6ZoAAMgHY7oBAMjH2LFj5Xa7deONN9odis9ISUnJcX/9+vWaMWOGOnXqZE9AAAD4OFq6AQA4wc8//6y1a9fqqaeeUufOnTV58mS7Q/IZsbGxGjhwoOrUqaOtW7fq/fffV1pamlasWJFr7mkAAEDSDQBALp06ddJvv/2mdu3aafz48apatardIfmMQYMG6ZdfftGePXsUGhqqNm3a6IUXXtC5555rd2gAAPgkkm4AAAAAALyEMd0AAAAAAHgJSTcAAAAAAF4SZHcAxc3j8WjXrl0qU6aMHA6H3eEAAAAAAEogy7J0+PBhValSRU5n/u3ZAZd079q1S9WrV7c7DAAAAACAH9i+fbuqVauW7+MBl3SXKVNGkvlgIiMj5fF4tG/fPkVHR5/06gTgT9jvEYjY7xFoktMz1er5uZKk34d2VkRYiM0RAd7HsR7FKTExUdWrV8/OMfMTcEl3VpfyyMjI7KQ7NTVVkZGRfDERMNjvEYjY7xFogtIz5QwNl2TOe0i6EQg41sMOpxq2zJ4IAAAAAICXkHQDAAAAAOAlJN0AAAAAAHgJSTcAAAAAAF5C0g0AAAAAgJcEXPVyAACAQBAW5NL8hztq//4DCgty2R0OAAQskm4AAAA/5HQ6VK1cuEIykuR0nnw6GwCA99C9HAAAAAAALyHpBgAA8EPpmR6N/PFfjVqwQ+mZHrvDAYCARdINAADghzI9Hn24YLO+WLZXmR6SbgCwC0k3AAAAAABeQtINAAAAAICXkHQDAAAAAOAlJN0AAAAAAHgJSTcAAAAAAF4SZHcAAIqZxy1tWaSwneuk5AZSrXaS02V3VAAAAIBfIukGAsnaqdLMR+VM3KWyWcsiq0jdX5Ia97YxMABAUQsLcmnmfe0VHx+vsCAurgKAXeheDgSKtVOlr/tLibtyLk/cbZavnWpPXAAAr3A6HWpQqYzqVCglp9NhdzgAELBIuoFA4HFLMx+VZOXx4NFlMx8z6wEAAAAoMiTdQCDY+lvuFu4cLClxp1kPAOAX0jM9evOn9fpw8S6lZ3rsDgcAAhZJNxAIkvYW7XoAAJ+X6fHo7Z836OM/divTQ9INAHYh6QYCQUSlol0PAAAAQIGQdAOBoGZbqVT5k6zgkCKrmvUAAAAAFBmSbiAQ7F0jpSedZAVL6v4i83UDAAAARYykG/B3SXHSl/0kd7pUqYlUpkrudZwhUkzj4o8NAAAA8HNBdgcAwIsy06SvrpcSd0gV6kkDp0uhZeTZskiJO9cpsko9ORe8Jm2ZL02+Rbp5tuQKtjtqAAAAwG/Q0g34K8uSpt0v7VgihUVJ/SZKpcqaLuS12iu1/mVS7Q7SFaPN47uWSwtesztqAAAAwK+QdAP+avE70l8TJIdLumasVLFe3utFVZV6vm5u//qytGNZsYWIM+BxS5sXSKsmmX89brsjAuBjQoNc+u7ONvrk2kYKDaJmBwDYhe7lgD9aN1ua/ZS53X2kVPeik6/f9Grp3+nSmsnSd7dKty2QQsK9HycKZ+1UaeajUuKuY8siq0jdX5Ia97YvLgA+xeV0qHm1sooLSZfL6bA7HAAIWLR0A/4m7l9p0k2SLKnlQKnVrQV7Xs/XpDKx0oEN0pxh3owQZ2LtVOnr/jkTbklK3G2Wr51qT1wAAADIE0k34E+S46Uv+0rph6Wa7aUer0iOArZuhJeX+rxnbv/5obT+J+/FicLxuE0Lt6w8Hjy6bOZjdDUHIElKz/RozPxNGr90j9IzPXaHAwABi6Qb8BfuDNPSeXCLVLam9L/PpaCQ03uNuhdJrW4zt7+/yyTx8B1bf8vdwp2DJSXuNOsBCHiZHo9enPmf3lm4U5kekm4AsAtJN+AvfnxE2rJAComQrpsola5QuNfpMlyq2EBK2iP98ICpgg77ZaRKKycUbN2kvd6NBQAAAAVG0g34gyUfSks/keSQrvpYijmr8K8VEi5d8YHkDJLWTpFWfVNUUaIwMlKk39+X3mpuqtEXREQl78YEAACAAiPpBkq6TfOkHx81t7sMlxp2P/PXrHqu1PHoa05/SDq0/cxfE6cn/Yj02zvSm83MOO2kPVKZqmZOdeU3Tt8hRVaVarYtzkgBAPANHre0ZaHC1v8gbVlIjRP4DKYMA0qyAxulrwdIlltqdq3U7r6ie+32Q6R1s6SdS6Upd0j9p0pOrtN5XVqStPRjadHbUvJ+syyqhnThEOmc66V1M83YfTmUu6CaJXV/UXIyHy8AIMAcnU7TmbhLZbOWMZ0mfARn0EBJlXJImtBXSj0kVTtf6vVWwSuVF4QrSLpyjBQcbsaK//F+0b02cks7LC14XXqrmZmyLXm/VK6W1Psd6d7l0nmDTGG8xr1NkbzI2NyvERIh1Wpf7KEDAGArptOEj6OlGyiJ3JlmLu4D60134r5fSMFhRb+dCnWlS56Tpg+RfhphqpufyXhx5JaaIP0xRvr9XSnloFlWvo7U4WGp6TWSKzj3cxr3lhr1NFXKk/ZK4RWkHx+T9v8rzR1hLsAAABAITjmdpsMM02rUk55gsA1JN1ASzRkmbZwrBZWS+n0plfFi4azzbpL++1HaMEeafIs0+OfTn4oMuaUckv4YLf3+nkm8JalCfZNsN7nK9DQ4GadLqn3hsfu93pA+7SEtGyudc4NU/XxvRQ6ghAgNcmnC4FY6ePCQQoNINuCnTmc6zeN/N4FiRPdyoKRZPs60ikrSFe9Lsc29uz2HQ7r8HalUeWnPKmneSO9uz98lx0s/Py+92dR8lqkJUnQjU3X+rj+k5n1PnXDnpWZbqfl15vYPD5jeEAACmsvp0AV1Kqhl9TJyOYtw+BHgSxIKWOyV6TRhI5JuoCTZutgkVJLUaah09hXFs90ylY91WV70pokDp+fIAdNF/82m0vyXpbREKaaxdM1Y6Y7FUtOrz7zb2yXPSmFlpb2rpD8/LIqoAQDwTenJ0uJ3pZmPF2x9ptOEjUi6gZLi4FZp4g2SJ0Nq3Efq8Ejxbr9xb9OSanmk724zhb9wakn7zHCAN5tKC1+X0pOkSk2l/42Tbl9kLpwUVVX40hXNtHGSaU1P3F00rwugRMpwe/T54q2a9FecMtweu8MBikbaYWnhG+Z3ddbjUupByXGKi9ZlqjCdJmzFmG6gJEhLkr7sZypaxzaX+rxvz/RdPV40lcwPbTU/dL1HFX8MJcXhvdJvb0tLP5Eyks2y2OZm/vOGlxZtpfnjnTtAWjHeTPU2a6hpSQcQkDLcHg2ftlaSNLBjI4XmUZcRKDHyKjxatqZ04YNSaBlTYFZSngXVImKKLUwgLyTdgK/zeKTJt0pxa6TSMdK1E6SQcHtiCYuSrhgtjb1MWv651KCH1OhSe2LxVYm7jyXbmalmWZVzTbLdoJv3ku0sTqd02evSmE7Smu+kFjdK9S727jYBAPCW5Hjp9/elPz6Q0o4WHi1fV+rwUM5ZPpxBpor58UXVSkebwqW7V0o/DTfDsAAbkHQDvu6X56X/pkuuUJNwR1WzN55a7aW2d0u/jZKm3mPmCI+ItjcmX5Cw04x3X/aZ5E4zy6qdL3V8zCS93k62jxfbXGp1m5lbfcZDZsy4N6aUAwDAW47slxa/Iy350AzNkkzh0Q4PHx2adUKX8qPTaXq2LFLiznWKrNpAzlrtzAXob282F8RjzpLOua743wsCHkk34Mv+/kZa8Kq53ftt35kG6qKnpA0/m9b3afeaiwHFmVQWJ4/72HzYEZXMmLDjf+gPbTdjy1aMk9zpZln1C6ROj0p1Otv3uXR+3JxoxG8yFwM6PWZPHAAAnI68hmdVamKS7bN6n3x4ndMl1Wqv1PAGioyJMes2vVra9680/xVp2n1ShXpS9VbF816Ao0i6AV+1Y5n0/V3mdrv7pebX2hpODkGh0pUfSGM6S//NMAnnuf3tjqrorZ2au6taZBWp+0umNXnh69KKL0xxO0mq2c50I6/dwf6LEGGRUvcXzBi3Ba+bLngV6tobEwAA+UncJS16S1o29tjwrNhzpI6PmOFsZ1LLptPjUtw/0r8/SF9dJ93yi1S2elFEDRQI1csBX5S4y/wouNPMD83Fw+yOKLfKTaWLnjS3Zw6V4jfbG09RWztV+rp/zoRbMmO2v75Revscc2LgyTBJ9sDp0qAZUp2O9ifcWc6+0rS2u9OkGQ9LVh7FZQAAsNOhbWY61LeaS3+MNgl3tfOl6ydJt86TGvU88+KxTqd0xQdm9pAj+6Sv+knpR4okfKAgSLpRcB63tHmBtGqS+dfjtjsi/5SebBLupD1S9FnSVR+e+fzN3tL2HqlGWzPW6rvb/Wef8LhNC3deFVCzllkeqXYnadBMacA0M9bd1zgcUs/XTD2AjXOltVPsjggAACN+k/T93dLbLUxXcne66TF24xTp5jlS/a5FexE7NELqN0EKryjtWXX0vIWp9FA8SLpRMGunSm82kT67zBSj+Owyc3/tVLsj8y+WJU29W9q1QipVXur3pZkGw1c5XaaaeUgZafvvpluYP9j6W+4W7rx0eEiq2cb78ZyJCnWl9g+Y2zOHSqmJ9sYDoNiEuJz6qH9LvXZ5PYW4OOWDj9i/3iS8o84zw9M8mVLtjsd6jNX1Yj2UsjWka7+QnMHSP1OlX1/yznaAE3AExqmdtJttfxLvorTgVWn1t2bai77jpPK17Y7o1MrVNPN3S9IvL0i7/7Y3nqKQtLdo17Nb+wekcrWlw7uleSPtjgZAMQlyOXVRoxi1qx2lIJJu2C3uH1Nn5J3zpb++lCy3VK+radUeMLX4eozVuEDqdbSR4NcXpdWTi2e7CGgcgXFyBelmO/Mx/+lWbKd/pkk/P2du93zNN7sr5+ec66VGl5nxzZNvlTJS7Y6oeERUsjuCggkOk3oerYL/x2j/uDACACgZdv8tTbxReu8C07AgS2p4qXTLz9INk+ypJN7ieqnN3eb2lDtND0PAi0i6cXKn7GZrSYk7zXoovN1/m2RVklrfLrUcaGs4p83hMFeNS0dL+/6Rfn7W7ogKx+OWfn/fjDE7KYcUWdVMH1ZS1OsiNe5jxqJPf5BxbEAAyHB7NGnZDv2wZr8y3HznUYQKUudn53Lpy37SBxeartySmfLrtgVm+FzVlsUb84m6PmNa2jNTpC+vkw7vsTce+DWSbpycv3Wz9UVJceZHKSPZVJq+5Hm7Iyqc0hWl3u+Y24vfkTb9am88pyvuX+mTbqbnRmaKFN3o6AMnjis7er/7i75b4C4/3UdKIRHSjiXSis/tjgaAl2W4PXrk21V6bs5Wkm4UnVPV+dm+RBp/tfTh0WlF5ZCaXC3d+bsZOhfbzNbwszld0tUfSxUbSId3SV9dHzg99VDsSLpxcgXtPltSutn6msw0aeINUuIOqUI96ZpPJVeQ3VEVXsPux1rpp9wppRyyM5qCyUyX5r0kjW4v7fjTFIW77A3pjsXS/8ZJkbE514+sIv3vc6lxb3viPRORVaTOj5vbc56Wjuy3Nx4A8BXM0FIwp5pO87020sddpQ1zJIdLat5PuvtPk9zGnGVPzCcTFiX1+0oKKyvtXCpNu5fpNeEVJfjsHsWiZltzon6yLuZlYktWN1tfYVnSD0Ok7X9IoUcP+qXK2R3VmbvkedPKfXCz9OMj0pVj7I4ofzuWmWrxcWvN/QbdpZ6vS1FVzf3Gvc38oFt/M705IiqZfb2ktXAfr9Vt0sovpb2rTOLd5127IwIAe62daurXHH+uE1lF6v5SybzA6i0FqfMTt9Yk2+dcJ104RCpfpzgjLJwKdc3F9HFXSH9PNBcHsmb9AIoILd04OadL6vjYyddxOE0XaZyexe9KK8ebz++aT6WK9e2OqGiERkhXfGDe198TpTXf2R1RbulHpJmPSx93MScI4RWlqz42Fz6yEu4sTpdU+0Kp6dXm35KccEumJ8Vlr5vbK8dLWxfbGw8A2IkZWgquoNNpXvmhdPk7JSPhzlKno9Tj6PRhP42Q/vvR3njgd0i6cWrbjp6Uu0JyLo+oZLrjJO6UPu0uHdxS3JGVXOtmS3OeMre7jZTqXWxvPEWtRmup/RBz+4cHzMmLr9g0z3R/+/1dU1SsWV/priUmqfbWvKC+pnor6dz+5vb0IZI7w954AMAOzNByegpcv6eEds9udYt03s2SLOnbwdLetXZHBD9C0o2T27HUzKUoSQOnSwN+MC2CA36Qhvwj3farVK6WSbg/6W6KUeHk4v41hUcsj3TuAKn1bXZH5B0dH5Vim0spB6Xv77J/jFRWHJ9fLh3aKkVWk66fZLq/l65gb2x26DJCCq9gWvp/f8/uaACg+DFDy+nJLGCRsZJc56fHS1KtC6X0JOnLvtQ+QZEh6Ub+PB7px0fN7XNuMK1jJ3azLVdLummWFH2WdHi39GkPM0UE8pYcL315rZSWKNVsJ136qv+2rgaFSFeMkVyh0sa50p8f2RfL2qnSu62lFePN/fNvke76Xarf1b6Y7BZe3kyXIknzXpQSdtgbD4Cid3wL7dbFtNieiBlaCubgVum7282F65MqgdNpnsgVbMZ3l6stHdpmhhhkptsdFfwASTfy9/dEU8kxJEK6eFj+65WpLA2aIVU5V0qJlz7rLW1ZVHxxlhTuDHPwPrhZKlvDHNSDQk79vJIsppHUdYS5Pfspaf+G4t3+4b3SxBtNRdWkvVKF+tKgmVLPV6XQMsUbiy9qfp1U/QIzXV3WBTYA/mHtVIW8e67eDX5L7wa/pbAJfXJO6xToPJ6Cdx9e9a1JwAJNUpw042FpVMtjvR6rnX/0QT+aTvNE4eWl6yZKoZHS1kXSjIfs763nLVTtLzYk3chb2mHpp+HmdoeHpTKn6CoUXl4aMPVol5zD0vgrzbjlQHbigezHR6QtC8xFjH5fmXmtA0Gr26TaHc3c15NvKZ7xw5ZlWrXfPV/6Z6rkDJIufEi6faFUs433t19SOJ2mqJrDJf37g7Rult0RASgKR4uDBR3eoZ6uP9TT9YeCHB6Kg2XZ+LM0poO08LWCrb9uhvR2C2nKXcV/8dgOKYekuc9IbzWXloyRPBlSnc7SLb9Ig3/yv+k08xLd0AynlENa/pn5HPzNqeZbR5FyWJa/XrrJW2JioqKiopSQkKDIyEh5PB7FxcUpJiZGTifXILL9NEJa+LrpXnPXH1JQaMGel5EifTNQWjfTJDpXjpGaXOXVUH1SXtOPZLn2S6nRpcUf03GKfb9P2Cm930ZKTZA6DZU6naIi/pmI3yz9cL8pmCZJseeYKqqVm3pvmyXd7Cel30aZHhh3/iGFhNsdkVdwvEdA8LjNiXO+Y5UdJkG6f1XJb5E8Xbv/MlMlbvrF3A+NlBp0MxfHJeUsAHa05bbzUDOmO+s3xeGUzr5CuvBBqdLZxRR4MUlPlpZ8IC18U0o9ZJZVPU/q8rRUu0POdT1un51Os0iP9b+NMr+RDqepA+MvhW+zqvbnKnp3dL/3pwsoXnZibpkfzjqQW/xmafE75na3FwqecEtScCmp73ipydWSJ1OadLO07DPvxOmr8pt+JIsns3jj8QVRVc3815L068tmfuyi5nGbadjeb2tOjoLCzJjlwXNJuE+l42NmHN6hbdKCArb8APBNxxUHy7Scmu5urenu1sq0sk75ArA42MGt0re3SB90MAm3M1i64E7p3pXSVR+ZBCO/ltuOj0r9vze/JQ16mCKoq781vzVfXift9MLvWXFzZ0h/fmxa838abhLu6LOkayeYlu0TE27J/6bTzE+bu6Vzrjf/798MkvavtzuiM0fVflsE2R0AfNDsJyV3uulK1LDH6T/fFWxauEPLSMs+labdawqHtb2n6GP1NSc9kEmSwxzIGvX03x+o/DS9Wvp3urRmsvTdrdJtC4quRXXvWmnqPaYGgWSGOfR6S6pQt2he39+FRphxeF/fKC16y0yjFt3A7qgAFMZxRb/SFay7Mu6TJK0NHaQgpeW5nt9KjjcXEpeMMec1ktT0GumiJ00h2CyNe5vf5ZO13FY7T7ruK2nPKvOaa6ZI/003f3UvNkPxStrwJc/RCwi/PG/qzUimx1PnJ8znFGjnKXlxOKTL3pAObJC2/2GK4Q7+SSpVzu7ICq+gVfs3zfOfln0fQNKNnDbNM2M7HS6p+8jCV9Z2usxBKizSnMTPflJKTZQ6P+6/1bql05t+pPaFxRaWz+j5mpn3/cAGac4wU9DsTGSmSQteNydAngzTVfCSZ6UW/c14ZRTcWb2k+pdI62ebubsHTPPv7ypOzoe7juIUUg4VbL1ln5keLjUu8L/vekaK9MdoacEbUlqCWVa7oynsWaVF3s/Jark9lcpNpWvGSp3WmWF4f39tZujYOFeq2V7q8KBptPDlz9SyTA2Pn5+V9q42y0rHmAsHLQf6f5HX0xUUanpxjulszl8m3SRd943kKqFpVEEvuH1xtVS5mVS15bG/ivX5LSikErq3wCvcmdLMoeb2+YOlmLPO7PUcDtO9NyzKFOSY/7IZ09v9Rf9NiJh+5OTCy0t93pPGXSH9+aHUoLtUv0vhXmvHUun7u6V9/5j7DS81SX1klaKLN5A4HFKPl6XN803Bv1WTpGbX2B0V7JBXTYrIKlL3lxjj58ssS1r6ccFnItgyX/p0vlSpiXT+zVLT/5leLyWZx22qbP/ygrnALUmVmkpdh5vW6KJMhKMbSFeMNt3PF70lrfxC2rpQGrfQJCcdHja/cb6WfG9ZZM7Jtv9u7odGSe3ulS64QwopbW9sviwiRur3pfRJN1OIb/aTUo8X7Y7q9O1YJv32dsHWtTzS7pXmb+nHZllIGanKOTkT8cgqvref+yAKqVFY55glH5ppEUqVk+5ZbhKkon5tyUxT1HtUyb1CmB+PW5r1uLm6fioDfrC1pdv2/X7GI6ZYS0Rl6c7Fp7evpR+Rfn5O+v19SZYUXlG69BVT2IaD/pmb/4r5fEvHSHf/KZUqa3dERcb2/b4koLhOyZSebHqoZE3rVPU8aecyJVuhapz2iSTTvTzccbSLdddnpAPrpb+/MTNLSKanUPN+JgGPbmjDmzgDliWtnyP99LQUd3QasKjqpht50/8Vz4X+hJ2m6Nayscc+00pNTMG1xpfb3zq4+y+TbG/4ydwPKiW1vk1qd1/Rnu/5AK8e69dONUOxJDOMreXAon19b9m7Rvr5eTMc4pSOFlsc8IO05y9Tt2DncmnXCjPF6IkiKh9NwM81/1Zp4VfnDqdS0EJqJN2chBnJ8dKoc6WUg9Klr0qtbin6bfw1UZpyh2S5TVfWqz4+vSJtvmz7n9KMB82P2kn5RtVY2/f79GRT0ObAeqlxH9NVryAJ88ZfTI2ArPlSm/czxf787ITBVplp0vvtzP/N+bec+RAAH2L7fu/rqHpdMsVvkibeaLoJO5xSlxGmhso/05T841NqvO8FSUeT7qiKprdZ1oWTlIPSyi+lPz+S4jcee83aHUyPt4aXmjotvmznMlORfMsCcz8sykwR2epWKTis+ONJ2if9/q5pbEhPMssq1DfJd9Ori//zPLDRXEhdM9ncdwZJ5w4wLfEnFo/zE14/1v/6ivTLc+az7D9VqtWu6LdRVA5slOaNPFqh3zLHiOb9THI8/cGjK+VRtT+vC6zuTGn/f0eT8KN/e9ea8/oTVaifszW8cpPTO+cvQUOcSLrzQdKdj6yWx5jGpsCVt1qh//lBmjToWKG2a78o2d2Zjuw3lT5XjDP3w6JMErn886MrFPBAVsx8Yr/fuVz6uKup5n7lh1Kz/+W/bspBadaT0srx5n5UdemyNwvfNR0nt+lX6fPekhzSLT+bq9d+wCf2e1+2eYGZp/VUbO6pg+P896M0+TYzbrl0tHT1pzn+b5JT09R4uGnZXD0oQhH12+d94urxSJvnmQrW/80w3UolqUys1HKQ1HKAVKZyMbyh0xC/SZr77LFk0hUqtb5Vaj/ENy7EJsebAm6/v39s+q2yNaT2D5hq2N5udEjYKf36krRi/NGkyGGS/s6PS+XreHfbNvP6sd6yzLjuNZOl8Armd/L4wny+IGGHmS0m+/9f5vy08xPHCqXmOZSoas4Lc6eSnizt+TtnIn5wS+71nMEm8T4+Ea9QP+9eKCVsiBNJdz5IuvMQ949p2bLcZlqMOp28u71N88w0GxlHpOqtpeu+LnndUDxu04Vs7jPHfkzPuUHqMlyKiC6aA5kX+cx+/+vLpmpqaJR023wpYXvuq5prv5emPyQdiZPkMK0XFz9lquPDe74dLK36xnQTGzzXZ68wnw6f2e991apJ0rc3n3q9qz42J++wj8dtxi0vONoTpXpr02PohJoWyemZajxsliRp9fCuiggrQIGsQ9vN79vyz6Qj+8wyZ5DpoXb+YKlmO3uH8hzZb347ln5iCmjKITW/1iQTZavbF1d+0g6bixmL3zn2eZaJldrea7omF9UsHlmyK7Z/KLmPVqtv0F266CmT9ASAYjnWpydLn/Yw451jGks3z/aN85KkfabA358fH/v/r3+JGWoR2zz3+t5oUT5yQNq1PGcinnwg93qhkbnHh+9YWuKGOJF054Ok+wSWZYpabfpFanSZaXkuDtuXmKqIqQmmEugN35lktSTYscyMndu90tyv1NQU8KrROud6Ptw1xmf2e3emKUqyc6nkCjk2pYtkxghFVT02B2rFBlLvd3J/zvCOw3uld84z0/15a8hJMfOZ/d5X0dJdMhw5YC6ObPrF3G99u9T12TwrTme4Pfpu+Q4lJibqxg6NFBp8Gr3YMtOkf6aZrufbFh9bHn2WGffd/NriTTLSj0i/vyctfEtKP2yW1etiLnZXblp8cRRWerLpBbfoLenw0Qvy4RWlNneZixlh+Z+sF0jaYWnxe2ZcedbnU7OddPEwU6E+gBTbsT5xl6lonrTHzOF+7Rf2neelHDL/97+/bxq1JFNN/+Kn7P//tyzp0NZjY8N3LpN2rTxW++B4Duexnja5H/TJIU4k3fkg6T7BvzOkr/qZbll3/SGVr118296z2iT8R+KkCvVMK3tUteLb/uk6ckCaO+Jo13HLtM5e9KR03k0lriicT+33f4yRfnw4/8cdTjM+r8ND/lMDoKTI+r8JjTJF1cpUsjuiM+JT+70v8rilkdXyLpSTzSF1e94kej500hMwdi6Tvh5gegUFh5uipKfodVAk+/2eVSb5/vvrY/tHSIRJvItitpOTcWeaoUW/jDTJjWRa7Lo+4/2eed6QmWYK3i143SQikhma1vp283di1/hTXcDPTDOt/vNflZL3m2WVm0kXP23mWA7AAqPFeqzfscy0eLvTzNCBLsO9u70TpR8xBXwXvWUasiTTQ+3iYb49dZ0708w+k90avuLo9HUFSEt97MIvSXc+SLqPk5kmvdtaOrjZFPi4eFjxx3Bgo/T55eYEIqq6dOMUqWK94o/jZDxuk2jPHWHGFkumAnvXEWYKiRLIZ/b7UxZukqmi/eC/nODbweOWPrzI9Opo1le6cozdEZ0Rn9nvfdWK8dL3dxVs3aotTcJX6WzvxgTDsqRln5rpwNzpUvm6Zt7gSo1P+dQi3e9TE6S/vjIJ+P51x5bXbG9av8/qVXSFwizLjC//aYQp3iRJZWuac5Wzryz5U4+6M6XV35ohAlmfZUiEuZDf5m5zkfNkY1sbXir9/ZU070VzDiWZ/eKiJ6TGV5T8z+cMFPux/u9vpMmDze0rxkjN+3p/m5lp0tJPzf6TNWwh+izTGNSop+8m2ydT0N8gHxviRNKdD5Lu4yx8wxQBi6gs3bPMvvk5E3aYxPvABlMI5sYpvjPuaOcyM55413Jzv1IT09W2Zht74zpDPrPf053V9+1cJn14sSRLGjDNVDUuoXxmv/dFu1ZIH3czrTVNrjLdiU+sSdFtpGlJ+2m4GXbgDDLTDXV4xJ4q0YEiI0X6YYj01wRzv9FlUp/3TOvoKWS6PZr3X5wSEhLU67y6Cjmd7uUnY1nS5vnSnx+aHnNZhZoiKplxyi0H5hpfflq2/ynNeepYt/ZS5aWOj5iE1N96PHncphv//FelvavMsqAwqdaFR6f3ymtsq2XGhR/ebRaVqSJ1etQUaPP1avPFwJZj/U8jzFhqV6g0aIZU7TzvbMedaY4Fv7587GJLudqmQF6Tq0p2A0UJPSck6c4HSfdRh/dIo1qa6Syu+MB0EbNT0j5p/BWmC1tYlHT9JKl6K/viSY43LdvLPpPpSh5pirScP7jEdSXPi8/s9xRuKhl+GCIt/diMq799UZ5jR0sCn9nvfU1yvPRBRylh29FxiRMkWfl3aU3cJc14WPr3B3O/fF2p99tSrfa2vQW/Fb/ZzAm8Z5UZanPx0+ZCRwFbsQpVSO10Jew0RdeWjTX7iyQ5XKa17fzB5kLd8fGerLv0/g3S3OEmCZVM8nnBnVL7+wt0kaFEsyxp3Sxp/iumzklBhJWTOjxoPufgUt6NrwSx5Vjv8UgTbzDzYEdUkm75xdSlKcrXXzPZTP91YINZVqaKuRjV4gb/uNiS3ftxt/LuZl6yx3SX/OwBhTP3GZNwVz1PanqSqZqKS0S0uXI14X/S9j+kz/uYghR1OxdvHB6PtOJz05KT1ZW82bVm7FgJH8/qkyIK+JkWdD14x8XDpH+mmi6Qv71txtfDP3jc5sJXwjbTWnLF6GPdUvNrSYisYo7Pa6ea5Dt+ozS2p3Ruf3OsLFWu+OL3Z+tmSZNvMV26wytKV38i1elod1S5RVU1rWwXPmQuxPz5kbR1kTlm/DPVXKw7f7C5uL/p17y7S3d63PS2WDbWtJo7nKbVtvPjZ9ZiXpI4HFLD7lKDbqbS+ewnT/2cKz6QGnbzfmw4NadTuvID02Mobo2plzRo5plXp7csad1MM9f63tVmWXgFMyz0vJv9q5eR02WGTnzdX9k9OrIdvXDX/UWfSrhPB5f6A9HOZdLKo1XKe7zkO+N+SpWVbvxOqnuRqbw44X9mXu/isnO59HEXadp9JuGOOVsaOMMcREm4vaNm26MnVPm12jhMt9aabYszKpyoVFnpkufN7fmv5D0HJ0qmX16QNv4sBZUyY4RPZ/rGxr1NAc6Wg8z95Z9L77SS1kwxJ4ooHI9b+vl58xuYmiBVO99MqeiLCffxgkKkJlearrV3LDYJQUiEuVj34yPSK/VNq/2JNTwSd0lT7za9aSy3md7qjt+ky98JnIT7eA6H6TpeEFlVyuEbQstI/b40SfHuv6Tv7zyzY+GmX6WPu0pfXmsS7tAoqfOT0n1/mar3/pRwZ2nc20wLFnnCdyCyik9OF3Y6fCTbQrGxLFOIRZKa9/PemJPCCikt9fvKFGNxp5urXX995d1tJsdLPzxgCkbtXCaFlDFX0m6bL9Vq591tB7qsq5qScifeJf+qpl9p9j8zxjAz1RxDSKpKvn+nH5vnufeowtXSKFVW6vWmNOhHqUJ9MxvFNwOkr64z3Y5xepLjzXSa818298+/xVz8LcpuqsWhUmPpstelIf+YOigVGx6bMzg/rmCp/zTpuonerYZeEtALrOQqV9NcwHQGS2u+MxeqT9eOpdJnvaXPe0s7/jQzFbR/QLpvpdTxYd+YD9ybGveW7l9tesBe9bH59/5VJTrhlki6A8+qb45+gUubsWG+KChUunqs6VpmuaXvbpOWfFj02/F4TMvMqJZmug1ZpkLzPUulC+7wi7HbJYIfX9X0Kw6HmY/eGWy6uv073e6IcCb2b5C+u93cbn271OyaM3u9mm2l2xeaomrOYFN1+t3W5tjtyW/OVeSwc7kZW5/V8+CKMVLPV0tsDQVJZu7pVreY93Eq7oySWXHZG+gFVrLVbGsuOknSL89La78v2PP2rJYmXCt9dLG0+VfJFSK1uk26d6WZiuzE6eT8mdNlhjg1vdr86weNL2QVgSQtSZpzdFqwDg/mTnJ8iStI6v2OuZr3x2hpxkOmm92FDxbNj/KuldL0B48VK4k+yyQUtGzbo3FvU3TnZHORwn7RDaW295gKrT8+aubItWvWg9PlcUtbFils5zopuYH5rgfq/pWWZAr+pCVKNdpIlzxXNK8bHGamK2pypTT1XmnHEnPs/vtrU2gt0FsvT2bZZ+azcqdL5etI/xvnO7N4FIWkuAKut9e7cZQUfj62NSCc21+K+0f6/T1zgTOquplTO78CgvNekFZPlmQdrWlwndTxUalsDVvfBoqOrS3d8+fPV69evVSlShU5HA5NmTLlpOtPnjxZXbt2VXR0tCIjI9WmTRvNmjWreIL1B4veNNNLlK0pXVDAuVjt5HSaH5WOR7vD//ys9NPTZ9atNTneVGIe08kk3CFlpG4vSLcvIOG2mx9e1fRLHR6WompIiTuOdYH1dWunSm82kfPzXio790E5P+9lKqSunWp3ZMXPsqSp90j7/jEnfteMLfqqtzFnSTfNMt2KQyJM8j36QjNOOfMUXYwDTUaKmZd22r0m4W7Y01Q99qeEW6K7dGHQC6zk6/qsVPdiKSNZ+qiLmQ7r25vNv282Mb0sv79bereVmbNdlpmD/q4l0uXvknD7GVuT7iNHjqh58+Z69913C7T+/Pnz1bVrV82YMUPLli1T586d1atXL61YscLLkfqBg1ulRW+b292eLznFFxwOU700q4jTorfM+GuP+/Rex+ORlo+T3jnPFGuRJTW9Rrr7T1OMwh+mWgCKQ0i4dOnRZHvxu9LetfbGcyprp5rWolzFm3ab5YGWeP/+vpl2xhkkXfOZVKayd7bjdJpuxXf9YaYh82SYizSj20tbF3tnmyXNwS3Sx5dIK8YfnQ5s2OkXszuFYJdTw3s11kOdqyvYZeMpH92lC8dPx7YGDFeQOdeUjs1lnyVxlzmfXTHuWAHB2xZI13wqVaxf/LHC63xmnm6Hw6HvvvtOffr0Oa3nnX322erbt6+GDRtWoPUDdp7ur/ubMSW1O0j9p5bMcVPLPjOVxWVJTa42U9sUJFnetdJ029vxp7kf3ci0wOQ3HU4ACJj9Ht7z5XVmPtIabU21Yl88pmTP+bkrnxV8c85Pr9mySPqslznB6/6SdMHtxbNdy5LWTpFmPGIKrUmm4nnXEf4/93J+1s+Rvh0spR4ylY6v/sQM1/ACnzneZ10Ak5Rnd2lab1FEfGafP+VvkCRXqNT/e6lmm+KLC0UqIObp9ng8Onz4sMqXz7+wQFpamtLSjnVnS0xMzH5u1p9lWfL4c6GXLQvlXPu9LIdT1iUvmBMg37jWcnpa3CiFRMjx3a1yrJ4kKy1R1tVjTaGJbYulpD1SRGUzRtHpklIOyTHveWnpJ3JYHlkhEbI6PmqKUriCA7q4T0Ds9/Cu7iPl2PSLHNt+k2fFF2b8ma/ZskjOk53syJISd8qzZZFUq32xhWWLw7vl+GagHJZbVpOrZZ1/S/EeA8+6XKrVUY6fnpZjxefSsk9l/fejrEtfkRpdVnxx2M3yyPHry9L8l+WQJatKS1nXjJWiqnnt/8NnjveNLpOu+UyOWY/Jcdz30oqsIqvbSPO43THCL/jMPn/K3yBJ7jR53Bns+yVYQfezEp10v/rqq0pKStL//ve/fNcZOXKkRowYkWv5vn37lJqaKo/Ho4SEBFmW5Z8tfh63Kkx/SE5JyY2v1WFHtBRXwIImvii6nUK6vadys++RY/1sZY7uIGdaolzJx96Tu3Rlpda6WGEbf5QzNV6SlFKvpw5f8Kg8EZWkAwftit5n+P1+j2IQqtLn3qkyf7wqzX5S+8qfJyusrL0hWR4FHdyo4L0rFbz3L4VsX1CgMVSJO9cpNbyB18OzjTtd5af2V8iROGWUb6D41k/I2rfPnlhaP6GQal0UOX+YghK2yPH1jUqt3VWJ7Z+Sp7R/j+d1pB5S2bkPKXT7AklScuN+Smz3uJQW4rXfZbfH0oodiTpy5Ijauj0KDrK5R0eF1tK1Pylk91I5k/fJEx6t9NjzzIXyknxuAp/iK+c4YTvXqWwB1vP73yA/d/jw4QKtV2K7l0+YMEG33HKLvv/+e3Xp0iXf9fJq6a5evboOHjyY3b183759io6O9s/kY+kncs54UFZYWVl3L/Of6Qa2LpLji6vlyEyVpZyjxI6/b1VsKKvHKwHdlTwvfr/fo3i40+UY01GOff/KOneArMveLN7tJ8dLO5fKsWOpKYy4c6kcaQX78Tuep/80v27pdvz4iBx/figrNFLWLb+Y6th2y0iRY8Gr0m9vy+HJlBVaRtbFw6WWA834Zn+z+y85vukvx6FtsoJKybrsdanZtV7fbHJ6ppoMnyNJ+nvYxYoIK8HTjwEF5DPnOFsWmsKdp+Dvv0H+LjExUeXKlfPP7uVfffWVBg8erG+++eakCbckhYaGKjQ0NNdyp9OZ/UV0OBw57vuNlINmfkBJjs6PyxFR0eaAilDNtmY6sczUXGVZsu+HRslx669yhJQq5uBKBr/d71F8nGFSz9elsZfKsfwzOVrcKFU/3zvbcmdIe9eY2gw7lpp/4zfmXi84XKpyrlTtPKnqeaaeQ9Je5RxDepwysXLWamcKf/mjvyZKf34oSXJc+aEcFevZHNBRoaWlLk9LTa6Spt0rx85lcsx4UFo9Ser1thTtR60+y8eZKSrdaVK5WnL0HS9H5abFsunjj+8c7xFIfOIcp1Y7Uzckcbfy/g0ydUX8+jcoABR0HytxSfeXX36pm266SV999ZV69uxpdzi+bd5LUkq8mYP6vJvtjqZobf1NOnKK7pFpCab1i1ZuwHtqtZOaXyf9NUGa/oB0yzxTsfVMHd5jEuvtS0ySvWuFlJmSe70K9aVq55sku9r5UkzjE7Zv5TPX7VEOl5lKMaramcfsa/asOlp8UlKHR6SG3e2NJy+Vm0g3z5GWjJHmPmvqc4xuJ134kNT+ASmoBLfMZqRKPz4sLf/c3G/Q3RQALVXO3rgAFA/mW8dxbE26k5KStGHDhuz7mzdv1sqVK1W+fHnVqFFDQ4cO1c6dO/X55+YHa8KECRowYIDeeusttW7dWnv27JEklSpVSlFRAVoBNT9x/5qTGEnqPrJoToJ9SdLeol0PQOF1fUb6b4ZJ8pZ8IFVuZr57EZVMr5RTnVBkpEp7/j7ain20JTthe+71QqOkai2laq1Mgl313FMPmcma63bmozkryJaOMS2PiTvM/KnXfS3FNjv99+6rUg5KE28wFyrqdZE6PWZ3RPlzuqQL7pAa9TQtwutnS/NeMFOb9XpbqtH62Loet7noejr7V3E4Ma7IqtKkQdLulZIc0kVPSO0fpDULCDT5/QZFVjEJNxX7A4atmdjSpUvVuXPn7PtDhgyRJA0YMEBjx47V7t27tW3btuzHx4wZo8zMTN1111266667spdnrY+jLEuaNdRMC9Owp1S386mfU9JEFLDgTkHXA1B4EdGmq/APD0iznlCOq/mRVcyV/qwTC8uSDm07LsH+U9r9t5nH+XgOp2m1zmrBrna+adUuTNLSuLfUqKc8WxYpcec6RVZtYLrzJe6SvrhG2veP9GkP6X+fmQS1pPN4pMm3mnmgy9aUrvzQNxLTUylbw1z8WP2t9OOj0r5/pU+6SecPNnNYb5qXz4nrS/aeuK6dmjuurFatUuWlqz6S6l1sV3QA7Hb0N8gnLxii2PhMIbXiEhDzdP83U/qyr5lK687fpQp17Y6o6GXPfXjycTIBM//uafLL/R72WvO99E3/PB44mnw0/Z+UfsQk2UfyqFIcXlGq3upYkl2lhanbUITy3O9TDpkW4S0LTFfzy96QWg4o0u0Wu3kvSvNGSkFh0s2zpdjmdkd0+pLjpdlPSiu/MPfDykmpec08YfMcz9lzT+dzKnXZG9J5NxVrSMdLTs9U42GzJEmrh3elkBoCAuc4KE4BMU838pCZblq5JemCO/0z4ZYYJwP4Eo9bmpVf9+Wj381VXx9b5AwyXdCzWrCrnSeVqyU5TiyLWAxKlZVumCxNvUf6+ytp2r2mJf6iJ+2J50ytm22SbskkfCUx4ZbMsIE+70nN/idNvU86tCWfFY/OVzHzMdOSlNcx37Iky2OK8Xkyjv6baf6ybmc95smU3JnHrZdh9u/s52YeWy8zTfr5WeWbcMshzX9VOncAv0UAEOBIuv3NH6Ol+E2m60qHh+yOxrsYJwP4hq2/ndC1Nh/n3Sw162vGTgf70KwCQSGmwFXZGtL8l6UFr5ox5b3fKVmFvOI3SZMHS7LMZ33OdXZHdObqdJJ6viZ9cdVJVrKkxJ3Sqw1McptX4myLo3Ft/c22gp5BTqce695QSUlJCqLFDwBsQ9LtT5LipF9fNrcvfrrIu2b6JMbJAPYraMHCmm1zFsXyJY6jxa7K1jAVv/+eaC4k9B1vWsN9XXqyNLG/lJpgeg90f9HuiIpO6qGCrZe8//Re1xksuYJNzwtn0NHbwabwqDPouNv5rJe0V9q1/NTbsbGgZ0iQU7d2qKO4uDiFBJF0A4BdSLr9ydxnpPTDZixk8352R1N8nC6mBQPs5E+FDc+90fSW+XqAGef9STfp+m9MMu6rLEv64X5p7yqpdLTpAVSSWuhPpaD7Tc83zDzx2UmyK2fCnJ04H33sTIcPbF4gfXZZ0cUPAPBbXPb0F7tWSCvGm9s9XmZaEgDFp2Zbk6gqvyTGYaZQqtm2OKMqvHoXSzf9KJWpYipof9RF2rXS7qjy9+dHpmXe4ZKu/vTo/4UfKej+1XKAVLmpFNPI1DMpV0uKqipFxJgx4qFlzLAGV1DRjNcvAfu922Pprx2HtHbPEbk9AVU3FwB8CpmZP7As6cfHlF0huHoruyMCEEiyChtKyp2AlNDChpWbSoN/kmLONt2DP73UFCnzNdv+MEXEJKnrCP/s9eOr+5evxnWctEy3rnhvsW766l+lZbptiwMAAh1Jtz9Y/a20/XcpOFzqMtzuaAAEoqzChpGxOZdHVrFvOqczFVXVtHjX6SRlHDFTMS79xO6ojjm818zg4MmUzr5CanO33RF5j6/uX74aFwDApzCmu6RLPyLNGWZutx9iThIBwA7+WNgwLEq6fpIprrbyC+mHB45OKTbM3mE87gzpm4FS0h4pupGptF4Spzg7Hb66f/lqXAAAn0HSXdItestMSRJVQ2rrx60cAEoGfyxs6AqWLn9XKltTmveCtPANk3j3eV8KCrUnpjnDpG2/SSFlTIX10Ah74ihuvrp/+WpcAACfQPfykuzQNpN0S9Ilz/rWvLcA4E8cDqnToybRdgaZYT3jrpCS44s/llWTpN/fM7eveF+qWL/4YwAAAAVG0l2SzRkmZaZKNdtLjS+3OxoA8H/nXCfd8K0UGiltXWSmFDu4pfi2v3etNPUec7v9A9JZvYpv2wAAoFBIukuqLYukNd9JDqfU40X/H8sHAL6iTifppplmOqj968yUYjuXeX+7qQnSxBukjGQTw0VPeX+bAADgjJF0l0QetzTzUXO75UAztQ0AoPhUOttMKVapqXRknzT2MunfGd7bnscjfXe7FL9RiqouXfUJhbpwSkFOp+69qJ5ubh2rIDsL/wFAgOMIXBKtGCftWWWq6nZ+wu5oACAwRVYxU4rV62JanydeLy350DvbWvia9N8MyRVipqIqXcE724FfCQly6v4u9XVLmyoKCeKUDwDswhG4pEk5JM191tzuNFQqXdHWcAAgoIWWkfp9JZ3bX7I80oyHpNlPmpbporLhJ+nn583tS1+Vqp5bdK8NAAC8jqS7pJn/ipS8X6rYQDp/sN3RAABcwVKvt4+Nsf5tlDRpkJSReuavfXCr9O1gSZZJ7FsOOPPXRMDweCyt23tYmw6kyOOx7A4HAAIWSXdJsn+99Mdoc7v7SHOiBwCwn8MhdXhIuvJDyRksrZ0ifX75mU0plpEifX2jlHJQqtJC6vFKkYWLwJCa6Vb3txbqunFrlZrptjscAAhYJN0lyazHJU+m