GAN_FraudDetection_Medium_2.ipynb

{
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        "<a href=\"https://colab.research.google.com/github/deeporigami/Portfolio/blob/main/GAN_FraudDetection_Medium_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ayRK9wy9peSv"
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      "source": [
        "\n",
        "# Fraud Detection Analysis: Binary Classification with Highly Imbalanced Dataset\n",
        "## Application of GANs for Data Augmentation: generate synthetic data for minority class\n",
        "\n",
        "Author: Michio Suginoo\n",
        "\n",
        "Fraud detection is an application of binary classification prediction. Fraud cases, which account for only a small fraction of the transaction universe, constitute a minority class that makes the dataset highly imbalanced. In general, the less balanced the dataset, the poorer the performance of the classification predictor would be.\n",
        "\n",
        "In order to address this classical problem of fraud detection associated with the imbalanced dataset, we can use GANs as a data augmentation tool. In other words, GANs can generate realistic synthetic data of the minority fraud class and transform the imbalanced dataset perfectly balanced.\n",
        "\n",
        "In this notebook, I will explore two approaches to develop fraud detection for a credit card dataset:\n",
        "1. Benchmark Scenario: Ensemble Method without data augmentation\n",
        "2. GANs Scenario: Ensemble Method with data augmenation with GANs (Generative Adversarial Networks).\n",
        "\n",
        "## Dataset\n",
        "\n",
        "I selected ther following dataset of credit card transactions from\n",
        "https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud\n",
        "\n",
        "In total the dataset contains\n",
        "The dataset contains 284,807 transactions. In the dataset, there are 492 frauds cases.\n",
        "\n",
        "I selected this dataset because of the following reasons.\n",
        "*   Fraud cases account for only 0.172% of all transactions.\n",
        "*   It is a finance related dataset.\n",
        "\n",
        "This dataset is appropriate for classification analysis of imbalanced dataset in the context of finance.   \n",
        "\n",
        "It has 30 features.\n",
        "\n",
        "   * V1, V2, ... V28: the principal components obtained with PCA. The source of the data is not disclosed for the privacy protection purpose.\n",
        "   * 'Time': the seconds elapsed between each transaction and the first transaction in the dataset.\n",
        "   * 'Amount': the transaction Amount.\n",
        "\n",
        "The label is set as 'Class'.\n",
        "   * 'Class': 1 in case of fraud; and 0 otherwise.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCmm5b8m1h4e"
      },
      "source": [
        "## Step 0: Configuration of Colab Notebook and Installation of relevant API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23TOba33L4qf",
        "outputId": "07f43fc9-a5d7-4355-9e29-8206735fd553"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Jan 25 15:40:11 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla V100-SXM2-16GB           Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   31C    P0              24W / 300W |      0MiB / 16384MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1G82GuO-tez",
