Diabet.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMRFxal1RmgXt8rYRp7kJy8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fjadidi2001/DiabetPrediction/blob/main/Diabet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "wlzrvkyr5M9U",
        "outputId": "684bc4fe-f6b5-4e77-af4f-23cd0a2abb5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Path to dataset files: /root/.cache/kagglehub/datasets/himanshu86503/dibetes33hi/versions/1\n"
          ]
        }
      ],
      "source": [
        "import kagglehub\n",
        "\n",
        "# Download latest version\n",
        "path = kagglehub.dataset_download(\"himanshu86503/dibetes33hi\")\n",
        "\n",
        "print(\"Path to dataset files:\", path)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch-tabnet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVLB-KnI8Api",
        "outputId": "05e04f5e-a04e-423f-cb3e-42f8243a724f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytorch-tabnet\n",
            "  Downloading pytorch_tabnet-4.1.0-py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pytorch-tabnet) (1.26.4)\n",
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            "Requirement already satisfied: scipy>1.4 in /usr/local/lib/python3.10/dist-packages (from pytorch-tabnet) (1.13.1)\n",
            "Requirement already satisfied: torch>=1.3 in /usr/local/lib/python3.10/dist-packages (from pytorch-tabnet) (2.5.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.36 in /usr/local/lib/python3.10/dist-packages (from pytorch-tabnet) (4.66.6)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn>0.21->pytorch-tabnet) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn>0.21->pytorch-tabnet) (3.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch-tabnet) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch-tabnet) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch-tabnet) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch-tabnet) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch-tabnet) (2024.10.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch-tabnet) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.3->pytorch-tabnet) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.3->pytorch-tabnet) (3.0.2)\n",
            "Downloading pytorch_tabnet-4.1.0-py3-none-any.whl (44 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.5/44.5 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pytorch-tabnet\n",
            "Successfully installed pytorch-tabnet-4.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import kagglehub\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import xgboost as xgb\n",
        "from pytorch_tabnet.tab_model import TabNetClassifier\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Step 1: Load the dataset\n",
        "\n",
        "df = pd.read_csv('diabetes.csv')\n",
        "# Step 2: Exploratory Data Analysis (EDA)\n",
        "def perform_eda(df):\n",
        "    print(\"\\nDataset Info:\")\n",
        "    print(df.info())\n",
        "\n",
        "    print(\"\\nDescriptive Statistics:\")\n",
        "    print(df.describe())\n",
        "\n",
        "    print(\"\\nMissing Values:\")\n",
        "    print(df.isnull().sum())\n",
        "\n",
        "    # Correlation matrix plot\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