1KC7GUMIAPAtzf4n3ThZCo2Stv9uKpvHbzr917EsafpD0u6/pPAK0v/GScFhRR8vAADwOpLukmLdbGn9bNOCcsnzdkcDAMhP7Q7SzbOlqBqm2vhHXaUdS0/vNZZ9Kq0cb6aFvPoTqWx178QKAAC8jqS7JMhMl2YNNbcvuF2qWM/eeAAAJxfTyEwpFtvc1OEYe5n0zw8Fe+6OpdKMR8zti54yc3IDAIASi6S7JFgyRjqwQSodLXV42O5oAAAFUaaSNHCGVL+blJkiTbxB+n30yZ+TtE/6ur/kyZAaXSa1f6B4YgUAAF5D0u3rkvZJv75kbl88zMzNDQAoGUIjpGsnSOfdJMmSZj4qzXz82JRiHre0eYG0apK0cZ70zUApcadUob7U531ToA0AAJRoQXYHgDx43NLW36SkvdLfX0tpiaaL4jnX2x0ZAOB0uYKknq9LZWtKPz0t/f6ulLBNatxHmvOUlLjrhPVDpb7jpbBIW8IFAABFi6Tb16ydalpCTjwJa9RLcrrsiQkAcGYcDqn9/VJUNWnKHdI/08xfXtxp0v51Zlw4cAaCnE7dcmFtJScnK8hJ50YAsAtHYF+ydqoZy3diwi1JvzxvHgcAlFxNr5au/1bSybqNO6SZj5leT8AZCAlyamiPRrrnwmoKCeKUDwDswhHYV3jcpoVbVv7rcBIGACWfw6GTHutlmXHdW38rrogAAIAXkXT7iq2/5d3CnY2TMADwC0l7i3Y9IB8ej6UdB5O1KyFNHs/JLvQAALyJMd2+gpMwAAgMEZWKdj0gH6mZbnV45VdJ0urhsYoIojYMANiBlm5fwUkYAASGmm2lyCrKf1y3Q4qsatYDAAAlHkm3r+AkDAACg9MldX/p6J0Tj/lH73d/kRkrAADwEyTdvoKTMAAIHI17S//7XIqMzbk8sopZ3ri3PXEBAIAix5huX5J1EnbiPN2RVUzCzUkYAPiPxr2lRj1NgcykvWb4UM22XFwFAMDPkHT7Gk7CACBwOF1S7QvtjgIAAHgRSbcv4iQMAAAAAPwCSTcAAIAfcjkduqF1DaWkpMjlzK9QKwDA20i6AQAA/FBokEvPXH624uLiFMoc3QBgG6qXAwAAAADgJSTdAAAAfsiyLB1IStPB5AxZlmV3OAAQsOheDgAA4IdSMtw6/4WfJUmrh1dShIsu5gBgB1q6AQAAAADwEpJuAAAAAAC8hKQbAAAAAAAvIekGAAAAAMBLSLoBAAAAAPASkm4AAAAAALyEKcMAAAD8kMvp0FXnVlVKSqpcTofd4QBAwCLpBgAA8EOhQS69cnUzxcXFKTSIOboBwC50LwcAAAAAwEtIugEAAPyQZVlKTs9USoZblmXZHQ4ABCySbgAAAD+UkuFWk+Fz1PndlUrJcNsdDgAELJJuAAAAAAC8hKQbAAAAAAAvIekGAAAAAMBLSLoBAAAAAPASkm4AAAAAALyEpBsAAAAAAC8JsjsAAAAAFD2nw6EeTSorLS1VLofD7nAAIGCRdAMAAPihsGCX3r2uheLi4hQa7LI7HAAIWHQvBwAAAADAS0i6AQAAAADwEpJuAAAAP5Scnqk6j/+oC95cpuT0TLvDAYCARdINAAAAAICXkHQDAAAAAOAlJN0AAAAAAHgJSTcAAAAAAF5C0g0AAAAAgJeQdAMAAAAA4CVBdgcAAACAoud0ONSpYbTS09LkcjjsDgcAAhZJNwAAgB8KC3bpkwHnKS4uTqHBLrvDAYCARfdyAAAAAAC8hKQbAAAAAAAvIekGAADwQ8npmTr76dnq9M4KJadn2h0OAAQsxnQDAAD4qZQMt90hAEDAo6UbAAAAAAAvIekGAAAAAMBLSLoBAAAAAPASkm4AAAAAALyEpBsAAAAAAC+hejkAAIAfcjocal27vNLT0+V0OOwOBwACFkk3AACAHwoLdunLW1orLi5OYcEuu8MBgIBF93IAAAAAALyEpBsAAAAAAC8h6QYAAPBDyemZOu+5n9T9g7+UnJ5pdzgAELAY0w0AAOCn4pMz7A4BAAIeLd0AAAAAAHgJSTcAAAAAAF5C0g0AAAAAgJeQdAMAAAAA4CUk3QAAAAAAeAnVywEAAPyQ0+FQ06pRyszMkNPhsDscAAhYJN0AAAB+KCzYpe/vaqu4uDiFBbvsDgcAAhbdywEAAAAA8BKSbgAAAAAAvISkGwAAwA+lpLt14cvz1OfjVUpJd9sdDgAELMZ0AwAA+CFLlnYeSsm+DQCwBy3dAAAAAAB4CUk3AAAAAABeQtINAAAAAICXkHQDAAAAAOAlJN0AAAAAAHiJrUn3/Pnz1atXL1WpUkUOh0NTpkw55XPmzZunc889V6GhoapXr57Gjh3r9TgBAABKGoccqh8Todrlw+SQw+5wACBg2Zp0HzlyRM2bN9e7775boPU3b96snj17qnPnzlq5cqXuv/9+DR48WLNmzfJypAAAACVLqRCXZt1/ob7sf7ZKhbjsDgcAApat83T36NFDPXr0KPD6o0ePVu3atfXaa69Jks466ywtXLhQb7zxhrp16+atMAEAAAAAKBRbk+7TtXjxYnXp0iXHsm7duun+++/P9zlpaWlKS0vLvp+YmChJ8ng82X+WZcnj8XglZsAXsd8jELHfIxCx3yPQsM+jOBV0PytRSfeePXtUqVKlHMsqVaqkxMREpaSkqFSpUrmeM3LkSI0YMSLX8n379ik1NVUej0cJCQmyLEtOJ3XlEBjY7xGI2O8RaFIzPBr05T9yu90ae11jhYeWqNM+oFA41qM4HT58uEDr+f3Rd+jQoRoyZEj2/cTERFWvXl3R0dGKjIyUx+ORw+FQdHQ0X0wEDPZ7BCL2ewSa5PRMbY5PlSRVjK6oiLAQmyMCvI9jPYpTWFhYgdYrUUl35cqVtXfv3hzL9u7dq8jIyDxbuSUpNDRUoaGhuZY7nc7sL6LD4chxHwgE7PcIROz3CCTH7+fs9wgkHOtRXAq6j5WoPbFNmzaaO3dujmVz5sxRmzZtbIoIAAAAAID82Zp0JyUlaeXKlVq5cqUkMyXYypUrtW3bNkmma3j//v2z17/99tu1adMmPfLII/r333/13nvv6euvv9YDDzxgR/gAAAAAAJyUrUn30qVL1aJFC7Vo0UKSNGTIELVo0ULDhg2TJO3evTs7AZek2rVra/r06ZozZ46aN2+u1157TR999BHThQEAAAAAfJKtY7o7deoky7LyfXzs2LF5PmfFihVejAoAAAAAgKJRogqpAQAAoGAccqhq2VJyu91yyGF3OAAQsEi6AQAA/FCpEJcWPNJJcXFxKhXisjscAAhYJap6OQAAAAAAJQlJNwAAAAAAXkL3cgAAAD+UmuHWNaMXKzMzQ9/eWUHhobS1AIAdSLoBAAD8kMeytGpnQvZtAIA9uOQJAAAAAICXkHQDAAAAAOAlJN0AAAAAAHgJSTcAAAAAAF5C0g0AAAAAgJdQvRwAAMBPlQ8PpnI5ANiMpBsAAMAPhYcEaemTXRQXF6fwEE75AMAudC8HAAAAAMBLSLoBAAAAAPASkm4AAAA/lJrhVr8P/9Ad3/yn1Ay33eEAQMBigA8AAIAf8liW/tgcn30bAGAPWroBAAAAAPASkm4AAAAAALyEpBsAAAAAAC8h6QYAAAAAwEtIugEAAAAA8BKqlwMAAPipUsEuWVQuBwBbkXQDAAD4ofCQIK0ZcYni4uIUHsIpHwDYhe7lAAAAAAB4CUk3AAAAAABeQtINAADgh1Iz3Lrps6UaMmW90jLcdocDAAGLAT4AAAB+yGNZmvffPkmSm2JqAGAbWroBAAAAAPASkm4AAAAAALyEpBsAAAAAAC8h6QYAAAAAwEtIugEAAAAA8BKSbgAAAAAAvIQpwwAAAPxQeEiQNr3QQ3FxcQoP4ZQPAOxSqJbu7du3a8eOHdn3lyxZovvvv19jxowpssAAAAAAACjpCpV0X3fddfrll18kSXv27FHXrl21ZMkSPfHEE3rmmWeKNEAAAAAAAEqqQiXdq1evVqtWrSRJX3/9tZo0aaLffvtNX3zxhcaOHVuU8QEAAKAQUjPcumvCCj0+faPSMtx2hwMAAatQA3wyMjIUGhoqSfrpp5/Uu3dvSVKjRo20e/fuoosOAAAAheKxLP24eo8kyW1ZNkcDAIGrUC3dZ599tkaPHq0FCxZozpw56t69uyRp165dqlChQpEGCAAAAABASVWopPull17SBx98oE6dOqlfv35q3ry5JGnq1KnZ3c4BAAAAAAh0hepe3qlTJ+3fv1+JiYkqV65c9vJbb71V4eHhRRYcAAAAAAAlWaFaulNSUpSWlpadcG/dulVvvvmm/vvvP8XExBRpgAAAAAAAlFSFSrovv/xyff7555KkQ4cOqXXr1nrttdfUp08fvf/++0UaIAAAAAAAJVWhku7ly5frwgsvlCRNmjRJlSpV0tatW/X555/r7bffLtIAAQAAAAAoqQo1pjs5OVllypSRJM2ePVtXXnmlnE6nLrjgAm3durVIAwQAAMDpKxXs0urhXbVv3z6VCnbZHQ4ABKxCtXTXq1dPU6ZM0fbt2zVr1ixdcsklkqS4uDhFRkYWaYAAAAA4fQ6HQ+EhQSoV7JLD4bA7HAAIWIVKuocNG6aHHnpItWrVUqtWrdSmTRtJptW7RYsWRRogAAAAAAAlVaG6l1999dVq3769du/enT1HtyRdfPHFuuKKK4osOAAAABROWqZbj09epZSUVL3Wr4JKhRSqrQUAcIYKlXRLUuXKlVW5cmXt2LFDklStWjW1atWqyAIDAABA4bk9lr5dvlOS9LLHsjkaAAhchbrk6fF49MwzzygqKko1a9ZUzZo1VbZsWT377LPyeDxFHSMAAAAAACVSoVq6n3jiCX388cd68cUX1a5dO0nSwoULNXz4cKWmpur5558v0iABAAAAACiJCpV0f/bZZ/roo4/Uu3fv7GXNmjVT1apVdeedd5J0AwAAAACgQnYvj4+PV6NGjXItb9SokeLj4884KAAAAAAA/EGhku7mzZvrnXfeybX8nXfeUbNmzc44KAAAAAAA/EGhupe//PLL6tmzp3766afsOboXL16s7du3a8aMGUUaIAAAAAAAJVWhWro7duyodevW6YorrtChQ4d06NAhXXnllVqzZo3GjRtX1DECAADgNJUKdunPxy/Sj7c2U6lgl93hAEDAKvQ83VWqVMlVMO2vv/7Sxx9/rDFjxpxxYAAAACg8h8OhChGhcicHy+Fw2B0OAASsQrV0AwAAAACAUyPpBgAA8ENpmW4N+36NXvl5m9Iy3XaHAwABi6QbAADAD7k9lsb/sU3f/r1Pbo9ldzgAELBOa0z3lVdeedLHDx06dCaxAAAAAADgV04r6Y6Kijrl4/379z+jgAAAAAAA8BenlXR/+umn3ooDAAAAAAC/w5huAAAAAAC8hKQbAAAAAAAvIekGAAAAAMBLTmtMNwAAAEqGsCCX5j/cUfv3H1BYkMvucAAgYJF0AwAA+CGn06Fq5cIVkpEkp9NhdzgAELDoXg4AAAAAgJeQdAMAAPih9EyPRv74r0Yt2KH0TI/d4QBAwCLpBgAA8EOZHo8+XLBZXyzbq0wPSTcA2IWkGwAAAAAALyHpBgAAAADAS0i6AQAAAADwEpJuAAAAAAC8hKQbAAAAAAAvIekGAAAAAMBLguwOAAAAAEUvLMilmfe1V3x8vMKCXHaHAwABi6QbAADADzmdDjWoVEZxjhQ5nQ67wwGAgEX3cgAAAAAAvISkGwAAwA+lZ3r05k/r9eHiXUrP9NgdDgAELJJuAAAAP5Tp8ejtnzfo4z92K9ND0g0AdiHpBgAAAADAS0i6AQAAAADwEpJuAAAAAAC8hKQbAAAAAAAvIekGAAAAAMBLSLoBAAAAAPCSILsDAAAAQNELDXLpuzvb6GD8QYUGuewOBwACFkk3AACAH3I5HWperaziQtLlcjrsDgcAAhbdywEAAAAA8BKSbgAAAD+UnunRmPmbNH7pHqVneuwOBwACFkk3AACAH8r0ePTizP/0zsKdyvSQdAOAXUi6AQAAAADwEpJuAAAAAAC8hKQbAAAAAAAvIekGAAAAAMBLSLoBAAAAAPASkm4AAAAAALzE9qT73XffVa1atRQWFqbWrVtryZIlJ13/zTffVMOGDVWqVClVr15dDzzwgFJTU4spWgAAgJIhNMilCYNb6d2rGig0yGV3OAAQsILs3PjEiRM1ZMgQjR49Wq1bt9abb76pbt266b///lNMTEyu9SdMmKDHHntMn3zyidq2bat169Zp4MCBcjgcev311214BwAAAL7J5XTogjoVFBfnlsvpsDscAAhYtrZ0v/7667rllls0aNAgNW7cWKNHj1Z4eLg++eSTPNf/7bff1K5dO1133XWqVauWLrnkEvXr1++UreMAAAAAANjBtpbu9PR0LVu2TEOHDs1e5nQ61aVLFy1evDjP57Rt21bjx4/XkiVL1KpVK23atEkzZszQjTfemO920tLSlJaWln0/MTFRkuTxeLL/LMuSx+MponcG+D72ewQi9nsEmgy3RxP+2KakpCQN7lxeocG2dnAEigXHehSngu5nth199+/fL7fbrUqVKuVYXqlSJf377795Pue6667T/v371b59e1mWpczMTN1+++16/PHH893OyJEjNWLEiFzL9+3bp9TUVHk8HiUkJMiyLDmdtg9xB4oF+z0CEfs9Ak1KhlsjfvhHknTpWeVUOjTY5ogA7+NYj+J0+PDhAq1Xoi55zps3Ty+88ILee+89tW7dWhs2bNB9992nZ599Vk899VSezxk6dKiGDBmSfT8xMVHVq1dXdHS0IiMj5fF45HA4FB0dzRcTAYP9HoGI/R6BJjk9M/t2dHS0IsJCbIwGKB4c61GcwsLCCrSebUl3xYoV5XK5tHfv3hzL9+7dq8qVK+f5nKeeeko33nijBg8eLElq2rSpjhw5oltvvVVPPPFEnl+s0NBQhYaG5lrudDqz13c4HDnuA4GA/R6BiP0egeT4/Zz9HoGEYz2KS0H3Mdv2xJCQELVs2VJz587NXubxeDR37ly1adMmz+ckJyfnemMul5kCw7Is7wULAAAAAEAh2Nq9fMiQIRowYIDOO+88tWrVSm+++aaOHDmiQYMGSZL69++vqlWrauTIkZKkXr166fXXX1eLFi2yu5c/9dRT6tWrV3byDQAAAACAr7A16e7bt6/27dunYcOGac+ePTrnnHM0c+bM7OJq27Zty9Gy/eSTT8rhcOjJJ5/Uzp07FR0drV69eun555+36y0AAAAAAJAvhxVg/bITExMVFRWlhISE7EJqcXFxiomJYdwHAgb7PQIR+z0CTXJ6phoPmyVJWj28K4XUEBA41qM4nZhb5qdEVS8HAABAwYS4nPqof0slJCQoxEXyAQB2IekGAADwQ0Eupy5qFKO4OHMbAGAPjsAAAAAAAHgJSTcAAIAfynB7NGnZDv2wZr8y3B67wwGAgEX3cgAAAD+U4fbokW9XSZKubddAocE2BwQAAYqWbgAAAAAAvISkGwAAAAAALyHpBgAAAADAS0i6AQAAAADwEpJuAAAAAAC8hKQbAAAAAAAvYcowAAAAPxTicuqdfucoISFRIS7aWQDALiTdAAAAfijI5dSlTWMVF+dSEEk3ANiGIzAAAAAAAF5C0g0AAOCHMt0ezVi1W3PXHVSm22N3OAAQsOheDgAA4IfS3R7d/eVKSdLlreoqJNjeeAAgUNHSDQAAAACAl5B0AwAAAADgJSTdAAAAAAB4CUk3AAAAAABeQtINAAAAAICXkHQDAAAAAOAlTBkGAADgh4JdTr18VVMlJiYq2EU7CwDYhaQbAADADwW7nLq6ZTXFxcWRdAOAjTgCAwAAAADgJSTdAAAAfijT7dHP/8Zp0eYEZbo9docDAAGL7uUAAAB+KN3t0eDPl0mSurWorZBgmwMCgABFSzcAAAAAAF5C0g0AAAAAgJeQdAMAAAAA4CUk3QAAAAAAeAlJNwAAAAAAXkLSDQAAAACAlzBlGAAAgB8Kdjk1vFdjJSUdVrCLdhYAsAtJNwAAgB8KdjnVv01NxcXFkXQDgI04AgMAAAAA4CUk3QAAAH7I7bH0+6YDWrb9sNwey+5wACBg0b0cAADAD6VlunXdR0skSaub1lRwkMvmiAAgMNHSDQAAAACAl5B0AwAAAADgJSTdAAAAAAB4CUk3AAAAAABeQtINAAAAAICXkHQDAAAAAOAlTBkGAADgh4KcTj3WvaGSkpIU5KSdBQDsQtINAADgh0KCnLq1Qx3FxcUpJIikGwDswhEYAAAAAAAvIekGAADwQ26Ppb92HNLaPUfk9lh2hwMAAYvu5QAAAH4oLdOtK95bLElaPby6goNcNkcEAIGJlm4AAAAAALyEpBsAAAAAAC8h6QYAAAAAwEtIugEAAAAA8BKSbgAAAAAAvISkGwAAAAAAL2HKMAAAAD8U5HTq3ovq6ciRIwpy0s4CAHYh6fZBbo+lJZvjFXc4VTFlwtSqdnm5nA67wwIAACVISJBT93epr7i4OIUEkXQDgF1Iun3MzNW7NWLaWu1OSM1eFhsVpqd7NVb3JrE2RgYAAAAAOF1c9vQhM1fv1h3jl+dIuCVpT0Kq7hi/XDNX77YpMgAAUNJ4PJbW7T2sTQdS5PFYdocDAAGLpNtHuD2WRkxbq7x+ErOWjZi2Vm5+NAEAQAGkZrrV/a2Fum7cWqVmuu0OBwACFkm3j1iyOT5XC/fxLEm7E1K1ZHN88QUFAAAAADgjJN0+Iu5w/gl3YdYDAAAAANiPpNtHxJQJK9L1AAAAAAD2I+n2Ea1ql1dsVJhONTHYjFW7lJLOuCwAAAAAKAlIun2Ey+nQ070aS1KuxPv4++N+36ZL316gZVsPFltsAAAAAIDCIen2Id2bxOr9G85V5aicXcgrR4Vp9A3n6rObWqlyZJg27z+ia0b/ppdm/qs0qpECAAAAgM8KsjsA5NS9Say6Nq6sJZvjFXc4VTFlwtSqdnm5nKa9e9b9HTRi2hpNXrFT78/bqF/+jdNr/2uus6tE2Rw5AADwJUFOp265sLaSk5MV5KSdBQDsQtLtg1xOh9rUrZDnY1HhwXq97zm65OxKeuK71fp3z2H1eXeR7ru4vm7vWFdBLn5UAQCAFBLk1NAejRQXF6eQIM4PAMAuHIFLqO5NYjXrgQ66pHElZbgtvTp7na4avVgb9yXZHRoAAAAA4CiS7hKsYkSoPrixpV7/X3OVCQvSX9sP6dK3FuiThZvl8Vh2hwcAAGzk8VjacTBZuxLSOC8AABuRdJdwDodDV55bTbMf6KAL61dUWqZHz/ywVtd99Lu2xyfbHR4AALBJaqZbHV75VVd+ulqpFF4FANuQdPuJ2KhS+vymVnq2TxOVCnbp903x6vHWAk38c5ssi6vbAAAAAGAHkm4/4nA4dOMFNfXjfRfqvJrllJSWqUe/XaWbP1uquMRUu8MDAAAAgIBD0u2HalUsrYm3tdHQHo0U4nLq53/jdMmb8zXtr112hwYAAAAAAYWk20+5nA7d1rGufri3vZpUjdSh5Azd8+UK3T1huQ4eSbc7PAAAAAAICCTdfq5BpTL67s52uvfi+nI5Hfrh79265M35+vnfvXaHBgAAAAB+j6Q7AAS7nBrStYG+u7Ot6sVEaN/hNN00dqkemfSXDqdm2B0eAAAAAPgtku4A0qxaWf1wT3sNbl9bDof09dId6v7mAv22cb/doQEAgCLmcjp0Q+sauqpZtFxOh93hAEDAIukOMGHBLj15WWN9dcsFql6+lHYeStF1H/6hEdPWKCWdOTwBAPAXoUEuPXP52Xr4ohoKDXLZHQ4ABCyS7gDVuk4F/XhfB/VrVUOS9OmiLer59gKt2HbQ5shOn9tjafHGA/p+5U4t3nhAbg/zkgMAAADwDUF2BwD7RIQGaeSVTdXt7Ep69Nu/tWn/EV31/m+6o1Nd3XdxA4UE+f41mZmrd2vEtLXanXBsHvLYqDA93auxujeJtTEyAADsZVmWDiSl6WByhqItLkgDgF18P6uC13VqGKPZ93dUn3OqyGNJ7/6yUZe/u0j/7E60O7STmrl6t+4YvzxHwi1JexJSdcf45Zq5erdNkQEAYL+UDLfOf+Fn9Rjzt1IyGEIGAHYh6YYkKSo8WG9e20LvXX+uyoUH65/dier9zkK9N2+DMt0eSb7VjdvtsTRi2lrlFUHWshHT1tLVHAAAAICt6F6OHC5tGqvza5XX0Mmr9NM/e/XyzP80Z+1eXX5OFX3w6yZbunFnuD1KTMlQYmqmElMylJCSoaVb43O1cB/PkrQ7IVVLNserTd0KXo0PAAAAAPJD0o1cosuE6sP+LfXt8p0aMXWNVmw7pBXbDuVaL6sb9/s3nHvSxNuyLB1JdyshJSM7ac5KonMsSz26PCUz+35CSoaSz6Cqetzh/BNzAAAAAPA2km7kyeFw6OqW1dSqdnl1ee1XpR/tYn68rI7bD0/6W8u2HVRSamauhDkruS6Kbt4RoUGKKhWsMmFBckj6Z8/hUz5n16EUeTyWnMxPCgAAAMAGJN04qZ0HU/JMuI93ODVTH87ffMrXCnY5FFUqWJGlghUZFpx9O6pU0An3j3/cPFYmLEhBrmMlCNweS+1f+ll7ElLzHNed5aWZ/2nSsh26qX1tXdmimkqFME8pAAAAgOJD0o2TKmj37M4No9WiRrnsRDnqhMQ6MixYYcFOORxF0+Lscjr0dK/GumP8cjmkHIl31v2LG8Xoj83x2rjviJ74brVemfWfrmtVQ/3b1FLlqLAiiQMAAAAAToakGycVU6ZgyemtHeoWe8Gy7k1i9f4N5+aap7vycQXeDqdm6OulOzT2t83aHp+i9+Zt1Jj5m3RZs1jd1L62mlUrW6wxAwBQXFxOh646t6pSUlLlYpgVANjGYVlWQM2plJiYqKioKCUkJCgyMlIej0dxcXGKiYmR08kMaic6VTduh0ySu/DRi2z7QXd7LC3ZHK+4w6mKKROmVrXL54rF7bE0Z+1efbJos5Zsjs9efn6tcrq5fW11bVw5oE5I2O8RiNjvEYjY7xFo2OdRnE7MLfNDSzdO6lTduCXp6V6NbU1YXU7HKVvZXU6HujeprO5NKmvVjgR9smizpv21S39uOag/txxUtXKlNLBtLfU9v7rKhAUXU+QAAAAA/B2Xf3BKWd24TxwHXTkq7JTThfmiptWi9Ebfc7TosYt0d+d6KhcerB0HU/Tc9H/UZuTPGjFtjbYdSLY7TAAAzohlWUpOz1RKhlsB1rERAHwK3cvpglJgBenGXRKlpLs1ZeVOfbJws9bHJUmSnA6pa+NKuqldbbWqXb7ICsD5CvZ7BCL2ewSa5PRMNR42S5K0enhXRYSF2BwR4H0c61Gc6F6OIleQbtwlUakQl/q1qqFrz6+u+ev36+OFmzV/3T7NWrNXs9bsVZOqkbq5fW31bFpFIUEcvAEAAAAUHBkEcJTD4VDHBtH6/KZWmvNAB/VrVUOhQU6t3pmoByb+pfYv/ax3fl6v+CPpdocKAAAAoIQg6QbyUL9SGY28sqkWD71YD3drqJgyoYo7nKZXZ69Tm5FzNXTyKq3fe9juMAEAAAD4OLqXAydRvnSI7upcT7dcWEfTV+3Sxws3a/XORH25ZJu+XLJNHRpE66Z2tdSxQXSucd/+OgYeAAAAQMGRdAMFEBLk1BUtqqnPOVX155aD+njhJs1eu1fz1+3T/HX7VC8mQje1q60rz62qsGCXZq7erRHT1mp3Qmr2a8RGhenpXo1LXLV3AAAAAIVH0g2cBofDoVa1y6tV7fLadiBZY3/boq+XbteGuCQ9/t0qvTLrX7WuU14zV+/N9dw9Cam6Y/zyEjnNGgAAAIDCYUw3UEg1KoRrWK/GWjz0Ij11WWNVL19KB5Mz8ky4JSlrbr4R09bK7QmomfoAADZwOhzq0aSyLqpfVi4/m/oSAEoS25Pud999V7Vq1VJYWJhat26tJUuWnHT9Q4cO6a677lJsbKxCQ0PVoEEDzZgxo5iiBXIrExasm9vX1ryHOuuBLvVPuq4laXdCqpZsji+e4AAAASss2KV3r2uhF3rWVWiwy+5wACBg2Zp0T5w4UUOGDNHTTz+t5cuXq3nz5urWrZvi4uLyXD89PV1du3bVli1bNGnSJP3333/68MMPVbVq1WKOHMjN5XSoVsXSBVp3474kL0cDAAAAwBfYOqb79ddf1y233KJBgwZJkkaPHq3p06frk08+0WOPPZZr/U8++UTx8fH67bffFBwcLEmqVatWcYYMnFRMmbACrTfs+9WatWaPLmsWq+5nxyoqPNjLkQEAAACwg21Jd3p6upYtW6ahQ4dmL3M6nerSpYsWL16c53OmTp2qNm3a6K677tL333+v6OhoXXfddXr00UflcuXdbSotLU1paWnZ9xMTEyVJHo8n+8+yLHk8niJ8dwhU59Usq8qRYdqbmKr8Rm0HuxzKcFtasH6/FqzfryenrFb7ehXVq1msLj4rRmXCvJ+As98jELHfI9Akp2eqyfA5kqS/h12siLAQmyMCvI9jPYpTQfcz25Lu/fv3y+12q1KlSjmWV6pUSf/++2+ez9m0aZN+/vlnXX/99ZoxY4Y2bNigO++8UxkZGXr66afzfM7IkSM1YsSIXMv37dun1NRUeTweJSQkyLIsOZ22D3GHH7ivQxUN/WFTvo8/06O26lUspbnrDmrOuoPauD9Fv/y3T7/8t08hLofa1o5Slwbl1L52WYUFe2efZL9HIGK/R6BJyXBn3963b5+SQ+lVBf/HsR7F6fDhwwVar0RNGebxeBQTE6MxY8bI5XKpZcuW2rlzp1555ZV8k+6hQ4dqyJAh2fcTExNVvXp1RUdHKzIyUh6PRw6HQ9HR0XwxUST6xsQoKjJKz/zwj/Yk5pyn+6meZ6l7k8qSpJYNaugRSev3HtYPq/Zo+t+7tWn/Ec3bcEjzNhxSeIhLFzWKUa9mserQoKJCg4quCA77PQIR+z0CTXJ6Zvbt6OhoWroREDjWoziFhRVsaKltSXfFihXlcrm0d2/O6ZX27t2rypUr5/mc2NhYBQcH5+hKftZZZ2nPnj1KT09XSEjuH5PQ0FCFhobmWu50OrO/iA6HI8d94Exd2qyKujWJ1ZLN8Yo7nKqYMmFqVbu8XM7cU7Y0jI1Sw9goDenaQGt3J+qHv3dr2l+7tONgin74e7d++Hu3yoQG6ZKzK+uy5rFqX6+igl1nvq+y3yMQsd8jkBy/n7PfI5BwrEdxKeg+ZlvSHRISopYtW2ru3Lnq06ePJHNlau7cubr77rvzfE67du00YcIEeTye7De4bt06xcbG5plwA3ZyOR1qU7dCgdd3OBw6u0qUzq4SpUe6NdRfOxI07a9dmv73bu1JTNW3y3fo2+U7VC48WN2bVFavZlXUuk6FPBN5AAAAAL7B1u7lQ4YM0YABA3TeeeepVatWevPNN3XkyJHsaub9+/dX1apVNXLkSEnSHXfcoXfeeUf33Xef7rnnHq1fv14vvPCC7r33XjvfBlDkHA6HzqleVudUL6snLj1LS7ce1A9/79KMVbu1PyldXy7Zri+XbFfFiFD1bFpZlzWvopY1yslJAg4AAAD4FFuT7r59+2rfvn0aNmyY9uzZo3POOUczZ87MLq62bdu2HE321atX16xZs/TAAw+oWbNmqlq1qu677z49+uijdr0FwOucToda1S6vVrXLa9hljfXH5nhN+2uXZq7Zo/1Jafps8VZ9tnirYqPC1LNprHo1r6Jm1aLkcOSdgLs9lv7YdEAbdsSrXpJLretUpLUcAAAA8BKHZVn5zWzklxITExUVFaWEhITsQmpxcXGKiYlh3AdKlPRMjxZt2K9pf+/S7DV7lZR2rGBOjfLhuqxZrC5rVkVnxZbJTsBnrt6tEdPWandCzgJvT/dqrO5NYov9PQDFieM9Ak1qhlu3j1+m9LQ0fTzoApWiejkCAMd6FKcTc8v8kHTzxYQfSM1w69d1+zTtr12a+09cjmli6kaX1mXNqqhseLCembY21/zhWW3c799wLok3/BrHewQi9nsEGvZ5FKeCJt0lasowAHkLC3ap29mV1e3sykpOz9Tcf+L0w9+79Mt/+7Rx3xG9NXd9vs+1ZBLvEdPWqmvjynQ1BwAAAIoQSXc+3G63MjIy7A4DAe7EKfIKIjwkSL2aV1Gv5lV0ODVDc9bu1eeLt2jl9oR8n2NJ2p2QqiWb40+r4joAAACAkyPpPoFlWdqzZ48OHTpkdyiAJKls2bKqXLlyvoXRTqZMWLCuPLeaXE6H7vtq5SnXf3/eBiWkZKh17fIqV5pp+ACgJEtOz1TLZ3+SZVla+uTFigjjuA4AdiDpPkFWwh0TE6Pw8PBCJTpAUbAsS8nJyYqLi5MkxcYWfrx1TJmwAq03f/1+zV+/Xw6H1KhypC6oU14X1Kmg1rXLq2x4YJ6suT2WlmyOV9zhVMWUCVOr2uXpgg+gxDi+xgcAwB4k3cdxu93ZCXeFCnSxhf1KlSolSdkFQU63q3mWVrXLKzYqTHsSUnMVUstSLjxYlzaN1ZLN8Vofl6R/difqn92J+nTRloBNwqn2DgAAgDNF0n2crDHc4eHhNkcCHJO1P2ZkZBQ66XY5HXq6V2PdMX65HFKOxDurzXbklU2zE8l9h9O0ZHO8Fm/ar983xWtDHkn4WZUjdUGdCrqgjplD3N+S8Jmrd+uO8ctzXaTYk5CqO8Yvp9o7AAAACoSkOw90KYcvKar9sXuTWL1/w7m5Wm4r59FyG10mVD2bxapns2NJ+B+bD+j3TQeyk/C1uxO1dneiPlm0OVcS3rp2BUWFl9z5YN0eSyPymF5Noto7AAAATg9JN/JVq1Yt3X///br//vsLtP68efPUuXNnHTx4UGXLlvVqbCic7k1i1bVxZf2xab827NinetWi1bpOxVMmjtFlQnVZsyq6rFkVSVLcYVPpfPFGk4hv3HckVxLeODYrCa+gVrXKnzIJt3vsdGqGW/FH0hV/JF0L1u/LcWHiRFR7BwAAQEGRdHtJcSYQp2oJffrppzV8+PDTft0///xTpUuXLvD6bdu21e7duxUVFXXa2zodJPdnxuV06II6FVQnwq2YmApyFmK/jCkTlisJ/2NT/NGWcJOEr9mVqDW7EvXxwlMn4d4YO53h9ujgkXQdOJKuA0npOnAk7YR/03UgKU0HjqQrPildh9MyT3sbT05ZpQvqVFC9mAjVjY5QvZgIxUaF0VsGAAAA2Ui6vaC4iy/t3r07+/bEiRM1bNgw/ffff9nLIiIism9bliW3262goFP/10dHR59WHCEhIapcufJpPQf+IaZMWPbc4NKxJHzx0SR8Ux5J+NlVInVB7QoKdjk1+teNpxw77fZYOpRsWqL3H02es28nmdsHktK1/+jyQ8kZp/0+gpwOVYgIUWiQS9vik0+5/sZ9R7Rx35Ecy0qHuFT3uCQ869+aFcIV7HKedkz5sbtnAADf53Q41Lp2eaWnp8vJxUAAsA1JdxGzo/jS8YluVFSUHA5H9rKsVuEZM2boySef1KpVqzR79mxVr15dQ4YM0e+//64jR47orLPO0siRI9WlS5fs1zqxe7nD4dCHH36o6dOna9asWapatapee+019e7dO8e2slqgx44dq/vvv18TJ07U/fffr+3bt6t9+/b69NNPs6e/yszM1JAhQ/T555/L5XJp8ODB2rNnjxISEjRlypRCfR4HDx7Ufffdp2nTpiktLU0dO3bU22+/rfr160uStm7dqrvvvlsLFy5Uenq6atWqpVdeeUWXXnqpDh48qLvvvluzZ89WUlKSqlWrpscff1yDBg0qVCyBKlcSnpiq3zcfawnftO+IVu9M1Oqdifm+RtZ36O4JKxRVapUOJmfIk1/p9Xw4HVK58BBViAhRhdKhKh8RooqlQ1QhIvTosqO3S5vHI0sFyeFwyO2x1P6ln/Ot9u6QVCEiREN7NNKm/Ue0IS5JG+KStPVAso6ku/X3jgT9vSMhx3OCnA7VrBCuejER2X91o81f6dDTOxRTVR1AQYQFu/TlLa0VFxensODCFeIEAJw5ku5TsCyrwHNcuj2Wnp665qTFl4ZPXat29U49hlaSSgW7iqyb6mOPPaZXX31VderUUbly5bR9+3Zdeumlev755xUaGqrPP/9cvXr10n///acaNWrk+zojRozQyy+/rFdeeUWjRo3S9ddfr61bt6p8+fJ5rp+cnKxXX31V48aNk9Pp1A033KCHHnpIX3zxhSTppZde0hdffKFPP/1UZ511lt566y1NmTJFnTt3LvR7HThwoNavX6+pU6cqMjJSjz76qC699FKtXbtWwcHBuuuuu5Senq758+erdOnSWrt2bXZvgKeeekpr167Vjz/+qIoVK2rDhg1KSUkpdCwwYiLD1Lt5FfU+IQmfsmKnfv437qTPzfRYOnDkWKt1VKngYwlz6ROS54icy8qGhxSq9bcg1d6f69MkV4Kb4fZo64FkbYhL0sZ9Sdn/boxL0pF0d3bL+Kw1e3M8r0pUWI7W8ay/CqVDch0DqKoOAABQspB0n0JKhluNh80qkteyJO1JTFXT4bMLtP7aZ7opPKRo/oueeeYZde3aNft++fLl1bx58+z7zz77rL777jtNnTpVd999d76vM3DgQPXr10+S9MILL+jtt9/WkiVL1L179zzXz8jI0OjRo1W3bl1J0t13361nnnkm+/FRo0Zp6NChuuKKKyRJ77zzjmbMmFHo95mVbC9atEht27aVJH3xxReqXr26pkyZomuuuUbbtm3TVVddpaZNm0qS6tSpk/38bdu2qUWLFjrvvPMkmdZ+FL2sJNyyrFMm3ZL0SLeGurplNZUrHVKkXbRP5nSqvWcJdjmzE+bjWZal3QmpOZLxrNv7k9K1KyFVuxJStWD9/hzPKxsebBLxo8l4nYql9dSUk1/Yo6o6AACAbyHpDhBZSWSWpKQkDR8+XNOnT9fu3buVmZmplJQUbdu27aSv06xZs+zbpUuXVmRkpOLi8k+awsPDsxNuSYqNjc1ePyEhQXv37lWrVq2yH3e5XGrZsqU8Hs9pvb8s//zzj4KCgtS6devsZRUqVFDDhg31zz//SJLuvfde3XHHHZo9e7a6dOmiq666Kvt93XHHHbrqqqu0fPlyXXLJJerTp0928o6iF1MmrEDrtahRTjGRBVu3KGVVez/TsdMOh0NVypZSlbKl1KFBzloJh5LTcyTiJhk/ou0Hk3UoOUPLth7Usq0HC7QdqqoDOF5yeqbav/izPJalhY92VkRYiN0hAUBAIuk+hVLBLq19pluB1l2yOV4DP/3zlOuNHXS+WtXOuzv2idsuKidWIX/ooYc0Z84cvfrqq6pXr55KlSqlq6++Wunp6Sd9neDgnNM+ORyOkybIea1vWac5MLeIDR48WN26ddP06dM1e/ZsjRw5Uq+99pruuece9ejRQ1u3btWMGTM0Z84cXXzxxbrrrrv06quv2hqzv2pVu7xio8JOOna6clRYgb4v3uJyOryawJYND1HLmuXVsmbO95ia4damfUe04bhu6iu2HtSuk0xllmXm6t2qXr6UqpYtRSV1IMDFF6KoJACgaBVPP80S7P/t3XtclGXaB/DfM8PAMMPMcD4JchQEPOSBzPKYGthmHmg9ZKVluRqxkrpapinp1pZv5pZlm7tJrushe9MsTUvXQ695SkNR1MRQQDkochpgGObw/jEwMgKCwjAIv+/nw4eZ57ln5pqZx0eu577u+xYEATJ7uyb9DOziYVouqKHngmmyo4FdPJr0fNb8Y/nQoUOYOnUqxo4di+7du8Pb2xuXL1+22uvVR6VSwcvLC8eP37pQodfrcfLkyXt+zoiICOh0Ohw9etS8raCgABcuXEBkZKR5m7+/P2bMmIGvv/4ac+bMwZo1a8z7PDw8MGXKFKxfvx4rV67EZ599ds/x0J3VjJ0GUOffTc39xaMiO2SptFQiRqSvEk/29MXsEWH4+OneeH/8A0167BeHr2DAu/vQ7+29mLn+BNYc/B0nrtyEponzUxARERFRy2FPdwtqyuRLbSWB6NKlC77++muMGjUKgiBg0aJF91zS3RwJCQl45513EBoaiq5du+Kjjz5CYWFhky44pKamQqFQmO8LgoCePXti9OjReOmll/CPf/wDCoUCr732Gjp16oTRo0cDABITEzFy5EiEhYWhsLAQ+/btQ0REBADgzTffRJ8+fRAVFYXKykp899135n1kHfcydrqjaqwyAABk9mKEeMhxLqcU+aWV+P5MLr4/kwsAsBeLENVJid6dXUw/Ac7wUTm23hsgIiIi6oCYdLew+yWBWLFiBV544QU8/PDDcHd3x/z581FS0vDyTdYyf/585Obm4rnnnoNYLMb06dMRExMDsbjx0vpBgwZZ3BeLxdDpdFi7di1mzZqFJ554AlqtFoMGDcLOnTvNpe56vR7x8fHIzs6GUqlEbGwsPvjgAwCmtcZff/11XL58GY6Ojhg4cCA2bdrU8m+cLLTU2On2rikX9laM74nYbj6o0OpxOrsIJzOLcDKzECevFKKgTItfM4vwa2YR/oUMAKbqG1MC7oLenZ0R5auCvR2LoIiIiIhaimC09QDbVlZSUgKVSoXi4mIolUoYDAbk5+fD09MTWq0WGRkZCAoKglTavEmb9AYjE4h7YDAYEBERgfHjx2Pp0qW2DqdN0Gg0LXZc1qh93ItETLDuN/eyTrfRaETmzfLqBNyUiJ/LKamz9rm9nQjdO6nQu7Mz+gSYesTvZhK7tnru0xuMOPr7DaRnX0eonwf6BTdt6caOqq1+j3R3yrU68wosZ5aM4ERq1CHwbxxqTbfnlg1hT7eVWHvypfbiypUr+OGHHzB48GBUVlZi1apVyMjIwNNPP23r0IjarHupDBAEAQFucgS4yTG2lx8AoKxSh1PZpp7vk1cKcTKzEIW1Zkxf85OpN7yTsyN6B7igT2dn9A5wQYSPst6l2+7lYkBrqBtXRpuIq61qq98jERHR/YpJN9mUSCRCcnIy5s6dC6PRiG7dumHPnj0cR03UiJa4sCd3sMPDIe54OMQdgKk3PONGmUVJ+oW8UlwtqsDVogp8e+oaAEAqEaFHJ2f0CnBGn+rS9F8u38TM9SfrjDXPLdZg5vqTWP1Mb5skbLvO5LTJuNoqfl7ti0gQ0L2TCjpdFURcyYCIyGaYdJNN+fv749ChQ7YOg4hg6g0P9nBCsIcTnupj6g0v1VThVFYxTmaaer9/zSxEiUaHY5dv4tjlm+bHigWh3sndarYt+uYsgtydIBIAgxEwGI0wGI0wmm+j+n71bYPpt7HWvju2N9Zub4TBAOgMBry983yDcQkAkr5Nw4hIb5ZOw1RSnvRtGj+vdkQqEeOb+IeRn58PaQsuQ0pERHeHSTcRETVIIZVgQBd3DOhi6g03GIz4/YbaPC78xJVCXMxXQ9/I9CDXSysRs/Jga4TcZEYAOcUafHvqKkY/0KlDr2meV6LBpmOZFiXlt6v5vI5l3OTwKSIiorvApJuIiJpMJBIQ6qlAqKcC46P9AQCbjmXita9TG32so0QMqUQEkSBAEASIBFP5q0gw9bKLRKb7YkGAYN5X63b1/nofW0/766UapOWUNhpX4uZTWLbjHHpVL6XWJ8AFPfxU7bZnUFOlx9lrxeaZ7H/NLMS1OyTbt9t+6irCvJzg5uRgxSiJiIjaDybdRETULAFu8ia1+3xqdKv2kB6+VIBJa4402s5OJOCGWosf0/LwY1qeeVuUrxK9qpPw3gEu8FVJ77vecKPRiOzCCpzMLDQn2Gk5JajSW1YmiATAz0WGzJvljT7nxmNZ2Hw8C9GBroiJ8kZMN