        "outputId": "9b568406-e84c-471a-be30-1e277109936b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 54.8 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ],
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Not using a high-RAM runtime')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uwMwC_VMY3PT"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wk300KaOt4jE",
        "outputId": "0e0f8cb7-5d0e-4817-d4ff-333236eb107d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
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      ],
      "source": [
        "!pip install --upgrade pip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpX-r99_sQfQ",
        "outputId": "effbe03e-50c0-4da1-e9dd-cbd07c9fb0ce"
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      "source": [
        "#!pip install pandas==2.0.3\n",
        "!pip install vision-datasets"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcKQXiJqcNDz",
        "outputId": "aec2cdf8-b906-4235-d3b3-2fcaf6cece33"
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            "  Downloading typeguard-4.1.5-py3-none-any.whl.metadata (3.7 kB)\n",
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            "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.10/dist-packages (from visions==0.7.5->visions[type_image_path]==0.7.5->ydata-profiling) (3.2.1)\n",
            "Collecting tangled-up-in-unicode>=0.0.4 (from visions==0.7.5->visions[type_image_path]==0.7.5->ydata-profiling)\n",
            "  Downloading tangled_up_in_unicode-0.2.0-py3-none-any.whl (4.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m32.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: visions 0.7.5 does not provide the extra 'type-image-path'\u001b[0m\u001b[33m\n",
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            "  Downloading annotated_types-0.6.0-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting pydantic-core==2.14.6 (from pydantic>=2->ydata-profiling)\n",
            "  Downloading pydantic_core-2.14.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.5 kB)\n",
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            "Downloading pandas-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.0/13.0 MB\u001b[0m \u001b[31m47.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ydata_profiling-4.6.4-py2.py3-none-any.whl (357 kB)\n",
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            "\u001b[?25hDownloading pydantic_core-2.14.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
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            "\u001b[?25hDownloading typeguard-4.1.5-py3-none-any.whl (34 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m346.6/346.6 kB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading annotated_types-0.6.0-py3-none-any.whl (12 kB)\n",
            "Building wheels for collected packages: htmlmin\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27081 sha256=a1345be9ce2951b939380ceccdf97ecb2e38d72f606619749e9e23692ec7b36c\n",
            "  Stored in directory: /root/.cache/pip/wheels/dd/91/29/a79cecb328d01739e64017b6fb9a1ab9d8cb1853098ec5966d\n",
            "Successfully built htmlmin\n",
            "Installing collected packages: htmlmin, tzdata, typeguard, tangled-up-in-unicode, pydantic-core, multimethod, dacite, annotated-types, pydantic, pandas, imagehash, visions, seaborn, phik, ydata-profiling\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 1.10.14\n",
            "    Uninstalling pydantic-1.10.14:\n",