        "    plt.title('Correlation Matrix')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Distribution of target variable\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    df['Outcome'].value_counts().plot(kind='bar')\n",
        "    plt.title('Distribution of Diabetes Outcome')\n",
        "    plt.xlabel('Outcome')\n",
        "    plt.ylabel('Count')\n",
        "    plt.show()\n",
        "\n",
        "perform_eda(df)\n",
        "\n",
        "# Step 3: Preprocess data and split\n",
        "def preprocess_data(df):\n",
        "    # Separate features and target\n",
        "    X = df.drop('Outcome', axis=1)\n",
        "    y = df['Outcome']\n",
        "\n",
        "    # Handle missing values if any\n",
        "    X = X.fillna(X.mean())\n",
        "\n",
        "    # Scale features\n",
        "    scaler = StandardScaler()\n",
        "    X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "    # Split data\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X_scaled, y, test_size=0.2, random_state=42\n",
        "    )\n",
        "\n",
        "    return X_train, X_test, y_train, y_test, scaler\n",
        "\n",
        "X_train, X_test, y_train, y_test, scaler = preprocess_data(df)\n",
        "\n",
        "# Step 4: Train Models\n",
        "def train_xgboost(X_train, X_test, y_train, y_test):\n",
        "    # XGBoost model\n",
        "    xgb_model = xgb.XGBClassifier(\n",
        "        n_estimators=100,\n",
        "        learning_rate=0.1,\n",
        "        max_depth=5,\n",
        "        random_state=42\n",
        "    )\n",
        "    xgb_model.fit(X_train, y_train)\n",
        "    return xgb_model\n",
        "\n",
        "def train_tabnet(X_train, X_test, y_train, y_test):\n",
        "    # TabNet model\n",
        "    tabnet_model = TabNetClassifier(\n",
        "        optimizer_fn=torch.optim.Adam,\n",
        "        optimizer_params=dict(lr=2e-2),\n",
        "        scheduler_params={\"step_size\":10, \"gamma\":0.9},\n",
        "        scheduler_fn=torch.optim.lr_scheduler.StepLR,\n",
        "        mask_type='entmax'\n",
        "    )\n",
        "\n",
        "    # Convert to PyTorch tensors with correct data type (This line is already present, no change needed)\n",
        "    X_train_tab = torch.tensor(X_train, dtype=torch.float32)\n",
        "    y_train_tab = torch.tensor(y_train.values, dtype=torch.long)\n",
        "    X_test_tab = torch.tensor(X_test, dtype=torch.float32)\n",
        "\n",
        "    # Convert back to NumPy arrays before passing to TabNet\n",
        "    X_train_np = X_train_tab.cpu().numpy()  # Convert to NumPy array\n",
        "    X_test_np = X_test_tab.cpu().numpy()   # Convert to NumPy array\n",
        "    y_train_np = y_train_tab.cpu().numpy() # Convert to NumPy array\n",
        "    y_test_np = torch.tensor(y_test.values, dtype=torch.long).cpu().numpy() # Convert to NumPy array\n",
        "\n",
        "    tabnet_model.fit(\n",
        "        X_train=X_train_np, y_train=y_train_np, # Pass NumPy arrays\n",
        "        eval_set=[(X_train_np, y_train_np), (X_test_np, y_test_np)], # Pass NumPy arrays\n",
        "        max_epochs=50,\n",
        "        patience=10,\n",
        "        batch_size=256\n",