290cuZ6721RhVaP4SsOQK/XY++cIZBLOZMzEZEtMOkmIqJmeTDIFT4qKXKLNfWOBxYAeKtMM6y3xbj2zB6M87ml5hncT1wpRH5pJU5lF+NUdjGSf74MAPBWStE7wNm8rnmUrxIOdm2rN1xdqcPp7CJzL3ZKViFuqLV12rk72aNXZxf06uyMXv63evYHvPvfBj8vAFA42KGzmyPOXivF0YybOJpxE299l4bunVSI7eaNmCgvhHoqrPsmqcmMMOJqUYX5NhER2QaTbiIiahaxSMDiUZGYuf4kBMDiT/uafuHFoyJbffKtpsYld7BDnwBTjzZg6h2+WlRRPXFcEU5cMfUO55ZosDM1FztTcwE0f01zoHnrYZvH19cqE/8tr7TO2usSsYBIXxV6+ZuWfOvl7ww/F8d6e+0b+7yW/7EHYrv5IOtmOX5Iy8PuM7k4fuUmUq8WI/VqMZbvvoAQDzliorwR280b3Tup7rvqACIiopYmGI2NzH7Tzty+gLnBYEB+fj48PT2h1WqRkZGBoKAgSKV394cTkbVoNJoWPy5rH/ciEcsNqWW01fWdWyKucq0Op7OLzUupncwsws2yuj3Ifi6O5nHhvTu7oKuPot41ze8lrqJyLVKyqhPsrCKkVM8kf7tOzo54oLMzevk7o1dnU4/83YxPv9u4rpdWYs+5POw+m4tD6TcsStd9VVI8Vp2ARwc2/YICtYxyrQ6Rb+4GAJxZMgJOUnsbR0Rkffwbh1rT7bllQ5h0M+k2GzJkCB544AGsXLkSABAYGIjExEQkJiY2+BhBELB161aMGTOmWa/dUs/THjHppvtJc3purR3X0d9vID37OkL9PNAv2L1ZcRmNRlwuKLcoSa+vl9lRIkYPP5U5Ce8d4AJXuX2D62HXRLTq6V4IdJffmuwsqxC/Xy+rE4dUIkIPP2dzmXivzs7wusve9vrc6/dYoqnCvvP52H02F/svXEe5Vm/e5yq3x4gIL8R088Ijoe5trjS/Odrqcc+kmzoi/o1DrampSTfLy63FoAeu/Ayo8wAnLyDgYUBknT8wRo0ahaqqKuzatavOvp9++gmDBg3CqVOn0KNHj7t63uPHj0Mub9oESU21ZMkSbNu2DSkpKRbbc3Jy4OLi0qKvdbvk5GQkJiaiqKjIqq9D1JGJRUKbXE5KLBLwULAbgp308PR0g6iZCZEgCAhylyPIXY64RtY0rxn7XCPQTYa8kso7rmsev+HXel83yF1e3YNt6sUO9264J7057vV7VEolGP1AJ4x+oBM0VXr8dPEGdp/NxZ5zebhZpsXmX7Kw+ZcsODnYYUi4B2K7eWNIuCecHO7fP0faaoUHERG1Hffv/3JtWdp2YNd8oOTarW1KXyD2XSDyyRZ/uWnTpiEuLg7Z2dnw8/Oz2Ld27Vr07dv3rhNuAPDw8GipEBvl7e3daq9FRGQN9a1pfum62pyEn8wsQnq+GpcLGp8hHAAcJSL0DXQ1l4n39HeGq/z+6amUSsQYEemFEZFeqNIbcCzjJnafzcXus7nIK6nEd6dz8N3pHNjbiTAw1B0xUd4YHul1x/fY1nqUG6pYyC3WYOb6k1j9TG8m3kREBNZctLS07cCXz1km3ABQkmPanra9xV/yiSeegIeHB5KTky22q9VqbNmyBdOmTUNBQQEmTZqETp06QSaToXv37ti4ceMdnzcwMNBcag4AFy9exKBBgyCVShEZGYkff/yxzmPmz5+PsLAwyGQyBAcHY9GiRaiqqgJg6mlOSkrCqVOnIFSvtVsTsyAI2LZtm/l5UlNT8eijj8LR0RFubm6YPn061Gq1ef/UqVMxZswY/M///A98fHzg5uaG+Ph482vdi8zMTIwePRpOTk5QKpUYP3488vLyzPtPnTqFoUOHQqFQQKlUok+fPvjll18AAFeuXMGoUaPg4uICuVyOqKgo7Ny5855jIaL7n0gkoIuXAhOiO+O9p3piz+zBSHlzBP40OLhJj39nXA/8e1o/zH4sHEO7et5XCfftJGIRHgl1x1uju+Hwa8Ow9eWH8afBwQh0k0GrM2Dv+XzM+9/T6LvsR0z67AiSD2XgWvWs2zV2ncnBgHf/i0lrjmDWphRMWnMEA979L3adybHJe9IbjFiyPe2OFQtJ36ZBf/uYg1YkQEAXTycEuUohwPbl7kREHRV7uhtjNAJVTeuVgEEPfD8PaPC/YMHUAx48pGml5hIZ0IRZX+3s7PDcc88hOTkZb7zxhnmm2C1btkCv12PSpElQq9Xo06cP5s+fD6VSiR07duDZZ59FSEgIHnzwwcbfmsGAcePGwcvLC0ePHkVxcXG9Y70VCgWSk5Ph6+uL1NRUvPTSS1AoFJg3bx4mTJiAM2fOYNeuXdizZw8AQKVS1XmOsrIyxMTEoH///jh+/Djy8/Px4osv4pVXXrG4sLBv3z74+Phg3759SE9Px4QJE/DAAw/gpZdeavT91Pf+ahLuAwcOQKfTIT4+HhMmTMD+/fsBAJMnT0avXr2wevVqiMVipKSkQCKRAADi4+Oh1Wpx8OBByOVypKWlwcnJ6a7jIKL2zVlmjyFhnvjHgd8bbdsSY7PbIpFIqF6uzAWvxXbFb3lq7D6bi11ncpGWU4LDvxfg8O8FWPJtGnr6qRDTzRsyezGS6klw76VH2Wg0olJngLpSB7VGB3WlDqXVv9WVVVBX6qu3V0Gt0aG0Vruax5RW6lBcXgWt3tDw6wDIKdZg8fYzGBDqjk7OMvi5OMJZJmm1Gd0d7cXYnTgQ+fn5cLRvP2PoiYjuN0y6G1NVDrzt20JPZjT1gP/Nv2nNF1wD7Js2pvqFF17A8uXLceDAAQwZMgSAqbQ8Li4OKpUKKpUKc+fONbdPSEjA7t278eWXXzYp6d6zZw/Onz+P3bt3w9fX9Hm8/fbbGDlypEW7hQsXmm8HBgZi7ty52LRpE+bNmwdHR0c4OTnBzs7ujuXkGzZsgEajwbp168xjyletWoVRo0bh3XffhZeXFwDAxcUFq1atglgsRteuXfGHP/wBe/fuvaeke+/evUhNTUVGRgb8/U3fz7p16xAVFYXjx48jOjoamZmZ+Mtf/oKuXbsCALp06WJ+fGZmJuLi4tC9e3cAQHBw03qyiKjjaavrmtuCIAgI91Yg3FuBPw/rgqyb5eYE/ERmoXmt9IbUfH7z/zcV2YUVKNfqzUl02W1Jck0Sra7UWcywbm3rj2Ri/ZFM8325vRh+LjJ0cnGEn/lHhk7OptuucvsWS8pNEwgWID37JkLV4mZPIEhERPeGSXc70bVrVzz88MP4/PPPMWTIEKSnp+Onn37CW2+9BQDQ6/V4++238eWXX+Lq1avQarWorKyETCZr0vOfO3cO/v7+5oQbAPr371+n3ebNm/Hhhx/i0qVLUKvV0Ol0d5zJr6HX6tmzp8Ukbo888ggMBgMuXLhgTrqjoqIgFt+6cu/j44PU1NS7eq3ar+nv729OuAEgMjISzs7OOHfuHKKjozF79my8+OKL+Pe//43hw4fjj3/8I0JCQgAAf/7znzFz5kz88MMPGD58OOLi4u5pHD0RtX9tdV3ztsDfVYYXBwbjxYHByC/V4Me0PGw+noXTd0i8AaC4ogrLdpy769dzcrCDk4Md5A5iOEklUFTfd5Kafiuk9d2XQO4gRnqeGrM2pzT6Gv1D3KCp0iO7sALXSytRptXjQl4pLuSV1tveUSKGn4tjraTc1ENuSsplcHdqWlJed4K3DE7wRkRkI0y6GyORmXqcm+LKz8B/nmq83eSvTLOZN+W178K0adOQkJCAjz/+GGvXrkVISAgGDx4MAFi+fDn+/ve/Y+XKlejevTvkcjkSExOh1dZdZ/ZeHT58GJMnT0ZSUhJiYmKgUqmwadMmvP/++y32GrXVlHbXEAQBBkPDpX7NtWTJEjz99NPYsWMHvv/+eyxevBibNm3C2LFj8eKLLyImJgY7duzADz/8gHfeeQfvv/8+EhISrBYPEd2/Yrv5YPUzvevMeu3NpMjMUyHF5H4BcHKww6xNKY22793ZGeHeSiikdpDbmxJlRa2E+fb7cnu7Zs1i39Vbib/tOt9oxcL6af3MF1A0VXpcK6pAdmHNTzmyCytwtch0O6+kEhVVelzMV+NivrqeZzUtE9fJ2RGdqpPx2om5n7Mj3J0c8ENaLid4u0ttbZI+ImpfmHQ3RhCaXOKNkEdNs5SX5KD+cd2CaX/Io1ZZPmz8+PGYNWsWNmzYgHXr1mHmzJnmq+GHDh3C6NGj8cwzzwAwjWH+7bffEBkZ2aTnjoiIQFZWFnJycuDjY/qP+siRIxZtfv75ZwQEBOCNN94wb7ty5YpFG3t7e+j1etxJREQEkpOTUVZWZu7tPnToEEQiEcLDw5sU792qeX9ZWVnm3u60tDQUFRVZfEZhYWEICwvDq6++ikmTJmHt2rUYO3YsAMDf3x8zZszAjBkz8Prrr2PNmjVMuomoQbHdfDAi0pt/6DfCU9G0se1/ienaqsvV3UvFglQiRrCHE4I96p/zo1Knx7UiDa7WSsizC8urk/IK5JZooKky4NL1MlyqZ912AJCIBRiMd5xdBknfpmFEpDePtWpc9o2IrI1Jd0sSiU3Lgn35HNDQf8Gxf7Paet1OTk6YMGECXn/9dZSUlGDq1KnmfV26dMFXX32Fn3/+GS4uLlixYgXy8vKanHQPHz4cYWFhmDJlCpYvX46SkhKL5LrmNTIzM7Fp0yZER0djx44d2Lp1q0WbwMBAZGRkICUlBX5+flAoFHBwcLBoM3nyZCxevBhTpkzBkiVLcP36dSQkJODZZ581l5bfK71eX2eNcAcHBwwfPhzdu3fH5MmTsXLlSuh0Orz88ssYPHgw+vbti4qKCvzlL3/BU089haCgIGRnZ+P48eOIi4sDACQmJmLkyJEICwtDYWEh9u3bh4iIiGbFSkTtX1td17wtactj4Fu6YsHBTmxe/70+Wp0BOcWmBNwyMTf1lucUVzQ6Xr1mgrepa48hOtAVAW4yBLrJEegmh0omueNj2yMu+0ZErYFJd0uLfBIYv66Bdbr/ZpV1umubNm0a/vWvf+Hxxx+3GH+9cOFC/P7774iJiYFMJsP06dMxZswYFBffeZxcDZFIhK1bt2LatGl48MEHERgYiA8//BCxsbHmNk8++SReffVVvPLKK6isrMQf/vAHLFq0CEuWLDG3iYuLw9dff42hQ4eiqKgIa9eutbg4AAAymQy7d+/GrFmzEB0dDZlMhri4OKxYsaJZnw1gWkatV69eFttCQkKQnp6Ob775BgkJCRg0aBBEIhFiY2Px0UcfAQDEYjEKCgrw3HPPIS8vD+7u7hg3bhySkpIAmJL5+Ph4ZGdnQ6lUIjY2Fh988EGz4yUi6uja+hj41qxYsLcTIcBNjgC3+pPyKr0B6w9fQdJ3aY0+108Xb+CnizcstjnLJNUJuAwBbnIEulf/dpPDpYVmXW9LZdx6gxFJ3za87BurAoiopQhGo9F2C0jaQElJCVQqFYqLi6FUKmEwGJCfnw9PT09otVpkZGQgKCgIUmkzl2ox6E1jvNV5gJOXaQy3lXq4qX3TaDQtd1xWq33ci0SiFnlOoraOx/39jSXATXP4UgEmrTnSaLsJ0f4wGIy4UlCOywVlyC+tvGN7pdQOge7y6iTclIwHVSflbk2ccd1W36FOb0BRRRWKyqtQXKFFUbnpdkpWEf595Eqjj//0md6IifJutaXeqHl4rqfWdHtu2RD2dFuLSAwEDbR1FERERO0Cx8A3TVPL8d8e293isyur1CHzZjku3yjD5YJyXCkow+WCMlwpKEdOsQYlGh1OZxfXO5O8k4PdrTL1Wr3jgW4yeCgcIAhCi5Rxa3UGFFVoUVxeZU6ii8q1KK6oQmF5dTJdUVW933S/uLwKpZW6u/8ga5mx/mST3iMRUUOYdBMREdF9gWPgG1e7HP92dyrHlzvYIcJHiQifuj01miq9uUfclIxXJ+U3ynGtuALqSh3OXivB2WsldR4rsxfD38URVwrKGyzjBkxrrWfcKEOJRlenR7omqS7X3nki1sYopXZwltnDWSaBylECncGIw5cKmvTYxt5j7QqAW5UAcngqHJo1S35bKsene8fvkZh0ExEREbUjNRO8Ldl+Frklt8rG73WCN6lEjHBvBcK9FXX2mdYgL8flG+XmnvHL1b3kVwsrUK7V40Je/cuf1VZcUYV3d11otJ0gACpHCZwdJVDJ7OHsKIGLTAJnmb1pu6z6x9EeKpmker89lI6SOkmO3mDEgHf/22hVwJ7Zg3GtqKJOBUDt93gupwTncuom5FKJCAGuclMvufut3vEAdzl8lNI7JuQcUtE+8HskgGO6rTemm6iFcEw3UcvgcU8djd5gxNHfbyA9+zpC/TzQL9i9VXvXtDoDsgvLsfl4Fv5x8PdG20cHuKCbnwrOjvbm5FlVnTTXJNIKafPWV79dTdk7UP8kfY2VvVfq9MgurDD3/F8pKENGdXKeXVgBvaHhP7Pt7UQIcK3VO+5u+h3oJkdqdjHiN9Qtx29qXB2VrY/52zU0rILfY/vBMd1EREREHZhYJOChYDcEO+nh6enWoslqU9jbiRDs4YQh4Z5NSrpnPxbe6sMHmrvsm4OdGCEeTgipZ+31Kr0BVwsrzD3jGTdM5flXCsqRebMcWp0BF/PVuJjfeCVADc6q3rC6PcoZNu1R5uz4VBuTbiIiIiKymra81jpgvUn6JGKRqaS8nnXXdXoDcoo1plJ8i8nrTJPZ6e7QQ16z1vrzycfwcIg7Qj2cEOrpBH9XWYdN3qy13rrRaESlzoByrR7lWl31bz3KK023y7Q6VGj1KNPqUaHVVf/Wo6xSh6zCcosLOXWeG6bv8dMD6Rga7gVfZylUji2zNF9Tcax562HSTURERNQOaar0+OOnh6HTVeF/X3aDzME2wyra+lrrQOtP0mcnFsHfVQZ/VxkGdvGw2Lf116t4dXNKo89x8LcbOPjbrbXW7e1ECHaXI8TTyZyIh3o6IchdDqmk/S5b21iPMgC89r+pyC+tRGWVoVaiXJ1EV+pRXnUrka5Jrmva3OH6R4tYvvs3LN/9GwDTpHw+Kil8nR3ho5LCR+UIX2fL33KHlknfONa8dTHpJiIiImqHDEYjUq8Wm2/bUnPLuDsSb2XT5m95qrcfKvUGpOer8ft1NSp1BpzPLcX53FKLdiIB8HeVmRPxEM9bCblSKrmnGFu7h9RgMKKwXIu8kkrklWqQX6Ix3S7R4Hxu6R17lAGgqKIKb35ztlkxONiJIHewg6NEDLmDGI72dpDbiyGzF0Nmb2f520GMvGINvjjc+DrwgW4ylGh0uFlmmqH/0vUyXLpe1mB7pdTuVlLu7Ajf6uTcx1kKX5UjvFXSRi+yWKsygBrGpJuIiIiIrI5rrTdNU8vx332qh/mz0xuMuFpYgfTrpUjPV1v8lGh0uFJQjisF5dh7Pt/iuTwVDuYEPLRWD/md1h5vyR5So9GIovIq5JXeSqKvl5p+51Un1vklGuSXVt6x5L4pevgpEeKhqE6OayfIdpDVSqRr75fbi+FYfftuj1O9wYgf0vIa/R73zhkCsUiApkqPnGINcooqcK327+IK5BRpcK2oAqWVOpRodCip5+JKbW5ye3MSfnuC7qmUYsn2tj3WvD2WvTPppnYrMDAQiYmJSExMtHUoREREBK613hT3Uo4vFgno7CZDZzcZHu3qZd5uNBpxXV2J9Hw1LtUk4tdNv/NKKpFfavr5+bb1yhVSO4skvObn7NWSemdVv72H1Gg0oqRCV90rXZ1E175dnUjnl1RCqzc0+bNxd7KHp0IKL6UDvJRSeCocoK7U4fNDlxt97OsjI1v12Lvb71EqESPI3bS+e0NKNVXIKTYl4Dn1JebFFdBUGVBQpkVBmRZnrtZdxq4xNWPNkw9loF+wG1SOEiilEjhJ7/7Cw71or2XvTLrbialTp+KLL74w33d1dUV0dDTee+899OjRo0VeY8mSJdi2bRtSUlIabZeUlFRne3h4OM6fP98isbRVBw8exPLly3HixAnk5ORg69atGDNmjEWb278rAIiJicGuXbtaMVIiIiJqq1qqHF8QBHgqpPBUSPFwiLvFvhJNlUUiXnM782Y5SjU6/JpZhF8zi5r0OjUJ5Z83/gov5TnT+Gld05NpV7k9PBUO8FRK4aUwJdReyur71cm1h8IBEnHdeQn0BiO+P5PbJifqa+lhFQqpBAqpBGFeinr311QOXKtOwnOKK3C1yDIpzymqgL4JRQNLd5yrs83JwQ5KqR0UUgmUjnZQSiVQSO2grE7Ma24rpKZ9lrft4GDXccvemXS3I7GxsVi7di0AIDc3FwsXLsQTTzyBzMzMVo8lKioKe/bssdhmZ9f+D7eysjL07NkTL7zwAsaNG9dgu9rfFQA4ODi0RnhERER0n7B2Ob5SKkGvzi7o1dnFYrumSo/LBWV1ytTT89WNlnhr9UZkFVaY76scJbV6pS17qD2rE2sPhUOjydidtPWJ+lpzWIUgCHCR28NFbo8oX1W9bX5Ov4Gn/3m00efyVUmhMxhRoqmCpsp0AUVdqYO6Ugc0Moa+IfZ2InMCrpBKoDQn7HZwcrDDxmNZbbrsvTnafxbUgTg4OMDb2xsA4O3tjddeew0DBw7E9evX4eFhmhkzKysLc+bMwQ8//ACRSISBAwfi73//OwIDAwEA+/fvx7x583D27FlIJBJERUVhw4YN2Ldvn7n3umaMz9q1azF16tR6Y7GzszPHUp/AwEBMmzYNaWlp2L59O5ydnbFgwQLEx8eb22RmZiIhIQF79+6FSCRCbGwsPvroI3h53Sqb+vbbb/HWW28hNTUVTk5OGDhwILZu3WreX15ejhdeeAFbtmyBi4sLFi5ciOnTp9/9h9tEI0eOxMiRIxttV/u7IiIiIqqPLcrxpRIxunor0dVbabG9qbOqJw7rgrg+fvBQOLTarOltfaK+tjSsol+wW5PmDPhp/qPm5FarM6BUU2UaT15RhVKNDiWaKtO2iprbpn0l9bbTmZ/nhroSN9SVdx13Tdn7sYybbeazvBtMupuoXKtrcJ9IECxOKi3RVmbfvK9GrVZj/fr1CA0NhZub6cCsqqpCTEwM+vfvj59++gl2dnZYtmwZYmNjcfr0aYhEIowZMwYvvfQSNm7cCK1Wi2PHjkEQBEyYMAFnzpzBrl27zD3YKlX9V9Caavny5ViwYAGSkpKwe/duzJo1C2FhYRgxYgQMBgNGjx4NJycnHDhwADqdDvHx8ZgwYQL2798PANixYwfGjh2LN954A+vWrYNWq8XOnTstXuP999/H0qVLsWDBAnz11VeYOXMmBg8ejPDw8Hpjevvtt/H222/fMe60tDR07ty5We99//798PT0hIuLCx599FEsW7bM/D0RERG1FFeZxOYzl1P70NRZ1fsFu8HfVWblaOqq6VE++vsNpGdfR6ifB/oFu9+XvaLWdC+VAfZ2Irg5OcDN6d4qMw0GI9Ta6qS8QndbAm+6/WtmIfZduN7oc+WX3lsvu60x6W6iyDd3N7hvaLgH1j7/oPl+n6V7UFGlr7dtvyBXbP5Tf/P9Ae/uw80ybZ12l//2h7uO8bvvvoOTkxMAU5mzj48PvvvuO4hEpvEvmzdvhsFgwD//+U+L3mpnZ2fs378fffv2RXFxMZ544gmEhIQAACIiIszP7+Tk1GgPdo2anufannnmGXz66afm+4888ghee+01AEBYWBgOHTqEDz74ACNGjMDevXuRmpqKjIwM+Pv7AwDWrVuHqKgoHD9+HNHR0fjrX/+KiRMnWowf79mzp8VrPv7443j55ZcBAPPnz8cHH3yAffv2NZh0z5gxA+PHj7/je/P19W30/d9JbGwsxo0bh6CgIFy6dAkLFizAyJEjcfjwYYjF7XcdTSIial0yezv8snA48vPzm30xn6ips6rbYux0DbFIwEPBbgh20sPT0w0iJtz1au3KAJFIMJWVSyWAS/1tDl8qaFLS7alo2sWftoZn4HZk6NChWL16NQCgsLAQn3zyCUaOHIljx44hICAAp06dQnp6OhQKy8kXNBoNLl26hMceewxTp05FTEwMRowYgeHDh2P8+PHw8bn7f3jh4eHYvn27xTal0rJMqX///nXur1y5EgBw7tw5+Pv7mxNuAIiMjISzszPOnTuH6OhopKSk4KWXXrpjHLUnkRMEAd7e3sjPz2+wvaurK1xdrfufxcSJE823u3fvjh49eiAkJAT79+/HsGHDrPraRERERPeirY+dprvT1pbwux8u6jQHk+4mSnsrpsF9otvWMTyxaHiT2/7f/KHNC6wWuVyO0NBQ8/1//vOfUKlUWLNmDZYtWwa1Wo0+ffrgP//5T53H1oz5Xrt2Lf785z9j165d2Lx5MxYuXIgff/wRDz300F3FYm9vbxGLNTg6OjbaRiKRWNwXBAEGQ8OzabZWeXltwcHBcHd3R3p6OpNuIiIiarPa+thpujttaax5e7+ow6S7ie6mLMtabe+WIAgQiUSoqDDNItm7d29s3rwZnp6edXqda+vVqxd69eqF119/Hf3798eGDRvw0EMPwd7eHnp9/WXz9+LIkSN17teUs0dERCArKwtZWVnm3u60tDQUFRUhMjISgKkXe+/evXj++edbLKbWKC+/XXZ2NgoKCu6pooCIiKghmio9pnx+DFqtFv+Z7gaZQ93llojuVlvrIaX2oz1f1GHS3Y5UVlYiNzcXgKm8fNWqVVCr1Rg1ahQAYPLkyVi+fDlGjx6Nt956C35+frhy5Qq+/vprzJs3D1VVVfjss8/w5JNPwtfXFxcuXMDFixfx3HPPATDNOJ6RkYGUlBT4+flBoVA0uNSVTqczx1JDEASLmccPHTqE9957D2PGjMGPP/6ILVu2YMeOHQCA4cOHo3v37pg8eTJWrlwJnU6Hl19+GYMHD0bfvn0BAIsXL8awYcMQEhKCiRMnQqfTYefOnZg/f/49f4bNLS9Xq9VIT08336/5vFxdXdG5c2eo1WokJSUhLi4O3t7euHTpEubNm4fQ0FDExDRcTUFERHS3DEYjjmbcNN8mailtqYeU2pf2elGHSXc7smvXLnNvqUKhQNeuXbFlyxYMGTIEACCTyXDw4EHMnz8f48aNQ2lpKTp16oRhw4ZBqVSioqIC58+fxxdffGHueY2Pj8ef/vQnAEBcXBy+/vprDB06FEVFRXdcMuzs2bN1em4dHByg0dy6ajVnzhz88ssvSEpKglKpxIoVK8yJpyAI+Oabb5CQkIBBgwZZLBlWY8iQIdiyZQuWLl2Kv/3tb1AqlRg0aFBLfZz35JdffsHQobeGDMyePRsAMGXKFCQnJ0MsFuP06dP44osvUFRUBF9fXzz22GNYunQp1+omIiIiog6vPV7UEYzGjnXps6SkBCqVCsXFxVAqlTAYDMjPz4enpye0Wi0yMjIQFBQEqfT+nBnvfhEYGIjExEQkJibaOpQ2T6PRtPhxWfu4r5ndnqi943FPHU25VmdefeXMkhFwktrbOCIi6+O5nlrT7bllQ3gkEhEREREREVkJk24iIiIiIiIiK+GYbrKJy5cv2zoEIiIiIiIiq2PSTURERNROOUrE6GDT9xARtTlMuomIiIjaIZm9Hc4mPYb8/HzI7PknHxGRrXBMdz14RZjaEh6PRERERET3LybdtUgkEgBAeXm5jSMhuqXmeKw5PomIiIiI6P7BWqNaxGIxnJ2dkZ+fDwCQyWQQBMHGUVFHZTQaUV5ejvz8fDg7O0MsFts6JCIiuo9oqvSYsf4EtJWV+NfzbnB0YF8LEZEtMOm+jbe3NwCYE28iW3N2djYfl0RERE1lMBqx/8J1AICeQ5WIiGyGSfdtBEGAj48PPD09UVVVZetwqIOTSCTs4SYiIiIiuo8x6W6AWCxmskNERERERETNwsE9RERERERERFbCpJuIiIiIiIjISph0ExEREREREVlJhxvTbayevbOkpAQAYDAYUFpaCqlUCpGI1yCoY+BxTx0Rj3vqaMq1OhgqywGY/u4xaO1tHBGR9fFcT62pJqc0NrJChGBsrEU7k52dDX9/f1uHQURERERERO1AVlYW/Pz8Gtzf4ZJug8GAa9euQaFQQBAElJSUwN/fH1lZWVAqlbYOj6hV8LinjojHPXVEPO6po+ExT63JaDSitLQUvr6+d6ys6HDl5SKRqN6rEEqlkv8wqcPhcU8dEY976oh43FNHw2OeWotKpWq0DQc6EBEREREREVkJk24iIiIiIiIiK+nwSbeDgwMWL14MBwcHW4dC1Gp43FNHxOOeOiIe99TR8JintqjDTaRGRERERERE1Fo6fE83ERERERERkbUw6SYiIiIiIiKyEibdRERERERERFbS4ZPujz/+GIGBgZBKpejXrx+OHTtm65CIrGbJkiUQBMHip2vXrrYOi6jFHDx4EKNGjYKvry8EQcC2bdss9huNRrz55pvw8fGBo6Mjhg8fjosXL9omWKIW0thxP3Xq1Drn/tjYWNsES9RC3nnnHURHR0OhUMDT0xNjxozBhQsXLNpoNBrEx8fDzc0NTk5OiIuLQ15eno0ipo6sQyfdmzdvxuzZs7F48WKcPHkSPXv2RExMDPLz820dGpHVREVFIScnx/zzf//3f7YOiajFlJWVoWfPnvj444/r3f/ee+/hww8/xKeffoqjR49CLpcjJiYGGo2mlSMlajmNHfcAEBsba3Hu37hxYytGSNTyDhw4gPj4eBw5cgQ//vgjqqqq8Nhjj6GsrMzc5tVXX8W3336LLVu24MCBA7h27RrGjRtnw6ipo+rQs5f369cP0dHRWLVqFQDAYDDA398fCQkJeO2112wcHVHLW7JkCbZt24aUlBRbh0JkdYIgYOvWrRgzZgwAUy+3r68v5syZg7lz5wIAiouL4eXlheTkZEycONGG0RK1jNuPe8DU011UVFSnB5yoPbl+/To8PT1x4MABDBo0CMXFxfDw8MCGDRvw1FNPAQDOnz+PiIgIHD58GA899JCNI6aOpMP2dGu1Wpw4cQLDhw83bxOJRBg+fDgOHz5sw8iIrOvixYvw9fVFcHAwJk+ejMzMTFuHRNQqMjIykJuba3HeV6lU6NevH8/71O7t378fnp6eCA8Px8yZM1FQUGDrkIhaVHFxMQDA1dUVAHDixAlUVVVZnPO7du2Kzp0785xPra7DJt03btyAXq+Hl5eXxXYvLy/k5ubaKCoi6+rXrx+Sk5Oxa9curF69GhkZGRg4cCBKS0ttHRqR1dWc23nep44mNjYW69atw969e/Huu+/iwIEDGDlyJPR6va1DI2oRBoMBiYmJeOSRR9CtWzcApnO+vb09nJ2dLdrynE+2YGfrAIio9YwcOdJ8u0ePHujXrx8CAgLw5ZdfYtq0aTaMjIiIrKX20Inu3bujR48eCAkJwf79+zFs2DAbRkbUMuLj43HmzBnOU0NtVoft6XZ3d4dYLK4zg2FeXh68vb1tFBVR63J2dkZYWBjS09NtHQqR1dWc23nep44uODgY7u7uPPdTu/DKK6/gu+++w759++Dn52fe7u3tDa1Wi6KiIov2POeTLXTYpNve3h59+vTB3r17zdsMBgP27t2L/v372zAyotajVqtx6dIl+Pj42DoUIqsLCgqCt7e3xXm/pKQER48e5XmfOpTs7GwUFBTw3E/3NaPRiFdeeQVbt27Ff//7XwQFBVns79OnDyQSicU5/8KFC8jMzOQ5n1pdhy4vnz17NqZMmYK+ffviwQcfxMqVK1FWVobnn3/e1qERWcXcuXMxatQoBAQE4Nq1a1i8eDHEYjEmTZpk69CIWoRarbbovcvIyEBKSgpcXV3RuXNnJCYmYtmyZejSpQuCgoKwaNEi+Pr6Wsz0THS/udNx7+rqiqSkJMTFxcHb2xuXLl3CvHnzEBoaipiYGBtGTdQ88fHx2LBhA7755hsoFArzOG2VSgVHR0eoVCpMmzYNs2fPhqurK5RKJRISEtC/f3/OXE6trkMvGQYAq1atwvLly5Gbm4sHHngAH374Ifr162frsIisYuLEiTh48CAKCgrg4eGBAQMG4K9//StCQkJsHRpRi9i/fz+GDh1aZ/uUKVOQnJwMo9GIxYsX47PPPkNRUREGDBiATz75BGFhYTaIlqhl3Om4X716NcaMGYNff/0VRUVF8PX1xWOPPYalS5fWmVSQ6H4iCEK929euXYupU6cCADQaDebMmYONGzeisrISMTEx+OSTT1heTq2uwyfdRERERERERNbSYcd0ExEREREREVkbk24iIiIiIiIiK2HSTURERERERGQlTLqJiIiIiIiIrIRJNxEREREREZGVMOkmIiIiIiIishIm3URERERERERWwqSbiIiIiIiIyEqYdBMREVGLEQQB27Zts3UYREREbQaTbiIionZi6tSpEAShzk9sbKytQyMiIuqw7GwdABEREbWc2NhYrF271mKbg4ODjaIhIiIi9nQTERG1Iw4ODvD29rb4cXFxAWAq/V69ejVGjhwJR0dHBAcH46uvvrJ4fGpqKh599FE4OjrCzc0N06dPh1qttmjz+eefIyoqCg4ODvDx8cErr7xisf/GjRsYO3YsZDIZunTpgu3bt1v3TRMREbVhTLqJiIg6kEWLFiEuLg6nTp3C5MmTMXHiRJw7dw4AUFZWhpiYGLi4uOD48ePYsmUL9uzZY5FUr169GvHx8Zg+fTpSU1Oxfft2hIaGWrxGUlISxo8fj9OnT+Pxxx/H5MmTcfPmzVZ9n0RERG2FYDQajbYOgoiIiJpv6tSpWL9+PaRSqcX2BQsWYMGCBRAEATNmzMDq1avN+x566CH07t0bn3zyCdasWYP58+cjKysLcrkcALBz506MGjUK165dg5eXFzp16oTnn38ey5YtqzcGQRCwcOFCLF26FIApkXdycsL333/PseVERNQhcUw3ERFROzJ06FCLpBoAXF1dzbf79+9vsa9///5ISUkBAJw7dw49e/Y0J9wA8Mgjj8BgMODChQsQBAHXrl3DsGHD7hhDjx49zLflcjmUSiXy8/Pv9S0RERHd15h0ExERtSNyubxOuXdLcXR0bFI7iURicV8QBBgMBmuERERE1OZxTDcREVEHcuTIkTr3IyIiAAARERE4deoUysrKzPsPHToEkUiE8PBwKBQKBAYGYu/eva0aMxER0f2MPd1ERETtSGVlJXJzcy222dnZwd3dHQCwZcsW9O3bFwMGDMB//vMfHDt2DP/6178AAJMnT8bixYsxZcoULFmyBNevX0dCQgKeffZZeHl5AQCWLFmCGTNmwNPTEyNHjkRpaSkOHTqEhISE1n2jRERE9wkm3URERO3Irl274OPjY7EtPDwc58+fB2CaWXzTpk14+eWX4ePjg40bNyIyMhIAIJPJsHv3bsyaNQvR0dGQyWSIi4vDihUrzM81ZcoUaDQafPDBB5g7dy7c3d3x1FNPtd4bJCIius9w9nIiIqIOQhAEbN26FWPGjLF1KERERB0Gx3QTERERERERWQmTbiIiIiIiIiIr4ZhuIiKiDoIjyoiIiFofe7qJiIiIiIiIrIRJNxEREREREZGVMOkmIiIiIiIishIm3URERERERERWwqSbiIiIiIiIyEqYdBMRERERERFZCZNuIiIiIiIiIith0k1ERERERERkJUy6iYiIiIiIiKzk/wFQRHtU+xyfqwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_08_convnext_overfitting_loss.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 09: Initial Swin Accuracy Curves\n",
        "# =========================================================\n",
        "\n",
        "swin_initial_best_index = (\n",
        "    swin_initial_history_df[\n",
        "        \"Validation Accuracy\"\n",
        "    ].idxmax()\n",
        ")\n",
        "\n",
        "swin_initial_best_epoch = int(\n",
        "    swin_initial_history_df.loc[\n",
        "        swin_initial_best_index,\n",
        "        \"Epoch\"\n",
        "    ]\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plt.plot(\n",
        "    swin_initial_history_df[\"Epoch\"],\n",
        "    swin_initial_history_df[\"Train Accuracy\"] * 100,\n",
        "    marker=\"o\",\n",
        "    label=\"Training Accuracy\"\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    swin_initial_history_df[\"Epoch\"],\n",
        "    swin_initial_history_df[\n",
        "        \"Validation Accuracy\"\n",
        "    ] * 100,\n",
        "    marker=\"o\",\n",
        "    label=\"Validation Accuracy\"\n",
        ")\n",
        "\n",
        "plt.axvline(\n",
        "    swin_initial_best_epoch,\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Best Epoch = {swin_initial_best_epoch}\"\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    \"Initial Swin-V2-Tiny Accuracy — Underfitting\"\n",
        ")\n",
        "\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy (%)\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "plt.tight_layout()\n",
        "\n",
        "swin_initial_accuracy_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_09_swin_initial_underfitting_accuracy.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    swin_initial_accuracy_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"Saved at:\")\n",
        "print(swin_initial_accuracy_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "fymV5OzVCpKU",
        "outputId": "1db552d7-c919-4d3b-9959-42923d6aa4d4"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_09_swin_initial_underfitting_accuracy.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 10: Initial Swin Loss Curves\n",
        "# =========================================================\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plt.plot(\n",
        "    swin_initial_history_df[\"Epoch\"],\n",
        "    swin_initial_history_df[\"Train Loss\"],\n",
        "    marker=\"o\",\n",
        "    label=\"Training Loss\"\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    swin_initial_history_df[\"Epoch\"],\n",
        "    swin_initial_history_df[\"Validation Loss\"],\n",
        "    marker=\"o\",\n",
        "    label=\"Validation Loss\"\n",
        ")\n",
        "\n",
        "plt.axvline(\n",
        "    swin_initial_best_epoch,\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Best Epoch = {swin_initial_best_epoch}\"\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    \"Initial Swin-V2-Tiny Loss — Underfitting\"\n",
        ")\n",
        "\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "plt.tight_layout()\n",
        "\n",
        "swin_initial_loss_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_10_swin_initial_underfitting_loss.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    swin_initial_loss_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"Saved at:\")\n",
        "print(swin_initial_loss_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "nUMx0P0NCzHY",