            "      Successfully uninstalled pydantic-1.10.14\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.5.3\n",
            "    Uninstalling pandas-1.5.3:\n",
            "      Successfully uninstalled pandas-1.5.3\n",
            "  Attempting uninstall: seaborn\n",
            "    Found existing installation: seaborn 0.13.1\n",
            "    Uninstalling seaborn-0.13.1:\n",
            "      Successfully uninstalled seaborn-0.13.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "bigframes 0.19.2 requires pandas<2.1.4,>=1.5.0, but you have pandas 2.2.0 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed annotated-types-0.6.0 dacite-1.8.1 htmlmin-0.1.12 imagehash-4.3.1 multimethod-1.10 pandas-2.2.0 phik-0.12.4 pydantic-2.5.3 pydantic-core-2.14.6 seaborn-0.12.2 tangled-up-in-unicode-0.2.0 typeguard-4.1.5 tzdata-2023.4 visions-0.7.5 ydata-profiling-4.6.4\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade pandas ydata-profiling typing-extensions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhAR91rk569t"
      },
      "source": [
        "# <font color=red>Caution: Please do the following\n",
        "Stop and Restart RUNTIME. Then run the code after this cell. </font>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "s-cfVcaB5wjg",
        "outputId": "e571a634-9fdf-4472-aebe-2fe85ec16be4"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-7-c4b5543e7d17>, line 1)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-c4b5543e7d17>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    Stop and Restart RUNTIME\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ],
      "source": [
        "Stop and Restart RUNTIME\n",
        "Then run the code after this cell."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBCIo_CRBUgI"
      },
      "source": [
        "# Step 1: Retrieve Dataset\n",
        "\n",
        "I selected Credit Card Fraud dataset from the following link of Kaggle. I use Kaggle API to download it: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cb2mqG-XHVjU"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp /content/kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCkNpl_RHVhM",
        "outputId": "1d887074-bcf6-447d-85d8-5b0ff05c7bab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading creditcardfraud.zip to /content\n",
            " 99% 65.0M/66.0M [00:02<00:00, 38.1MB/s]\n",
            "100% 66.0M/66.0M [00:02<00:00, 31.0MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d mlg-ulb/creditcardfraud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xXcNVJ3LHVfA",
        "outputId": "7a343b9d-c6d7-471d-a65f-cf80b9fef828"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/creditcardfraud.zip\n",
            "  inflating: creditcard.csv          \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/creditcardfraud.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrYh6foYE62z"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "DATA=pd.read_csv(\"/content/creditcard.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "lU-XzSaPabTL",
        "outputId": "85cbecb8-a031-413e-bcdd-3f31916a5d64"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
              "4  0.502292  0.219422  0.215153   69.99      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5884272e-5278-453f-af71-b51e5211b272\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-5884272e-5278-453f-af71-b51e5211b272 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5884272e-5278-453f-af71-b51e5211b272');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-d33d61e1-aadd-45af-b16f-a38ad04541ad button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "DATA.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bl5m2O0SNq4",