        "    )\n",
        "\n",
        "    return tabnet_model\n",
        "\n",
        "# Train both models\n",
        "xgb_model = train_xgboost(X_train, X_test, y_train, y_test)\n",
        "tabnet_model = train_tabnet(X_train, X_test, y_train, y_test)\n",
        "\n",
        "# Step 5: Evaluate Models\n",
        "def evaluate_models(xgb_model, tabnet_model, X_test, y_test):\n",
        "    # XGBoost predictions\n",
        "    xgb_pred = xgb_model.predict(X_test)\n",
        "    print(\"\\nXGBoost Results:\")\n",
        "    print(\"Accuracy:\", accuracy_score(y_test, xgb_pred))\n",
        "    print(\"\\nClassification Report:\")\n",
        "    print(classification_report(y_test, xgb_pred))\n",
        "\n",
        "    # TabNet predictions\n",
        "    tabnet_pred = tabnet_model.predict(torch.FloatTensor(X_test))\n",
        "    print(\"\\nTabNet Results:\")\n",
        "    print(\"Accuracy:\", accuracy_score(y_test, tabnet_pred))\n",
        "    print(\"\\nClassification Report:\")\n",
        "    print(classification_report(y_test, tabnet_pred))\n",
        "\n",
        "    # Feature importance plot for XGBoost\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    xgb.plot_importance(xgb_model)\n",
        "    plt.title('XGBoost Feature Importance')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "evaluate_models(xgb_model, tabnet_model, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q8tI_Sng-Kc9",
        "outputId": "a9914815-5ac5-4801-8f49-78c04bbd5f76"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n",
            "None\n",
            "\n",
            "Descriptive Statistics:\n",
            "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
            "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
            "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
            "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
            "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
            "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
            "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
            "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
            "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
            "\n",
            "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
            "count  768.000000                768.000000  768.000000  768.000000  \n",
            "mean    31.992578                  0.471876   33.240885    0.348958  \n",
            "std      7.884160                  0.331329   11.760232    0.476951  \n",
            "min      0.000000                  0.078000   21.000000    0.000000  \n",
            "25%     27.300000                  0.243750   24.000000    0.000000  \n",
            "50%     32.000000                  0.372500   29.000000    0.000000  \n",
            "75%     36.600000                  0.626250   41.000000    1.000000  \n",
            "max     67.100000                  2.420000   81.000000    1.000000  \n",
            "\n",
            "Missing Values:\n",
            "Pregnancies                 0\n",
            "Glucose                     0\n",
            "BloodPressure               0\n",
            "SkinThickness               0\n",
            "Insulin                     0\n",