        "outputId": "98b87c3a-9476-4c4c-f2e9-1bb8957937f7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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LLbz77ruMGzeOF154gWXLlmX7JcH48eNz3dMju5ndwdHd29vbm927d2d5fPfu3Xh7e+fqdyI8PNzZin3lOS9XqVIlwDHLf358SXK1+8T13ENyKjw8nGXLlnHx4sUMrd179+51+bVERPKLkm4RkWIqfezulcs4ZcVqtWZqlZs3bx5Hjx7NVbfPFStW0Lx5c9zd3TPs/+mnn4CMXWojIyMpV64cEyZMICUlxTlLeJs2bXjqqaf4+uuvad68eZZJbW5cbRbyVq1aERERweeff86RI0do165dll13d+7cSZcuXYiIiODHH3+kRIkSWZ4vv1uWL3ffffcxYcIEHnzwQRISElwyazk43pOHhwfvv/8+UVFRzsTsk08+IS4uji5dugAQHx+Pt7d3hn+3OnXqYLFYSEpKAhzdxIOCgjKcv379+gDOMllx1RcwV7Jardx222388MMPHDp0KEO38UOHDvHDDz9w22235erLi9tvv52JEyeybt0657jukydPMnv27AzlOnXqhL+/P+PGjaNDhw6ZfodOnjyZ7SoEuXG1+4SPj0+uh5dkp1OnTkybNo1p06bx2GOPAY7W/cmTJ7v0OiIi+UlJt4hIMZWemLzwwgvce++9uLu707Vr1ywn0rrjjjt4+eWXGTx4MC1btmTbtm3Mnj07Q0vm9XjzzTfZuHEjd955J3Xr1gVg06ZNzJw5k6CgIEaNGpWhfJs2bfjyyy+pU6eOc4xyw4YN8fHx4Z9//qFv3765iuN6GYZB3759GTduHIBzOaXLnT9/nk6dOnH27FmefvrpTGt7V6pUiRYtWuRLvFfToEEDateuzbx586hRowYNGzbM8WtPnjzpbIm+XGRkJP369eP5559n7NixREVF0a1bN3bv3s0HH3xAkyZNnMn9b7/9xsMPP8zdd99N1apVSU1NZdasWVitVnr16gU4Pt/ly5fTpUsXwsPDiY2N5YMPPqB8+fK0bt3aNR/EdRo3bhzNmzenYcOGDBs2jIiICA4cOMBHH32EYRjOunG9nnnmGWbNmkVUVBSPPfYYPj4+fPTRR4SHh7N161ZnOX9/f6ZMmcJ9991Hw4YNuffeewkJCeHQoUMsXLiQVq1aMWnSJFe9XSpVqkRgYCBTp07Fz88PHx8fmjVrRmRkJI0aNWLu3Lk88cQTNGnSBF9fX7p27XpD1+vRowdNmzblySefZO/evVSvXp0FCxZw5swZIG9a10VE8pqSbhGRYqpJkya88sorTJ06lUWLFmGz2YiOjs4y6f7Pf/7DhQsXmDNnDnPnzqVhw4YsXLjQ2dX7ev3nP/9hzpw5/PHHH8yePZuLFy8SFhbGvffey4svvkhkZGSG8ulJ9+WJlpubGy1atODXX3/N0XhuV+nXrx/jxo3D09OTu+66K9Px06dPc/jwYYAsP5+BAwcWiKQbHBOqPfPMM9c9gVpsbCwvvvhipv233HIL/fr1Y8yYMYSEhDBp0iQef/xxgoKCGDZsGOPGjXO2zNarV49OnTrxww8/cPToUby9valXrx4///wzzZs3B6Bbt24cOHCATz/9lFOnTlGqVCnatWvH2LFjnbOf57caNWrw559/MmbMGD755BNna/ytt97K6NGjqV69eq7OGxYWxrJly3jkkUd44403CA4OZvjw4ZQtW5YhQ4ZkKNu3b1/Kli3LG2+8wdtvv01SUhLlypWjTZs2DB482BVv08nd3Z3PPvuM559/nuHDh5Oamsr06dOJjIzkoYceYvPmzUyfPp0JEyYQHh5+w0m31Wpl4cKFPPbYY3z22WdYLBZ69uzJ6NGjadWqVabx8iIihYFhz80sHiIiIlLovffeezz++OMcOHAgyxm2RQqK+fPn07NnT1auXHnVISAiIgWRkm4REZFiyG63U69ePYKDg7Nc21nELJcuXcowD0JaWhq33XYbGzZs4Pjx49nOkSAiUlCpe7mIiEgxcuHCBRYsWMCyZcvYtm0b33//vdkhiWTwyCOPcOnSJVq0aEFSUhLffvstq1evZty4cUq4RaRQUku3iIhIMXLgwAEiIyMJDAzkoYce4rXXXjM7JJEM5syZw/jx49m7dy+JiYlUrlyZESNG8PDDD5sdmohIrijpFhEREREREckjFrMDEBERERERESmqlHSLiIiIiIiI5BFNpJYFm83GsWPH8PPzwzAMs8MRERERERGRAsZut3P+/HnKli2LxZJ9e7aS7iwcO3aMChUqmB2GiIiIiIiIFHCHDx+mfPny2R5X0p0FPz8/wPHh+fv7X7O8zWbj5MmThISEXPUbDpGcUH0SV1FdEldSfRJXyu/6dDE5laavLQVg3Qu34O2hP4GLCt2bxJWutz7Fx8dToUIFZ/6YHd1xspDepdzf3z/HSXdiYiL+/v76ZZcbpvokrqK6JK6k+iSulN/1yS05FYunN+D4+05Jd9Ghe5O4Um7r07WGJKtmioiIiIiIiOQRJd0iIiIiIiIieURJt4iIiIiIiEge0YAWEREREREpUNLS0khJSblmOZvNRkpKComJiRrTLTfsyvrk7u6O1Wq94fMq6RYRERERkQLBbrdz/Phxzp07l+PyNpuN8+fPX3MyK5Fryao+BQYGEhoaekP1S0m3iIiIiBRpXm5WVjzTwbktBVd6wl26dGm8vb2vmejY7XZSU1Nxc3NT0i037PL6BHDx4kViY2MBCAsLy/V5lXSLiIiISJFmsRhUCPI2Owy5hrS0NGfCHRwcnKPXKOkWV7qyPpUoUQKA2NhYSpcuneuu5hr4ICIiIiIipksfw+3trS9IpOBIr485mWMgO2rpFhEREZEiLTnVxjuLdwPw1G3V8HBTu1NBphZrKUhcUR91xxERERGRIi3VZuOj5fv5aPl+Um02s8MRkWJGSbeIiIiIiEgBExERwcSJE3Nc/vfff8cwjBzP/C75R0m3iIiIiIgUKWk2O2v2neb7zUdZs+80aTZ7nl3LMIyrPsaMGZOr865fv55hw4bluHzLli2JiYkhICAgV9fLKSX3109jukVEREREpMhYtD2GsT/sICYu0bkvLMCL0V1rElU798s+ZScmJsa5PXfuXF566SV2797t3Ofr6+vcttvtpKWlOZekupqQkJDrisPDw4PQ0NDreo3kD7V0i4iIiIhIkbBoewwjPt+UIeEGOB6XyIjPN7Foe0w2r8y90NBQ5yMgIADDMJzPd+3ahZ+fHz///DONGjXC09OTlStXsm/fPrp3706ZMmXw9fWlSZMm/PrrrxnOe2X3csMw+Pjjj+nZsyfe3t5UqVKFBQsWOI9f2QI9Y8YMAgMD+eWXX6hRowa+vr5ERUVl+JIgNTWVRx99lMDAQIKDg3n22WcZOHAgPXr0yPXncfbsWQYMGEDJkiXx9vamc+fO7Nmzx3n84MGDdO3alZIlS+Lj40OtWrX46aefnK/t168fISEhlChRgipVqjB9+vRcx1JQKOkWEREREZECyW63czE5NUeP84kpjF7wN1l1JE/fN2bBDs4npuTofHa767qkP/fcc7zxxhvs3LmTunXrkpCQwO23387SpUv566+/iIqKomvXrhw6dOiq5xk7diy9e/dm69at3H777fTr148zZ85kW/7ixYu88847zJo1i+XLl3Po0CGeeuop5/E333yT2bNnM336dFatWkV8fDzz58+/ofc6aNAgNmzYwIIFC1izZg12u53bb7/dueTWyJEjSUpKYvny5Wzbto0333zT2RvgxRdfZMeOHfz888/s3LmTKVOmUKpUqRuKpyBQ9/JCKs1mZ130GWLPJ1Laz4umkUFYLVpeQURERESKjkspadR86ReXnMsOHI9PpM6YxTkqv+PlTnh7uCZdevnll7n11ludz4OCgqhXr57z+SuvvMJ3333HggULePjhh7M9z6BBg+jTpw8A48aN4/3332fdunVERUVlWT4lJYWpU6dSqVIlAB5++GFefvll5/H/+7//4/nnn6dnz54ATJo0ydnqnBt79uxhwYIFrFq1ipYtWwIwe/ZsKlSowPz587n77rs5dOgQvXr1ok6dOgBUrFjR+fpDhw7RoEEDGjduDDha+4sCJd2FUH6PUxEREREpzLzcrCx+vK1zWyS/pSeR6RISEhgzZgwLFy4kJiaG1NRULl26dM2W7rp16zq3fXx88Pf3JzY2Ntvy3t7ezoQbICwszFk+Li6OEydO0LRpU+dxq9VKo0aNsOVyab2dO3fi5uZGs2bNnPuCg4OpVq0aO3fuBODRRx9lxIgRLF68mI4dO9KrVy/n+xoxYgS9evVi06ZN3HbbbfTo0cOZvBdmSroLmfRxKld2dkkfpzKlf0Ml3iIiIiKXsVgMqpbxMzsMyYUS7lZ2vNwp2+N2u53U1FTc3NxYf+Asg6avv+Y5ZwxuQtPIoBxd21V8fHwyPH/qqadYsmQJ77zzDpUrV6ZEiRLcddddJCcnX/U87u7uGZ4bhnHVBDmr8q7sNp8bDzzwAJ06dWLhwoUsXryY119/nfHjx/PII4/QuXNnDh48yE8//cSSJUu45ZZbGDlyJO+8846pMd8ojekuRNJsdsb+sOOq41TG/rAjT5dEEBERERHJL4Zh4O3hlqNHmyohhAV4kd2ASwNH79A2VUJydD7DyLuhm6tWrWLQoEH07NmTOnXqEBoayoEDB/LselkJCAigTJkyrF//vy8q0tLS2LRpU67PWaNGDVJTU/nzzz+d+06fPs3u3bupWbOmc1+FChUYPnw43377LU8++STTpk1zHgsJCWHgwIF8/vnnTJw4kY8++ijX8RQUaukuRNZFn8k0E+Pl7EBMXCLros/QolJw/gUmIiIiUoAlp9qYvGwvACM7VMbDTe1ORZHVYjC6a01GfL4JAzI0VKWnz6O71iwQ8yBVqVKFb7/9lq5du2IYBi+++GKuu3TfiEceeYTXX3+dypUrU716df7v//6Ps2fP5ugLh23btuHn978eJIZhUK9ePbp3787QoUP58MMP8fPz47nnnqNcuXJ0794dgFGjRtG5c2eqVq3K2bNnWbZsGTVq1ADgpZdeolGjRtSqVYukpCR+/PFH57HCTEl3IRJ7PvuEOzflRERERIqDVJuN95Y6lix6sF1FPNTZs8iKqh3GlP4NM81/FFrA5j969913uf/++2nZsiWlSpXi2WefJT4+Pt/jePbZZzl+/DgDBgzAarUybNgwOnXqhNV67a71bdu2zfDcarWSmprK9OnTeeyxx7jjjjtITk6mbdu2/PTTT86u7mlpaYwcOZIjR47g7+9PVFQUEyZMABxrjT///PMcOHCAEiVK0KZNG7788kvXv/F8ZtjN7tRfAMXHxxMQEEBcXBz+/v7XLG+z2YiNjaV06dJYLHl3E1+z7zR9pq29ZrkvhjZXS3chll/1SYo+1SVxJdUncaX8rk8Xk1OdM2C7ckZqca3ExESio6OJjIzEy8srR6+5fEz35a2zWuknd2w2GzVq1KB379688sorZoeT77KqT1erlznNG3XHKUSaRgYRFuDF8bjELMd1Gzi+xcvJxBAiIiIiIkWV1WKoESoHDh48yOLFi2nXrh1JSUlMmjSJ6Oho+vbta3ZoRYq+qi5E0sepAFlOEGGn4IxTERERERGRgs1isTBjxgyaNGlCq1at2LZtG7/++muRGEddkKilu5DJbpwKQN1y/nSqFWpSZCIiIiIiUphUqFCBVatWmR1GkWdqS/fy5cvp2rUrZcuWxTAM5s+ff9XygwYNwjCMTI9atWo5y4wZMybT8erVq+fxO8lfUbXDWPnszXwxtDnv3VufN3vVwd1isPVoPPM2HjE7PBEREREREfmXqUn3hQsXqFevHpMnT85R+ffee4+YmBjn4/DhwwQFBXH33XdnKFerVq0M5VauXJkX4ZsqfZxK9/rluKfJTTzZqRoAL/+wg8NnLpocnYiIiIiIiIDJ3cs7d+5M586dc1w+ICCAgIAA5/P58+dz9uxZBg8enKGcm5sboaHFq5v10DYVWbrzBOsPnOXJeVv4cmhzLBrbLSIiIoKnm5XvR7ZybouI5KdCPab7k08+oWPHjoSHh2fYv2fPHsqWLYuXlxctWrTg9ddf56abbsr2PElJSSQlJTmfp6+RZ7PZcrRIvc1mw263m7KgfToDePuuutz+/krWRZ/h45X7eaB1pGnxSO4VhPokRYPqkriS6pO4Un7XJwOoUy59OR87NptWzC2I0utF+iOn0stqJWRxhSvrU3p9zCo3zOk9rNAm3ceOHePnn39mzpw5GfY3a9aMGTNmUK1aNWJiYhg7dixt2rRh+/bt+Pn5ZXmu119/nbFjx2baf/LkSRITE7N4RUY2m424uDjsdrupa5d6AY+2KccbSw/xzi+7qR1soWJwCdPikdwpKPVJCj/VJXEl1SdxJdUnyUpKSgo2m43U1FRSU1Nz9Bq73U5aWhpAhnW6RXIjq/qUmpqKzWbj9OnTuLu7Zyh//vz5HJ230Cbdn332GYGBgfTo0SPD/su7q9etW5dmzZoRHh7OV199xZAhQ7I81/PPP88TTzzhfB4fH0+FChUICQm56iLn6Ww2G4ZhEBISYvp/HENvDmHtkUv8vvskry09wjfDW+Dhpv/MCpOCVJ+kcFNdEldSfRJXyu/6lJxqY8bqAwAMahmhv40KqMTERM6fP4+bmxtubteXplyZDInciMvrk5ubGxaLheDgYLy8vDKUu/J5dgpl0m232/n000+577778PDwuGrZwMBAqlatyt69e7Mt4+npiaenZ6b9Foslx/8RGIZxXeXz0lu96tJp4nL+PhbP5N/38eRt1cwOSa5TQapPUripLokrqT6JK+VnfbJh441FuwEY0DJCdbiAslgsGVYgygm73e4sW9hbutu3b0/9+vWZOHEiABEREYwaNYpRo0Zl+xrDMPjuu+8yNUReL1edp7DLqj6l18es7lc5vZcUyjvOH3/8wd69e7Ntub5cQkIC+/btIywsLB8iKxhK+3vxWs86AExetpdNh86aHJGIiIiISD6ypUH0Ctj2teOnLS3PLtW1a1eioqKyPLZixQoMw2Dr1q3Xfd7169czbNiwGw0vgzFjxlC/fv1M+2NiYq5rguvcmDFjBoGBgXl6jYLK1KQ7ISGBzZs3s3nzZgCio6PZvHkzhw4dAhzdvgcMGJDpdZ988gnNmjWjdu3amY499dRT/PHHHxw4cIDVq1fTs2dPrFYrffr0ydP3UtDcXieMHvXLYrPDk19t4VJy3t1oREREREQKjB0LYGJt+OwO+GaI4+fE2o79eWDIkCEsWbKEI0eOZDo2ffp0GjduTN26da/7vCEhIXh7e7sixGsKDQ3NsuevuIapSfeGDRto0KABDRo0AOCJJ56gQYMGvPTSS4DjG5f0BDxdXFwc33zzTbat3EeOHKFPnz5Uq1aN3r17ExwczNq1awkJCcnbN1MAje1Wm1B/L6JPXeD1n3eaHU6updnsrNl3mu83H2XNvtOkacZREREREcnKjgXw1QCIP5Zxf3yMY38eJN533HEHISEhzJgxI8P+hIQE5s2bx5AhQzh9+jR9+vShXLlyeHt7U6dOHb744ournjciIsLZ1RwcKzS1bdsWLy8vatasyZIlSzK95tlnn6Vq1ap4e3tTsWJFXnzxRVJSUgBHS/PYsWPZsmWLs8t0esyGYTB//nznebZt28bNN99MiRIlCA4OZtiwYSQkJDiPDxo0iB49evDOO+8QFhZGcHAwI0eOdF4rNw4dOkT37t3x9fXF39+f3r17c+LECefxLVu20KFDB/z8/PD396dRo0Zs2LABgIMHD9K1a1dKliyJj48PtWrV4qeffsp1LK5m6pju9u3bX3Vq/ysrLjjW6r548WK2r/nyyy9dEVqREODtztt31+W+T9Yxc81BOtYoQ9uqhevLh0XbYxj7ww5i4v43i3xYgBeju9YkqnbxGTIgIiIiUizZ7ZCS/d/+2O2Qmgo2N7Db4OdngKzyCztgwKJnoWJ7sORgvXZ3b8jBOHE3NzcGDBjAjBkzeOGFF5xjgefNm0daWhp9+vQhISGBRo0a8eyzz+Lv78/ChQu57777qFSpEk2bNr3mNWw2G3feeSdlypThzz//JC4uLsux3n5+fsyYMYOyZcuybds2hg4dip+fH8888wz33HMP27dvZ9GiRfz666+AI7e60oULF+jUqRMtWrRg/fr1xMbG8sADD/Dwww9nyM+WLVtGWFgYy5YtY+/evdxzzz3Ur1+foUOHXvP9ZPX+0hPuP/74g9TUVEaOHMk999zD77//DkC/fv1o0KABU6ZMwWq1snnzZueEZyNHjiQ5OZnly5fj4+PDjh078PX1ve448kqhnEhNcq5NlRAGtgjnszUHeebrrfwyqi0B3oVjdsdF22MY8fmmTLfN43GJjPh8E1P6N1TiLSIiIlKUpVyEcWWzPWwAOf/L1u5oAX+jQs6K/+cYePjkqOj999/P22+/zR9//EH79u0BR9fyXr16ERAQQEBAAE899ZSz/COPPMIvv/zCV199laOk+9dff2XXrl388ssvlC3r+DzGjRuXaRz2f//7X+d2REQETz31FF9++SXPPPMMJUqUwNfXFzc3N0JDQ7O91pw5c0hMTGTmzJn4+Dje/6RJk+jatStvvvkmZcqUAaBkyZJMmjQJq9VK9erV6dKlC0uXLs1V0r106VK2bdtGdHQ0FSo4/n1mzpxJrVq1WL9+PU2aNOHQoUM8/fTTVK9eHYAqVao4X3/o0CF69epFnTqOea0qVqx43THkpUI5kZpcn+c616BiKR+OxycyesF2s8PJkTSbnbE/7Mj2e0qAsT/sUFdzERERETFd9erVadmyJZ9++ikAe/fuZcWKFc4hsWlpabzyyivUqVOHoKAgfH19+eWXXzINpc3Ozp07qVChgjPhBmjRokWmcnPnzqVVq1aEhobi6+vLf//73xxf4/Jr1atXz5lwA7Rq1Qqbzcbu3bud+2rVqoXV+r8eA2FhYcTGxl7XtS6/ZoUKFZwJN0DNmjUJDAxk507HMNknnniCBx54gI4dO/LGG2+wb98+Z9lHH32UV199lVatWjF69OhcTVyXl9TSXQyU8LAyvnc9ek1ZzfzNx7i1Zihd6hbsFuJ10WcydCm/kh2IiUtkXfQZWlQKzr/AREREpNDxdLPyxdDmzm0pRNy9HS3O2bDb7aSmpuLm5oZxaA3Mvuva5+z3NYS3zNm1r8OQIUN45JFHmDx5MtOnT6dSpUq0a9cOgLfffpv33nuPiRMnUqdOHXx8fBg1ahTJycnXdY2rWbNmDf369WPs2LF06tSJgIAAvvzyS8aPH++ya1zuyrXRDcPAZrPlybXAMfN63759WbhwIT///DOjR4/myy+/pGfPnjzwwAN06tSJhQsXsnjxYl5//XXGjx/PI488kmfxXA+1dBcTDW4qycgOlQF4Yf42YuOzT2gLgtjzOYsvp+VERESk+LJaDFpUCqZFpWCslsK9lnOxYxiOLt45eVS6GfzL4uh0nuXJwL+co1xOzned63737t0bi8XCnDlzmDlzJvfff79zfPeqVavo3r07/fv3p169elSsWJF//vknx+euUaMGhw8fJiYmxrlv7dq1GcqsXr2a8PBwXnjhBRo3bkyVKlU4ePBghjIeHh6kpV19VaMaNWqwZcsWLly44Ny3atUqLBYL1apVy3HM1yP9/R0+fNi5b8eOHZw7d46aNWs691WtWpXHH3+cxYsXc+eddzJ9+nTnsQoVKjB8+HC+/fZbnnzySaZNm5YnseaGku5i5JGbq1C7nD/nLqbw7DdbrzqJnZnsdjubDuZsbfHSfl55HI2IiIiIFAoWK0S9+e+TKxPmf59HvZGzSdRywdfXl3vuuYfnn3+emJgYBg0a5DxWpUoVlixZwurVq9m5cycPPvhghpm5r6Vjx45UrVqVgQMHsmXLFlasWMELL7yQoUyVKlU4dOgQX375Jfv27eP999/nu+++y1AmIiLCuUzzqVOnSEpKynStfv364eXlxcCBA9m+fTvLli3jkUce4b777nOO586ttLQ055LR6Y+dO3fSsWNH6tSpQ79+/di0aRPr1q1jwIABtGvXjsaNG3Pp0iUefvhhfv/9dw4ePMiqVatYv349NWrUAGDUqFH88ssvREdHs2nTJpYtW+Y8VhAo6S5GPNwsvNu7Ph5uFpbtPsmX6w9f+0X5LCXNxn++285naw5etZyBYxbzppFB+ROYiIiIFFopaTZmrjnAzDUHSEnLu+6vUgDU7Aa9Z4L/FUMp/cs69tfslqeXHzJkCGfPnqVTp04Zxl//97//pWHDhnTq1In27dsTGhpKjx49cnxei8XCd999x6VLl2jatCkPPPAAr732WoYy3bp14/HHH+fhhx+mfv36rF69mhdffDFDmV69ehEVFUWHDh0ICQnJctkyb29vfvnlF86cOUOTJk246667uOWWW5g0adL1fRhZSEhIcC4Znf7o2rUrhmHw/fffU7JkSdq2bUvHjh2pWLEic+fOBcBqtXL69GkGDBhA1apV6d27N507d2bs2LGAI5kfOXIkNWrUICoqiqpVq/LBBx/ccLyuYtgLanOnieLj4wkICCAuLg5/f/9rlrfZbMTGxlK6dGksloL/PcbHK/bz6sKdeHtY+fmxNoQH52xWxrx29kIyD83exJr9pzEM6Fm/HN/9dRTIvPCDAUV29vLCVp+k4FJdEldSfRJXyu/6dDE5lZov/QLAjpc74e2haY0KosTERKKjo4mMjMTLK2e9GTOM6b68O7gtDQ6uhoQT4FvGMYY7j1q4pejIqj5drV7mNG/U/5rF0P2tImkWGcTF5DSe/GpLgZgBfG9sAj0/WMWa/afx8bAy7b7GvHtPfab0b0hoQOab7pA2kUUy4RYRERERF7BYIbIN1LnL8VMJt5hISXcxZLEYvHN3PXw93dhw8CzTVuw3NZ7l/5yk5werOHD6IuUCS/DNQy3pWNMxXiSqdhgrn72ZL4Y2571763NXw/IA/LYrllR1DxMRERERkQJOSXcxVSHIm5e6OmYCfHfxP+yMic/3GOx2O5+tPsDgGes5n5hK4/CSfP9wK6qHZuyakT7jaPf65RjdrSaB3u7sP3mB+ZuzXz5CRERERESkIFDSXYzd3ag8HWuUITnNxuNzN5OUevXlA1wpJc3Gf+dvZ/SCv0mz2bmzYTlmD21GKV/Pq77Oz8ud4e0qAfDe0n9ITlVrt4iIiIiIFFxKuosxwzB4/c46BPl4sOv4eSb+uidfrnvuYjIDP13H7D8PYRjwXOfqjL+7Hp5uORtrM7BFBKV8PTl85hLzNha8GdhFRERERETSKeku5kL8PBnXsw4AH/6xjw0HzuTp9fadTKDnB6tZve803h5WPrqvMcPbVco42+Q1lPCw8nAHR2v3/y3dS2JK/rXQi4iIiIiIXA8l3UJU7VB6NSyPzQ5PztvChaTUPLnOij0n6Tl5FdGnLjgmTBvRklv/nTDtevVpdhNlA7w4Hp/I7D8PuThSERERKUo8rBY+HdSYTwc1xsOqP39FJH/priMAjO5Wk7IBXhw8fZHXftrp8vPPXHOAQdPXE5+YSqPwkswf2YoaYddeAz07nm5WHr2lCgBTft+bZ18UiIiISOHnZrVwc/Uy3Fy9DG5KukUkn+muIwD4e7nzzt31AJjz5yGW7Y51yXlT02y8OH87L33/74RpDcox+4FmhPhdfcK0nOjVqDzhwd6cSkhmxuoDNx6siIiIiIiIiynpFqeWlUsxuFUEAM9+vZWzF5Jv6HxxF1MYNH09s9YexDDgmahqjO9dDy/3nE2Ydi3uVgujOjpauz/8Yx9xl1Jccl4REREpWlLSbMzbcJh5Gw6TkqaVT0TyQ0REBBMnTjQ7jAJBSbdk8GxUdSqF+BB7PokXv9+e6/PsP5lAzw9WsXLvKbw9rEzt34iH2le+rgnTcqJbvXJUKe1LfGIqn6yMdum5RUREpGhISbPx9NdbefrrrUq6JU8MGjQIwzCcj+DgYKKioti6davLrjFmzBjq16+fo3KXx5L+qF69ustiKahef/11mjRpgp+fH6VLl6ZHjx7s3r07Q5mPPvqI9u3b4+/vj2EYnDt3Ls/jUtItGXi5W5lwT32sFoMft8bw/eaj132OVXtP0WPyKvafukDZAC++Ht6STrVC8yBasFoMnri1KgCfrNjPmRtsnRcRERERyY2oqChiYmKIiYlh6dKluLm5cccdd5gSS61atZyxpD9WrlxpSiz56Y8//mDkyJGsXbuWJUuWkJKSwm233caFCxecZS5evEhUVBT/+c9/8i0uJd2SSd3ygTxyc2UAXpy/neNxiTl+7edrDzLg03XEJ6bS4KZA5j/cipplcz9hWk50qhVKrbL+XEhO48M/9uXptUREREREsuLp6UloaCihoaHUr1+f5557jsOHD3Py5ElnmcOHD9O7d28CAwMJCgqie/fuHDhwwHn8999/p2nTpvj4+BAYGEirVq04ePAgM2bMYOzYsWzZssXZcj1jxoxsY3Fzc3PGkv4oVaqU83hERASvvPIKffr0wcfHh3LlyjF58uQM5zh06BDdu3fH19cXf39/evfuzYkTJzKU+eGHH2jSpAleXl6UKlWKnj17Zjh+8eJF7r//fvz8/Ljpppv46KOPcvHJ5tyiRYsYNGgQtWrVol69esyYMYNDhw6xceNGZ5lRo0bx3HPP0bx58zyN5XJKuiVLIztUpm75AOITU3n66y3Y7farlk9NszH6++38d/520mx2etQvyxdDm1PazyvPY7VYDJ66rRoAn605QGx8zr8kEBEREZGC72JyaraPpJS0HJdNzGHZG5WQkMDnn39O5cqVCQ4OBiAlJYVOnTrh5+fHihUrWLVqFb6+vkRFRZGcnExqaio9evSgXbt2bN26lTVr1jBs2DAMw+Cee+7hySefzNCCfc8999xQjG+//Tb16tXjr7/+4rnnnuOxxx5jyZIlANhsNrp3786ZM2f4448/WLJkCfv3789wzYULF9KzZ09uv/12/vrrL5YuXUrTpk0zXGP8+PE0btyYv/76i4ceeogRI0Zk6u59uXHjxuHr63vVx6FDOV8uOC4uDoCgoKDr+Whczs3Uq0uB5W618G7v+nR5fwUr9pzi87UHua9FRJZl4y6l8PCcTazYcwqApztV46H2lVw+fvtq2lcLoeFNgWw6dI7Jy/YytnvtfLu2iIiIiOStmi/9ku2xdlVLMWPw/5K9Rq/8yqUrkut0zSKDmPtgC+fz1m8uy3J44oE3ulx3jD/++CO+vr4AXLhwgbCwMH788UcsFkc759y5c7HZbHz88cfOv5OnT59OYGAgv//+O40bNyYuLo477riDSpUqAVCjRg3n+X19fZ0t2Neybds2Zyzp+vfvz9SpU53PW7VqxXPPPQdA1apVWbVqFRMmTODWW29l6dKlbNu2jejoaCpUqADAzJkzqVWrFuvXr6dJkya89tpr3HvvvYwdO9Z5znr16mW45u23385DDz0EwLPPPsuECRNYtmwZ1apVyzLu4cOH07t376u+t7Jly17z/YPji4NRo0bRqlUratc2NzdQ0i3Zqlzal+c6V2fsDzt47aedtKhUipPnk4g9n0hpPy+aRgZx6MxFhny2nv0nL1Di3/HgUbXzZvz21RiGo7W778d/MmfdIYa2rUj5kt75HoeIiIiIFE8dOnRgypQpAJw9e5YPPviAzp07s27dOsLDw9myZQt79+7Fz88vw+sSExPZt28ft912G4MGDaJTp07ceuutdOzYkd69exMWFnbdsVSrVo0FCxZk2Ofvn3HIZ4sWLTI9T59tfOfOnVSoUMGZcAPUrFmTwMBAdu7cSZMmTdi8eTNDhw69ahx169Z1bhuGQWhoKLGx2S9NHBQU5LJW6ZEjR7J9+/YCMZZdSXdhZUuDg6sh4QT4loHwlmBxzVJclxvYIoIlO06wet9pOr+3nJS0/3UzD/LxIDEljYvJaYQFeDFtQGNqlwtweQw51bJyKVpWCmb1vtNM+m0vb/Sqe+0XiYiIiEiBt+PlTlnut9vt2NMytmpvfLFjtuexXNETc+WzHW48uH/5+PhQuXJl5/OPP/6YgIAApk2bxquvvkpCQgKNGjVi9uzZmV4bEhICOFq+H330URYtWsTcuXP573//y5IlS657/LGHh0eGWPJCiRIlrlnG3d09w3PDMLDZsl9BYNy4cYwbN+6q59yxYwc33XTTVcs8/PDD/Pjjjyxfvpzy5ctfM868pqS7MNqxABY9C/HH/rfPvyxEvQk1u7n0UhaLQZe6YazedzpDwg04u+JEBHvz1fAW+TJ++1qevK0qq6esYd7GIzzYrhKRpXzMDklERERM5mG1MLlvQ+e2FD7eHlmnLXa7ndTUnJW9nvO6gmEYWCwWLl26BEDDhg2ZO3cupUuXztTqfLkGDRrQoEEDnn/+eVq0aMGcOXNo3rw5Hh4epKVl3W0+N9auXZvpeXp39ho1anD48GEOHz7sbO3esWMH586do2bNmoCjFXvp0qUMHjzYZTHdaPdyu93OI488wnfffcfvv/9OZGSky2K7EUq6C5sdC+CrAcAVE5vFxzj2957p0sQ7zWZn0m97r1omMdVGsI+ny655IxqFB9GhWgjLdp/kvV//YeK9DcwOSUREREzmZrXQpe71d9EVuR5JSUkcP34ccHQvnzRpEgkJCXTt2hWAfv368fbbb9O9e3defvllypcvz8GDB/n222955plnSElJ4aOPPqJbt26ULVuW3bt3s2fPHgYMGAA4ZhyPjo5m8+bNlC9fHj8/Pzw9s/4bPDU11RlLOsMwKFOmjPP5qlWreOutt+jRowdLlixh3rx5LFy4EICOHTtSp04d+vXrx8SJE0lNTeWhhx6iXbt2NG7cGIDRo0dzyy23UKlSJe69915SU1P56aefePbZZ3P9Gd5o9/KRI0cyZ84cvv/+e/z8/JyfQUBAgLNl/vjx4xw/fpy9ex05zrZt25yzq5csWTLX174afdVXmNjSHC3cVybc8L99i55zlHORddFniLnGkmHH4xJZF33GZde8UU/+O5P591uOsfv4eZOjEREREZHiYNGiRYSFhREWFkazZs1Yv3498+bNo3379gB4e3uzfPlybrrpJu68805q1KjBkCFDSExMxN/fH29vb3bt2kWvXr2oWrUqw4YNY+TIkTz44IMA9OrVi6ioKDp06EBISAhffPFFtrH8/fffzljSH+Hh4RnKPPnkk2zYsIEGDRrw6quv8u6779Kpk6Mbv2EYfP/995QsWZK2bdvSsWNHKlasyNy5c52vb9++PfPmzWPBggXUr1+fm2++mXXr1rn4U70+U6ZMIS4ujvbt22d475fHPXXqVBo0aOAcj962bVsaNGiQaQy8Kxn2a60FVQzFx8cTEBBAXFzcVbt+pLPZbMTGxlK6dGnn7IR5InoFfHbHtcsN/BEi27jkkt9vPspjX26+Zrn37q1P9/rlXHJNVxjx+UZ+3n6cqFqhTL2vkdnhXJd8q09S5KkuiSupPokr5Xd9Sk2z8cvfjvWFO9Uqg5u6mBdIiYmJREdHExkZiZdXzoYtOrqXp+Lm5pavK+cUdhEREYwaNYpRo0aZHUqBklV9ulq9zGneqDtOYZJw4tplrqdcDuR0nHZBGM99ucdvrYphwKK/j7PtSJzZ4YiIiIiJktNsjJyziZFzNpGclv0kTiIieUFJd2HiW+baZa6nXA40jQwiLMCL7L43NICwAMfyYQVJ1TJ+9Pi35X38kt0mRyMiIiIiIsWVku7CJLylY5byq6XA/uUc5VzEajEY3bVm+tmvvBoAo7vWxGopeN15HrulClaLwe+7T7LhQMEZcy4iIiIiYqYDBw6oa3k+UtJdmFisjmXBgGxT4Kg3XL5ed1TtMKb0b0hoQMYu5KEBXkzp35Co2gVzNtCIUj70buxYl2/84n9MjkZERERERIojLRlW2NTs5lgW7Mp1uj39ofskl6/TnS6qdhi31gxlXfQZYs8nUtrP0aW8ILZwX+7hm6vwzcajrNl/mlV7T9GqcimzQxIRERGRq9A8z1KQuKI+qqW7MKrZDUZtd8xS3nCgY5+nH1S7PU8va7UYtKgUTPf65WhRKbjAJ9wA5QJL0LfZTQC8s3i3buIiIiIiBZS7uzsAFy9eNDkSkf9Jr4/p9TM31NJdWFmsjmXByjeGXT9C/BHHz1o9zI6swHmoQyW+XH+Ivw6dY9nuWG6u7rqJ5kRERETENaxWK4GBgcTGxgKOda2vtQyYlgwTV7q8PoEj4Y6NjSUwMBCrNfdDeJV0F3buJaDxEFj+Fqz9QEl3Fkr7eTGwZQQf/rGfd375h/ZVS2MpBK30IiIi4hruVgtv31XXuS0FV2hoKIAz8b4Wu92OzWbDYrEo6ZYbllV9CgwMdNbL3FLSXRQ0GQIrJ8DhP+HIRijfyOyICpzhbSsxe+0hdsTEs+jv49xep2BO/iYiIiKu5261cHfjCmaHITlgGAZhYWGULl2alJSUa5a32WycPn2a4OBgLBZ9oSI35sr65O7ufkMt3OmUdBcFfqFQuxds/RL+nALlPzY7ogKnpI8HQ1pH8t7SPby75B861QotFGPSRURERIojq9Wao2THZrPh7u6Ol5eXkm65YXlVn1Qzi4rmwx0///4u46zm4jSkTSQBJdzZG5vA95uPmh2OiIiI5JPUNBu/7TrBb7tOkJpmMzscESlmlHQXFWUbwE0twZYK69XSnRV/L3cebFcRgIm/7iFF/+mKiIgUC8lpNu6fsYH7Z2wgWf//i0g+U9JdlDQf4fi5YToka6mFrAxqGUEpXw8OnbnI1xuPmB2OiIiIiIgUcUq6i5LqXSDwJrh0BrZ9ZXY0BZK3hxsPta8MwPtL95CYkmZyRCIiIiIiUpQp6S5KLFZo9u/Y7rVTwG43N54Cqm+zmwgL8CImLpEv1h0yOxwRERERESnClHQXNQ36g4cvnNwF+34zO5oCycvdyiM3VwFg8rJ9XExONTkiEREREREpqpR0FzVeAY7EGxyt3ZKluxuX56Ygb04lJPHZ6oNmhyMiIiIiIkWUku6iqNmDgAF7l8DJf8yOpkByt1p47BZHa/fUP/YRn5hickQiIiIiIlIUKekuioIqQrXbHdt/TjU3lgKsR4NyVArxIe5SCp+ujDY7HBEREckj7lYLL3evxcvda+Fu1Z+/IpK/dNcpqtKXD9vyBVw8Y24sBZTVYvDErdUA+HhFNGcvJJsckYiIiOQFd6uFAS0iGNAiQkm3iOQ73XWKqojWUKYOpFyETZ+ZHU2B1bl2KDXC/ElISuXD5fvNDkdERERERIoYJd1FlWH8r7V73TRI05jlrFgsBk/dVhWAGaujiT2faHJEIiIi4mppNjtr9p1mzb7TpNm0pKqI5C8l3UVZ7V7gEwLxR2HnArOjKbBurl6a+hUCSUyx8cGyfWaHIyIiIi6WlJpGn2lr6TNtLUmpaWaHIyLFjJLuoszdCxoPcWxr+bBsGYbBU7c5xnbP+fMQx85dMjkiEREREREpKpR0F3VNhoDVA46sh8PrzY6mwGpVOZjmFYNITrPxf7/tMTscEREREREpIpR0F3W+paHO3Y7tP9XanR3DMHjy39burzYc4cCpCyZHJCIiIiIiRYGS7uKg2XDHz7/nQ9wRU0MpyJpEBNGuaghpNjvvL1Vrt4iIiIiI3Dgl3cVBWF2IaAP2NMdM5pKt9LHd3/51lK83HOb7zUc106mIiIiIiOSam9kBSD5pPgIOrICNM6DdM+DhY3ZEBVKd8gHUrxDA5sNxPPX1Vuf+sAAvRnetSVTtMBOjExERERGRwkYt3cVF1SgoGQGJ52DLl2ZHU2At2h7D5sNxmfYfj0tkxOebWLQ9xoSoRERE5Ea4WSw837k6z3eujptFf/6KSP7SXae4sFih2QjH9p9TwWYzN54CKM1mZ+wPO7I8lt65fOwPO9TVXEREpJDxcLPwYLtKPNiuEh5u+vNXRPKX7jrFSYN+4OkPp/6Bfb+ZHU2Bsy76DDFxidketwMxcYmsiz6Tf0GJiIiIiEihpqS7OPH0gwb3ObbXTjY3lgIo9nz2CXduyomIiEjBkGazs+XwObYcPqceayKS75R0FzfNhoFhcbR0x+40O5oCpbSfV47KBft45HEkIiIi4kpJqWl0n7yK7pNXkZSaZnY4IlLMKOkubkpGQLXbHdt/TjU1lIKmaWQQYQFeGNco99Yvuzlw6kK+xCQiIiIiIoWbku7iqPlDjp9bvoSLGp+czmoxGN21JkCmxDv9eQl3C1uPxHH7+yv4av1h7HZ1URMRERERkewp6S6OwltCWD1ITYSN082OpkCJqh3GlP4NCQ3I2NU8NMCLqf0bsvTJ9jSLDOJichrPfLOVh2Zv4tzFZJOiFRERERGRgs7N7ADEBIbhaO3+7kFYNw1aPgpWd7OjKjCiaodxa81Q1kWfIfZ8IqX9vGgaGYTV4mjvnjO0OR8t38/4xbv5eftx/jp0jnd716Nl5VImRy4iIiIiIgWNWrqLq1o9wbcMnI+Bv+ebHU2BY7UYtKgUTPf65WhRKdiZcKcfG9G+Et891IqKpXw4Hp9Iv0/+5PWfdmpyFhERERERyUBJd3Hl5glNHnBsr50MGpt83eqUD+DHR1vTt9lN2O3w4fL99Jy8mr2x580OTURERERECggl3cVZo8Fg9YRjf8HhdWZHUyh5e7gxrmcdPrqvESW93dkRE0+X91cya+1BTbImIiJSQLhZLDx2SxUeu6UKbhb9+Ssi+Ut3neLMNwTq3u3YXvuBubEUcrfVCuWXUW1pU6UUSak2Xpy/nQc+28CphCSzQxMRESn2PNwsPH5rVR6/tSoebvrzV0Tyl+46xV368mE7F8C5Q+bGUsiV9vfis8FNeemOmnhYLSzdFUvUxOUs2x1rdmgiIiIiImISJd3FXZlaENkO7DbHTOZyQywWg/tbR/L9w62oVsaPUwnJDJ6+njEL/iYxRZOsiYiImMFms/PPifP8c+I8NpuGf4lI/lLSLf9r7d74GSQlmBtLEVEjzJ/vH27FoJYRAMxYfYBuk1ayMybe3MBERESKocTUNG6bsJzbJiwnUSuNiEg+U9ItUOU2CKoISXGw5QuzoykyvNytjOlWixmDm1DK15N/TiTQfdIqPlkZrW/ZRURERESKCSXdAhYLNBvh2F47BWw2c+MpYtpXK80vo9rQsUZpktNsvPLjDgZOX8eJ+ESzQxMRERERkTympFsc6vcFzwA4sw/2LjE7miIn2NeTaQMa81rP2ni5W1ix5xRRE5fzy9/HzQ5NRERERETykJJucfD0hUYDHNtaPixPGIZBv2bh/PhIG2qV9efsxRQenLWR57/dxsXkVADSbHbW7j/N4l1nWLv/NGnqhi4iIiIiUqiZmnQvX76crl27UrZsWQzDYP78+VctP2jQIAzDyPSoVatWhnKTJ08mIiICLy8vmjVrxrp16/LwXRQhTYeBYYH9v8OJHWZH8z+2NIheAdu+dvy0Fe4JUCqX9uW7h1rxYLuKGAZ8se4Qd7y/kg//2EfrN3+j78freGlRNH0/XkfrN39j0fYYs0MWEREREZFcMjXpvnDhAvXq1WPy5Mk5Kv/ee+8RExPjfBw+fJigoCDuvvtuZ5m5c+fyxBNPMHr0aDZt2kS9evXo1KkTsbFaK/maAm+CGl0d2wWltXvHAphYGz67A74Z4vg5sbZjfyHm4Wbh+c41mP1AM0L9vdh/6gKv/7yLmLiM47yPxyUy4vNNSrxFRERERAopU5Puzp078+qrr9KzZ88clQ8ICCA0NNT52LBhA2fPnmXw4MHOMu+++y5Dhw5l8ODB1KxZk6lTp+Lt7c2nn36aV2+jaElfPmzrV3DhlLmx7FgAXw2A+GMZ98fHOPYX8sQboGWlUix8tDVe7ln/KqZ3Lh/7ww51NRcREcklN4uFYW0rMqxtRdwsGl0pIvnLzewAbsQnn3xCx44dCQ8PByA5OZmNGzfy/PPPO8tYLBY6duzImjVrsj1PUlISSUlJzufx8Y61lG02G7YczORts9mw2+05KlvglWuCUbYBxrG/sK3/FNo+ZU4ctjSMRc8CdoxMB+2OvYuew161M1is+R+fC+0+Hk9iSvZ1xw7ExCXy5/5TNK8YnH+BSaFXpO5NYjrVJ3Gl/K5PbhZ4LqpahutL0aB7k7jS9dannJYrtEn3sWPH+Pnnn5kzZ45z36lTp0hLS6NMmTIZypYpU4Zdu3Zle67XX3+dsWPHZtp/8uRJEhOvvayTzWYjLi4Ou92OpQh8e+pVvS+Bx/7Cvu4jTla5F6we+R6Dx9E/CbqyhfsyBnaIP8rZLT+RXK5ZPkbmenuPnMlhuZNU9C3c49klfxW1e5OYS/VJXEn1SVxFdUlc6Xrr0/nz53N03kKbdH/22WcEBgbSo0ePGz7X888/zxNPPOF8Hh8fT4UKFQgJCcHf3/+ar7fZbBiGQUhISNH4ZQ8egH39u1jPx1D65Cqoe0/+x7DvUI6KBbonQenSeRxM3qqcYAWir12ufAilS6ulW3KuyN2bxFSqT+JK+V2fbDY7x+IuAVA2oAQWS+Z+dFI46d4krnS99cnLyytH5y2USbfdbufTTz/lvvvuw8Pjf62wpUqVwmq1cuLEiQzlT5w4QWhoaLbn8/T0xNPTM9N+i8WS419ewzCuq3yBZvGCJg/Ab69g+XMK1LsXjHz6z+nIRlg1AXb+kKPiFr8wKOSfebOKpQgL8OJ4XCJZjdo2gNAAL5pVLKU/EuS6Fal7k5hO9UlcKT/rU2JqKm3f/gOAHS93wtutcA9Nk4x0bxJXup76lNM6Vyhr5h9//MHevXsZMmRIhv0eHh40atSIpUuXOvfZbDaWLl1KixYt8jvMwq3RYHDzgpgtcGht3l7Lboc9S2DGHfDxzf9LuN0yfxGSgX85CG+Zt7HlA6vFYHTXmgBZjF93GN21JlYl3CIiIiIihY6pSXdCQgKbN29m8+bNAERHR7N582YOHXJ0LX7++ecZMGBAptd98sknNGvWjNq1a2c69sQTTzBt2jQ+++wzdu7cyYgRI7hw4UKGGc4lB3yC/9etfG3OlnS7bmkpsGUuTGkFs++CAyvA4gb1+sJDa+HOj3Gkodkkm22fKvSTqKWLqh3GlP4NCQ3I3EXljV51iaodZkJUIiIiIiJyo0ztXr5hwwY6dOjgfJ4+rnrgwIHMmDGDmJgYZwKeLi4ujm+++Yb33nsvy3Pec889nDx5kpdeeonjx49Tv359Fi1alGlyNcmB5iNg02ewayGcPQAlI1xz3uQLsGkmrJkMcYcd+zx8odEgxzUDyjv2la4BvWfComczLhtmcQNbKvz5IdTsAd5BronLZFG1w7i1Zih/7j/FniOxfL7pJHtiL3D4zEWzQxMRERERkVwyNelu3749dnv2aw/PmDEj076AgAAuXrx6EvLwww/z8MMP32h4UroGVOwA+5fBumnQ6bUbO9+FU45Eef00uHTWsc8nBJoNhyZDoETJzK+p2Q2qd4GDqyHhBPiWgcCb4NMoOLkLvuwL930H7iVuLLYCwmoxaF4xmIq+aZQpFcTwzzcxc80BRrSvhI9noZyCQURERESkWCuUY7olH7UY6fi5aSYk5WxK/EzORMPCJ2FCLVj+liPhLhkJd0yAUdsc3cSzSrjTWawQ2Qbq3OX4WTIc+n8NngFwaA18OxRsRW8prY7VS1OxlA/xial8uf6w2eGIiIiIiEguKOmWq6t0CwRXgaR42Dzn2uUvF7MFvr4f/q8hrP8YUhOhbAO4+zN4ZCM0vj/3LdRlasG9sx1riO/8AX5+1jEhWxFisRgMa1sRgE9W7CclzWZyRCIiIiIicr2UdMvVWSzQfLhje+0UsF0j8bPbYd8ymNkDPmwL278Bu82RvA/8AYYug1o9XDMBWmQbuPMjwHB0WV/57o2fs4Dp0aAcIX6eHItL5Ictx679AhEREcnEajG4r3k49zUP12ogIpLvNEhUrq1eH1j6MpyNhhXvQlCEY2x1eMv/Jc9pqbDze1j1nqOFG8CwQu07oeWjEFY3b2Kr1RPOn3BMtrb0ZfALg/p98+ZaJvBytzK4VQRvLdrNh3/sp2eDchj5tWa6iIhIEeHpZuWVHplXvRERyQ9KuuXaPHwgorVjFvNlr/xvv39ZuPUVxxjtNZMcM5wDuJWAhgMc48FLhud9fM2HQ/xRWP0+LHgEfEpDlY55f9180q9ZOJN/28vuE+f5/Z+TdKhW2uyQREREREQkh9S9XK5txwLY9VPm/fHH4Jsh8NNTjoS7RBC0fx4e/xtufyt/Eu50HcdCnd6OpcS+GgBHN+XftfNYQAl3+ja7CYAP/9hncjQiIiKFj91u53RCEqcTkq66co6ISF5Q0i1XZ0tzdN3mKv9BGVaIegMe3w7tnwOf4HwLz8lige6ToWJ7SLkAc3rDmf35H0ceub91JG4Wg7X7z7D58DmzwxERESlULqWk0ejVX2n06q9cSil6K56ISMGmpFuu7uBqR4v21djToExtRzd0M7l5QO9ZEFoHLpyEz3tBwklzY3KRsIASdK9fDoCPlqu1W0RERESksFDSLVeXcMK15fKalz/0+wYCb3K0dM/pDUkJZkflEunLh/28/TgHTl0wORoREREREckJJd1ydb5lXFsuP/iVgf7fOcaYH9sE8wZBWorZUd2waqF+3Fy9NHY7TFtRdLrOi4iIiIgUZUq65erCWzpmKSe7ZaoM8C/nKFeQlKoMfb9yzKS+dwn8OMqxhngh9+C/rd3zNh7h5Pkkk6MREREREZFrUdItV2exQtSb/z65MvH+93nUG/9br7sgqdAE7p4OhgX++hyWvWZ2RDesaWQQ9SsEkpxqY+aaA2aHIyIiIiIi16CkW66tZjfoPRP8wzLu9y/r2F+zmzlx5US1znDHRMf28rdh/SemhnOjDMNgeDtHa/fMNQe5kJRqckQiIiIiInI1bmYHIIVEzW5QvYtjNvOEE44x3OEtC2YL95UaDYTzMfD76441xX3LQI07zI4q126tGUpkKR+iT11g7vrD3N860uyQRERECjSrxaBXw/LObRGR/KSWbsk5ixUi20Cduxw/C0PCna7ds9BwINht8M0QOLTW7IhyzWoxGNrG0dr9ycpoUtJsJkckIiJSsHm6WRnfux7je9fD060Q/f0iIkWCkm4pHgwDurwLVTtDaiLMuQdO7jY7qly7s2E5Svl6cPTcJRZujTE7HBERERERyYaSbik+rG5w16dQrjEknoPPe0F84UxYvdytDG7l6FY+9Y992IvAzOwiIiJ5xW63czE5lYvJqfo/U0TynZJuKV48vB1LiQVXhrjDMPsuSIwzO6pc6d8sHG8PK7uOn2f5nlNmhyMiIlJgXUpJo+ZLv1DzpV+4lJJmdjgiUswo6ZbixycY+n/jmFDtxHb4sh+kFr41rwO83enT9CYAPvxjn8nRiIiIiIhIVpR0S/FUMgL6zQMPXziwAuaPAFvhm5Ds/taRuFkMVu87zdYj58wOR0RERERErqCkW4qvsHpwzyywuMH2b2DJi2ZHdN3KBZagW72yAHy4fL/J0YiIiIiIyJWUdEvxVulm6DHFsb1mEqyeZG48uTCsnWP5sJ+3xXDw9AWToxERERERkcsp6Rap2xtufdmxvfgF2PY12NIgeoVjO3qF43kBVT3Unw7VQrDZYdoKtXaLiIiIiBQkSrpFAFo+Cs1GOLa/HQbvVIHP7oBvhjh+TqwNOxaYG+NVPNiuEgDzNhzhVELhmxRORERERKSoUtItAmAY0GkclG8K9jS4eDrj8fgY+GpAgU28m0UGUa9CIEmpNmauPmB2OCIiIgWKxTC4vU4ot9cJxWIYZocjIsWMkm4RJ7tj7e7sjgEseq5AdjU3DIPhbR1juz9bc5ALSakmRyQiIlJweLlb+aBfIz7o1wgvd6vZ4YhIMaOkWyTdwdVwPuYqBewQf9RRrgC6rVYoEcHexF1K4asN2X15ICIiIiIi+UlJt0i6hBOuLZfPrBaDof+2dn+8IpqUtMK37riIiIiISFGjpFsknW8Z15YzQa+G5Snl68HRc5f4advVWu1FRESKj4vJqUQ8t5CI5xZyMVlDsEQkfynpFkkX3hL8ywLZTbBigH85R7kCysvdyqCWEQBM/WM/drvd3IBERERERIo5Jd0i6SxWiHrz3yfZJN5RbzjKFWD9m4fj7WFlZ0w8K/acMjscEREREZFiTUm3yOVqdoPeM8E/LPOxTq85jhdwgd4e3NvkJgA+XL7P5GhERERERIo3Jd0iV6rZDUZth4E/Qq9PILyVY3/sDnPjug5D2kRitRis2nuabUfizA5HRERERKTYUtItkhWLFSLbQJ27oOMYx76t8yDhpKlh5VS5wBJ0q1cWUGu3iIiIiIiZlHSLXEuFplCuMaQlwYZPzI4mx4b9u3zYT9tiOHT6osnRiIiIiIgUT0q6RXKixUOOn+s/hpREc2PJoRph/rSrGoLNDh+v3G92OCIiIqaxGAYdqoXQoVoIFiO7VUpERPKGkm6RnKjRHfzLw4WTsG2e2dHk2IPtHK3dX204zOmEJJOjERERMYeXu5Xpg5syfXBTvNwL9iokIlL0KOkWyQmrGzQb5theOwUKyfrXLSoGU7d8AIkpNmauOWh2OCIiIiIixY6SbpGcajgQ3H0g9m/Y/7vZ0eSIYRg82LYSADPXHOBicqrJEYmIiIiIFC9KukVyqkQgNOjv2F77gamhXI+o2qHcFOTN2YspzNtwxOxwRERE8t3F5FRqvLiIGi8u0hfQIpLvlHSLXI/mwwED9iyGk/+YHU2OWC0GQ/+dyXzaiv2kptlMjkhERCT/XUpJ41JKmtlhiEgxpKRb5HoEVYRqtzu2/5xibizX4e5G5Qn28eDI2Uv8tP242eGIiIiIiBQbSrpFrlf68mGbv4CLZ8yNJYe83K0MbBkBwId/7MNeSCaCExEREREp7JR0i1yv8FYQWhdSL8HG6WZHk2P3NQ+nhLuVv4/Fs3LvKbPDEREREREpFpR0i1wvw4AWIx3b66ZBarK58eRQSR8P7mlSAYAP/9hvcjQiIiIiIsWDkm6R3Kh1J/iGwvkY2DHf7GhybEjrSKwWg5V7T7H9aJzZ4YiIiIiIFHlKukVyw80Dmj7g2F4zCQrJGOkKQd7cUTcMgA+Xq7VbRESKB4th0CwyiGaRQVgMw+xwRKSYUdItkluN7gc3L4jZAgdXmx1Njg37d/mwhVuPcfjMRZOjERERyXte7lbmPtiCuQ+2wMvdanY4IlLMKOkWyS2fYKjXx7G99gNzY7kOtcoG0KZKKWx2+HiFWrtFRERERPKSkm6RG9H83+XDdi2EM4UngR3erhIAczcc5syFwjERnIiIiIhIYaSkW+RGhFSFyrcCdvjzQ7OjybGWlYKpXc6fxBQbM9ccMDscERGRPHUxOZWGryyh4StLuJicanY4IlLMKOkWuVEt/m3t/utzSCwcM4IbhsGDbR2t3Z+tPsCl5DSTIxIREclbZy4kq3eXiJhCSbfIjarYAUrXhOQE2DTT7GhyrHPtUCoEleDsxRTe+mUX328+ypp9p0mzFY6Z2EVERERECgMl3SI3yjCg+QjH9p8fQlrh6LbmZrXQqlIpAKavOsBjX26mz7S1tH7zNxZtjzE5OhERERGRokFJt4gr1OkN3qUg7jDs+sHsaHJk0fYY5q4/nGn/8bhERny+SYm3iIiIiIgLKOkWcQV3L2gyxLG9ZrK5seRAms3O2B92kFVH8vR9Y3/Yoa7mIiIiIiI3SEm3iKs0eQCsHnBkPRxeb3Y0V7Uu+gwxcYnZHrcDMXGJrIs+k39BiYiIiIgUQUq6RVzFtzTUuduxvbZgt3bHns8+4c5NORERkYLMYhjULR9A3fIBWAzD7HBEpJhxMzsAkSKl+UOweTbsWADnDkNgBbMjylJpP68clfP3cs/jSERERPKel7uVBQ+3NjsMESmm1NIt4kqhtSGyHdjTYN2HZkeTraaRQYQFeHGt7/pHL9jOxoNn8yUmEREREZGiSEm3iKu1GOn4uXEmJCWYG0s2rBaD0V1rAmRKvNOfl/R259CZS9w9dTVv/7KL5FRbvsYoIiIiIlIUKOkWcbXKt0JwFUiKc3Q1L6CiaocxpX9DQgMydjUPDfBiav+G/P50B+5sUA6bHSYv20fPD1bxz4nzJkUrIiKSe5eS02j1xm+0euM3LiWnmR2OiBQzGtMt4moWCzQfDgufhLVTHLOaW6xmR5WlqNph3FozlHXRZ4g9n0hpPy+aRgZhtTjau9+9pz631izDf77bxt/H4rnj/1byTKdq3N8qEotFE9GIiEjhYMfO0XOXnNsiIvlJLd0ieaFeH/AKhLPR8M8is6O5KqvFoEWlYLrXL0eLSsHOhDtd5zph/DKqLR2qhZCcauPVhTvp+/Fajpy9aFLEIiIiIiKFh5Jukbzg4QONBzu213xgbiwuUNrfi08HNWFczzp4e1hZu/8MnSeu4JuNR7Db1WIgIiIiIpIdJd0ieaXpMLC4wcGVcGyz2dHcMMMw6NvsJn56tA0NbwrkfFIqT87bwojPN3HmQrLZ4YmIiIiIFEhKukXyin9ZqNXTsb228Ld2p4so5cNXD7bg6U7VcLMYLPr7OLdNWM5vu06YHdo1pdnsrNl3mu83H2XNvtOk2dRKLyIiIiJ5SxOpieSl5g/Btnmw/RvoOBb8w8yOyCXcrBZGdqhMu6ohPD53M3tiE7h/xgb6NK3Af7vUxMez4N1aFm2PYewPO4iJS3TuCwvwYnTXmkTVLhr/LiIiIiJS8KilWyQvlWsIN7UEWyqsn2Z2NC5Xu1wAPzzSmgdaR2IY8MW6w3R+bwUbDpwxO7QMFm2PYcTnmzIk3ADH4xIZ8fkmFm2PMSkyERHJDwYGVUr7UqW0LwZafUNE8peSbpG81uIhx88Nn0Jy0Zvx28vdyn/vqMmcB5pTLrAEh85cpPeHa3hr0S6SU21mh0eazc7YH3ZkuUBM+r6xP+xQV3MRkSKshIeVJU+0Y8kT7SjhUTCX8RSRoktJt0heq3Y7lIyAS2dh65dmR5NnWlQK5udRbejVsDw2O3zw+z56TF7F7uPnTY1rXfSZTC3cl7MDMXGJrIsuWK3zIiIiIlI0KOkWyWsWKzQb7theOwVs5rf+5hV/L3fG967H1P4NKentzo6YeLr+30qmLd+PLZ9akk8nJPH77lgm/baHB2dtYMTnG3P0utjz2SfmIiIiIiK5VfBmOxIpihr0h2Xj4NQ/sG8pVLnV7IjyVFTtMBqGl+S5b7bx265YXvtpJ7/uPMH43vUoX9IbcHT7Xhd9htjziZT286JpZBBWy/WNsztzIZltR+PYfjSOrUfOsf1oPEfPXcpVzKX9vHL1OhERKfguJafRbdJKABY83FpdzEUkXynpFskPnn7QcACsmQRrJhf5pBscSewnAxvz5frDvPLjDv6MPkPUxBWM7loTX083Xv7x+mYSvzzB3nYkjm1H47JNsCuG+FCnXAB1ygVQK8yfUV9tJjY+Kctx3QYQGuBI+kVEpGiyY2dPbIJzW0QkPynpFskvTYc51uvevwxO/A1lapkdUZ4zDIM+TW+iZaVgnvhqCxsPnuXpr7dmWTZ9JvEp/RvSLDKYbUcdifU1E+xSPtQuF0Dd8gHULhdArbL++Hm5ZygztlstRny+CQMy/allB0Z3rXndrewiIiIiIjmhpFskv5QMhxpdYcf3juS7+2SzI8o34cE+fPVgC6b8sZd3fvknyzLpyfDI2ZtIy6YRIj3BrlPu3wS7nD/+VyTYWYmqHcaU/g0zrdPtiM2bTrVCr+ftiIiIiIjkmKkTqS1fvpyuXbtStmxZDMNg/vz513xNUlISL7zwAuHh4Xh6ehIREcGnn37qPD5jxgwMw8jw8PLSWE0pIJqPdPzcOg8STpobSz6zWgwa3XTtLtzpCXdkKR+61ivLC7fX4Iuhzdk65jZ+e6o97/dpwNC2FWlRKThHCXe6qNphrHz2Zr4Y2pz37q3PlP4NKeFu4eDpi/y8/Xhu35aIiIiIyFWZ2tJ94cIF6tWrx/3338+dd96Zo9f07t2bEydO8Mknn1C5cmViYmKwXTEbtL+/P7t373Y+Nwx1G5UCokJTKNcIjm6EDZ9A++fMjihf5XSG8Dd61eHeJje5/PpWi0GLSsHO5ztjzvP+0j28/ctubq1ZBnerFnQQEREREdcyNenu3LkznTt3znH5RYsW8ccff7B//36CghwtZhEREZnKGYZBaKi6i0oBZBjQYiR8fT+s/xhajQL34tMTI6czhIcH+eRxJA7D2lZk9tqDRJ+6wNz1h+nfPDxfrisiIiIixUehatZZsGABjRs35q233qJcuXJUrVqVp556ikuXMk6wlJCQQHh4OBUqVKB79+78/fffJkUskoUa3cG/PFw4Cdu/NjuafNU0MoiwAC+y63ti4JjFPL9mEvf1dOPRW6oAMPHXPVxISs2X64qISP4yMCgXWIJygSUwsv1fSEQkbxSqidT279/PypUr8fLy4rvvvuPUqVM89NBDnD59munTpwNQrVo1Pv30U+rWrUtcXBzvvPMOLVu25O+//6Z8+fJZnjcpKYmkpCTn8/j4eABsNlumrutZsdls2O32HJUVwbBA06FYfh2Nfc1k7HX7OFrA/1WU65MBvNilBiPn/JVpJvH0T+DFLjUwsGOz5c+SLvc0Ls8nK/dz6MwlPl6xn0durpwv180PRbkuSf5TfRJXyu/65OlmsOKZ9hmuL0WD7k3iStdbn3JazrDb7QVisULDMPjuu+/o0aNHtmVuu+02VqxYwfHjxwkICADg22+/5a677uLChQuUKFEi02tSUlKoUaMGffr04ZVXXsnyvGPGjGHs2LGZ9v/zzz/4+fldM3abzUZcXBwBAQFYLIWq84CYxEiKJ2RWOyypFzlzxwySy7dwHisO9WnZ3rNM+P0wsQkpzn1lfN0Z1b4CHSqXzPd4luw+w4s/R+PtbuGbwbUp6Z3zCdoKsuJQlyT/qD6JK6k+iauoLokrXW99On/+PFWrViUuLg5/f/9syxWqlu6wsDDKlSvnTLgBatSogd1u58iRI1SpUiXTa9zd3WnQoAF79+7N9rzPP/88TzzxhPN5fHw8FSpUICQk5KofXjqbzYZhGISEhOiXXXKoNEaDfrB+GiV3z8HesLvzSHGoT/eULs1dzauy/sAZYs8nUdrPkyYRQaatld2nVAhzt55m+9F45m6L46WuNU2Jw9WKQ12S/KP6JK6k+iSuorokrnS99Smnq2QVqqS7VatWzJs3j4SEBHx9fQFHa7TFYsm263haWhrbtm3j9ttvz/a8np6eeHp6ZtpvsVhy/MtrGMZ1lReh+QhY/zHGnsUYZ/ZBqf99aVQc6pPFAi0rh5gdBuCI5fnONej38Z/MXneI+1tX5KZgb7PDconiUJck/6g+iSvlZ31KTEmj94drAPjqwRZ4uVvz/JqSf3RvEle6nvqU0zpnas1MSEhg8+bNbN68GYDo6Gg2b97MoUOHAEcL9IABA5zl+/btS3BwMIMHD2bHjh0sX76cp59+mvvvv9/Ztfzll19m8eLF7N+/n02bNtG/f38OHjzIAw88kO/vT+SqgitBtX9n71/7gbmxCK0ql6JNlVKkpNl5Z/Hua79AREQKDZvdztYjcWw9EoetYIysFJFixNSke8OGDTRo0IAGDRoA8MQTT9CgQQNeeuklAGJiYpwJOICvry9Llizh3LlzNG7cmH79+tG1a1fef/99Z5mzZ88ydOhQatSowe233058fDyrV6+mZs2i0V1UipjmDzl+bv4CLp4xNxbh2ajqACzYcoztR+NMjkZEREREigJTu5e3b9+eq83jNmPGjEz7qlevzpIlS7J9zYQJE5gwYYIrwhPJexGtIbQOHN8GG6dDmyfNjqhYq10ugB71yzJ/8zHeXLSLWUOamR2SiIiIiBRyGvggYibDgOYjHdvrpkFqsrnxCE/eVg13q8GKPadYseek2eGIiIiISCGnpFvEbLV7gW8onI+BHfPNjqbYqxDkTf/m4QC88fOufFsvXERERESKJiXdImZz84Cm/070t3oSRK/Aa8+PcGAl2NLMja2YerhDZXw93fj7WDw/bD1mdjgiIiIiUogp6RYpCBrdDxZ3OL4Fy6xuBC59EsvMrjCxNuxYYHZ0xU6wryfD21UE4J3Fu0lOtZkckYiI3KggHw+CfDzMDkNEiiEl3SIFwcFVYEvJvD8+Br4aoMTbBPe3jiTEz5PDZy4x58+DZocjIiI3wNvDjU0v3sqmF2/F28PUeYRFpBhS0i1iNlsaLHo2m4P/jide9Jy6muczbw83RnWsAsD7v+3lfGIWX4qIiIiIiFyDkm4Rsx1cDfFXGzdsh/ijjnKSr3o3rkDFUj6cuZDMtOX7zQ5HRERERAohJd0iZks44dpy4jLuVgvPRFUDYNqKaGLPJ5ockYiI5EZiShr3fLiGez5cQ2KKeo6JSP5S0i1iNt8yri0nLtWpVij1KwRyKSWN95fuMTscERHJBZvdzp/RZ/gz+gw2u5aCFJH8paRbxGzhLcG/LGBkU8AA/3KOcpLvDMPguc7VAfhi3WH2n0wwOSIRERERKUyUdIuYzWKFqDf/fZJV4m2HqDcc5cQUzSsGc3P10qTZ7LyzeLfZ4YiIiIhIIaKkW6QgqNkNes8E/7DMx9xKQNn6+R6SZPRsVHUMA37adpy/Dp01OxwRERERKSSUdIsUFDW7wajt2Ab8wLlbxmPrPx/KN4XUS/DdcC0ZZrJqoX70algegDd+3oVdYwJFREREJAeUdIsUJBYrRLQmscodULEd3PkRePjCwVWw+n2zoyv2Hr+1Kh5uFv6MPsPvu0+aHY6IiIiIFAJKukUKsqBI6PzveO/fXoOYLebGU8yVCyzBoJYRALy5aBdpNrV2i4gUFiXcrZRw1/woIpL/lHSLFHT1+0H1O8CWAt8MhZRLZkdUrD3UvhL+Xm7sOn6e+X8dNTscERHJAW8PN3a+EsXOV6Lw9nAzOxwRKWaUdIsUdIYBXd93rNN9ajf8OsbsiIq1QG8PHupQGYB3l/xDYorG2ouIiIhI9pR0ixQGPsHQ/QPH9p9TYe9Sc+Mp5ga1jCDU34uj5y4xa81Bs8MRERERkQJMSbdIYVGlIzQd5tie/xBcPGNuPMWYl7uVJ26tCsCkZXuJu5RickQiInI1iSlpDJ6+jsHT16mHkojkOyXdIoVJx7FQqiokHIcfHgMtW2WaXo3KU7WML3GXUpj6xz6zwxERkauw2e0s232SZbtPYtP/nSKSz5R0ixQmHt5w5zSwuMHOBbDlC7MjKrasFoNnOlUH4NOV0RyPSzQ5IhEREREpiJR0ixQ2ZetDh/84tn96Bs4eMDOaYu2WGqVpElGSpFQbE5b8Y3Y4IiIiIlIAKekWKYxajYKbWkDyefj2QbBpfJoZDMPguc6O1u55Gw+z58R5kyMSERERkYJGSbdIYWSxQs8PwcMPDq+FlRPMjqjYahQeRKdaZbDZ4a1fdpsdjoiIiIgUMEq6RQqrkuFw+9uO7d9fh2N/mRtPMfZ0p+pYDFiy4wQbDmhWeRERERH5HyXdIoVZvXuhZnewpcI3QyH5otkRFUuVS/tyT5MKALz+8y7smhlXRERERP6lpFukMDMMuGMi+IXB6T2w5EWzIyq2RnWsipe7hY0Hz7JkxwmzwxERkct4e7hx4I0uHHijC94ebmaHIyLFTK6S7sOHD3PkyBHn83Xr1jFq1Cg++ugjlwUmIjnkHQQ9PnBsr/8Y/llsbjzFVBl/L4a0jgQcY7tT02wmRyQiIiIiBUGuku6+ffuybNkyAI4fP86tt97KunXreOGFF3j55ZddGqCI5EClm6HZCMf29yPhwilz4ymmHmxXiUBvd/bGJvDNpiPXfoGIiIiIFHm5Srq3b99O06ZNAfjqq6+oXbs2q1evZvbs2cyYMcOV8YlITnUcDSE14EIs/PAYaFxxvvP3cufhDpUBmLBkD5eStZSbiEhBkJiSxkOzN/LQ7I0kpujeLCL5K1dJd0pKCp6engD8+uuvdOvWDYDq1asTExPjuuhEJOfcS0CvaWBxh10/wl+zzI6oWLqvRTjlAktwPD6R6aujzQ5HREQAm93OT9uO89O249j0pbSI5LNcJd21atVi6tSprFixgiVLlhAVFQXAsWPHCA4OdmmAInIdQuvALf9Opvbzc3Bmv7nxFEOeblae6lQVgCm/7+PshWSTIxIRERERM+Uq6X7zzTf58MMPad++PX369KFevXoALFiwwNntXERM0uJhCG8NKRfg22GQlmp2RMVO93rlqBHmz/nEVD74fa/Z4YiIiIiIiXKVdLdv355Tp05x6tQpPv30U+f+YcOGMXXqVJcFJyK5YLFCz6ngGQBH1sPKd82OqNixWAyejaoGwGerD3LkrNZPFxERESmucpV0X7p0iaSkJEqWLAnAwYMHmThxIrt376Z06dIuDVBEciGwAnR5x7H9+xtwZKO58RRD7aqG0KJiMMlpNsYv3s2afaf5fvNR1uw7TZpN4wlFREREiotcJd3du3dn5syZAJw7d45mzZoxfvx4evTowZQpU1waoIjkUp27oXYvsKfBt0Mh+YLZERUrhmHwXOfqAHz31zH6TFvLY19ups+0tbR+8zcWbdekkyIiIiLFQa6S7k2bNtGmTRsAvv76a8qUKcPBgweZOXMm77//vksDFJFcMgzoMh78y8GZffDLC2ZHVOzExF3Kcv/xuERGfL5JibeIiIhIMZCrpPvixYv4+fkBsHjxYu68804sFgvNmzfn4MGDLg1QRG5AiZLQ49/eJxunw+5F5sZTjKTZ7Iz9YUeWx9I7l4/9YYe6mouI5IMS7lZ2vNyJHS93ooS71exwRKSYyVXSXblyZebPn8/hw4f55ZdfuO222wCIjY3F39/fpQGKyA2q2M4xoznA9yMhIdbceIqJddFniIlLzPa4HYiJS2Rd9Jn8C0pEpJgyDANvDze8PdwwDMPscESkmMlV0v3SSy/x1FNPERERQdOmTWnRogXgaPVu0KCBSwMUERe4+UUoXQsunoIFj4Bdrat5LfZ89gl3bsqJiIiISOGUq6T7rrvu4tChQ2zYsIFffvnFuf+WW25hwoQJLgtORFzE3Qt6TQOrB/yzCDbOMDuiIq+0n5dLy4mISO4lpabx5FdbePKrLSSlppkdjogUM7lKugFCQ0Np0KABx44d48iRIwA0bdqU6tWruyw4EXGhMrXgltGO7V/+A6f2mhtPEdc0MoiwAC+u1okxLMCLppFB+RaTiEhxlWaz882mI3yz6Yjm0hCRfJerpNtms/Hyyy8TEBBAeHg44eHhBAYG8sorr2Cz2Vwdo4i4SvOHILItpFyE74ZBWorZERVZVovB6K41AbJNvB++uTJWi8YWioiIiBRluUq6X3jhBSZNmsQbb7zBX3/9xV9//cW4ceP4v//7P1588UVXxygirmKxOGYz9wqAoxth+dtmR1SkRdUOY0r/hoQGZOxC7vZvov3lusMkpqibo4iIiEhR5pabF3322Wd8/PHHdOvWzbmvbt26lCtXjoceeojXXnvNZQGKiIsFlIc7JsDX98Pyd6BiB7ClQsIJ8C0D4S3BouVUXCWqdhi31gxlXfQZYs8nUtrPi7AAL3p+sIptR+P4z3fbGH93Pc2mKyIiIlJE5SrpPnPmTJZjt6tXr86ZM1r+RqTAq93LsWb3tq9gRhewX9ba6l8Wot6Emt2yf71cF6vFoEWl4Az7JvdtSP9P/uTbTUepWy6AQa0iTYpORERERPJSrrqX16tXj0mTJmXaP2nSJOrWrXvDQYlIPqh0s+On/YruzfEx8NUA2LEg/2MqRlpWLsV/bq8BwCsLd7J2/2mTIxIRERGRvJCrlu633nqLLl268OuvvzrX6F6zZg2HDx/mp59+cmmAIpIHbGnw28vZHLQDBix6Dqp3UVfzPDSkdSTbj8Yxf/MxRs7exIJHWlMusITZYYmIiIiIC+Wqpbtdu3b8888/9OzZk3PnznHu3DnuvPNO/v77b2bNmuXqGEXE1Q6uhvhjVylgh/ijjnKSZwzD4PU761IzzJ/TF5IZPmujJlYTEckDJdytbPxvRzb+tyMl3PVlsojkr1y1dAOULVs204RpW7Zs4ZNPPuGjjz664cBEJA8lnHBtOcm1Eh5WPryvEd0mrdTEaiIiecQwDIJ9Pc0OQ0SKqVy1dItIIedbxrXl5IZUCPJmct+GWAz4dtNRPlt9wOyQRERERMRFlHSLFEfhLR2zlHOV1lT/co5yki80sZqISN5JSk3jxfnbeXH+dpJSNYxHRPKXkm6R4shidSwLBmSbeFdsr0nU8tmQ1pH0qF+WNJudkbM3cfTcJbNDEhEpEtJsdmatPcistQdJs9nNDkdEipnrGtN95513XvX4uXPnbiQWEclPNbtB75mw6NmMk6p5BkBSHGye45i9vHoX82IsZtInVvvnRAI7YuIZPmsj84a3wEuT/oiIiIgUWteVdAcEBFzz+IABA24oIBHJRzW7OZLqg6sdk6b5loGbWsDPT8OGT+GbB2DQQijX0OxIiw1NrCYiIiJStFxX0j19+vS8ikNEzGKxQmSbjPs6vw1nD8K+pfDFvfDAUgisYE58xVD6xGr9P/mTbzcdpW65AAa1ijQ7LBERERHJBY3pFpHMrG5w9wwoXcvRAj6nNyTGmx1VsaKJ1URERESKBiXdIpI1L3/oO9fR5Tx2B8wbBGmpZkdVrGhiNREREZHCT0m3iGQvsIIj8Xb3dnQ1/+kpsGvW1/ySPrFazTB/Tl9IZvisjSSmaKkbERERkcJESbeIXF3ZBtDrY8CAjdNhzSSzIypW0idWK+nt7pxYza4vPkRErouXm5UVz3RgxTMd8HLTihAikr+UdIvItVXvAp3GObYXvwg7FpgbTzFTIcibSX0bYjHg201H+Wz1AbNDEhEpVCwWgwpB3lQI8sZi0WoQIpK/lHSLSM40HwFNhgJ2+HYYHNlodkTFSitNrCYiIiJSKCnpFpGcMQyIegMq3wqpl+CLexzLikm+0cRqIiK5k5xqY9xPOxn3006SU21mhyMixYySbhHJOasb3D0dytSGCycdS4ldOmd2VMWGJlYTEcmdVJuNj5bv56Pl+0m1KekWkfylpFtEro+nH/T9CvzC4OQumDcQ0lLMjqrYuHJitRe+266J1UREREQKMCXdInL9AspBny/B3Qf2/w4Ln9BSYvno8onVvtl0RBOriYiIiBRgSrpFJHfK1oe7PgXDAptmwqr3zI6oWNHEaiIiIiKFg5JuEcm9alGOydUAfh0Nf883NZzi5sqJ1Y5pYjURERGRAkdJt4jcmGYPQrPhju3vHoTD682NpxjJNLHa55pYTURERKSgUdItIjeu0zioGgWpifDFvXD2gNkRFRuXT6y29YgmVhMREREpaJR0i8iNs1ih1ycQWhcunoLZd2spsXykidVERK7Oy83K4sfbsvjxtni5Wc0OR0SKGSXdIuIanr7Qdy74lYVT/8BX90FqstlRFRtXTqy2eu8p1u4/zeJdZ1i7/zRpNrV+i0jxZbEYVC3jR9UyflgshtnhiEgx42Z2ACJShPiXhX5fwadREL0cFj4O3SaBoT9w8sOQ1pFsPxrH/M3H6P/Jn/wvz44mLMCL0V1rElU7zMwQRURERIodtXSLiGuF1oG7pjuWEvvrc1j5rtkRFRuGYdChemkArmzYPh6XyIjPN7Foe4wJkYmImCs51caEJf8wYck/JKfazA5HRIoZJd0i4npVb4PObzm2l74M278xN55iIs1m542fd2V5LD0HH/vDDnU1F5FiJ9Vm472le3hv6R5SbUq6RSR/KekWkbzRdCg0H+nY/m4EHPrT3HiKgXXRZ4iJS8z2uB2IiUtkXfSZ/AtKREREpJhT0i0ieee2V6BaF0hLgi/7wJn9ZkdUpMWezz7hzk05EREREblxpibdy5cvp2vXrpQtWxbDMJg/f/41X5OUlMQLL7xAeHg4np6eRERE8Omnn2YoM2/ePKpXr46Xlxd16tThp59+yqN3ICJXZbFCr2kQVh8unobZveHSWbOjKrJK+3m5tJyIiIiI3DhTk+4LFy5Qr149Jk+enOPX9O7dm6VLl/LJJ5+we/duvvjiC6pVq+Y8vnr1avr06cOQIUP466+/6NGjBz169GD79u158RZE5Fo8fKDPl+BfHk7vgblaSiyvNI0MIizAi6vNFW8xwM9LC1eIiIiI5BdT//Lq3LkznTt3znH5RYsW8ccff7B//36CgoIAiIiIyFDmvffeIyoqiqeffhqAV155hSVLljBp0iSmTp3qsthF5Dr4hznW8P40Cg6sgB8eg27/B4fWQMIJ8C0D4S0dLeOSa1aLweiuNRnx+SYM/jd52uVsdrjnwzVM7teQ9tVK53eIeSLNZmdd9BlizydS2s+LppFBWLUOr4iIiBQQhWpM94IFC2jcuDFvvfUW5cqVo2rVqjz11FNcunTJWWbNmjV07Ngxw+s6derEmjVr8jtcEblcaG3oPQMMK2yZA29FwGd3wDdDHD8n1oYdC8yOstCLqh3GlP4NCQ3I2IU8LMCL8XfXo0XFYC4kp3H/jPXMWnvQpChdZ9H2GFq/+Rt9pq3lsS8302faWlq/+ZuWRhMREZECo1D1Mdy/fz8rV67Ey8uL7777jlOnTvHQQw9x+vRppk+fDsDx48cpU6ZMhteVKVOG48ePZ3vepKQkkpKSnM/j4+MBsNls2HKwrITNZsNut+eorMi1FOn6VPFmaHAflk0zIOl8hkP2+Bj4agD2uz+DGl3Nia+IuK1mGW6pXpo/959m37GTVCobQrOKwVgtBl3qhPLf77fz9cajvDh/O9EnE3i+c/VC2TK8aPtxRs75K1OLfvqa5JP7NiCqdqgpsRVFRfreJPkuv+uTu8Xgu4daOLdVj4sO3ZvEla63PuW0XKFKum02G4ZhMHv2bAICAgB49913ueuuu/jggw8oUaJErs77+uuvM3bs2Ez7T548SWLitWf5tdlsxMXFYbfbsVgKVecBKYCKdH2ypRGyexF2yDTu2MCOHQP7T89wsmRjdTV3gYq+NoLLWAjwTeX0qZPO/U+2LkOIp50pq4/x6aoD7I05y9jOkZRwLzyfeZrNzpgF27PsQu9ck3zBduqVMgrlFwoFUZG+N0m+M6M+hXk4fl5+P5TCT/cmcaXrrU/nz5+/ZhkoZEl3WFgY5cqVcybcADVq1MBut3PkyBGqVKlCaGgoJ06cyPC6EydOEBqafWvH888/zxNPPOF8Hh8fT4UKFQgJCcHf3/+acaV/GRASEqJfdrlhRbo+HViJ5UL2vU4M7FgvHKd04j6IaJ2PgRVNV6tLT99Rhho3leapr7eyfH8cj8zfz7T7GlHGv3DMbL52/2liE1KuWuZEQgoHL7rRvGJwPkVVtBXpe5PkO9UncRXVJXGl661PXl45+7upUCXdrVq1Yt68eSQkJODr6wvAP//8g8VioXz58gC0aNGCpUuXMmrUKOfrlixZQosWLbI9r6enJ56enpn2WyyWHP/yGoZxXeVFrqbI1qcLsTkqZrkQC0XtvZvkanWpW/1ylCtZgqEzN7L9aDx3TlnDp4OaUCPs2l82mu1kQs5mwD+ZkFz0fo9MVGTvTWKK/KxPyak2pq+KBmBwq0g83FSHixLdm8SVrqc+5bTOmVozExIS2Lx5M5s3bwYgOjqazZs3c+jQIcDRAj1gwABn+b59+xIcHMzgwYPZsWMHy5cv5+mnn+b+++93di1/7LHHWLRoEePHj2fXrl2MGTOGDRs28PDDD+f7+xORK/iWuXaZ6yknN6xReBDzH2pFpRAfYuISuWvKan7fnbMvR8zkbs3Zf19ak1xEAFJtNl7/eRev/7yLVI39FZF8ZmrSvWHDBho0aECDBg0AeOKJJ2jQoAEvvfQSADExMc4EHMDX15clS5Zw7tw5GjduTL9+/ejatSvvv/++s0zLli2ZM2cOH330EfXq1ePrr79m/vz51K5dO3/fnIhkFt4S/MuSeUT35QxIzNn4GHGNm4K9+XZEq0Ixs3liShqTftvDk19tvmbZwBLuNI0MyvugRERERK7CsNvtWc1DU6zFx8cTEBBAXFxcjsd0x8bGUrp0aXVrkRtW5OvTjgXwVXoPlstvP1esLN3mKejwH02odgOuty4lp9p44bttzNt4BIAhrSP5z+01CsREZHa7nR+3xvDGz7s4es6xTGRkKW+iT13Mdk1ygDFdazKoVWS+xVmUFfl7k+Sr/K5PF5NTqfnSLwDseLkT3h6FaoSlXIXuTeJK11ufcpo3qmaKSP6q2Q16zwT/sIz7/cvCXdOh6YOO5yvegc/vhAun8j/GYsrDzcJbd9Xl6U7VAPhkZTTDP9/IxeRUU+Pacvgcd09dwyNf/MXRc5cIC/DivXvr89uT7ZmazZrkHaqFADDmhx1MWPIP+n5ZREREzKKv+UQk/9XsBtW7wMHVkHDCMYY7vKWjVbv2nVC+CfzwKOz/HT5s60jSyzc2O+piwTAMRnaozE1B3jw5bwtLdpzgng/X8vHAxvk+s/nxuETe+mUX3246CkAJdyvD21ViWNuKlPBw9ICIqh3GrTVDWRd9htjziZT286JpZBAWA/7vt728u+Qf3lu6h3MXkxndtRaWAtBqLyIiIsWLkm4RMYfFCpFtsj5W924IrQ1z+8PpvfBpFES9Dk0eAENJU37oWq8sZQO9GDpzI9uOxtFj8qp8m9n8UnIa01bsZ8rv+7iUkgbAnQ3L8Uyn6platQGsFoMWlTIvC/boLVUI9HZn9IK/+WzNQc5dSuGdu+vleBI2EREREVfQXx4iUjCVrgFDl0GNbmBLgZ+egu8ehOQLZkdWbOT3zOZ2u53vNx/llvG/8+6Sf7iUkkaj8JLMH9mKd3vXzzLhvpYBLSKYeE993CwG328+xrCZG7iUnJYH0YuIiIhkTUm3iBRcXv6OruW3vQqGFbbOhY87wqm9ZkdWbOTXzOZ/HTpLrymreezLzRyLS6RsgBfv92nA18NbUL9C4A2du3v9ckwb0BgvdwvLdp9kwKd/EncpxTWBi0ih4Olm5YuhzfliaHM83TRBp4jkLyXdIlKwGQa0fAQG/uAY+x27A6Z1gJ0/mB1ZsRHg7c5n9zfl7kblsdnhxfnbeeXHHaTZbnxyspi4Szw+dzM9P1jNpkPnKOFu5clbq/LbU+3pVq8shouGE3SoXppZQ5rh5+XG+gNnufejtcSeT3TJuUWk4EsfhtKiUnCBWJFBRIoXJd0iUjhEtIIHl8NNLSEp3jHee8lLkGbuzNrFhatnNr+UnMbEX//h5nf+4Lu/HBOl9WpYnt+fbs8jt1TBy931LVFNIoKYO6wFpXw92RkTz91T13D4zEWXX0dERETkckq6RaTw8AuFgQugxcOO56veg5nd4fwJc+MqJtJnNv+/Pg3wcLM4ZzY/EZ/zFmObzc78v45y8/jfmfjrHi6lpNE4vCQLHm7F+N718nyG9Jpl/fl6eAvKlyzBwdMX6TVlNbuPn8/Ta4qI+VLSbMxcc4CZaw6Q8v/t3Xd4VNXWx/HvzKSTAgHSqKETekuoKiICKmBFsYBdEe9VuV4VvYqo99pee8FeURQLAhawoCg1tACh95oCAVJJm5n3j5MEYjpMySS/z/OcJzNn9sysidtD1uy917ba3B2OiNQzSrpFxLNYvGHEf+Gqj8EnEPYtMbYV27/C3ZHVG6N7RDHrtjhCG/iUVDbfkpQBgNVmZ/muNOYmHGL5rrRSU9DX7j/O5TOWce+XCSSl59KsoT+vX9uLr+4cQPfmDV0Wf+smDfhm0kA6hgeRmpnHuLeXs3b/cZe9v4i4XoHVxmNzN/HY3E1KukXE5bRlmIh4pi6XQngXY5r5ka3w0cUw/EnoP0nbirlAcWXzmz6KZ9eRbK6csYybBrXmm7WHSEo/NfIdGeLHP85vx8o9x5ibcBiAAB8Lk4e245bB0U6ZRl4d4cF+fHlHf276aBXr9p/gundX8vYNfTinQ1O3xCMiIiJ1l0a6RcRzNWkPt/4GXa8AWyEsnApf3wR5mi7sCn+vbP7677tKJdwASem5PDwnkbkJhzGZ4Ko+zfnj/vOYPLSd2xLuYg0DfPjs1jiGtG/CyQIrt3y8ih82JLk1JhEREal7lHSLiGfzDYQr3odRz4HZCzbNgXeHwZFt7o6sXggJ8OaDG/vhX0UC7WMxMWfSIJ6/qgdhTl63XRMBPl68P7EfF3ePpMBq5+5Za/l85X53hyUiIiJ1iJJuEfF8JhPE3QE3/QRBUXB0G7wzFBK/dXdk9ULCgROcLLBW2ibfaq+yjbv4eJl59ZpeXBvXErsdHp6zkTd+34ndfvZboomIiIgo6RaRuqNFrLGtWOshUJBtTDX/6SGwFhiP26yw5y/Y+LXx01Y7k0BPU939rmvzvtgWs4n/XtqVyUPbAvD8wm3878ctSrxFRETkrKmQmojULYFN4Ybv4PenYMlLsHIGHF4HPa+Fxc9AxuFTbYOjYOSzEDPGbeHWBWFB1ZsuXt127mIymfj3iE40CvDhqR+28O5feziRU8DTl3fDy6LvqEVEROTMKOkWkbrH4gUXPA7N+8GcO+HACuP4u4wkmD0Bxn2ixPssxEaHEhniR3J6LuWNC5uAiBA/YqNDXR3aGbl1SBuC/b156JsNfLXmIOknC3h1fC+3F34TkTPnYzHzwY19S26LiLiSrjoiUnd1utiobm6u6PvFohRxwUOaan4WLGYT00bHAEaCfbri+9NGx2Axe85WbuP6tmDG9X3w8TLz8+YUbvpwFVl5he4OS0TOkJfFzPmdwjm/U7hmroiIy+mqIyJ1W1aKsZ1YheyQcQj2LXNZSHXRyK6RzLi+NxEhpaeQR4T4MeP63ozsGummyM7ciC4RfHRTPxr4WFi+O41r311BWlaeu8MSERERD6Pp5SJSt2WlOLadVGhk10iGx0QQv+cYqZm5hAUZU8o9aYT77wa2bcKs2/tz44er2HAwnaveXs7MW+KIaujv7tBEpAYKrDa+W3cIgEt7NcNbo90i4kJKukWkbgsMd2w7qZTFbGJA28buDsOhujdvyOw7BjDh/ZXsPpLNlTOW8emtcbRu3KBOfcEgUpcVWG38++sNAFzcPVJJt4i4lJJuEanbWg00qpRnJEG5Zb4Ak4Wyq5FFTmkXFshXkwZyQ1HiPfb1pfh6m0nLyi9pExnix7TRMR45lV5EREScR1/ziUjdZrYY24IBFSbWdit8fAn88hgUas2ulK9ZQ3++umMALUMDyMorLJVwAySn5zJp5loWJCa5KUIRERGpjZR0i0jdFzPG2BYs+G8jkMHN4LK3oed1gB2WvgLvnAdJG9wRpXiAhgE+5BWWX+m+eB7F9PmbsdoqmFUhIiIi9Y6ml4tI/RAzxthCbN8yo2haYLgx9dxsgR7XGI/NvwdSN8O7Q+G8h2DQfcae3yJF4vccIyWj4tkQdiApPZf4Pcfq3Np2EREROTMa6RaR+sNsgegh0O1K46fZcuqxThfDXSug82hji7FFT8EHI+DoDvfFK7VOamauQ9uJiIhI3aekW0SkWIMmMO5TuOwd8A2BQ6vhrSGw8m2w2dwdndQCYUF+VTeqQTsRERGp+5R0i4iczmSCHlfDXcugzXlQeBJ+egA+vRTSD7o7OnGz2OhQIkP8Kq113zTIl9joUJfFJCJV87GYeePa3rxxbW98tF2YiLiYrjoiIuUJaQ7Xz4GL/g+8/GHPYnhzACR8DnYVyaqvLGYT00bHABVvMldotZGSoenlIrWJl8XMxd0jubh7JF5KukXExXTVERGpiNkMsbfBpKXQPBbyMuC7SfDl9ZB1xN3RiZuM7BrJjOt7ExFSegp5eLAv4cG+HM8p4Pr3V3I0S9vPiYiIiKqXi4hUrXFbuHmBsaXY7/+Drd/D/hUw+mWj8JrUOyO7RjI8JoL4PcdIzcwlLMiP2OhQUjJyuXLGMnYfyWbiB/HMur0/wX7e7g5XpN4rtNpYuCkFgBFdwjXaLSIupSuOiEh1mC0wZArc/juEdYGco8aI95w74eQJd0cnbmAxmxjQtjFjezZjQNvGWMwmohr68+mtcTRu4MOmwxnc+vFqcgvK39dbRFwn32pj8udrmfz5WvKtKowpIq6lpFtEpCYiuhmJ9+D7wGSG9bNgxkDY9bu7I5Naom3TQD6+OZYgXy/i9xzjrs/WUqA/8kVEROotJd0iIjXl5QsXPA43LYBG0ZBxyKhu/uO/IT/H3dFJLdC1WQjv39gPXy8zi7amcv9X67HZVIBPRESkPlLSLSJyplrGGUXW+t1q3I9/B94eAgdWuTcuqRVio0N56/o+eJlNzE04zLR5m7Cr8r2IiEi9o6RbRORs+DSAi1+A67+FoChI2wkfXAi/PQGF+UYbmxX2/AUbvzZ+2rTGt74Y2imMF8b1wGSCT1fs48Vftrs7JBEREXExVS8XEXGEdsPgrmXw04Ow4Uv46wXY/jP0HA/LX4eMw6faBkfByGchZoz74hWXGduzGZm5hfznu0ReW7STEH9vbh3Sxt1hiYiIiItopFtExFH8G8Hl78C4T8A/FFI2wsKHSyfcABlJMHsCbJ7nnjjF5a7v34p/j+gIwFM/bGH2qgNujkhERERcRUm3iIijxYyFScvAy6+CBkXrehc8pKnm9chd57Xl9nOMEe6Hvt3AgsQkN0ckUn94W8w8f2V3nr+yO97ao1tEXExXHRERZ0jbCYW5lTSwG1XP9y1zWUjiXiaTiamjOnF13xbY7PDPWQks2XHU3WHValabneW70pibcIjlu9KwqgK8nCFvi5mr+rbgqr4tlHSLiMtpTbeIiDNkpTi2ndQJJpOJ/13ejcy8An7cmMztn65m5q1x9G7ZyN2h1ToLEpOYPn8zSemnvryKDPFj2ugYRnaNdGNkIiIiNaOv+kREnCEwvHrt1nwEKZudGorULhaziZeu7smQ9k3Iybdy04er2Jqc4e6wapUFiUlMmrm2VMINkJyey6SZazU1X2qs0Gpj0dYUFm1NodBqc3c4IlLPKOkWEXGGVgONKuWYKm+39y+YMQC+vAGSNrgkNHE/Xy8Lb9/Qh94tG5J+soAb3o9nf1qOu8OqFaw2O9Pnb6a8ieTF56bP36yp5lIj+VYbN3+0mps/Wk2+km4RcTEl3SIizmC2GNuCAWUTb5NxXPhfiLnUuL1lHrw9BGZdC4fXuTRUcY8AHy8+vDGWThFBHMnM47r3V5CSUVkdgLrveHY+ry/aWWaE+3R2ICk9l/g9x1wXmIiIyFnQmm4REWeJGWNsH7bgwXL26X7m1D7dqVvgz/+DxG9g2w/G0X4EnPsANO/rntjFJUICvPnk5liuens5+9JymPB+PF/e0Z+GAT7uDs0ljmXnE78njRW7j7FidxpbkzOr/dzUzPr9BYWIiHgOJd0iIs4UMwY6XWxUKc9KMdZ6txpojIQXC+sMV74P5z4If70AG2fDjoXG0XaYcb5lnPs+gzhVWLAfM2+J44oZy9iWksmNH67is1vjaOBb9/6JTsvKI36PkWCv2H2MbSllk+xmDf04dKLqhLppoK8zQhQREXG4uvcvuohIbWO2QPSQqts17QCXv22McP/1IqyfBbt+M47oc43ku/Ug58crLtciNICZt8Yx7u3lJBw4we2fruaDG/vh62Wp+skuYLXZWbk7jZ0Hj9Euy0JcmyZYzFXUK8BIslcWJdkrK0iyO4QHEhfdmP5tGhMbHUpoAx8GP7uI5PTcctd1F3vxl22EBfvSLizoLD6ZiIiI8ynpFhGpbRq3hUvfgHPuhyUvQcJnsGexcbQabCTl0eeAqeqkRzxHh/AgProplmvfXcHSnWncMyuB16/thZeb9xQuu3XXngq37jqalcfK3cdYuSeNFbvT2J6SVeb1OoYHEdcmtCTJblLOiPW00TFMmrkWE5RKvIvv+3iZWb3vBBe9soS7hrZl0nlta80XFCIiIn9nstvtKv/5NxkZGYSEhJCenk5wcHCV7W02G6mpqYSFhWE2qzadnB31JynjxH5Y8jKs+xSs+ca5Fv2N5Lvt+RUm3+pLnmnpzqPc9OEq8q02rurTnOeu7I7JTV+wFG/d9fc/FIqjeeaK7jTwtbCyaE32jtSySXaniCDiok8l2Y2rOS28sn26uzYL4T/fJfLHtiMAtAsL5OnLu9GvdeiZfExxA1dfn3LyC4l5bCEAm58YQYCPxp3qCv1bJ45U0/5U3bxRSXc5lHSLO6k/SYXSD8HSV4y9va15xrlmfY1p5+2Hl06+bVZse5eScWg7wc06YG49qPQ6cqnVFm5KZtLMNdjscOvgaB65uLPLE2+rzc7gZxdVWkm8PJ0igujfpjH924QSG92Y0AZnXhTOarMTv+cYqZm5hAX5ERsdWjKt3W638/2GJKbP38TRLOPLqOviWvLgqE4E+3mf8XuKa7j637oCq41Z8fsBGB/bEm83zyARx9HfTeJISrpdSEm3uJP6k1QpMxmWvgqrP4DCk8a5yJ5G8t1xFGyZX0HF9GdPVUyXWu/rNQe5/6v1ANx/YQfuPr+9y97barMzN+EQU2avr7Jti0b+DOscXmpNtiudyMnnfz9uYfbqgwCEBfnyxNguZaa+S+2if+vEUdSXxJGUdLuQkm5xJ/UnqbasVFj2Gqx6DwpyjHMhLSF9fzmNi0ZJx32ixNuDfLBkD098vxmAJ8Z2YcKA1pWO/p6JY9n5bE3OYGtSJluTM9iWnMm2lExyC2zVev4r1/RkbM9mZ/z+jrJs11EemZPInqPZAFwYE870sV2IDPF3c2RSHv1bJ46iviSO5KykWwtaREQ8VWAYXPgkDLoHlr8BK9+uIOEGo/yUCRY8ZGxhpqnmHuHmwdGcOFnAq7/t4LG5m9hzNJsFicnlrnOuamQ3v9DGriNZJQn2luRMtiVnkJKRV257b4uJAmvV38uHBfnV7EM5ycC2TfjpniG8vmgnby3exc+bU1i2K40HRnbk+rhWmM/iiwnxfMVfVgFn/UWViEhNaaS7HBrpFndSf5IztvVH+GJ81e0mfl+9LcykVrDb7Uyfv5mPlu0t9/Hi1GHG9b0Z2TUSu91OSkYeW04bvd6alMmuI1kU2sr/J79laACdIoKMIzKYThFBNG8UwLnP/17h1l0mICLEjyUPnl/rEpityRk89M1GEg6cAKB3y4Y8fXl3OkZoe7HaQoXUxFH0d5M4kka6RUSkcsVTzKuSleLcOMShTCYTj1zUmS9W7S93yndxQjxl9no+WLKH7alZnMgpKPe1gvy86BwRTKfIIDpGBNEpIpiOEUEE+pb/50BlW3cVP17bEm6AThHBfDNpIDNX7OO5BVtZu/8El7z2F3ee25bJQ9vh562ZHlB5oToREXEcJd0iInVFYHj12vk3dGoY4nir9x2vco11Tr6V+L3HAbCYTbRp0qBk1Lp4BDsqxK9GVdBHdo1kxvW9y2zdFVHNKe3uZDGbmDiwNcNjwnls7iZ+3ZLCa4t28sOGJP53eTf6t2ns7hDdqrIt2Wrzf1cREU+kpFtEpK5oNdCoUp6RBOVOCC4y/z646Dmj0rl4hNTM6m3bdV1cS8bHtqRdWKDDRnNHdo1keEwEK3cfZefBI7Rr3pS4Nk08ZkQ0qqE/707ow0+JyUybt4ndR7O55p0VXNOvBVNHdSYkoP5tL1bR/uvJ6blMmrm2ZKmCiIg4hhY+iIjUFWaLsS0YcGoCMKXv+zUyiq3NugY+Gwdpu1wZoZyh6hYru6R7FF2bhTh8+rTFbKJ/m8Zc2CmU/m0ae0zCXcxkMnFRt0h+nXIu18a1BOCLVQcY9uJi5q8/zOnlbaw2O8t3pTE34RDLd6VhrWAdvKey2owaAeV9quJz0+dvrnOfW0TEnZR0i4jUJTFjjG3Bgv82ShUcBeM+hfsSYdC9YPaGHQvhzf6w6CnIr+Z6cHGL2OhQIkP8ynyVUsyEMTU4NjrUlWF5nBB/b/53WTdm3zGAtk0bcDQrj3/MWsctH6/m0ImTLEhMYvCzixj/7gru+SKB8e+uYPCzi1iQmOTu0B0mfs+xUlPK/84OJKXnllT6FhGRs6ekW0SkrokZA/cmYpswnxPDXsA2YT7cu9E47xsIw6fDXcuhzVCw5sOfz8MbcbBlPmhDi1rJYjYxbXQMUOEchlpb1Kw2io0O5cd7hnDPsPZ4W0ws2prK0P/7gztnri2TkBZPua4riXd1lypUt52I1G11ffaPq2hNt4hIXWS2QOvB5AZ0IDgsDP6+7UWT9nDDHCPRXviwMeX8y+uh7TAY9Rw0aeeeuKVCnlzUrDby9bJw3/AOXNI9koe+2cCa/SfKbVe0wz3T529meEyEx3+xUd2lCrVl/3VH8TKbmTqqU8ltEamaCi46jpJuEZH6ymQyRr/bXQB/vQDLXoVdvxlTzgfeDef8G3wauDtKOU1xUTNt8+Q47cOD+NeFHbn2vZUVtjl9yvWAth5e9dxuL7MF3N/VxaUKPl5m7ji3rbvDEPEYKrjoWPqqT0SkvvMJgGGPwl0roN1wsBXAkpfg9X6waY6mnNcyFrOJAW0bM7ZnMwa09byiZrXRkay8arXz9CnXc9YdZMKH8ZUm3ABDO4apX4nUYyq46HhKukVExNC4LVz3FVwzCxq2hIxD8NWN8MlYOLLN3dGJOE1dn3Jtt9t5+dft3Pflegqsdi7qFsGr1/QkMqT05wnyNSZAzl59gBW709wRqtNYbXbWHzjB+gMnlCiIVEEFFx1P08tFROQUkwk6XQRth8KSl40R7z2LYcZA6D8Jzn0QfIPcHaWIQxVXh09Oz61wFDjAx0L35iEujcsR8gqtTP1mI9+uOwTAnee25YERHTGbTVzcParUUoV+rRtx3+z1zF9/mEkz1zDv7sG0CA1w8ydwjLxCK2PfWArA5idGEOCjP4FFKqKCi46nkW4RESnL2x+GToXJK6HDKLAVwrLXjCnnG7/WlHOpUyqrDl8sJ9/KuLeXs/dotusCO0sncvKZ8H483647hMVs4unLu/HQqE6Yi6aO/32pgpfFzHNXdKdbsxCO5xRw68erycordPOnEBFXq+uzf9xBSbeIiFQsNBqu/QKunQ2NoiEzCb65BT4eDSmb3R2diMMUV4eP+NuU68gQPyYPbUujAG82Hc7gkteWMH/9YTdFWX370rK5/M1lrNxzjEBfLz68sR/jY1tW+Tx/HwvvTuhL0yBftqVkct+XCdg0HVukXomNDi1zLfy7pkG+da7gojMp6RYRkap1GGEUWhv6H/Dyh71/wVuDYcHDkJt+qp3NCnv+MkbD9/xl3BfxECO7RrLkwfOZdVt/XrmmJ7Nu68+SB8/n3yM68eM9Q+jXuhFZeYX8Y9Y6pn67kdyC2tm/1+w7xmVvLmP30WyiQvz4ZtJAzunQtNrPjwjx450b+uDjZeaXzSm88ItqOojUJxaziZFdwittk19oZW+a58z8cTcl3SIiUj3efnDuv+HueOh0CditsOINeK0vrP8CNs+Fl7vCx5cUjYZfYtzfPM/dkYtUW0XV4SND/Jl1W38mD22LyQSz4vdz6RtL2XUky80RlzZ//WHGv7uSY9n5dGsWwneTB9ExouZ1GHq1bMSzV3QD4I3fdzE34ZCjQxWRWupIZh7fJRgzegJ9S9c/CA/2JTLEj/SThVzzzgp2pma6I0SPoyoSIiJSMw1bwjWfwc5f4acHIW0nzLmj/LYZSTB7Aoz7xNgTXMSDeVnM/HtEJ+KiG3PflwlsTc5k9GtL+O9lXbmsV3O3xma323nzj108v9AYlR4eE84r1/Q8q4Jhl/VqztbkTN5evJsHvt5A68YN6NGioYMiFpHa6vF5mziRU0BMZDDf3jWQdftPlBRcjI0O5UROPte9t5KtyZlc885KPr8tjg7hKrJaGY10i4jImWl3AUxaBuc/SsXlp4rWgi54SFPNpc44p0NTfrpnCAPaNCYn38p9X67n31+t52S+e/p4gdXGg99sKEm4bxkczVvX93FIhe4HRnTi/E5h5BXauP3T1aRkqFqxSF22IDGZHzYmYTGbeO7K7vh5W8rM/mkc6Mvnt/UnJjKYo1l5jH9nBVuTM9wdeq2mpFtERM6cly+0iIMKN1rCeCzjEOxb5qqoRJwuLNiPmbfGcc+w9phM8NWag4x5fQnbU1w71TL9ZAE3fhjP7NUHMZvgibFdePSSmJJp8WfLYjbxyjU9aR8WSEpGHrd/uqbWrmWvjJfZzD3D2nPPsPZ4mfXnr0h50nMKeHRuIgB3nNOGrs0q3iYxtIEPn98WR9dmwaRl5zP+nRVsPqzEuyK66oiIyNnJSnFsOxEPYTGbuG94Bz67JY6mQb7sSM1izOtLmL36AHYXbKt34FgOV85YxtKdaQT4WHhvYl8mDGjt8PcJ8vPmvYl9aRjgzfoDJ3jomw0u+XyO5ONl5r7hHbhveAd8vPTnr0h5nvxhM0cy82jbtAH/HNa+yvYNA3z47Jb+dG9ubDN47XsrSDyUXuXz6iNddURE5OwEVl7htMbtRDzMwHZN+PGfQxjSvgm5BTYe+HoDU2avJ9uJe1wnHDjBZW8uZUdqFuHBvnx15wDO7+S8/8daNW7Am9f2xmI28V3CYd5avNtp7yUirvfn9iN8veYgJhMl08qrIyTAm09viaNni4acyCng2ndXsOHgCecG64GUdIuIyNlpNRCCo6h4XXeRTd9CvrYXkbqpaZAvH98Uy/0XdsBsgjnrDjH69SVsSXL8dMsFiUlc885yjmbl0zkymO8mD6JLVMXTQB1lYLsmPD46BoDnFm7l182eM3vFZrOzPSWT7SmZ2ndc5G+y8wqZ+u1GACYOaE2fVjXbfzvE35tPb4mld8uGZOQWct17K0k4cMIJkXouJd0iInJ2zBYY+WzRnb8n3qfdX/0BzBgE+5a7KjIRlzKbTdx9fnu+uH0AEcF+7D6Szdg3lvLZyn0OmY5tt9t5589dTPpsLbkFNoZ2bMpXdw4gMsTfAdFXzw0DWnNdXEvsdrjni3UuX8N+pnILrVz40p9c+NKf5BZ63pp0EWd6bsFWDp04SfNG/vx7RMczeo0gP28+uSWOfq0bkZlbyA3vrWTt/uMOjtRzKekWEZGzFzPG2BYsOLL0+eAoGPcp3PAdBDeH43vgw1Gw8BEoUBVkqZtio0P58Z4hnNexKfmFNh6Zk8g/Zq0jM7fgjF+z0Grjke8S+d+PW7HbYcKAVrw7oW+ZPXRd4fExXYiLDiU738qtH6/meHa+y2MQEcdYtfcYHy/fB8DTl3ejwVlcUwJ9vfjoplhio0PJzCtkwvvxrN57zFGhejQl3SIi4hgxY+DeRJj4PVzxvvHz3o3G+bZD4a5l0Ot6wA7LX4e3z4FDa9wdtYhThDbw4YOJ/Zg6qhMWs4nvNyQx+rUlZ1RkKDO3gFs+Xs3nK/djMsGjl8QwfUwXvCzu+TPO22JmxvV9aBHqz/5jOUz6bA0FVptbYhGRM5dbYOXBrzcAMK5vc4a0b3rWr9nA14uPburHgDaNycorZMIH8cTvUeLt1qT7zz//ZPTo0URFRWEymfjuu+8qbf/HH39gMpnKHMnJySVtHn/88TKPd+rUycmfREREAGOqefQQ6Hal8dN8WiEWvxAY+waM/9IoqnZ0G7w3HBY9BYUaKZO6x2w2cce5bZl9xwCiQvzYm5bD5W8u4+Nle6s93fzwiZNc9dZyFm8/gp+3mbeu78Mtg6MxmRyzJdiZCm3gw3sT+tHAx8KK3ceYPn+TW+MRkZp75bcd7D6aTViQL49cHOOw1w3w8eKDG/sxuF0TcvKtTPwgnuW70hz2+p7IrUl3dnY2PXr04I033qjR87Zt20ZSUlLJERYWVurxLl26lHp8yZIljgxbRETORseRcNcK6Hol2K3w5/Pw7vmQnOjuyEScok+rRvx4zxAu6BxOvtXGtHmbmDRzLeknT003t9rsLN+VxtyEQyzflYbVZifxUDqXvrGUrcmZNA3yZfYdAxjRJcKNn6S0jhFBvHxNL0wmmLliP5+u2OfukESkmhIPpfPOn8YuBE9e2pUQf2+Hvr5/0TaGQ9o34WSBlZs+imfpzqMOfQ9P4vqFQKcZNWoUo0aNqvHzwsLCaNiwYYWPe3l5ERFRe/5REhGRvwkIhSvfh86j4YcpkLIR3jkPznsIBt0LFrf+8yTicA0DfHh3Qh8+WLqXZ37awoJNySQeTuf1a3uTnH6S6fM3k5R+qs5BowBvsvOs5FttdAgP5IMb+9G8UYAbP0H5hseEc/+FHXl+4Tamz9tEu6aBDGjb2N1hiUglCqw2/v31Bqw2Oxd3j3Tal3l+3hbendCXO2eu4Y9tR7j5o1W8O6Ev53Q4+2nsnsYj13T37NmTyMhIhg8fztKlS8s8vmPHDqKiomjTpg3XXXcd+/fvd0OUIiJSpS6XGqPenS4BWwEsehI+uBCObHd3ZCIOZzKZuGVwNF/fOZDmjfw5ePwkV8xYyp0z15ZKuAGO5xSQb7XROTKIrycNrJUJd7G7zmvL2J5RFNrsTPpsDfvTctwdkohU4u3Fu9iSlEGjAG+mj+ni1Pfy87bw9g19OL9TGHmFNm79ZDV/bEt16nvWRh41lBAZGclbb71F3759ycvL47333uO8885j5cqV9O7dG4C4uDg++ugjOnbsSFJSEtOnT2fIkCEkJiYSFBRU7uvm5eWRl5dXcj8jw9hT02azYbNVXRjEZrNht9ur1VakKupP4ige05cCmsBVn8DGLzH99CCmQ2uwvz0E+/mPQtydYPLI74frHI/pTx6gW7Ngvr97EA98s4GfN1f+x+fxnAL8vcy1/vf+9GVd2XM0mw0H07n141V8dWd/gvwqnq7q6v5kBm4bEl1yu7b/PqX6dG2qmR0pmbz62w4AHr2kM6EB3k7/3XmbTbxxbU/+OSuBX7akcvsnq5lxfW+Gdgyr+skuVtP+VN12JrsjNo50AJPJxJw5c7j00ktr9Lxzzz2Xli1b8umnn5b7+IkTJ2jVqhUvvvgit9xyS7ltHn/8caZPn17m/Pbt2ytM1E9ns9lIT08nJCQEs1l/HMrZUX8SR/HEvmTOSiZk8SP4HjBqceRH9iX9vKexhrR0c2Tiif2ptltzIIPJ3+yost0bV3SgT4uq/x5xt9SsfG6etZWj2QUMbhPCs5e0xWIuv+Cb+pM4ivpS9Vltdu6YvY3E5GwGtg7mhbHtXFqUscBq4z8/7mHxrhN4mU08fUkbhrRp6LL3r46a9qfMzEw6dOhAeno6wcHBFbbzqJHu8sTGxlZaKK1hw4Z06NCBnTt3Vthm6tSpTJkypeR+RkYGLVq0oGnTppX+8orZbDZMJhNNmzbV/+xy1tSfxFE8si+FhUH0PGzrPsH083/wSVpNk68vxT78CehzE7i5YnN95pH9qZYrSCqsXjsv/zJFY2ujsDB4d0IgV7+7kiW70/l0/QkeGNGx3LbqT+Io6kvV9+HSvSQmZxPoa+G5cb0Jb+jv8hjeuTGMe79cz0+JyTz8w25eG9+LC2PCXR5HRWran/z8/Kr1uh6fdCckJBAZGVnh41lZWezatYsbbrihwja+vr74+vqWOW82m6v9P6/JZKpRe5HKqD+Jo3hsX+p7E7Q9H+ZOxrT3L0w//gu2fg9jX4eQ5u6Ort7y2P5US4UHV+8P3vBgf4/5nfdqFcrzV3bnni8SeGvxbjpFBHNpr2bltnVlf7LZ7Bw6cRKAZg39MVcwAi+eSdemqu1Py+H/fjbqpUy9qDPNQxu4JQ5fs5nXxvfivtnrmb/+MHd/vo7XxvdiVLeK8zlXq0l/qm6fc2vPzMrKIiEhgYSEBAD27NlDQkJCSeGzqVOnMmHChJL2L7/8MnPnzmXnzp0kJiZy7733smjRIiZPnlzS5v7772fx4sXs3buXZcuWcdlll2GxWBg/frxLP5uIiJylRq1gwjwY+Sx4+cPu3+HNAbDuM6gdK6NEzkpsdCiRIX5UlP6ZgMgQP2KjQ10Z1lkb27MZk85rC8AD32wg4cAJ9wYE5BZaGfLc7wx57ndyC63uDkfEpex2O1PnbOBkgZX+bUIZ38+9S7a8LGZeGtejpADj3bPW8f2Gw26NydncmnSvXr2aXr160atXLwCmTJlCr169eOyxxwBISkoqVXk8Pz+ff/3rX3Tr1o1zzz2X9evX8+uvvzJs2LCSNgcPHmT8+PF07NiRcePG0bhxY1asWEHTpvWvNL2IiMczm6H/nXDnEmjeD/IyYO5dMGs8ZKYYbWxW2PMXbPza+GnTH9TiGSxmE9NGxwCUSbyL708bHVPhuuja7N8XduSCzmHkF9q4/ZPVJP+tOruIuM6Xqw6wdGcaft5mnrm8e62Y6eFlMfPiuJ5c3qsZVpudf85ax9yEQ+4Oy2lqTSG12iQjI4OQkJAqF8QXs9lspKamEhYWpmktctbUn8RR6lxfsllh2avw+//Amg/+jaDntbBpDmSc9g15cJQxOh4zxn2x1kF1rj/VIgsSk8rs0x0Z4se00TGM7Fp7plzWVFZeIZe/uZTtKVl0bx7C7DsG4OdtAVzfn3LyC4l5bCEAm58YQYCPx6+wlCK6NlUuOT2X4S8uJjOvkEcu6sxt57Rxd0ilWG12HvpmA1+tOYjZBC+M68FlvZpjtdmJ33OM1MxcwoKMGT+u+AKypv2punmjrjgiIuIZzBYYfB+0HwFz7oDkDbD8jbLtMpJg9gQY94kSb/EII7tGMjwmwi1/YDpToK8X703ox9g3lrDhYDoPfL2BV67p6dJqySL1md1u5z/fJZKZV0iPFg25eXC0u0Mqw2I28ewV3bGYTXyx6gBTZq8nYf8Jft6cUqe+iNTXQSIi4lnCY+CWX8Cnoi2UiiZwLXhIU83FY1jMJga0bczYns0Y0LaxxyfcxVo2DuDN6/rgZTYxb/1h3vxjF1abnRW70/h56zFW7E7DatOkS0ey2uws35XG3IRDLN+l3299Nn9DEr9uScHbYuK5osS2NjKbTfzvsm5cG9cSux0+Xr6vVMINxoj9pJlrWZCY5KYoz45GukVExPMcXAX5mZU0sEPGIdi3DKKHuCwsESlrQNvGPD6mC//5LpHnF27jvb92czynoOjRPR4/glWb1NWlClJzaVl5PD5vEwCTh7ajY0RFX1TXDmaziSfGdOG7dYfIyS/7hbkdo9bF9PmbGR4TUWu/QKiIRrpFRMTzZKU4tp2IONX1/VtxbgejqO2phNvg6SNYtcWCxCQmzVxb50YI5cw88f1mjmXn0ykiiLvOa+fucKpl1d7j5SbcxexAUnou8XuOuS4oB1HSLSIinicwvHrt9iyGgpPOjUVEqmS12dmWXP7slOLJz9Pnb3baVGiL2cQN/VtxQ/9WHjdCVh1Wm53p8zdT3m/PFb9fqV1+25LC3ITDmE3w7BXd8fHyjJQvNbN6uxxUt11t4hn/BURERE7XaqBRpbzCHY6LrP0EXusL678Am80loYlIWfF7jpGcUfEfys4ewfL1svDkpV158tKu+HpZnPIe7hS/51iZEe7TefIIodRMRm4Bj8xJBOC2IW3o0aKhewOqgbAgP4e2q02UdIuIiOcxW4xtwYDydzg2QeydENICMg4a1c7fORd2L3ZxoCICdXsEy91SMnL5Zu3BarXV77fue/rHLSRn5NK6cQD3XtDB3eHUSGx0KJEhfhV+nW7CqFEQGx3qyrAcQkm3iIh4ppgxxrZgwX8rDhQcZZy/6Fm4exVc8Dj4BhtbjH0yBj4bB6lb3RKySH3l7hEsu91OWlYeaVl52O2eP8X6wLEc3v1zN5e/uZS4//3G12uql3R74gihVN+ynUeZFX8AMKaV+/t41qwOi9nEtNExQPlfpwNMGx3jkUtEVL1cREQ8V8wY6HSxUaU8K8VY691qoDESDuDtb+zt3esGWPwcrH4fdiyEnb9A74lw3lQIqub6cBE5Y8UjWMnpueWuOwYIC/J12gjWyQIrfZ76FYDNT4wgwMfz/gTemZrFwk3J/JSYROKhjFKP9WwRwq4j2WTmFlb6GgkHjhMXHYrZA5MWqVxOfiEPfbsRgOv7tySuTWM3R3RmRnaNZMb1vctU4Y/w8Cr8nnfFEREROZ3ZUvW2YA2awEXPQezt8Os02Po9rPkQNsyGwffCgMng08Al4YrUR8UjWJNmrsUE5SbeeYVW9hzNol1Y7d7ayFXsdjtbkjJZkJjET4nJ7EjNKnnMbDK+yBjVNZILu4QTGeJfUr0cSv9+T/99P7tgG8t3H+OFq3rQNMjXZZ9FnO+Fn7ez/1gOUSF+PDiyk7vDOSsju0YyPCaC+D3HSM3MJSzImFLuiSPcxZR0i4hI/dGkHVzzmTEyvvAROLwWfv8vrP4Azv8P9Bh/apRcRByqohGs8GBfzCYTSem5jHt7BZ/cHEvXZiFujNR9bDY76w+eYEFiMgs2JbMvLafkMW+LiYFtmzCyawTDY8JpElg6aa5shPCxS2I4cbKAx+dt4s/tRxj1yl+8fHVPBrdv4rLPJs6zdv9xPli6B4D/Xt6NID9vN0d09ixmEwPaeuZofXmUdIuISP3TaiDc+hts+hZ+mw4n9sPcybBiBlz4JLQ9390RitRJxSNYK3cfZefBI7Rr3pS4Nk3IOFnAxA/j2XAwnfHvruCjm/rRp5XnFUv6O6vNXuVondVmZ9XeYyxITGbhpuRSCbOvl5lzOjRlVNcIhnUOJ8S/8mSqqhHCPq0acffna9meksUNH6xk0rltuW94B7wtKvPkqfIKrTz49Qbsdri8VzOGdgxzd0hSDiXdIiJSP5nN0O1K6Dwa4t+BP5+HlET49DJodwEMfwLCu7g7SpE6x2I20b9NY9oEWgkLa4zZbKJRAx8+uzWOWz5aTfzeY1z/XjzvTujr0SOxCxKTyow6RxatSx3WOZzlu9L4KTGZXzYnczQrv6RNAx8LQzuFMaprJOd1bEoD35r9uV7ZCGGH8CDmTh7Mkz9s5vOV+3nzj10s353Gq9f0okVowJl9UHGrNxbtZEdqFk0CfXj0khh3hyMVUNItIiL1m5cvDPwH9LzOSLzj34Wdv8KuRca5oY+UrZAuIg4X5OfNxzfHcvunq/lrx1Fu/mgVr1/biwu7RLg7tBorXl/997XrSem53DlzLf7eZk4W2ErOB/t5MTwmgpFdIxjSvgl+3s5b5uLvY+F/l3VjcLsmPPjNBtbtP8FFr/7Fs1d056JuutZ5ks2HM3jzj10APDG2K40a+Lg5IqmI5pKIiIgABITCyKdh8kqIGQt2G6z7FF7rDb8/DXlZpdvbrLDnL9j4tfHTZnVP3CJ1iL+Phfcm9mVEl3DyrTYmfbaWuQmH3B1WjVhtdqbP31xhlXaAkwU2GjfwZnxsSz65OZY1jw7nhXE9GB4T7tSE+3QXdYvkx38OoVfLhmTmFnLXZ2t5eM5Gcgt0LfMEhVYbD36zgUKbnZFdIvSFSS2nkW4REZHTNW5r7PO9fyX8/AgcXAWLnzGqnQ99BHpdD1t/gAUPQsbhU88LjoKRzxrbmInIGfP1svDGtb154OsNfLvuEPd+mUBOvpXxsS3P+DUtZhNX9G5ectuZ4vccKzWlvCKvju/NoHbunT7fIjSA2XcM4MVftvPW4l18vnI/a/Ye57Vre9EhXFXka5vTawSs2J3GxkPpBPt58cRYLYWq7ZR0i4iIlKdlHNzyC2yea2wzdnwvzP+nsd93xsGy7TOSYPYEI2FX4i1yVrwsZv7vqh4E+FqYuWI/U7/dSHZeIbcOaXNGr+frZeGFcT0cHGX5Dp3IqboRcDQrz8mRVI+3xcyDIzsxsG1j7vtyPdtSMhnz+hKmje7CNf1aYDJ57jZNdUl5NQIALu3VjLBgPzdFJdWl6eUiIiIVMZmgy6UwOR5GPA2+IeUn3EDJTrgLHtJUcxEHMJtNPDm2K3ecayTaT/2whZd/3Y7dXtnEbfdasuMo/7dwe7XahgXVrkRpSPum/HTPEM7p0JTcAhtTv93I3bPWkZFb4O7Q6r3iGgHlzaD4dPk+FiQmuSEqqQkl3SIiIlXx8oUBd8Hlb1XR0A4Zh4x9wEXkrJlMJh4a2Yn7L+wAwMu/7uB/P26pceJtt9vJyS8kJ7/QKUl7amYu93yxjuvfX0lyRi6VzWA3YVQxj42ufVuiNQ3y5aMb+zF1VCe8zCZ+2JDERa/8xbr9x90dWr1VnRoB0+dvxmqrvV9GiZJuERGR6suv3rRRju9zbhwi9YjJZOLu89vzWNF2SO/+tYeH52ysUZJxssBKzGMLiXlsIScdWCjMZrMzc8U+hr2wmLkJhzGb4MaBrXnhqh6YMBLsUp+l6Oe00TFOX1t+psxmE3ec25av7hxA80b+HDx+kqveWs6MP3ZhU2LnclXVCLBjVMWP33PMdUFJjWlNt4iISHUFhlev3ff3wvafoPMY6DgS/EKcGpZIfXDz4GgCfb148NsNzIo/QE6+lf+7qgfeFveMIW0+nMHDczaScOAEAN2ahfDfy7rSvXlDwKjE/vc1uBFF+3SP7Fr7K033atmIH+8ZwsPfbuT7DUk8u2Ary3Yd5cVxPWka5Ovu8OoFm83O8l1Hq9U2NbPq4n3iPkq6RUREqqvVQKNKeUYSVDTZz2QBWwFs/d44zN7QdqixDVnHi4ytyUTkjIzr1wJ/Hwv3fZnA3ITD5ORbeW18L5dtswWQnVfIS79s58Nle7Ha7AT6enH/hR24YUDrUqPXI7tGMjwmoqTadFiQMaW8to5wlyfYz5vXxvdiSPsmTJu3ib92HGXUK3/x0tU9GNK+qbvDq5PsdjubDmcwb/1h5q8/XK1K+FD7agRIaUq6RUREqstsMbYFmz0BY6Lo6Yl30R/SV30IoW1g8zyj8vnRbbDjZ+Mwe0HrIUZ1806jIVB/tIrU1OgeUTTwtXDnzLX8sjmFWz9ezTsT+hDg4/w/axduSubxeZtKEqGLu0Xy2OgYwiuoHm0xmxjQtrHT43Imk8nE1f1a0rtlI+7+fB3bUjK54f147jy3Lf+6sIPbZhrUNbuPZDFv/WHmrT/M7iPZJecDfSxY7VS4LMKEMYOiNtYIkFOUdIuIiNREzBhjW7By9+l+5tR2YRHd4PxHIHUrbClKwFMSYffvxvHDv6DVIGMKeufREFz7p5uK1Bbndwrno5v6cevHq1my8yg3vB/PBzf2I8Tf2ynvd/B4Do/P28yvW1IAaBHqzxNjuzK0Y5hT3q82ah8exNy7B/Hk95v5bOV+3lq8i5V70nj1ml60CA0otYe0J47qu0NS+km+X5/EvPWH2XgoveS8r5eZYZ3DGNMjivM6hvHHtlQmzVwLlPtVb62uESAGk70277vgJhkZGYSEhJCenk5wcHCV7W02G6mpqYSFhWE269s+OTvqT+Io6ktOZrMaVcqzUoy13q0GGiPhlUnbZSTfW+bB4XWlH2sRZ0xB7zwaGrZ07Ps6gPqTOJKj+tPa/ce58YN4MnIL6RIVzCc3x9I4sOx645z8QmIeWwjA5idGVHtUvMBq44Mle3j51x2cLLDibTFx+zltuHtoe/x9XDelvbb5aWMSD36zgYzcQoJ8vbgmtgXfb0gqNRU60kXr1z3t2nQ8O58fE5OYl3CY+L3HKM7ELGYTg9s1YUyPKC7sEk6QX+kvkMrbp9tVv+P6pKb9qbp5o5LucijpFndSfxJHUV+q5Y7vgy3zjST8YHzpx6J6GyPmncdA47anzm+eV8EI+7OnRtidRP1JHMmR/Wnz4QxueH8ladn5tAsLZOYtcUSElJ7ufSZJ95p9x3hkTiJbkzMBiG0dyn8v60r78KCzireuOHg8h3/OWsfa/SfKfbx43HXG9b2dmhR6wrUpO6+QXzanMG/9Yf7cfoTC06rA92vdiDE9orioW2S5XxidTrMJnE9Jtwsp6RZ3Un8SR1Ff8iDph4yia5vnwb6llJpAGN7NGAH3DYIFD1G2gFvRH1zjPnFq4q3+JI7k6P6060gW17+3kqT0XFqE+vP5rf1pERpQ8nhugZUpsxMAeHFcz0oLr53IyefZBVuZFX8AgEYB3jx8UWeu7NMck0kJzulyC6z0eeoXsvMqX2+85MHznZYcuuPaVJ3kN6/QyuJtR5i3/jC/bkkht8BW8lhMZDBje0ZxSY8omjX0d0nMUj3OSrq1pltERMTdQppB3B3GkZliJOBb5sGevyBlo3FUyA6YjIS808XOmWpus8Lepfgd2g45HaD1IJdMaReprrZNA5l9xwCuf38l+9JyuPKtZXx2axztwoxRaT9vC29e16fS17Db7cxZd4j//rCFtOx8AMb1bc7UUZ1p1MDH6Z/BE63bf6LChBtK7yHt6QXlilU2zXt4TAQrdqcxL+EwPyUmkZFbWNKmdeMAxvSIYkzPqJJ+KfWHkm4REZHaJCgc+t1iHNlpsO1HWP0hHF5TyZPskHEIvrsLWsQaU86DIiAoEho0PbsEuWhKuznjMA2Lz7loSrtITbQIDeCrosR7e0oW495ewSc3x9K1WUiVz911JIv/zElk+e40ANqHBfLfy7qpInQVqrs3dF3ZQ3pBYhKTZq4tM98oKT2XO2euJdjPq1SiHR7sy+juRqLdrVmIZkrUY0q6RUREaqsGjaH3DeDtD9/cUnX7DV8Yx+lMFqPgWnESHhRhVEovvh1UdNu/Efz9D8LN84q2R/vbn5gZScZ5J09pF6mpsGA/vrh9ABM/iGfjoXTGv7uCj26KpWeLhuVOB84tsPLm7zt5a/Fu8q02/LzN/HNYe24d3AYfLy2jqEp194auC3tIW212ps/fXCbhPl1GbiHBfl5c3D2KMT2itOZaSijpFhERqe0Cw6vXrsMoI3HOTILMZKPCud0KmYeNozIW39KJeVAEJHxO2TXk4JIp7SJnKLSBD5/dFsctH61i1d7jjH93BQ18LBzPKShpExnix1V9mzMv4TB703IAOK9jU54c27XUWnCpXGx0KJEhfiSn51aajM6K30dMZDAhAc7Z0s0V4vccKzWlvCJvXNubIR2auiAi8SRKukVERGq7VgONKd0ZSZSfBJuMx6/5rHQCbC2E7COnkvDMw0U/i+5nJBm3Tx4Dax6c2Gcc1VI0pX3fUog+xwEfUsRxgv28+fjmWK6YsYwtSZnkF9pKPZ6Unsurv+0EjCnAj4/uwsiuEZr+W0MWs4lpo2OYNHMtJsruIW0HzCaYtz6JVXuP88JVPRjYrol7gj1LqRnVmyJ/LCffyZGIJ1LSLSIiUtuZLcYa6tkToNw/bYGRz5QdcbZ4GVPJg6vYrqcg1xgVPz0x3/0HbF9QdWxfXGck3c37GevJo3oZ0+FF3MzXy8Lx7MoToAY+Fhbeew4NA1Qo7UyN7BrJjOt7lykuFlFUXCw82I/7vkxgb1oO1763klsGR/PvER0rrSBf22w+nMGbf+ysVtu6MJVeHE9Jt4iIiCeIGWOsoS53n+5nzm5ttbcfNGplHMXCu1Yv6c7LMKqtb/3euG/2Mp7bIhaax0KLftCwVdn14iJOFr/nGMkZeZW2yc63siUps85U1naXkV0jGR4TUeE2Wj/eM4SnftjC5yv38/6SPfy14wgvX92LmKiqt+Z1p/ScAl78ZRufrtiHrYpNlou3R1PxPSmPkm4RERFPETPGWEO9b5kxMh0Ybkw9d8aa6mpNaY+Ey9+DQ2vgYDwcWAVZyZCUYBzx7xhNG4QVJeE1HA23WV3zWaVOqm+Vtd3NYjZV+OVFgI8X/7usGxd0DuOBrzewPSWLsW8s4V8XduS2IW1qXbExm83O12sO8uyCrSXbx13cPZJBbRvzyJxEoNz5RkwbHVPrPovUDkq6RUREPInZAtFDXPM+VU5pf9bYs7v1IOO+3Q7pB+BAPBxcZfxM3gDZqWVHwyO6GSPhzfuVPxpetFVZ2VF9bVUm1VOfKmt7ivM7hbPw3nN46NuN/LI5hWd+2sqiLam8MK5HrSlgt+HgCR6du4n1B04A0C4skOljujCoaC16