        "outputId": "677915ae-db90-4cdb-b6c1-98d028885877"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Check the missing values\n",
        "DATA.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-ddOWvf03BL"
      },
      "source": [
        "# Step 2: Data Preprocessing\n",
        "\n",
        "## 1. Removal of Duplicated Data.\n",
        "\n",
        "I will address the dupulicated data problems here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kV_Bcp2SLkn",
        "outputId": "d689c8c6-1434-4b67-f725-567dc7ede818"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#Check the shape of the dataset\n",
        "DATA.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "25JqT0vz6j20"
      },
      "outputs": [],
      "source": [
        "df=DATA.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCHK5OJ66ugr",
        "outputId": "baee50d4-d2a3-4723-d2de-59227e8fedee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(283726, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xx2L5Qf-7GNK",
        "outputId": "879ecc90-2aaa-4eb6-d929-3d6a826c176d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1081"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Check the number of duplicate observations removed.\n",
        "DATA.shape[0]-df.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4ANCiXAStRZ"
      },
      "source": [
        "## 2. Profile Report\n",
        "\n",
        "I use ydata_profiling ProfileReport for summarizing dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O1T-6yA0UOuT"
      },
      "outputs": [],
      "source": [
        "from ydata_profiling import ProfileReport\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaFk75Ck0Zuf"
      },
      "source": [
        "Since I encountered difficulties in displaying the result of the profile report, I decided to export the result to a html file in the following cell. Please open it in a browser. I apologize for any inconvenience arising from this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287,
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          ]
        },
        "id": "A1LMJD_0qJgt",
        "outputId": "e574df10-789b-42fa-95d2-f010c15a2204"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ydata_profiling/profile_report.py:354: UserWarning: Try running command: 'pip install --upgrade Pillow' to avoid ValueError\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/ydata_profiling/utils/dataframe.py:137: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.rename(columns={\"index\": \"df_index\"}, inplace=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60eb810bfacb4c6e8b6842084f30d5aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38564c04a82144cc9f28e7289da76933"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fd8185a50384b9eb14ac4e16804a551"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "792500f8cbd44d14bd00a6aedcadaa44"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "report_raw = ProfileReport(df)\n",
        "report_raw.to_file('profile_report_raw.html')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CiKBR2gMCJyA"
      },
      "source": [
        "## 3.  Define the outliers:\n",