            "BMI                         0\n",
            "DiabetesPedigreeFunction    0\n",
            "Age                         0\n",
            "Outcome                     0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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D3njjDVWrVk29e/fOd9y5c+dyLcv5MoSMjAxJUunSpSUp3wAqqI8++sjln4iXLl2qM2fOqGPHjs5lNWrU0NatW5WZmelctnz5cp06dcrluQoyt8cff1xZWVl69913XZZPmzZNNpvNZfu34/HHH1diYqI+/fRT57Jr165p1qxZ8vPzU6tWrYpkOzkuX76sPn366Ny5cxo9evQNg9/DwyPXkedZs2blezTy/fffdzm/9b333tO1a9ecr1VkZKTsdrvefPPNPM+DPXv2rPPP+f2sevTooaysLL3xxhu5Hn/t2jXn+PPnz+ea+/Xv1bz4+vrqlVde0dGjRzV69Ohc61esWKG4uDhFRkaqWbNmzuU1atTIdR7q+++/n+u1ym+/OnToIH9/f02cOFFXrlxxWZezH02aNFFQUJDmzp3rsg+rVq3S4cOHFRUVle9+FVSPHj2UkJCgNWvW5FqXkpKia9euFdm2gLsFR26Be9SqVat05MgRXbt2TUlJSdqwYYPWrVunqlWratmyZTe8kP+ECRO0efNmRUVFqWrVqkpOTtacOXN03333qUWLFpJ++598QECA5s6dK39/f5UuXVpNmzZVtWrVCjXfwMBAtWjRQv3791dSUpKmT5+umjVrauDAgc4xAwYM0NKlS/XYY4+pR48e+u677/Txxx+7fMCroHN74okn1KZNG40ePVonTpzQAw88oLVr1+rLL7/UsGHDcj13YQ0aNEj/+Mc/1K9fP+3atUuhoaFaunSp/v3vf2v69Om3dV7nzz//rI8//ljSb0dRDx06pCVLligxMVEvv/yynnvuuRs+vlOnTlqwYIEcDofCwsKUkJCg9evXq2zZsnmOz8zMVLt27dSjRw8dPXpUc+bMUYsWLfTkk09K+u2c2vfee099+vTRQw89pJ49e6p8+fI6efKkVqxYoebNmzv/MtG4cWNJ0osvvqjIyEh5eHioZ8+eatWqlZ577jlNnDhRe/bsUYcOHeTl5aVjx45pyZIlmjFjhrp3764PP/xQc+bM0Z///GfVqFFDFy5c0AcffCC73e48kpyfUaNG6T//+Y8mTZqkhIQEdevWTaVKldK3336rjz/+WPXq1dOHH37o8pgBAwbo+eefV7du3dS+fXvt3btXa9asUbly5VzGNWrUSB4eHpo0aZJSU1Pl4+Ojtm3bKigoSNOmTdOAAQP08MMP6+mnn1aZMmW0d+9eXbp0SR9++KG8vLw0adIk9e/fX61atVKvXr2UlJSkGTNmKDQ0VMOHD7/hfhXEyJEjtWzZMnXq1En9+vVT48aNdfHiRe3fv19Lly7ViRMncu0bcM9z34UaABTG9ReQ9/b2tkJCQqz27dtbM2bMcLnkVI7rLwUWHx9vde7c2apYsaLl7e1tVaxY0erVq1euywV9+eWXVlhYmOXp6ZnnlzjkJb9LgX3yySdWbGysFRQUZJUqVcqKioqyfvzxx1yPnzJlilWpUiXLx8fHat68ubVz585cz3mjueX1JQ4XLlywhg8fblWsWNHy8vKyatWqdcMvcbhefpcou15SUpLVv39/q1y5cpa3t7fVoEGDPC9XVtBLgeX8rG02m2W326369etbAwcOtLZt25bnY3Td5aDOnz/vnJefn58VGRlpHTlyJNd+Xf8lDmXKlLH8/Pys3r17W7/++muu7WzcuNGKjIy0HA6HVbJkSatGjRpWv379rJ07dzrHXLt2zRo6dKhVvnx5y2az5bos2Pvvv281btzYKlWqlOXv7281aNDA+utf/2qdPn3asizL2r17t9WrVy+rSpUqzi966NSpk8s2biQrK8uaP3++1bx5c8tut1slS5a06tevb40fP95KT0/Pc/yrr75qlStXzvL19bUiIyOt48eP5/ke+OCDD6zq1atbHh4euS4LtmzZMuuRRx6xSpUqZdntdutPf/qT9cknn7g8/tNPP7UefPBBy8fHxwoMDLzhlzhcL7//BvN6b124cMGKjY21atasaXl7e1vlypWzHnnkEeudd95xueQbYAqbZd2ln5IAAAAACohzbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMbgSxz023dvnz59Wv7+/kX+laMAAAC4fZZl6cKFC6pYsaLL15xfj7iVdPr0aVWuXNnd0wAAAMBNnDp1Svfdd1++64lbyfm1mKdOnZLdbnfzbAAAAHC9tLQ0Va5c+aZfZ07cSs5TEex2O3ELAABwF7vZKaR8oAwAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxnBr3I4bN042m83lVrduXef6K1euKCYmRmXLlpWfn5+6deumpKQkl+c4efKkoqKi5Ovrq6CgII0cOVLXrl0r7l0BAADAXcDT3ROoX7++1q9f77zv6fn/Uxo+fLhWrFihJUuWyOFwaMiQIeratav+/e9/S5KysrIUFRWlkJAQbdmyRWfOnFHfvn3l5eWlN998s9j3BQAAAO7l9rj19PRUSEhIruWpqan65z//qUWLFqlt27aSpPnz56tevXraunWrmjVrprVr1+rQoUNav369goOD1ahRI73xxht69dVXNW7cOHl7exf37gAAAMCN3H7O7bFjx1SxYkVVr15dvXv31smTJyVJu3bt0tWrVxUREeEcW7duXVWpUkUJCQmSpISEBDVo0EDBwcHOMZGRkUpLS9PBgwfz3WZGRobS0tJcbgAAALj3ufXIbdOmTRUXF6c6derozJkzGj9+vB599FEdOHBAiYmJ8vb2VkBAgMtjgoODlZiYKElKTEx0Cduc9Tnr8jNx4kSNHz++aHfmDyh01Ap3TwF/ECfeinL3FAAA9wi3xm3Hjh2df27YsKGaNm2qqlWr6rPPPlOpUqXu2HZjY2M1YsQI5/20tDRVrlz5jm0PAAAAxcPtpyX8XkBAgGrXrq3jx48rJCREmZmZSklJcRmTlJTkPEc3JCQk19UTcu7ndR5vDh8fH9ntdpcbAAAA7n13Vdymp6fru+