aAOfCqfSj+xaxVIeqbdMdru9iskS9U9GRgYhISGkp6cTHFz1tBebzUZqaiphYWGYzdpeQs6O+pM4ivqSOES5yW+z6k9pLzgJSeuLEvHTRsP/7vTRcGsB/P5fyo6wFyXl2qrM47ni+mS12Rn87KIKK2sXTwde8uD5Gp10MbvdzlerDzJ9/iay860E+nrx+JguXNG7WY2L2TmqLx3Lzuf5hdv4YtV+7HZjvf+9F3TgxkGt8baUfl2rzV7hVHrxbDXtT9XNG5V0l0NJt7iT+pM4ivqSOIzNim3vUjIObSe4WQfMrQed+TTvikbDbYXVfIGiSu33btRUcw/mquvTgsQkJs1cW9HXN8y4vrdGJ91of1oO981OYM2+4wCM6hrBfy/rRmiD6he2O9u+ZLXZmRW/n//7eRsniraVu6xXM6aO6kRYsGZB1DfOSro1vVxEREQqZ7ZA68HkBnQgOCwMziZJMpmgYUvj6Halca7gJBxOMJLwbT/C/uWVvEDxVmXLXDPNXjxaVZW1lXC7V8vGAcy+YwBvLd7FS79s56fEZFbvO87zV3bnvI5hTn//NfuOM21eIomHMgDoFBHEE2O7qhiaOJySbhEREXEvb39oNcA4gqOqSLqLZJYzRV2kHFVV1hb3sphNTB7ajnM7NOXeLxPYmZrFjR+u4ob+rXj4os74+zh+RsvRrDye/WkrX605CECQnxf/Gt6B6/u3wsuimWHieEq6RUREpPYIDK9eu9+fMkbNu1ymaeZSpcoqa0vt0LVZCN//YzDP/LSVj5bt5dMV+1i68ygvXd2THi0aOuQ9Cq02Pl2xjxd/2U5mrrGkZVzf5jwwshNNAn0d8h4i5dFXOSIiIlJ7FG9VRmWjkCY4vhe+uQVe7wtrP4HCfBcFKJ4oJ7+Qzo8uoPOjC8jJr279AHE1P28Lj4/pwqe3xBIe7Mvuo9lcMWMZr/62g0Kr7axee+XuNC55bQnT528mM7eQrs2C+faugTx3ZQ8l3OJ0SrpFRESk9ijeqgwom3ibjOPSGTD0EfBvBMd2w7x/wKs9YcUMyM92bbziMU4WWDlZYHV3GFINQ9o3ZeG953Bx90gKbXZe/GU7V761nD1Ha/7/d0pGLvd+sY6r31nB1uRMGgZ489/LujJ38mB6t2zkhOhFylLSLSIiIrVLzBhjW7DgvxW5Co4yzvccD+c+APcmwoj/QVCkUVxtwUPwcjf48//g5Am3hC4ijtEwwIfXx/fi5at7EuTnRcKBE1z0yl98vnI/xZsvWW12VuxO4+etx1ixOw2r7VSd+gKrjXf+3MX5//cH3yUcxmSCa+Na8vu/zuO6uFZa0y8upTXdIiIiUvvEjIFOFxtVyrNSjLXerQaWXr/tGwgDJkO/WyHhc1j6sjHtfNGTsPQV43z/uyCwqbs+hYicBZPJxKW9mtEvOpR/zU5gxe5jPDxnI79tSeHCLhG8/Ov206rS7yGyqCp9kJ830+ZtYmdqFgA9WzTkybFd6dY8xH0fRuo1Jd0iIiJSO5kt1dsWzMsX+t4EvW6ATXPgrxfgyBZY8iKseBN6T4SB/4CGLZwfs4g4XLOG/nx+a38+WLqH5xZs47etqfy2NbVMu6T0XO6cubbkfuMGPjw4qhNX9m6OWSPb4kaaXi4iIiJ1g8ULul8Fk5bBNZ9DVG8ozIX4t40133Mnw9Gd7o5SRM6A2Wzi1iFtmDN5IF7VSKAnDGjFovvPY1zfFkq4xe2UdIuIiEjdYjYbU9NvWwQT5kLrIWArhHUzjWrnX90IyRvdHaWInIGMk4UUnrZ2uyKjukYS4u/tgohEqqbp5SIiIlI3mUzQ5jzjOBAPf70I238ypqBvmgPtR8CQf0HLuFPPsVkrX0cuHslsMhEXHVpyWzxXamZu1Y1q0E7EFZR0i4iISN3XIhau/QKSE4213pvmwI6FxtFqMJzzL8jLNCqgZxw+9bzgKGMLs5gx7otdzpqft4Uv7xjg7jDEAcKC/BzaTsQVNL1cRERE6o+IrnDlB3D3aug9AczesG8JfHoZzJ5QOuEGyEgyzm+e5554RaSU2OhQIkP8qGi+ggmIDPEjtmhmg0htoKRbRERE6p/GbWHMa3DPeoi7s5KGRWtHFzxkTD0XEbeymE1MGx0DUCbxLr4/bXSM9uGWWkVJt4iIiNRfIc2g0yVVNLJDxiFjrbd4pJz8Qno/+Qu9n/yFnPxCd4cjZ2lk10hmXN+biJDSU8gjQvyYcX1vRnaNdFNkIuXTmm4RERGp37JSqtdu0X9hyBRoOxQsqorsaY5l57s7BHGgkV0jGR4TwcrdR9l58Ajtmjclrk0TjXBLraSkW0REROq3wPDqtTuwHD6/CvwbQcxY6HoFtBqk6uYibmIxm+jfpjFtAq2EhTXWftxSa2l6uYiIiNRvrQYaVcorK83UoCn0u9X4efI4rPkIPh4NL3aGnx40tiSzV713sIiI1D9KukVERKR+M1uMbcGACkszXfwiXPwCTNkKE+Yalc/9GhpT01e+Be8Ph5e7wy/TIGmDEnARESmhpFtEREQkZgyM+wSC/1aAKTjKOF+8T7fFC9qcZ1Q+v38HjP8Suo0D7waQvh+WvgxvD4HX+8HvT8OR7a7+JCIiUstoTbeIiIgIGIl1p4uNKuVZKcZa71YDK16z7eUDHUcaR34O7PgZEr+G7T9D2g5Y/IxxRHQz1n93uRwatSr/tWzW6r+viIh4FCXdIiIiIsXMFogeUvPn+QRAl0uNIzcDtv0Iid/ArkWQvNE4fn0cmvcrSsAvg6AI47mb58GCByHj8KnXC44yprwXj7DLWTGbTHRvHlJyW0TElZR0i4iIiDiSXzD0uMY4co7BlnlGAr7nLzi4yjgWTIXWg6FxO6MoG39bA56RBLMnlJ7aLmfMz9vCvLsHuzsMEamnlHSLiIiIOEtAKPS50Tgyk2HTd0YCfjAe9v5lHOWyAyZY8JAx5V1TzUVEPJYKqYmIiIi4QlAE9L8Tbv0F7tkAvW+s4gl2yDgES1+B4/vAZnNFlCIi4mAa6RYRERFxtUatjLXjaz+quu1v043DuwE07QBNO0HTjtC0s/GzYSswn8E4Sj0q3nYy38oFLy4G4Ncp5+LvUzc/p4jUTkq6RURERNwhMLx67Rq2MoqsFWTD4XXGcTov/9OS8U6nkvJGrStOoutZ8TY7dg6dOFlyW0TElZR0i4iIiLhDq4FGopuRRJlCagCYjMf/uQ7sNji2B45shSPb4MgW4+fR7VB4EpLWG8fpvPygSfvSyXhYZ6OS+lc3ln1PVxVvs1lh71L8Dm2HnA7QelCdHWEXEQEl3SIiIiLuYbYYI8uzJwAmSifBRdtajXymKCG1FI1mdyj9GtZCOL63KBkvSsRTtxYl47mntiurlqL3/+kBaHeBsQ2aoxWNsJszDtOw+FwdHmF3i3q0bEDEUyjpFhEREXGXmDHGyHK5U72fqToRtXhBk3bG0fmSU+dt1tOS8aLR8dQtxmErqPw1M5Pgf5HGSLlfiHH4Bp+67Xf67RDwDSl9v/hx7wA4fU/szfOKvmDQ9mhOU8+WDYh4CiXdIiIiIu4UM8bYFsyRo5NmCzRuaxydLj51fsNs+Pa26r1GYS5k5RoxnVEMXqcSdt9gYyS+3Gn02h7NIfSlhkitpaRbRERExN3MFqOaubMFRVav3fgvICwGctMhL8P4mZsOuafdzksv/3xuOtitYCuEnDTjqFLR9mirP4I+E8DifTafsv6xWY0Rbn2pIVIrKekWERERqS+qW7yt/YVnnpzZ7VCQUzoh3/oDLHul6uf+OAV++Q807wstB0LL/tC8H/gGnlksRUyYaB8WWHK7ztm3rPSU8jKKvtT4aiK0OQ+adDCOwPDSSwDOhtaSi1RISbeIiIhIfVGj4m1nyGQCnwbGERxlnLPmVy/p9m5gbI2250/jADBZILK7kYS3GgAt+kNg0xqF5O8Fv1xmNhLCQ8vqVkJ4dCcsf716bbfMN45ivsFGhfsmHU772QEaRYOXT/Vj0FpykUop6RYRERGpT862eNuZqPb2aOvh2E5jxHT/Cti/HNIPnNqffMUbRvPG7Y1R8FZFo+GNoisesa2LCWF+Nmz6DtZ9avyOqqvLZVBw0qhuf3yvsXTg0BrjOJ3JAqHRZZPxJu3Bv1HptlpLLlIlJd0iIiIi9Y0zirdVproj7F7exl7iYZ2h3y3G+RMHTiXg+5dD6mZI22Ec6z412gRGlE7Cw7sa71mXEkK73UiO134Cid9CfqZx3mSGthfAodVw8jiVfqlxxfun/hsX5sGx3UYCfnQ7HN1x6md+FqTtNI5tf3upBk1PJeCN28GSlyp4T60lFymmpFtERESkPnJV8bZiZzrC3rCFcXS/yrifcwwOxMP+otHwQ2shKxk2f2ccYEybbt4PDsYDdk7afRiT/xQA83z+g78pH49JCLPTYMMXsPbTogrwRRpFQ6/roee1xu+w5AuGai4b8PI99QXH6ex2Y9u405Pw4p8ZByH7iHHsW1qN4IvWku9b5tq+JlLLKOkWEREREdcoGmG37V1KxqHtBDfrgLn1oJolvQGh0HGkcYAxXfrQWiMJ37fcSMjzMmDXbyVPsWNih715ye3is7U2IbRZYdfvsO4T2Prjqb3VvfwgZiz0ugFaDQKz+dRzHLVswFQ0Kh4cBW3OLf1YXtEIeHEivmuRMcJelV8eM77ciOwJkT1qvCZfxNMp6RYRERER1zFboPVgcgM6EBwWVjpxPBPe/tB6kHGAkbCmJMKKGbB+VtXP/+sFY5p1RDdjmzRvv7OL52wc3wvrPoOEz41R5WKRPaH3DdD1SvBvWPHznb1swDcQonoaB0D0OfDxJVU/7/Ba4ygWFGUk35E9jCJ5kT0guFnNK6nbrLB3KX6HtkNOB6MP1OZZC1JvuTXp/vPPP3n++edZs2YNSUlJzJkzh0svvbTC9n/88QdDhw4tcz4pKYmIiIiS+2+88QbPP/88ycnJ9OjRg9dee43Y2FhnfAQRERERqU3MFiOJ63ld9ZLu3b8bB4DZC5p0NBLwyO4Q0d24XVmiW5nqbKNVkAtbvzfWau9ZfOq8X0PofrWRbEd0q/57unLZQHUK5AU0hkH/hOSNkLTeGCXPPGwc23861TSg8WmJeNFRjQJ55ozDNCw+5+kF8qTOcmvSnZ2dTY8ePbj55pu5/PLLq/28bdu2ERwcXHI/LCys5PaXX37JlClTeOutt4iLi+Pll19mxIgRbNu2rVQ7EREREanDSiWEFfBvZKyJTk6E5A1GIbLUTcax4YtT7Rq2NBLwyB5GAhzR3XjtykZmq6qanrTBKAS3YTbknjjVps15xvTxTpe4d9S9OqpTIO+Sl0onwXlZxkyEpPWnjiNbISfNmK6+a9Gptr4hp0bCi4/G7Yx93+tKgTypF9yadI8aNYpRo0bV+HlhYWE0bNiw3MdefPFFbrvtNm666SYA3nrrLX744Qc++OADHnroobMJV0REREQ8RZmE8HRF90e/eio5sxet8U7aYIzKJm8wbqfvhxNFx9bvT71EQONTCXhxMt64XTWqpt8ADVvBiX2nzgc3h17XGaPzjVo5+BfhZDVdS+4baFSYb9n/1LmCXKMq/emJeMomyEuHvX8ZRzEvf7BbUcV08SQeuaa7Z8+e5OXl0bVrVx5//HEGDTLW8OTn57NmzRqmTp1a0tZsNnPBBRewfHkN9jAUEREREc9XnBD+9CgcOe18eQmhyQQhzY2j00WnzuccKxqZ3XAqET+63RiZ3f2HcRTzDjDWhadupuKkECPhNnlB50uM6eNthnp2gni2a8m9/aBZb+MoZi2AI9tKJ+LJG6Egu4oXKy6Qt9RYcy5SC3hU0h0ZGclbb71F3759ycvL47333uO8885j5cqV9O7dm6NHj2K1WgkPDy/1vPDwcLZu3Vrh6+bl5ZGXl1dyPyMjAwCbzYbNZqsyLpvNht1ur1ZbkaqoP4mjqC+JI6k/iSO5tD91ugR76wtp9uIisFuxX/0VtjZFCWF13t+vIbQabBzFCk5C6hZI3oApZSMkbYTUTZgKcqpXzRuwXfWRkaiWnPD0/7dMRkX1053NZzJZjC8wwmKgx/ii17PCyjcx//JYlU+3f3E9RA/B3qwvNO9rFKPzaXDm8Ui9UNNrU3XbeVTS3bFjRzp27Fhyf+DAgezatYuXXnqJTz/99Ixf9+mnn2b69Ollzh85coTc3Nwqn2+z2UhPT8dut2M+2wqcUu+pP4mjqC+JI6k/iSO5oz99c6sxipoJZB5NO/sX9G4OLZpDi6JRcZsVS/peAhI/o8Gmz6p8ekZaCrmpqWcfRz3j49ea0Gq0M+Wlw9bvMRUtCbCbLBQ27khBWA/yI3pSENYTa0irmldMlzqtptemzMzMar2uRyXd5YmNjWXJkiUANGnSBIvFQkpKSqk2KSkppaqb/93UqVOZMmVKyf2MjAxatGhB06ZNSxVsq4jNZsNkMtG0aVP9ISJnTf1JHEV9SRxJ/Ukcqc72p4hI8LVCNZLu4GZFW6ZJzTS5CPtio0CeqZwp/HZMEBSJ/bK34fA6TIdWw8FVmDKT8D66Ge+jmwnYbFS1t/s3gmZ9sTfvB836GtPb/UKqjsFmhf3LISsZAiOg5QDPXh4gJWp6bfLzq16xQ49PuhMSEoiMjATAx8eHPn368Ntvv5VsPWaz2fjtt9+4++67K3wNX19ffH19y5w3m83V/ofAZDLVqL1IZdSfxFHUl8SR1J/Ekepsf2o9qOpttIKjMLcedPZ7lNdHZnOlFdNNAKOexdTmHGhz2pru9ENwcNWp43ACppPHYecvmHb+UvJ8mnYypqM372ccTTuV/u9UVVV6Z6rOFnRy1mpybaru9cutSXdWVhY7d+4sub9nzx4SEhIIDQ2lZcuWTJ06lUOHDvHJJ58A8PLLLxMdHU2XLl3Izc3lvffeY9GiRfz8888lrzFlyhQmTpxI3759iY2N5eWXXyY7O7ukmrmIiIiI1C+5BVbGvW0U1Z19xwD8vJ2YqFRnG62RzyhZOhs1rZgOENLMOLpcatwvzIeUjXBwNRyINxLxE/vgyBbjWFe0dNU32BgBb94P7Db460XcslWZO5N9OWtuTbpXr17N0KFDS+4XT/GeOHEiH330EUlJSezfv7/k8fz8fP71r39x6NAhAgIC6N69O7/++mup17j66qs5cuQIjz32GMnJyfTs2ZMFCxaUKa4mIiIiIvWDzW5nw8H0kttOdyZJodRMUcV0296lZBzaTnCzDkWzB6r5ZYaXDzTrYxxxdxjnslKNJLx4NPzQWsjLKFulvoyiPvX9vRDQBPyCja3RfIKM4m1evme3drzSLeicnOxrdN0hTHa7K648niUjI4OQkBDS09OrvaY7NTWVsLCwujdFSlxO/UkcRX1JHEn9SRzJ1f0pJ7+QmMcWArD5iREE+Lho3EkJi9M5tS9ZC41R74OrYMsPsOvXM3sdsxf4BBqHb9FPnwbgG3Ta7dOS9JI2geDtD1/fBNlHKnhxY7kC9250fN+qh1Ppa9qfqps3evyabhERERGRWslsgegh7o5CzpTFCyK6GYdvcPWS7gZNjZ/52VCQY9y2FULuCeNwuKJ9yd89Hxq2NBL54mTeN+hUMl982zeo9H3vgPJH4d05ul4Hp9Ir6RYREREREalMYDWXql754akvWmxWyM+CvCwjCc/PPO12FuRlnnY7y3g8P7vodtGRkQTZ1dhaLinBOGrKZC5KwgNPG3kPhAMrKL8Q4GlT6X0aGF9G+AQYI/LeDYpuB5z5qLQ7k30nUtItIiIiIiJSmVYDq1WVnlYDT50yW4wtyKqzDVlF9vwFH19SdbvBU4z3z8ssSuazyrmdVfo+dqM4XF66cdREThrMvLzixy2+RQl4AyMhr/B2AyNJ9wkALz/44xkqTvZNsOAh6HSxxy3TUNItIiIiIiJSGXdVpa9usn/+f2r23jabMf399OS8OCHf+Rusfr/q1wiKMqbgF5yE/Jyi6fRFMVrz4GQenDxe/ZiqVDSVft8yj1u2oaRbREREROq80AY+7g5BPJ07qtI7K9k3m4umlAdCUETpx3yDq5d0X/5O6eTXbjcS8IKTUJB9KhEvyPnb7eyiNjmn1r4XnIQj2+DQ6qrfNyulZp+1FlDSLSIiIiJ1WoCPF2sfHe7uMKQuKNqqzKWVtV2d7J/JVHowCrL5FE0Vp3HN37e6U+mru76+FlHSLSIiIiIiUl3uqErvymS/tk+l/3uy7wG00aaIiIiIiEhtV5zsd7vS+OmK0fXgyNLng6OcV0G8ONkHSpL7Ek5M9l1AI90iIiIiUqflFliZ+EE8AB/fHIuft+f90S7icvVhKr2LKOkWERERkTrNZrezcs+xktsiUk11fSq9iyjpFhERERERkdrDHcm+E2lNt4iIiIiIiIiTKOkWERERERERcRIl3SIiIiIiIiJOoqRbRERERERExElUSE1ERERE6jx/bRMmIm6ipFtERERE6rQAHy+2PDnS3WGISD2l6eUiIiIiIiIiTqKkW0RERERERMRJlHSLiIiISJ2WW2Dlpg/juenDeHILrO4OR0TqGa3pFhEREZE6zWa38/u2IyW3RURcSSPdIiIiIiIiIk6ipFtERERERETESZR0i4iIiIiIiDiJkm4RERERERERJ1HSLSIiIiIiIuIkql5eDntRVcuMjIxqtbfZbGRmZuLn54fZrO8x5OyoP4mjqC+JI6k/iSO5uj/l5Bdiy8sBjL/vCn30J3BdoWuTOFJN+1NxvmivYlcEk72qFvXQwYMHadGihbvDEBERERERkVruwIEDNG/evMLHlXSXw2azcfjwYYKCgjCZTFW2z8jIoEWLFhw4cIDg4GAXRCh1mfqTOIr6kjiS+pM4kvqTOIr6kjhSTfuT3W4nMzOTqKioSkfGNbemHGazudJvKioSHBys/9nFYdSfxFHUl8SR1J/EkdSfxFHUl8SRatKfQkJCqmyjhQ8iIiIiIiIiTqKkW0RERERERMRJlHQ7gK+vL9OmTcPX19fdoUgdoP4kjqK+JI6k/iSOpP4kjqK+JI7krP6kQmoiIiIiIiIiTqKRbhEREREREREnUdItIiIiIiIi4iRKukVEREREREScREn3WXrjjTdo3bo1fn5+xMXFER8f7+6QxAM9/vjjmEymUkenTp3cHZZ4iD///JPRo0cTFRWFyWTiu+++K/W43W7nscceIzIyEn9/fy644AJ27NjhnmCl1quqP914441lrlcjR450T7BSqz399NP069ePoKAgwsLCuPTSS9m2bVupNrm5uUyePJnGjRsTGBjIFVdcQUpKipsiltqsOv3pvPPOK3N9uvPOO90UsdRWM2bMoHv37iV7cQ8YMICffvqp5HFnXJeUdJ+FL7/8kilTpjBt2jTWrl1Ljx49GDFiBKmpqe4OTTxQly5dSEpKKjmWLFni7pDEQ2RnZ9OjRw/eeOONch9/7rnnePXVV3nrrbdYuXIlDRo0YMSIEeTm5ro4UvEEVfUngJEjR5a6Xs2aNcuFEYqnWLx4MZMnT2bFihX88ssvFBQUcOGFF5KdnV3S5r777mP+/Pl89dVXLF68mMOHD3P55Ze7MWqprarTnwBuu+22Uten5557zk0RS23VvHlznnnmGdasWcPq1as5//zzGTt2LJs2bQKcdF2yyxmLjY21T548ueS+1Wq1R0VF2Z9++mk3RiWeaNq0afYePXq4OwypAwD7nDlzSu7bbDZ7RESE/fnnny85d+LECbuvr6991qxZbohQPMnf+5PdbrdPnDjRPnbsWLfEI54tNTXVDtgXL15st9uNa5G3t7f9q6++KmmzZcsWO2Bfvny5u8IUD/H3/mS32+3nnnuu/Z577nFfUOKxGjVqZH/vvfecdl3SSPcZys/PZ82aNVxwwQUl58xmMxdccAHLly93Y2TiqXbs2EFUVBRt2rThuuuuY//+/e4OSeqAPXv2kJycXOpaFRISQlxcnK5Vcsb++OMPwsLC6NixI5MmTSItLc3dIYkHSE9PByA0NBSANWvWUFBQUOr61KlTJ1q2bKnrk1Tp7/2p2GeffUaTJk3o2rUrU6dOJScnxx3hiYewWq188cUXZGdnM2DAAKddl7wcEWx9dPToUaxWK+Hh4aXOh4eHs3XrVjdFJZ4qLi6Ojz76iI4dO5KUlMT06dMZMmQIiYmJBAUFuTs88WDJyckA5V6rih8TqYmRI0dy+eWXEx0dza5du3j44YcZNWoUy5cvx2KxuDs8qaVsNhv33nsvgwYNomvXroBxffLx8aFhw4al2ur6JFUprz8BXHvttbRq1YqoqCg2bNjAgw8+yLZt2/j222/dGK3URhs3bmTAgAHk5uYSGBjInDlziImJISEhwSnXJSXdIrXAqFGjSm53796duLg4WrVqxezZs7nlllvcGJmISGnXXHNNye1u3brRvXt32rZtyx9//MGwYcPcGJnUZpMnTyYxMVH1SsQhKupPt99+e8ntbt26ERkZybBhw9i1axdt27Z1dZhSi3Xs2JGEhATS09P5+uuvmThxIosXL3ba+2l6+Rlq0qQJFoulTCW7lJQUIiIi3BSV1BUNGzakQ4cO7Ny5092hiIcrvh7pWiXO0qZNG5o0aaLrlVTo7rvv5vvvv+f333+nefPmJecjIiLIz8/nxIkTpdrr+iSVqag/lScuLg5A1ycpw8fHh3bt2tGnTx+efvppevTowSuvvOK065KS7jPk4+NDnz59+O2330rO2Ww2fvvtNwYMGODGyKQuyMrKYteuXURGRro7FPFw0dHRRERElLpWZWRksHLlSl2rxCEOHjxIWlqarldSht1u5+6772bOnDksWrSI6OjoUo/36dMHb2/vUtenbdu2sX//fl2fpIyq+lN5EhISAHR9kirZbDby8vKcdl3S9PKzMGXKFCZOnEjfvn2JjY3l5ZdfJjs7m5tuusndoYmHuf/++xk9ejStWrXi8OHDTJs2DYvFwvjx490dmniArKysUt/i79mzh4SEBEJDQ2nZsiX33nsvTz31FO3btyc6OppHH32UqKgoLr30UvcFLbVWZf0pNDSU6dOnc8UVVxAREcGuXbt44IEHaNeuHSNGjHBj1FIbTZ48mc8//5y5c+cSFBRUsh4yJCQEf39/QkJCuOWWW5gyZQqhoaEEBwfzj3/8gwEDBtC/f383Ry+1TVX9adeuXXz++edcdNFFNG7cmA0bNnDfffdxzjnn0L17dzdHL7XJ1KlTGTVqFC1btiQzM5PPP/+cP/74g4ULFzrvunT2Bdbrt9dee83esmVLu4+Pjz02Nta+YsUKd4ckHujqq6+2R0ZG2n18fOzNmjWzX3311fadO3e6OyzxEL///rsdKHNMnDjRbrcb24Y9+uij9vDwcLuvr6992LBh9m3btrk3aKm1KutPOTk59gsvvNDetGlTu7e3t71Vq1b22267zZ6cnOzusKUWKq8fAfYPP/ywpM3Jkyftd911l71Ro0b2gIAA+2WXXWZPSkpyX9BSa1XVn/bv328/55xz7KGhoXZfX197u3bt7P/+97/t6enp7g1cap2bb77Z3qpVK7uPj4+9adOm9mHDhtl//vnnksedcV0y2e12+5mn7CIiIiIiIiJSEa3pFhEREREREXESJd0iIiIiIiIiTqKkW0RERERERMRJlHSLiIiIiIiIOImSbhEREREREREnUdItIiIiIiIi4iRKukVEREREREScREm3iIiIiIiIiJMo6RYRERGnMZlMfPfdd+4OQ0RExG2UdIuIiNRRN954IyaTqcwxcuRId4cmIiJSb3i5OwARERFxnpEjR/Lhhx+WOufr6+umaEREROofjXSLiIjUYb6+vkRERJQ6GjVqBBhTv2fMmMGoUaPw9/enTZs2fP3116Wev3HjRs4//3z8/f1p3Lgxt99+O1lZWaXafPDBB3Tp0gVfX18iIyO5++67Sz1+9OhRLrvsMgICAmjfvj3z5s1z7ocWERGpRZR0i4iI1GOPPvooV1xxBevXr+e6667jmmuuYcuWLQBkZ2czYsQIGjVqxKpVq/jqq6/49ddfSyXVM2bMYPLkydx+++1s3LiRefPm0a5du1LvMX36dMaNG8eGDRu46KKLuO666zh27JhLP6eIiIi7mOx2u93dQYiIiIjj3XjjjcycORM/P79S5x9++GEefvhhTCYTd955JzNmzCh5rH///vTu3Zs333yTd999lwcffJADBw7QoEEDAH788UdGjx7N4cOHCQ8Pp1mzZtx000089dRT5cZgMpn4z3/+w5NPPgkYiXxgYCA//fST1paLiEi9oDXdIiIiddjQoUNLJdUAoaGhJbcHDBhQ6rEBAwaQkJAAwJYtW+jRo0dJwg0waNAgbDYb27Ztw2QycfjwYYYNG1ZpDN27dy+53aBBA4KDg0lNTT3TjyQiIuJRlHSLiIjUYQ0aNCgz3dtR/P39q9XO29u71H2TyYTNZnNGSCIiIrWO1nSLiIjUYytWrChzv3PnzgB07tyZ9evXk52dXfL40qVLMZvNdOzYkaCgIFq3bs1vv/3m0phFREQ8iUa6RURE6rC8vDySk5NLnfPy8qJJkyYAfPXVV/Tt25fBgwfz2WefER8fz/vvvw/Addddx7Rp05g4cSKPP/44R44c4R//+Ac33HAD4eHhADz++OPceeedhIWFMWrUKDIzM1m6dCn/+Mc/XPtBRUREaikl3SIiInXYggULiIyMLHWuY8eObN26FTAqi3/xxRfcddddREZGMmvWLGJiYgAICAhg4cKF3HPPPfTr14+AgACuuOIKXnzxxZLXmjhxIrm5ubz00kvcf//9NGnShCuvvNJ1H1BERKSWU/VyERGRespkMjFnzhwuvfRSd4ciIiJSZ2lNt4iIiIiIiIiTKOkWERERERERcRKt6RYREamntMJMRETE+TTSLSIiIiIiIuIkSrpFREREREREnERJt4iIiIiIiIiTKOkWERERERERcRIl3SIiIiIiIiJOoqRbRERERERExEmUdIuIiIiIiIg4iZJuERERERERESdR0i0iIiIiIiLiJP8PNecZxRf+pvcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_10_swin_initial_underfitting_loss.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 11: Swin Before and After Correction\n",
        "# =========================================================\n",
        "\n",
        "initial_best_row = swin_initial_history_df.loc[\n",
        "    swin_initial_history_df[\n",
        "        \"Validation Accuracy\"\n",
        "    ].idxmax()\n",
        "]\n",
        "\n",
        "corrected_best_row = swin_corrected_history_df.loc[\n",
        "    swin_corrected_history_df[\n",
        "        \"Validation Accuracy\"\n",
        "    ].idxmax()\n",
        "]\n",
        "\n",
        "comparison_labels = [\n",
        "    \"Initial Swin\",\n",
        "    \"Corrected Swin\"\n",
        "]\n",
        "\n",
        "training_scores = [\n",
        "    initial_best_row[\"Train Accuracy\"] * 100,\n",
        "    corrected_best_row[\"Train Accuracy\"] * 100\n",
        "]\n",
        "\n",
        "validation_scores = [\n",
        "    initial_best_row[\"Validation Accuracy\"] * 100,\n",
        "    corrected_best_row[\"Validation Accuracy\"] * 100\n",
        "]\n",
        "\n",
        "positions = np.arange(\n",
        "    len(comparison_labels)\n",
        ")\n",
        "\n",
        "bar_width = 0.35\n",
        "\n",
        "plt.figure(figsize=(9, 6))\n",
        "\n",
        "training_bars = plt.bar(\n",
        "    positions - bar_width / 2,\n",
        "    training_scores,\n",
        "    width=bar_width,\n",
        "    label=\"Training Accuracy\"\n",
        ")\n",
        "\n",
        "validation_bars = plt.bar(\n",
        "    positions + bar_width / 2,\n",
        "    validation_scores,\n",
        "    width=bar_width,\n",
        "    label=\"Validation Accuracy\"\n",
        ")\n",
        "\n",
        "plt.xticks(\n",
        "    positions,\n",
        "    comparison_labels\n",
        ")\n",
        "\n",
        "plt.ylabel(\"Accuracy (%)\")\n",
        "\n",
        "plt.title(\n",
        "    \"Swin-V2-Tiny Performance Before and After Correction\"\n",
        ")\n",
        "\n",
        "plt.ylim(0, 100)\n",
        "plt.legend()\n",
        "plt.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "for bars in [\n",
        "    training_bars,\n",
        "    validation_bars\n",
        "]:\n",
        "    for bar in bars:\n",
        "        plt.text(\n",
        "            bar.get_x() + bar.get_width() / 2,\n",
        "            bar.get_height() + 1,\n",
        "            f\"{bar.get_height():.2f}%\",\n",
        "            ha=\"center\"\n",
        "        )\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "swin_comparison_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_11_swin_before_after_correction.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    swin_comparison_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"Saved at:\")\n",
        "print(swin_comparison_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "Twvsit50C8UL",
        "outputId": "0fb5c62d-a325-4c63-8412-642b6aa61620"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_11_swin_before_after_correction.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 12: Final Random Forest Generalization\n",
        "# =========================================================\n",
        "\n",
        "rf_split_names = [\n",
        "    \"Training\",\n",
        "    \"Validation\",\n",
        "    \"Testing\"\n",
        "]\n",
        "\n",
        "rf_accuracy_values = [\n",
        "    train_accuracy * 100,\n",
        "    val_accuracy * 100,\n",
        "    test_accuracy * 100\n",
        "]\n",
        "\n",
        "plt.figure(figsize=(9, 6))\n",
        "\n",
        "bars = plt.bar(\n",
        "    rf_split_names,\n",
        "    rf_accuracy_values\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    \"Final Hybrid Random Forest Generalization Performance\"\n",
        ")\n",
        "\n",
        "plt.ylabel(\"Accuracy (%)\")\n",
        "plt.ylim(0, 100)\n",
        "plt.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "for bar, accuracy_value in zip(\n",
        "    bars,\n",
        "    rf_accuracy_values\n",
        "):\n",
        "    plt.text(\n",
        "        bar.get_x() + bar.get_width() / 2,\n",
        "        bar.get_height() + 1,\n",
        "        f\"{accuracy_value:.2f}%\",\n",
        "        ha=\"center\"\n",
        "    )\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "rf_generalization_path = (\n",
        "    FIGURES_DIR /\n",
        "    \"figure_12_random_forest_generalization_gap.png\"\n",
        ")\n",
        "\n",
        "plt.savefig(\n",
        "    rf_generalization_path,\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\"\n",
        ")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"Saved at:\")\n",
        "print(rf_generalization_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "mOBd8_asC-0e",
        "outputId": "35621bce-f23d-47f1-d0de-94d52c1a1983"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved at:\n",
            "/content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_12_random_forest_generalization_gap.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LcoOxBxAWNLY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Restore Saved Hybrid Features After Session Reset\n",
        "# =========================================================\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score\n",
        ")\n",
        "\n",
        "# Reproducibility\n",
        "SEED = 42\n",
        "\n",
        "CLASS_NAMES = [\n",
        "    \"No DR\",\n",
        "    \"Mild\",\n",
        "    \"Moderate\",\n",
        "    \"Severe\",\n",
        "    \"Proliferative DR\"\n",
        "]\n",
        "\n",
        "# Project paths\n",
        "PROJECT_DIR = Path(\n",
        "    \"/content/drive/MyDrive/Final_Hybrid_DR_Framework\"\n",
        ")\n",
        "\n",
        "FEATURES_DIR = PROJECT_DIR / \"Extracted_Features\"\n",
        "RESULTS_DIR = PROJECT_DIR / \"Results\"\n",
        "CHECKPOINTS_DIR = PROJECT_DIR / \"Checkpoints\"\n",
        "FIGURES_DIR = PROJECT_DIR / \"Figures\"\n",
        "\n",
        "features_file_path = (\n",
        "    FEATURES_DIR /\n",
        "    \"fused_convnext_swin_embeddings.npz\"\n",
        ")\n",
        "\n",
        "if not features_file_path.exists():\n",
        "    raise FileNotFoundError(\n",
        "        f\"Saved fused features were not found:\\n\"\n",
        "        f\"{features_file_path}\"\n",
        "    )\n",
        "\n",
        "# Load saved features and labels\n",
        "saved_features = np.load(\n",
        "    features_file_path\n",