        "Based on the profile report displayed in the html file, I selected the following lower and upper percentile thresholds to detect outliers.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5tpAqFoaCuoc"
      },
      "outputs": [],
      "source": [
        "lp = 0.05\n",
        "up = 99.95"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i8zbqAPq_SzZ"
      },
      "outputs": [],
      "source": [
        "lower_percentile_thresholds ={\"V1\": lp, \"V2\": lp, \"V3\": lp, \"V4\": 0, \"V5\": lp, \"V6\": 0, \"V7\": 0, \"V8\": lp, \"V9\": 0, \"V10\": 0, \"V11\": 0, \"V12\": lp, \"V13\": 0, \"V14\": lp, \"V15\": 0, \"V16\": 0, \"V17\": 0, \"V18\": lp, \"V19\": 0, \"V20\": 0, \"V21\": 0, \"V22\": 0, \"V23\": lp, \"V24\": 0, \"V25\": lp, \"V26\": 0, \"V27\": 0, \"V28\": 0, \"Amount\":0}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfgadenGGYNQ",
        "outputId": "158aac9e-29b8-4da3-f374-45d9a69b2aec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "len(lower_percentile_thresholds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A6bTzqRzCIzw"
      },
      "outputs": [],
      "source": [
        "upper_percentile_thresholds ={\"V1\": 100, \"V2\": 100, \"V3\": 100, \"V4\": up, \"V5\": 100, \"V6\": up, \"V7\": up, \"V8\": 100, \"V9\": up, \"V10\": 100, \"V11\": up, \"V12\": 100, \"V13\": up, \"V14\": 100, \"V15\": 100, \"V16\": 100, \"V17\": 100, \"V18\": 100, \"V19\": 100, \"V20\": 100, \"V21\": 100, \"V22\": 100, \"V23\": 100, \"V24\": up, \"V25\": 100, \"V26\": 100, \"V27\": 100, \"V28\": up,\"Amount\":up}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g48BLJGqGbtS",
        "outputId": "cd42d622-9cf6-4ad5-f7d5-a776b38485aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "len(upper_percentile_thresholds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KcFWAcuHI0X4"
      },
      "outputs": [],
      "source": [
        "# Dictionary to store outliers for each feature\n",
        "outliers_dict = {}\n",
        "\n",
        "# Step 1: Detect Outliers using Percentiles\n",
        "for feature, lower_percentile in lower_percentile_thresholds.items():\n",
        "    # Calculate the lower percentile threshold for the feature\n",
        "    lower_threshold = np.percentile(df[feature], lower_percentile)\n",
        "\n",
        "    # Calculate the upper percentile threshold for the feature\n",
        "    upper_percentile = upper_percentile_thresholds[feature]\n",
        "    upper_threshold = np.percentile(df[feature], upper_percentile)\n",
        "\n",
        "    # Identify outliers for the current feature for Class 0 and Class 1 separately\n",
        "    outliers_0 = df[((df[feature] < lower_threshold) | (df[feature] > upper_threshold)) & (df['Class'] == 0)]\n",
        "    outliers_1 = df[((df[feature] < lower_threshold) | (df[feature] > upper_threshold)) & (df['Class'] == 1)]\n",
        "\n",
        "    # Store outliers in the dictionary\n",
        "    outliers_dict[feature] = {\n",
        "        'Class_0': outliers_0,\n",
        "        'Class_1': outliers_1\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_FCyPkWNJCCW"
      },
      "outputs": [],
      "source": [
        "# Step 2: Remove Outliers except for Class 1\n",
        "filtered_data = df.copy()  # Make a copy to keep original intact\n",
        "\n",
        "for feature in outliers_dict:\n",
        "    # Remove outliers for Class 0\n",
        "    filtered_data.loc[outliers_dict[feature]['Class_0'].index, feature] = np.nan\n",
        "\n",
        "# Drop rows with NaN values after handling outliers\n",