++U4UKFdS4cWN5eXkpPj7euf7o0aM6efKkwsPDJUnh4eHav3+/kpOTnWPWrVsnu92usLCwYp8/AAAA3MutpyW88soreuKJJ1S1alWdPn1aY8eOlYeHh3r16iWHw6Fnn31WI0aMUGBgoOx2u4YOHarw8HA1a9ZMktShQweFhYWpT58+mjx5shITEzVmzBjFxMTIx8fHnbsGAAAAN3Br3P7000/q1auXfv31V5UvX14tWrTQ1q1bVb58eUnStGnTVKJECXXr1k0ZGRmKjIzUnDlznI/38PDQ8uXLNXjwYIWHh6t06dKKjo7WhAkT3LVLAAAAcCObZVmWuyfhbmlpaXI4HEpNTeX82wLgagkoLlwtAQBwq712V51zCwAAANwO4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYIy7Jm7feust2Ww2DRs2zLnsypUriomJUdmyZeXn56du3bopKSnJ5XEnT55UVFSUfH19FRQUpJEjR+ratWvFPHsAAADcDe6KuN2xY4f+8Y9/qGHDhi7Lhw8frq+++kpLlizRpk2bdPr0aXXt2tW5PisrS1FRUcrMzNSWLVv04YcfKi4uTq+//npx7wIAAADuAm6P2/T0dPXu3VsffPCBypQp41yempqqf/7zn5o6daratm2rxo0ba/78+dqyZYu2bt0qSVq7dq0OHTqkjz/+WI0aNVLHjh31xhtvaPbs2crMzHTXLgEAAMBN3B63MTExioqKUkREhMvyXbt26erVqy7L69atqypVqighIUGSlJCQoAYNGig4ONg5JjIyUmlpaTp48GC+28zIyFBaWprLDQAAAPc+T3dufPHixdq9e7d27NiRa11iYqK8vb0VEBDgsjw4OFiJiYnOMb8P25z1OevyM3HiRI0fP/42Zw8AAIC7jduO3J46dUovvfSSFi5cqJIlSxbrtmNjY5Wamuq8nTp1qli3DwAAgDvDbXG7a9cuJScn66GHHpKnp6c8PT21adMmzZw5U56engoODlZmZqZSUlJcHpeUlKSQkBBJUkhISK6rJ+TczxmTFx8fH9ntdpcbAAAA7n1ui9t27dpp//792rNnj/PWpEkT9e7d2/lnLy8vxcfHOx9z9OhRnTx5UuHh4ZKk8PBw7d+/X8nJyc4x69atk91uV1hYWLHvEwAAANzLbefc+vv76/7773dZVrp0aZUtW9a5/Nlnn9WIESMUGBgou92uoUOHKjw8XM2aNZMkdejQQWFhYerTp48mT56sxMREjRkzRjExMfLx8Sn2fQIAAIB7ufUDZTczbdo0lShRQt26dVNGRoYiIyM1Z84c53oPDw8tX75cgwcPVnh4uEqXLq3o6GhNmDDBjbMGAACAu9gsy7LcPQl3S0tLk8PhUGpqKuffFkDoqBXungL+IE68FeXuKQAA3OxWe83t17kFAAAAigpxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwBnELAAAAYxC3AAAAMAZxCwAAAGMQtwAAADAGcQsAAABjELcAAAAwhlvj9r333lPDhg1lt9tlt9sVHh6uVatWOddfuXJFMTExKlu2rPz8/NStWzclJSW5PMfJkycVFRUlX19fBQUFaeTIkbp27Vpx7woAAADuAm6N2/vuu09vvfWWdu3apZ07d6pt27bq3LmzDh48KEkaPny4vvrqKy1ZskSbNm3S6dOn1bVrV+fjs7KyFBUVpczMTG3ZskUffvih4uLi9Prrr7trlwAAAOBGNsuyLHdP4vcCAwP19ttvq3v37ipfvrwWLVqk7t27S5KOHDmievXqKSEhQc2aNdOqVavUqVMnnT59WsHBwZKkuXPn6tVXX9XZs2fl7e19S9tMS0uTw+FQamqq7Hb7Hds304SOWuHuKeAP4sRbUe6eAgDAzW611+6ac26zsrK0ePFiXbx4UeHh4dq1a5euXr2qiIgI55i6deuqSpUqSkhIkCQlJCSoQYMGzrCVpMjISKWlpTmP/uYlIyNDaWlpLjcAAADc+9wet/v375efn598fHz0/PPP6/PPP1dYWJgSExPl7e2tgIAAl/HBwcFKTEyUJCUmJrqEbc76nHX5mThxohwOh/NWuXLlot0pAAAAuIXb47ZOnTras2ePtm3bpsGDBys6OlqHDh26o9uMjY1Vamqq83bq1Kk7uj0AAAAUD093T8Db21s1a9aUJDVu3Fg7duzQjBkz9NRTTykzM1MpKSkuR2+TkpIUEhIiSQoJCdH27dtdni/nago5Y/Li4+MjHx+fIt4TAAAAuJvbj9xeLzs7WxkZGWrcuLG8vLwUHx/vXHf06FGdPHlS4eHhkqTw8HDt379fycnJzjHr1q2T3W5XWFhYsc8dAAAA7uXWI7exsbHq2LGjqlSpogsXLmjRokX6+uuvtWbNGjkcDj377LMaMWKEAgMDZbfbNXToUIWHh6tZs2aSpA4dOigsLEx9+vTR5MmTlZiYqDFjxigmJoYjswAAAH9Abo3b5ORk9e3bV2fOnJHD4VDDhg21Zs0atW/fXpI0bdo0lShRQt26dVNGRoYiIyM1Z84c5+M9PDy0fPlyDR48WOHh4SpdurSio6M1YcIEd+0SAAAA3Oiuu86tO3Cd28LhOrcoLlznFgBwz13nFgAAALhdxC0AAACMQdwCAADAGMQtAAAAjEHcAgAAwBjELQAAAIxB3AIAAMAYxC0AAACMUai4rV69un799ddcy1NSUlS9evXbnhQAAABQGIWK2xMnTigrKyvX8oyMDP3888+3PSkAAACgMDwLMnjZsmXOP69Zs0YOh8N5PysrS/Hx8QoNDS2yyQEAAAAFUaC47dKliyTJZrMpOjraZZ2Xl5dCQ0M1ZcqUIpscAAAAUBAFitvs7GxJUrVq1bRjxw6VK1fujkwKAAAAKIwCxW2OH374oajnAQAAANy2QsWtJMXHxys+Pl7JycnOI7o55s2bd9sTAwAAAAqqUHE7fvx4TZgwQU2aNFGFChVks9mKel4AAABAgRUqbufOnau4uDj16dOnqOcDAAAAFFqhrnObmZmpRx55pKjnAgAAANyWQsXtgAEDtGjRoqKeCwAAAHBbCnVawpUrV/T+++9r/fr1atiwoby8vFzWT506tUgmBwAAABREoeJ23759atSokSTpwIEDLuv4cBkAAADcpVBxu3HjxqKeBwAAbhc6aoW7p4A/iBNvRbl7CsYq1Dm3AAAAwN2oUEdu27Rpc8PTDzZs2FDoCQEAAACFVai4zTnfNsfVq1e1Z88eHThwQNHR0UUxLwAAAKDAChW306ZNy3P5