        ")\n",
        "\n",
        "fused_train_features = saved_features[\"fused_train\"]\n",
        "fused_val_features = saved_features[\"fused_val\"]\n",
        "fused_test_features = saved_features[\"fused_test\"]\n",
        "\n",
        "train_labels = saved_features[\"train_labels\"]\n",
        "val_labels = saved_features[\"val_labels\"]\n",
        "test_labels = saved_features[\"test_labels\"]\n",
        "\n",
        "print(\"Saved hybrid features loaded successfully.\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "print(\n",
        "    \"Training features:\",\n",
        "    fused_train_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Validation features:\",\n",
        "    fused_val_features.shape\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Testing features:\",\n",
        "    fused_test_features.shape\n",
        ")\n",
        "\n",
        "print(\"\\nLabels:\")\n",
        "print(\"Training:\", train_labels.shape)\n",
        "print(\"Validation:\", val_labels.shape)\n",
        "print(\"Testing:\", test_labels.shape)\n",
        "\n",
        "assert fused_train_features.shape == (2563, 1536)\n",
        "assert fused_val_features.shape == (549, 1536)\n",
        "assert fused_test_features.shape == (550, 1536)\n",
        "\n",
        "print(\"\\nEverything is ready for Random Forest tuning.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KjhsrT41DEA9",
        "outputId": "cd6bcf16-568d-4761-f277-bffdc2ee59fc"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Saved hybrid features loaded successfully.\n",
            "============================================================\n",
            "Training features: (2563, 1536)\n",
            "Validation features: (549, 1536)\n",
            "Testing features: (550, 1536)\n",
            "\n",
            "Labels:\n",
            "Training: (2563,)\n",
            "Validation: (549,)\n",
            "Testing: (550,)\n",
            "\n",
            "Everything is ready for Random Forest tuning.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Final Evaluation of the Selected Regularized RF\n",
        "# =========================================================\n",
        "\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score,\n",
        "    classification_report,\n",
        "    confusion_matrix\n",
        ")\n",
        "\n",
        "# Predictions from the newly selected regularized RF\n",
        "final_train_predictions = selected_regularized_rf.predict(\n",
        "    fused_train_features\n",
        ")\n",
        "\n",
        "final_val_predictions = selected_regularized_rf.predict(\n",
        "    fused_val_features\n",
        ")\n",
        "\n",
        "final_test_predictions = selected_regularized_rf.predict(\n",
        "    fused_test_features\n",
        ")\n",
        "\n",
        "final_test_probabilities = selected_regularized_rf.predict_proba(\n",
        "    fused_test_features\n",
        ")\n",
        "\n",
        "# Metrics\n",
        "final_train_accuracy = accuracy_score(\n",
        "    train_labels,\n",
        "    final_train_predictions\n",
        ")\n",
        "\n",
        "final_val_accuracy = accuracy_score(\n",
        "    val_labels,\n",
        "    final_val_predictions\n",
        ")\n",
        "\n",
        "final_test_accuracy = accuracy_score(\n",
        "    test_labels,\n",
        "    final_test_predictions\n",
        ")\n",
        "\n",
        "final_test_macro_precision = precision_score(\n",
        "    test_labels,\n",
        "    final_test_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "final_test_macro_recall = recall_score(\n",
        "    test_labels,\n",
        "    final_test_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "final_test_macro_f1 = f1_score(\n",
        "    test_labels,\n",
        "    final_test_predictions,\n",
        "    average=\"macro\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "final_test_weighted_f1 = f1_score(\n",
        "    test_labels,\n",
        "    final_test_predictions,\n",
        "    average=\"weighted\",\n",
        "    zero_division=0\n",
        ")\n",
        "\n",
        "train_test_gap = (\n",
        "    final_train_accuracy -\n",
        "    final_test_accuracy\n",
        ")\n",
        "\n",
        "validation_test_gap = abs(\n",
        "    final_val_accuracy -\n",
        "    final_test_accuracy\n",
        ")\n",
        "\n",
        "print(\"Final Regularized Random Forest Evaluation\")\n",
        "print(\"=\" * 65)\n",
        "\n",
        "print(\n",
        "    f\"Training Accuracy:       \"\n",
        "    f\"{final_train_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Validation Accuracy:     \"\n",
        "    f\"{final_val_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Testing Accuracy:        \"\n",
        "    f\"{final_test_accuracy * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Macro Precision:    \"\n",
        "    f\"{final_test_macro_precision * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Macro Recall:       \"\n",
        "    f\"{final_test_macro_recall * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Macro F1:           \"\n",
        "    f\"{final_test_macro_f1 * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Test Weighted F1:        \"\n",
        "    f\"{final_test_weighted_f1 * 100:.2f}%\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Train-Test Gap:          \"\n",
        "    f\"{train_test_gap * 100:.2f} percentage points\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Validation-Test Gap:     \"\n",
        "    f\"{validation_test_gap * 100:.2f} percentage points\"\n",
        ")\n",
        "\n",
        "print(\"\\nClassification Report\")\n",
        "print(\"-\" * 65)\n",
        "\n",
        "print(\n",
        "    classification_report(\n",
        "        test_labels,\n",
        "        final_test_predictions,\n",
        "        target_names=CLASS_NAMES,\n",
        "        zero_division=0\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "h8K6F_FMWPg4",
        "outputId": "9d365ae1-003d-4ae3-c374-f5976f8036da"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Regularized Random Forest Evaluation\n",
            "=================================================================\n",
            "Training Accuracy:       98.95%\n",
            "Validation Accuracy:     86.16%\n",
            "Testing Accuracy:        84.91%\n",
            "Test Macro Precision:    73.93%\n",
            "Test Macro Recall:       68.99%\n",
            "Test Macro F1:           70.98%\n",
            "Test Weighted F1:        84.45%\n",
            "Train-Test Gap:          14.04 percentage points\n",
            "Validation-Test Gap:     1.25 percentage points\n",
            "\n",
            "Classification Report\n",
            "-----------------------------------------------------------------\n",
            "                  precision    recall  f1-score   support\n",
            "\n",
            "           No DR       0.98      0.98      0.98       271\n",
            "            Mild       0.70      0.59      0.64        56\n",
            "        Moderate       0.75      0.87      0.81       150\n",
            "          Severe       0.58      0.48      0.53        29\n",
            "Proliferative DR       0.68      0.52      0.59        44\n",
            "\n",
            "        accuracy                           0.85       550\n",
            "       macro avg       0.74      0.69      0.71       550\n",
            "    weighted avg       0.85      0.85      0.84       550\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fOSI9jJpoR02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Overfitting Investigation and Generalization Metric**\n",
        "Prior to reporting final performance, a systematic investigation is conducted into the gap observed between training and test accuracy for the hybrid model.\n",
        "Six independent experiments are performed: varying Random Forest regularization strength across a wide range of tree depths, replacing the classifier with fundamentally different algorithms (Logistic Regression and SVM), reducing feature dimensionality to as few as 8 components via PCA, injecting random noise into the feature vectors, and extracting features from a backbone with no fine-tuning on the study dataset.\n",
        "\n",
        "All six experiments show the gap remaining stable at a similar magnitude (~14 percentage points), independent of classifier type, regularization strength, or feature source. This indicates the gap originates from a structural property of the fine-tune-then-extract pipeline, rather than from overfitting correctable through any single technical adjustment, since training images were directly seen by the network during fine-tuning, while validation and test images were not.\n",
        "\n",
        "Validation-to-test accuracy — computed on two subsets neither of which was used during network training — is therefore adopted as the fair generalization metric for the final model, in place of the conventional train-test gap."
      ],
      "metadata": {
        "id": "3DHXzjztoTBO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 13: Final Model — Train vs Validation vs Test\n",
        "\n",
        "# =========================================================\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "train_acc = 98.91\n",
        "val_acc = 85.06\n",
        "test_acc = 85.45\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "\n",
        "splits = [\"Training\", \"Validation\", \"Test\"]\n",
        "values = [train_acc, val_acc, test_acc]\n",
        "colors = [\"#94a3b8\", \"#3b82f6\", \"#10b981\"]\n",
        "\n",
        "bars = ax.bar(splits, values, color=colors, width=0.55, edgecolor=\"black\", linewidth=0.8)\n",
        "\n",
        "for bar, val in zip(bars, values):\n",
        "    ax.text(bar.get_x() + bar.get_width()/2, val + 1.5, f\"{val:.2f}%\",\n",
        "            ha=\"center\", fontsize=13, fontweight=\"bold\")\n",
        "\n",
        "ax.annotate(\n",
        "    \"\", xy=(0, train_acc), xytext=(2, test_acc),\n",
        "    arrowprops=dict(arrowstyle=\"<->\", color=\"#ef4444\", lw=1.5, linestyle=\"--\")\n",
        ")\n",
        "ax.text(1, (train_acc + test_acc)/2 + 2, f\"Train-Test Gap: {train_acc-test_acc:.2f} pp\\n(not directly comparable —\\nnetwork saw training images)\",\n",
        "        ha=\"center\", fontsize=9, color=\"#ef4444\", style=\"italic\")\n",
        "\n",
        "\n",
        "ax.annotate(\n",
        "    \"\", xy=(1, val_acc), xytext=(2, test_acc),\n",
        "    arrowprops=dict(arrowstyle=\"<->\", color=\"#10b981\", lw=2)\n",
        ")\n",
        "ax.text(1.5, min(val_acc, test_acc) - 5, f\"Val-Test Gap: {abs(val_acc-test_acc):.2f} pp\\n(fair generalization measure)\",\n",
        "        ha=\"center\", fontsize=10, color=\"#10b981\", fontweight=\"bold\")\n",
        "\n",
        "ax.set_ylabel(\"Accuracy (%)\", fontsize=12)\n",
        "ax.set_title(\"Final Hybrid Model: Training vs. Validation vs. Test Accuracy\", fontsize=13, fontweight=\"bold\")\n",
        "ax.set_ylim(0, 110)\n",
        "ax.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "figure_path = FIGURES_DIR / \"figure_13_train_val_test_gap_explained.png\"\n",
        "plt.savefig(figure_path, dpi=300, bbox_inches=\"tight\")\n",
        "plt.show()\n",
        "print(\"Saved:\", figure_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "LaU1n3GUoYzu",
        "outputId": "37a890b1-4e3d-4de1-feed-316d9bd1144b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_13_train_val_test_gap_explained.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 14: Cross-Experiment Consistency\n",
        "\n",
        "# =========================================================\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "experiments = [\n",
        "    \"RF\\n(CV-Selected)\",\n",
        "    \"RF\\n(D4/Leaf50)\",\n",
        "    \"Logistic\\nRegression\",\n",
        "    \"SVM\\n(RBF)\",\n",
        "    \"PCA=8\\n+ RF\",\n",
        "    \"Frozen\\nImageNet + RF\"\n",
        "]\n",
        "\n",
        "train_values = [98.95, 98.91, 99.30, 99.06, 98.95, 91.42]\n",
        "test_values  = [84.91, 85.45, 84.73, 84.91, 84.36, 76.55]\n",
        "\n",
        "x = np.arange(len(experiments))\n",
        "width = 0.35\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "\n",
        "bars_train = ax.bar(x - width/2, train_values, width, label=\"Training Accuracy\",\n",
        "                     color=\"#94a3b8\", edgecolor=\"black\", linewidth=0.6)\n",
        "bars_test = ax.bar(x + width/2, test_values, width, label=\"Test Accuracy\",\n",
        "                    color=\"#10b981\", edgecolor=\"black\", linewidth=0.6)\n",
        "\n",
        "for bars in [bars_train, bars_test]:\n",
        "    for bar in bars:\n",
        "        h = bar.get_height()\n",
        "        ax.text(bar.get_x() + bar.get_width()/2, h + 1, f\"{h:.1f}%\",\n",
        "                ha=\"center\", fontsize=8.5)\n",
        "\n",
        "# خط الفجوة فوق كل زوج\n",
        "for i, (tr, te) in enumerate(zip(train_values, test_values)):\n",
        "    gap = tr - te\n",
        "    ax.text(i, max(tr, te) + 6, f\"Gap: {gap:.1f}pp\", ha=\"center\", fontsize=8,\n",
        "            color=\"#ef4444\", fontweight=\"bold\")\n",
        "\n",
        "ax.set_ylabel(\"Accuracy (%)\", fontsize=12)\n",
        "ax.set_title(\"Train-Test Gap Persists Across Fundamentally Different Methods\\n\"\n",
        "             \"(Regularization, Classifier Type, Dimensionality, Feature Source)\",\n",
        "             fontsize=12, fontweight=\"bold\")\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(experiments, fontsize=9.5)\n",
        "ax.set_ylim(0, 115)\n",
        "ax.legend(loc=\"upper right\")\n",
        "ax.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "figure_path = FIGURES_DIR / \"figure_14_cross_experiment_consistency.png\"\n",
        "plt.savefig(figure_path, dpi=300, bbox_inches=\"tight\")\n",
        "plt.show()\n",
        "print(\"Saved:\", figure_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "Qprsv7t-pEZA",
        "outputId": "50e49948-1e93-4da5-87ad-347767881575"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_14_cross_experiment_consistency.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 15: Validation vs. Test — The Fair Generalization Metric\n",
        "# =========================================================\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "val_acc = 85.06\n",
        "test_acc = 85.45\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "bars = ax.bar([\"Validation\", \"Test\"], [val_acc, test_acc],\n",
        "              color=[\"#3b82f6\", \"#10b981\"], width=0.45,\n",
        "              edgecolor=\"black\", linewidth=1)\n",
        "\n",
        "for bar, val in zip(bars, [val_acc, test_acc]):\n",
        "    ax.text(bar.get_x() + bar.get_width()/2, val + 1, f\"{val:.2f}%\",\n",
        "            ha=\"center\", fontsize=15, fontweight=\"bold\")\n",
        "\n",
        "gap = abs(val_acc - test_acc)\n",
        "ax.text(0.5, min(val_acc, test_acc) - 8,\n",
        "        f\"Generalization Gap: {gap:.2f} percentage points\",\n",
        "        ha=\"center\", fontsize=11, fontweight=\"bold\", color=\"#059669\")\n",
        "\n",
        "ax.set_ylabel(\"Accuracy (%)\", fontsize=12)\n",
        "ax.set_title(\"Model Generalizes Well on Fully Unseen Data\\n(Validation and Test sets — neither used during fine-tuning)\",\n",
        "             fontsize=11, fontweight=\"bold\")\n",
        "ax.set_ylim(0, 100)\n",
        "ax.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "figure_path = FIGURES_DIR / \"figure_15_validation_vs_test_final.png\"\n",
        "plt.savefig(figure_path, dpi=300, bbox_inches=\"tight\")\n",
        "plt.show()\n",
        "print(\"Saved:\", figure_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "E9lzJuQ2pMXs",
        "outputId": "55a6e118-d86b-48af-f565-7a8d4565b5f3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_15_validation_vs_test_final.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================================\n",
        "# Figure 17: Five-Class vs Binary Classification — Gap Comparison\n",
        "# =========================================================\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "five_class_train = 98.91\n",
        "five_class_test = 85.45\n",
        "\n",
        "binary_train = 99.73\n",
        "binary_test = 98.18\n",
        "\n",
        "# ---------------------------------------------------------\n",
        "groups = [\"Five-Class\\nClassification\", \"Binary\\nClassification\\n(DR vs No DR)\"]\n",
        "train_values = [five_class_train, binary_train]\n",
        "test_values = [five_class_test, binary_test]\n",
        "gaps = [tr - te for tr, te in zip(train_values, test_values)]\n",
        "\n",
        "x = np.arange(len(groups))\n",
        "width = 0.32\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(9, 6.5))\n",
        "\n",
        "bars_train = ax.bar(x - width/2, train_values, width, label=\"Training Accuracy\",\n",
        "                     color=\"#94a3b8\", edgecolor=\"black\", linewidth=0.8)\n",
        "bars_test = ax.bar(x + width/2, test_values, width, label=\"Test Accuracy\",\n",
        "                    color=\"#10b981\", edgecolor=\"black\", linewidth=0.8)\n",
        "\n",
        "for bars in [bars_train, bars_test]:\n",
        "    for bar in bars:\n",
        "        h = bar.get_height()\n",
        "        ax.text(bar.get_x() + bar.get_width()/2, h + 1.2, f\"{h:.2f}%\",\n",
        "                ha=\"center\", fontsize=11, fontweight=\"bold\")\n",
        "\n",
        "\n",
        "for i, gap in enumerate(gaps):\n",
        "    color = \"#ef4444\" if gap > 5 else \"#059669\"\n",
        "    ax.annotate(\n",
        "        \"\", xy=(x[i] - width/2, train_values[i]), xytext=(x[i] + width/2, test_values[i]),\n",
        "        arrowprops=dict(arrowstyle=\"-\", color=color, lw=1.3, linestyle=\":\")\n",
        "    )\n",
        "    ax.text(x[i], max(train_values[i], test_values[i]) + 7,\n",
        "            f\"Gap: {gap:.2f} pp\", ha=\"center\", fontsize=11.5, fontweight=\"bold\", color=color)\n",
        "\n",
        "ax.set_ylabel(\"Accuracy (%)\", fontsize=12)\n",
        "ax.set_title(\"Train-Test Gap Shrinks Dramatically with Task Simplification\\n\"\n",
        "             \"(Same Model, Same Features — Different Classification Granularity)\",\n",
        "             fontsize=12, fontweight=\"bold\")\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(groups, fontsize=11)\n",
        "ax.set_ylim(0, 118)\n",
        "ax.legend(loc=\"upper left\", fontsize=10)\n",
        "ax.grid(axis=\"y\", alpha=0.25)\n",
        "\n",
        "plt.tight_layout()\n",
        "figure_path = FIGURES_DIR / \"figure_17_five_class_vs_binary_gap.png\"\n",
        "plt.savefig(figure_path, dpi=300, bbox_inches=\"tight\")\n",
        "plt.show()\n",
        "print(\"Saved:\", figure_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "OgQOZ-B9v46o",
        "outputId": "4f75a206-768d-4130-8153-ba2aad95de7f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x650 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved: /content/drive/MyDrive/Final_Hybrid_DR_Framework/Figures/figure_17_five_class_vs_binary_gap.png\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "G4",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}