        "filtered_data.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZlKUcoRaKFBy"
      },
      "source": [
        "Let's check the change made in terms of shapes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eil1UupHwmf",
        "outputId": "ee9595b8-e071-479c-b463-6bd42271e7a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282513, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "filtered_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4rDHfEfHS3B",
        "outputId": "bba30fc6-994b-4484-d207-4395b4a289cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(473, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "filtered_data[filtered_data[\"Class\"]==1].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qw1A7furHml0",
        "outputId": "5f9afa07-9e74-405b-c82f-6126933c1abe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(473, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df[df[\"Class\"]==1].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzRRaTZmH1CI",
        "outputId": "a50f3ff7-ab32-479f-9e5c-e1acde82a1c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "DATA.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAlgLa4AJ3M-"
      },
      "source": [
        "Check how many rows were removed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8MUzIrKJs2F",
        "outputId": "2bd99d97-7fae-4541-811a-d0f819ac0768"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1213"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df.shape[0]-filtered_data.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXLnwYSJKKFd"
      },
      "source": [
        "How much % of the original data was removed?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDtP92inJyGk",
        "outputId": "d9ad9998-f962-4d8c-d307-dd8bebd60c1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0042752514750146265"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "(df.shape[0]-filtered_data.shape[0])/df.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0KUK9URR5Kq"
      },
      "source": [
        "## 5.  Feature Selection\n",
        "\n",
        " We can use SelectFromModel feature selection function to run Random Forest to generate the feature importance. </ font>\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3oWxH9eYQ3Z4"
      },
      "outputs": [],
      "source": [
        "X = filtered_data.drop(columns=[\"Class\"])\n",
        "y = filtered_data[\"Class\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MF7H7XgRa0Y",
        "outputId": "071f3f61-e5ba-4c07-ce35-915938f1b2fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282513, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# the shape of the feature dataframe\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RhU8KZ_qW2Xt",
        "outputId": "9b6992fa-0555-4cff-de14-8a5fd0b437d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282513,)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# the shape of the label dataframe\n",
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LlOEgH1h-jp9"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler,  MinMaxScaler\n",
        "import os # accessing directory structure\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix,roc_auc_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZlnYJXvDhAW",
        "outputId": "72e83278-06db-45dd-f18b-ed102c94272c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "X.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDVHtrnw1s4x",