uHHjlJ6eflsTAgAAAAqrSM+5/ctf/qJ58+YV5VMCAAAAt6xI4zYhIUElS5YsyqcEAAAAblmhTkvo2rWry33LsnTmzBnt3LlTr732WpFMDAAAACioQsWtw+FwuV+iRAnVqVNHEyZMUIcOHYpkYgAAAEBBFSpu58+fX9TzAAAAAG5boeI2x65du3T48GFJUv369fXggw8WyaQAAACAwihU3CYnJ6tnz576+uuvFRAQIElKSUlRmzZttHjxYpUvX74o5wgAAADckkJdLWHo0KG6cOGCDh48qHPnzuncuXM6cOCA0tLS9OKLLxb1HAEAAIBbUqgjt6tXr9b69etVr14957KwsDDNnj2bD5QBAADAbQp15DY7O1teXl65lnt5eSk7O/u2JwUAAAAURqHitm3btnrppZd0+vRp57Kff/5Zw4cPV7t27YpscgAAAEBBFCpu3333XaWlpSk0NFQ1atRQjRo1VK1aNaWlpWnWrFlFPUcAAADglhTqnNvKlStr9+7dWr9+vY4cOSJJqlevniIiIop0cgAAAEBBFOjI7YYNGxQWFqa0tDTZbDa1b99eQ4cO1dChQ/Xwww+rfv36+uabb+7UXAEAAIAbKlDcTp8+XQMHDpTdbs+1zuFw6LnnntPUqVOLbHIAAABAQRQobvfu3avHHnss3/UdOnTQrl27bntSAAAAQGEUKG6TkpLyvARYDk9PT509e/a2JwUAAAAURoHitlKlSjpw4EC+6/ft26cKFSrc9qQAAACAwihQ3D7++ON67bXXdOXKlVzrLl++rLFjx6pTp05FNjkAAACgIAp0KbAxY8boX//6l2rXrq0hQ4aoTp06kqQjR45o9uzZysrK0ujRo+/IRAEAAICbKVDcBgcHa8uWLRo8eLBiY2NlWZYkyWazKTIyUrNnz1ZwcPAdmSgAAABwMwX+EoeqVatq5cqVOn/+vI4fPy7LslSrVi2VKVPmTswPAAAAuGWF+oYySSpTpowefvjhopwLAAAAcFsK9IEyAAAA4G5G3AIAAMAYxC0AAACMQdwCAADAGMQtAAAAjEHcAgAAwBjELQAAAIxB3AIAAMAYxC0AAACMQdwCAADAGMQtAAAAjEHcAgAAwBjELQAAAIxB3AIAAMAYxC0AAACMQdwCAADAGMQtAAAAjEHcAgAAwBjELQAAAIxB3AIAAMAYxC0AAACMQdwCAADAGMQtAAAAjEHcAgAAwBjELQAAAIxB3AIAAMAYxC0AAACMQdwCAADAGG6N24kTJ+rhhx+Wv7+/goKC1KVLFx09etRlzJUrVxQTE6OyZcvKz89P3bp1U1JSksuYkydPKioqSr6+vgoKCtLIkSN17dq14twVAAAA3AXcGrebNm1STEyMtm7dqnXr1unq1avq0KGDLl686BwzfPhwffXVV1qyZIk2bdqk06dPq2vXrs71WVlZioqKUmZmprZs2aIPP/xQcXFxev31192xSwAAAHAjm2VZlrsnkePs2bMKCgrSpk2b1LJlS6Wmpqp8+fJatGiRunfvLkk6cuSI6tWrp4SEBDVr1kyrVq1Sp06ddPr0aQUHB0uS5s6dq1dffVVnz56Vt7f3TbeblpYmh8Oh1NRU2e32O7qPJgkdtcLdU8AfxIm3otw9BfxB8HsNxYXfawV3q712V51zm5qaKkkKDAyUJO3atUtXr15VRESEc0zdunVVpUoVJSQkSJISEhLUoEEDZ9hKUmRkpNLS0nTw4ME8t5ORkaG0tDSXGwAAAO59d03cZmdna9iwYWrevLnuv/9+SVJiYqK8vb0VEBDgMjY4OFiJiYnOMb8P25z1OevyMnHiRDkcDuetcuXKRbw3AAAAcIe7Jm5jYmJ04MABLV68+I5vKzY2Vqmpqc7bqVOn7vg2AQAAcOd5unsCkjRkyBAtX75cmzdv1n333edcHhISoszMTKWkpLgcvU1KSlJISIhzzPbt212eL+dqCjljrufj4yMfH58i3gsAAAC4m1uP3FqWpSFDhujzzz/Xhg0bVK1aNZf1jRs3lpeXl+Lj453Ljh49qpMnTyo8PFySFB4erv379ys5Odk5Zt26dbLb7QoLCyueHQEAAMBdwa1HbmNiYrRo0SJ9+eWX8vf3d54j63A4VKpUKTkcDj377LMaMWKEAgMDZbfbNXToUIWHh6tZs2aSpA4dOigsLEx9+vTR5MmTlZiYqDFjxigmJoajswAAAH8wbo3b9957T5LUunVrl+Xz589Xv379JEnTpk1TiRIl1K1bN2VkZCgyMlJz5sxxjvXw8NDy5cs1ePBghYeHq3Tp0oqOjtaECROKazcAAABwl3Br3N7KJXZLliyp2bNna/bs2fmOqVq1qlauXFmUUwMAAMA96K65WgIAAABwu4hbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAx3Bq3mzdv1hNPPKGKFSvKZrPpiy++cFlvWZZef/11VahQQaVKlVJERISOHTvmMubcuXPq3bu37Ha7AgIC9Oyzzyo9Pb0Y9wIAAAB3C7fG7cWLF/XAAw9o9uzZea6fPHmyZs6cqblz52rbtm0qXbq0IiMjdeXKFeeY3r176+DBg1q3bp2WL1+uzZs3a9CgQcW1CwAAALiLeLpz4x07dlTHjh3zXGdZlqZPn64xY8aoc+fOkqSPPvpIwcHB+uKLL9SzZ08dPnxYq1ev1o4dO9SkSRNJ0qxZs/T444/rnXfeUcWKFYttXwAAAOB+d+05tz/88IMSExMVERHhXOZwONS0aVMlJCRIkhISEhQQEOAMW0mKiIhQiRIltG3btnyfOyMjQ2lpaS43AAAA3Pvu2rhNTEyUJAUHB7ssDw4Odq5LTExUUFCQy3pPT08FBgY6x+Rl4sSJcjgczlvlypWLePYAAABwh7s2bu+k2NhYpaamOm+nTp1y95QAAABQBO7auA0JCZEkJSUluSxPSkpyrgsJCVFycrLL+mvXruncuXPOMXnx8fGR3W53uQEAAODed9fGbbVq1RQSEqL4+HjnsrS0NG3btk3h4eGSpPDwcKWkpGjXrl3OMRs2bFB2draaNm1a7HMGAACAe7n1agnp6ek6fvy48/4PP/ygPXv2KDAwUFWqVNGwYcP0t7/9TbVq1VK1atX02muvqWLFiurSpYskqV69enrsscc0cOBAzZ07V1evXtWQIUPUs2dPrpQAAADwB+TWuN25c6fatGnjvD9ixAhJUnR0tOLi4vTXv/5VFy9e1KBBg5SSkqIWLVpo9erVKlmypPMxCxcu1JAhQ9SuXTuVKFFC3bp108yZM4t9XwAAAOB+NsuyLHdPwt3