        "outputId": "9a938761-d42c-41d3-885d-6be4ddbca8b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282513, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Train the model\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import StandardScaler,  MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaled_array = scaler.fit_transform(X)\n",
        "scaled_X=pd.DataFrame(scaled_array, columns = X.columns)\n",
        "scaled_X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVvU2EtR59mV",
        "outputId": "a07d5f97-23cf-4ad6-8cfe-91dc6f1cd2d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "scaled_X.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "hrZugYA81s4y",
        "outputId": "f248f173-6f19-424e-d513-580632167cb5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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XVaFCBZUsWVJ33nmn7r33Xr344ouKi4tTo0aNtHnzZs2ePdtpL5sr7Ha7pk6dqvvuu0+1atVSXFycIiIitGPHDm3dulXfffedpEsXc2nSpImqV6+uPn36qHz58kpKStLq1at16NChTN8zdqXFixdr+PDhatu2rRo0aKCgoCDt3btX06dPV1pamtN3v7311ltq0qSJ6tSpo759+6pcuXLav3+/vvnmG23atEmS9PLLL2vx4sVq0qSJHn30UXl7e+vtt99WWlqaxo4de91tzu7r4ffff3f8X1etWlXe3t764osvlJSUpM6dO+fo8QaAXMmfiywCAK6U1aXVs7Js2TITExNjQkJCjL+/v7nllltMjx49zC+//OIYc+jQIfPAAw+Y0NBQExISYjp06GCOHDmS6dLrxhjz0ksvmdKlSxu73e502fnU1FTTq1cvExISYgoXLmw6duxojh07dtVLzR8/fjzLfv/v//7PNGnSxAQGBprAwEBTuXJl89hjj5mdO3e6/Hh0797dBAYGZhrbvHlzc9ttt2WaX7ZsWdOmTZtMNVesWGH69u1rihQpYoKCgky3bt3MyZMnM91/0qRJpnLlysbHx8eEhYWZRx55JNOl3K+2bmMuXfa8TZs2pnDhwkaS47Lz586dM08++aSJiIgwhQoVMo0bNzarV682zZs3d7o0/eVLzX/66adOda/2VQA//vijueuuu0zhwoVNYGCgqVGjhpk4caLTmD179pjY2FgTHh5ufHx8TOnSpc29995rPvvssyy34bK9e/eaYcOGmQYNGpiSJUsab29vU6JECdOmTRuzdOnSTOO3bNnieA76+/ubSpUqmRdeeMFpzIYNG0xMTIwJCgoyAQEB5o477jCrVq1yGnO918X1Xg8nTpwwjz32mKlcubIJDAw0ISEhJioqynzyySfX3F4AcBebMRacjQwAQD6bOXOm4uLi9PPPP2d5RUkAANyNc74AAAAAwAKELwAAAACwAOELAAAAACzAOV8AAAAAYAH2fAEAAACABQhfAAAAAGCBf8WXLE+ePFmvvfaaEhMTVbNmTU2cOFH169fPcuy7776rWbNmacuWLZIufYnlK6+84jTeGKPhw4fr3Xff1enTp9W4cWNNnTpVFStWdIw5deqUBgwYoAULFshut6t9+/Z68803FRQUlK2eMzIydOTIERUuXFg2my0XWw8AAADAkxlj9Ndff6lUqVKy26+xfys/v2TMGGPmzp1rfH19zfTp083WrVtNnz59TGhoqElKSspyfNeuXc3kyZPNxo0bzfbt202PHj1MSEiIOXTokGPMmDFjTEhIiJk/f7759ddfTdu2bU25cuXM33//7RjTqlUrU7NmTbNmzRrzww8/mAoVKpguXbpku++DBw8aSUxMTExMTExMTExMTEaSOXjw4DUzRL5fcCMqKkq33367Jk2aJOnSHqUyZcpowIABGjJkyHXvn56eriJFimjSpEmKjY2VMUalSpXSk08+qaeeekqSlJycrLCwMM2cOVOdO3fW9u3bVbVqVacv2ly0aJHuueceHTp0SKVKlbruepOTkxUaGqqDBw8qODg4F48AAAAAAE+WkpKiMmXK6PTp0woJCbnquHw97PD8+fNav369hg4d6phnt9sVHR2t1atXZ6tGamqqLly4oKJFi0qS9u3bp8TEREVHRzvGhISEKCoqSqtXr1bnzp21evVqhYaGOoKXJEVHR8tut2vt2rV64IEHMq0nLS1NaWlpjtt//fWXJCk4OJjwBQAAAOC6pyPl6wU3Tpw4ofT0dIWFhTnNDwsLU2JiYrZqPPPMMypVqpQjbF2+37VqJiYmqmTJkk7Lvb29VbRo0auud/To0QoJCXFMZcqUyVZ/AAAAACB5+NUOx4wZo7lz5+qLL76Qv7+/W9c1dOhQJScnO6aDBw+6dX0AAAAACpZ8PeywePHi8vLyUlJSktP8pKQkhYeHX/O+48aN05gxY7RkyRLVqFHDMf/y/ZKSkhQREeFUs1atWo4xx44dc6p38eJFnTp16qrr9fPzk5+fX7a3DQAAAACulK97vnx9fVW3bl0lJCQ45mVkZCghIUENGza86v3Gjh2rl156SYsWLXI6b0uSypUrp/DwcKeaKSkpWrt2raNmw4YNdfr0aa1fv94xZunSpcrIyFBUVFRebR4AAAAAOOT793zFx8ere/fuqlevnurXr68JEybo7NmziouLkyTFxsaqdOnSGj16tCTp1Vdf1bBhwzRnzhxFRkY6ztEKCgpSUFCQbDabnnjiCb388suqWLGiypUrpxdeeEGlSpVSu3btJElVqlRRq1at1KdPH02bNk0XLlxQ//791blz52xd6RAAAAAAXJXv4atTp046fvy4hg0bpsTERNWqVUuLFi1yXDDjwIEDTl9UNnXqVJ0/f14PPfSQU53hw4drxIgRkqSnn35aZ8+eVd++fXX69Gk1adJEixYtcjovbPbs2erfv79atmzp+JLlt956y/0bDAAAAOCGlO/f8+WpUlJSFBISouTkZC41DwAAANzAspsNPPpqhwAAAADgKQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABbwzu8GAABA/