S0tLkcDiUmprK+bcFEDpqhbungD+IE29FuXsK+IPg9xqKC7/XCu5We+2uPecWAAAAKCjiFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxiBuAQAAYAziFgAAAMYgbgEAAGAM4hYAAADGIG4BAABgDGPidvbs2QoNDVXJkiXVtGlTbd++3d1TAgAAQDEzIm4//fRTjRgxQmPHjtXu3bv1wAMPKDIyUsnJye6eGgAAAIqREXE7depUDRw4UP3791dYWJjmzp0rX19fzZs3z91TAwAAQDHydPcEbldmZqZ27dql2NhY57ISJUooIiJCCQkJeT4mIyNDGRkZzvupqamSpLS0tDs7WcNkZ1xy9xTwB8F/mygu/F5DceH3WsHlvGaWZd1w3D0ft7/88ouysrIUHBzssjw4OFhHjhzJ8zETJ07U+PHjcy2vXLnyHZkjgNvjmO7uGQBA0eL3WuFduHBBDocj3/X3fNwWRmxsrEaMGOG8n52drXPnzqls2bKy2WxunBlMl5aWpsqVK+vUqVOy2+3ung4A3DZ+r6G4WJalCxcuqGLFijccd8/Hbbly5eTh4aGkpCSX5UlJSQoJCcnzMT4+PvLx8XFZFhAQcKemCORit9v5nwAAo/B7DcXhRkdsc9zzHyjz9vZW48aNFR8f71yWnZ2t+Ph4hYeHu3FmAAAAKG73/JFbSRoxYoSio6PVpEkT/elPf9L06dN18eJF9e/f391TAwAAQDEyIm6feuopnT17Vq+//roSExPVqFEjrV69OteHzAB38/Hx0dixY3OdFgMA9yp+r+FuY7Nudj0FAAAA4B5xz59zCwAAAOQgbgEAAGAM4hYAAADGIG4BAABgDOIWAAAAxjDiUmDA3eqXX37RvHnzlJCQoMTERElSSEiIHnnkEfXr10/ly5d38wwBADALR26BO2THjh2qXbu2Zs6cKYfDoZYtW6ply5ZyOByaOXOm6tatq507d7p7mgBQZE6dOqVnnnnG3dPAHxzXuQXukGbNmumBBx7Q3LlzZbPZXNZZlqXnn39e+/btU0JCgptmCABFa+/evXrooYeUlZXl7qngD4zTEoA7ZO/evYqLi8sVtpJks9k0fPhwPfjgg26YGQAUzrJly264/vvvvy+mmQD5I26BOyQkJETbt29X3bp181y/fft2viIawD2lS5custlsutE/+ub1F3qgOBG3wB3yyiuvaNCgQdq1a5fatWvnDNmkpCTFx8frgw8+0DvvvOPmWQLAratQoYLmzJmjzp0757l+z549aty4cTHPCnBF3AJ3SExMjMqVK6dp06Zpzpw5znPQPDw81LhxY8XFxalHjx5uniUA3LrGjRtr165d+cbtzY7qAsWBD5QBxeDq1av65ZdfJEnlypWTl5eXm2cEAAX3zTff6OLFi3rsscfyXH/x4kXt3LlTrVq1KuaZAf+PuAUAAIAxuM4tAAAAjEHcAgAAwBjELQAAAIxB3AIAAMAYxC0AFINTp07pmWeeUcWKFeXt7a2qVavqpZde0q+//nrLz3HixAnZbDbt2bPnzk0UAO5xxC0A3GHff/+9mjRpomPHjumTTz7R8ePHNXfuXMXHxys8PFznzp1z9xQBwBjELQDcYTExMfL29tbatWvVqlUrValSRR07dtT69ev1888/a/To0ZJ+uwD+F1984fLYgIAAxcXFSZKqVasmSXrwwQdls9nUunVr57h58+apfv368vHxUYUKFTRkyBDnupMnT6pz587y8/OT3W5Xjx49lJSU5Fw/btw4NWrUSPPmzVOVKlXk5+enF154QVlZWZo8ebJCQkIUFBSkv//97y5zS0lJ0YABA1S+fHnZ7Xa1bdtWe/fuLcJXDgAKjrgFgDvo3LlzWrNmjV544QWVKlXKZV1ISIh69+6tTz/99Ja+1Wn79u2SpPXr1+vMmTP617/+JUl67733FBMTo0GDBmn//v1atmyZatasKUnKzs5W586dde7cOW3atEnr1q3T999/r6eeesrlub/77jutWrVKq1ev1ieffKJ//vOfioqK0k8//aRNmzZp0qRJGjNmjLZt2+Z8zH/9138pOTlZq1at0q5du/TQQw+pXbt2HIkG4FZ8/S4A3EHHjh2TZVmqV69enuvr1aun8+fP6+zZszd9rvLly0uSypYtq5CQEOfyv/3tb3r55Zf10ksvOZc9/PDDkqT4+Hjt379fP/zwgypXrixJ+uijj1S/fn3t2LHDOS47O1vz5s2Tv7+/wsLC1KZNGx09elQrV65UiRIlVKdOHU2aNEkbN25U06ZN9e2332r79u1KTk6Wj4+PJOmdd97RF198oaVLl2rQoEGFeLUA4PYRtwBQDO7Ul0EmJyfr9OnTateuXZ7rDx8+rMqVKzvDVpLCwsIUEBCgw4cPO+M2NDRU/v7+zjHBwcHy8PBQiRIlXJYlJydLkvbu3av09HSVLVvWZXuXL1/Wd999V2T7BwAFRdwCwB1Us2ZN2Ww2HT58WH/+859zrT98+LDKlCmj8uXLy2az5Yrgq1ev3vD5rz/VobC8vLxc7ttstjyXZWdnS5LS09NVoUIFff3117meKyAgoEjmBACFwTm3AHAHlS1bVu3bt9ecOXN0+fJll3WJiYlauHChnnrqKdlsNpUvX15nzpxxrj927JguXbrkvO/t7S1JysrKci7z9/dXaGio4uPj89x+vXr1dOrUKZ06dcq57NChQ0pJSVFYWFih9+uhhx5SYmKiPD09VbNmTZdbuXLlCv28AHC7iFsAuMPeffddZWRkKDIyUps3b9apU6e0evVqtW/fXpUqVXJehaBt27Z699139Z///Ec7d+7U888/73L0NCgoSKVKldLq1auVlJSk1NRUSb9d7WDKlCmaOXOmjh07pt27d2vWrFmSpIiICDVo0EC9e/fW7t27tX37dvXt21etWrVSkyZNCr1PERERCg8PV5cuXbR27VqdOHFCW7Zs0ejRo7Vz587beLUA4PYQtwBwh9WqVUs7d+5U9erV1aNHD9WoUUODBg1SmzZtlJCQoMDAQEnSlClTVLlyZT366KN6+umn9corr8jX19f5PJ6enpo5c6b+8Y9/qGLFiurcubMkKTo6WtOnT9ecOXNUv359derUSceOHZP026kEX375pcqUKaOWLVsqIiJC1atX16effnpb+2Sz2bRy5Uq1bNlS/fv3V+3atdWzZ0/9+OOPCg4Ovq3nBoDbYbPu1KccAAAAgGLGkVsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAxiFsAAAAYg7gFAACAMYhbAAAAGIO4BQAAgDGIWwAAABiDuAUAAIAx/g/2ETQLbpRRJAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cpu\n",
            "  warnings.warn(f\"Device used : {self.device}\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0  | loss: 0.98486 | val_0_auc: 0.65306 | val_1_auc: 0.60882 |  0:00:00s\n",
            "epoch 1  | loss: 0.67784 | val_0_auc: 0.71532 | val_1_auc: 0.6303  |  0:00:00s\n",
            "epoch 2  | loss: 0.58876 | val_0_auc: 0.73345 | val_1_auc: 0.72489 |  0:00:00s\n",
            "epoch 3  | loss: 0.56123 | val_0_auc: 0.73423 | val_1_auc: 0.74913 |  0:00:00s\n",
            "epoch 4  | loss: 0.51412 | val_0_auc: 0.75758 | val_1_auc: 0.75445 |  0:00:00s\n",
            "epoch 5  | loss: 0.50377 | val_0_auc: 0.7769  | val_1_auc: 0.77888 |  0:00:00s\n",
            "epoch 6  | loss: 0.48941 | val_0_auc: 0.79556 | val_1_auc: 0.78182 |  0:00:01s\n",
            "epoch 7  | loss: 0.48051 | val_0_auc: 0.81279 | val_1_auc: 0.79137 |  0:00:01s\n",
            "epoch 8  | loss: 0.47215 | val_0_auc: 0.82408 | val_1_auc: 0.80165 |  0:00:01s\n",
            "epoch 9  | loss: 0.45522 | val_0_auc: 0.83484 | val_1_auc: 0.79357 |  0:00:01s\n",
            "epoch 10 | loss: 0.45058 | val_0_auc: 0.84156 | val_1_auc: 0.79743 |  0:00:01s\n",
            "epoch 11 | loss: 0.43603 | val_0_auc: 0.84862 | val_1_auc: 0.78825 |  0:00:01s\n",
            "epoch 12 | loss: 0.41102 | val_0_auc: 0.85184 | val_1_auc: 0.78255 |  0:00:01s\n",
            "epoch 13 | loss: 0.42395 | val_0_auc: 0.85553 | val_1_auc: 0.773   |  0:00:01s\n",
            "epoch 14 | loss: 0.4189  | val_0_auc: 0.86019 | val_1_auc: 0.7719  |  0:00:01s\n",
            "epoch 15 | loss: 0.4286  | val_0_auc: 0.86418 | val_1_auc: 0.76694 |  0:00:01s\n",
            "epoch 16 | loss: 0.43957 | val_0_auc: 0.86565 | val_1_auc: 0.75849 |  0:00:02s\n",
            "epoch 17 | loss: 0.41634 | val_0_auc: 0.8695  | val_1_auc: 0.74068 |  0:00:02s\n",
            "epoch 18 | loss: 0.42762 | val_0_auc: 0.86986 | val_1_auc: 0.73444 |  0:00:02s\n",
            "\n",
            "Early stopping occurred at epoch 18 with best_epoch = 8 and best_val_1_auc = 0.80165\n",
            "\n",
            "XGBoost Results:\n",
            "Accuracy: 0.7142857142857143\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.75      0.77        99\n",
            "           1       0.59      0.65      0.62        55\n",
            "\n",
            "    accuracy                           0.71       154\n",
            "   macro avg       0.69      0.70      0.70       154\n",
            "weighted avg       0.72      0.71      0.72       154\n",
            "\n",
            "\n",
            "TabNet Results:\n",
            "Accuracy: 0.7467532467532467\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.91      0.82        99\n",
            "           1       0.74      0.45      0.56        55\n",
            "\n",
            "    accuracy                           0.75       154\n",
            "   macro avg       0.74      0.68      0.69       154\n",
            "weighted avg       0.74      0.75      0.73       154\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
            "  warnings.warn(wrn_msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pU9DdWC8-LP8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}