osc8k2e1do/pk2e1QKAgoQ9XwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGCBfA9fkydPVmRkpPz9/RUVFaV169ZddezWrVvVvn17RUZGymazacKECZnGXF72z+mxxx5zjGnRokWm5f369XPH5gEAAACApHwOX/PmzVN8fLyGDx+uDRs2qGbNmoqJidGxY8eyHJ+amqry5ctrzJgxCg8Pz3LMzz//rKNHjzqmxYsXS5I6dOjgNK5Pnz5O48aOHZu3GwcAAAAAV8jX8DV+/Hj16dNHcXFxqlq1qqZNm6aAgABNnz49y/G33367XnvtNXXu3Fl+fn5ZjilRooTCw8Md09dff61bbrlFzZs3dxoXEBDgNC44ODjPtw8AAAAALsu38HX+/HmtX79e0dHR/2vGbld0dLRWr16dZ+v46KOP1LNnT9lsNqdls2fPVvHixVWtWjUNHTpUqampebJOAAAAAMiKd36t+MSJE0pPT1dYWJjT/LCwMO3YsSNP1jF//nydPn1aPXr0cJrftWtXlS1bVqVKldJvv/2mZ555Rjt37tTnn39+1VppaWlKS0tz3E5JScmTHgEAAADcGPItfFnh/fffV+vWrVWqVCmn+X379nX8XL16dUVERKhly5bas2ePbrnllixrjR49WiNHjnRrvwAAAAAKrnw77LB48eLy8vJSUlKS0/ykpKSrXkzDFX/88YeWLFmi3r17X3dsVFSUJGn37t1XHTN06FAlJyc7poMHD+a6RwAAAAA3jnwLX76+vqpbt64SEhIc8zIyMpSQkKCGDRvmuv6MGTNUsmRJtWnT5rpjN23aJEmKiIi46hg/Pz8FBwc7TQAAAACQXfl62GF8fLy6d++uevXqqX79+powYYLOnj2ruLg4SVJsbKxKly6t0aNHS7p0AY1t27Y5fj58+LA2bdqkoKAgVahQwVE3IyNDM2bMUPfu3eXt7byJe/bs0Zw5c3TPPfeoWLFi+u233zRo0CA1a9ZMNWrUsGjLAQAAANxo8jV8derUScePH9ewYcOUmJioWrVqadGiRY6LcBw4cEB2+/92zh05ckS1a9d23B43bpzGjRun5s2ba/ny5Y75S5Ys0YEDB9SzZ89M6/T19dWSJUscQa9MmTJq3769nn/+efdtKAAAAIAbns0YY/K7CU+UkpKikJAQJScncwgiAMDjRQ75Js9q7R9z/UP+AaAgyW42yNcvWQYAAACAGwXhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAAC+R7+Jo8ebIiIyPl7++vqKgorVu37qpjt27dqvbt2ysyMlI2m00TJkzINGbEiBGy2WxOU+XKlZ3GnDt3To899piKFSumoKAgtW/fXklJSXm9aQAAAADgkK/ha968eYqPj9fw4cO1YcMG1axZUzExMTp27FiW41NTU1W+fHmNGTNG4eHhV61722236ejRo47pxx9/dFo+aNAgLViwQJ9++qlWrFihI0eO6MEHH8zTbQMAAACAK+Vr+Bo/frz69OmjuLg4Va1aVdOmTVNAQICmT5+e5fjbb79dr732mjp37iw/P7+r1vX29lZ4eLhjKl68uGNZcnKy3n//fY0fP1533nmn6tatqxkzZmjVqlVas2ZNnm8jAAAAAEj5GL7Onz+v9evXKzo6+n/N2O2Kjo7W6tWrc1V7165dKlWqlMqXL69u3brpwIEDjmXr16/XhQsXnNZbuXJl3Xzzzddcb1pamlJSUpwmAAAAAMiufAtfJ06cUHp6usLCwpzmh4WFKTExMcd1o6KiNHPmTC1atEhTp07Vvn371LRpU/3111+SpMTERPn6+io0NNSl9Y4ePVohISGOqUyZMjnuEQAAAMCNJ98vuJHXWrdurQ4dOqhGjRqKiYnRwoULdfr0aX3yySe5qjt06FAlJyc7poMHD+ZRxwAAAABuBN75teLixYvLy8sr01UGk5KSrnkxDVeFhobq1ltv1e7duyVJ4eHhOn/+vE6fPu209+t66/Xz87vmeWYAAAAAcC35tufL19dXdevWVUJCgmNeRkaGEhIS1LBhwzxbz5kzZ7Rnzx5FRERIkurWrSsfHx+n9e7cuVMHDhzI0/UCAAAAwJXybc+XJMXHx6t79+6qV6+e6tevrwkTJujs2bOKi4uTJMXGxqp06dIaPXq0pEsX6di2bZvj58OHD2vTpk0KCgpShQoVJElPPfWU7rvvPpUtW1ZHjhzR8OHD5eXlpS5dukiSQkJC1KtXL8XHx6to0aIKDg7WgAED1LBhQzVo0CAfHgUAAAAAN4J8DV+dOnXS8ePHNWzYMCUmJqpWrVpatGiR4yIcBw4ckN3+v51zR44cUe3atR23x40bp3Hjxql58+Zavny5JOnQoUPq0qWLTp48qRIlSqhJkyZas2aNSpQo4bjfG2+8Ibvdrvbt2ystLU0xMTGaMmWKNRsNAAAA4IZkM8aY/G7CE6WkpCgkJETJyckKDg7O73YAAMiVyCHf5Fmt/WPa5FktAPAE2c0GBe5qhwAAAADwb0T4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwQI7C14cffqjGjRurVKlS+uOPPyRJEyZM0JdffpmnzQEAAABAQeFy+Jo6dari4+N1zz336PTp00pPT5ckhYaGasKECXndHwAAAAAUCC6Hr4kTJ+rdd9/Vc889Jy8vL8f8evXqafPmzXnaHAAAAAAUFC6Hr3379ql27dqZ5vv5+ens2bN50hQAAAAAFDTert6hXLly2rRpk8qWLes0f9GiRapSpUqeNQYA+HeKHPJNntXaP6ZNntUCAODfzuXwFR8fr8cee0znzp2TMUbr1q3Txx9/rNGjR+u9995zR48AAAAA4PFcPuywd+/eevXVV/X8888rNTVVXbt21dSpU/Xmm2+qc+fOLjcwefJkRUZGyt/fX1FRUVq3bt1Vx27dulXt27dXZGSkbDZblhf4GD16tG6//XYVLlxYJUuWVLt27bRz506nMS1atJDNZnOa+vXr53LvAAAAAJBdObrUfLdu3bRr1y6dOXNGiYmJOnTokHr16uVynXnz5ik+Pl7Dhw/Xhg0bVLNmTcXExOjYsWNZjk9NTVX58uU1ZswYhYeHZzlmxYoVeuyxx7RmzRotXrxYFy5c0N13353pfLQ+ffro6NGjjmns2LEu9w8AAAAA2eXyYYf79u3TxYsXVbFiRQUEBCggIECStGvXLvn4+CgyMjLbtcaPH68+ffooLi5OkjRt2jR98803mj59uoYMGZJp/O23367bb79dkrJcLl069+xKM2fOVMmSJbV+/Xo1a9bMMT8gIOCqAQ4AAAAA8prLe7569OihVatWZZq/du1a9ejRI9t1zp8/r/Xr1ys6Ovp/zdjtio6O1urVq11t66qSk5MlSUWLFnWaP3v2bBUvXlzVqlXT0KFDlZqaes06aWlpSklJcZoAAAAAILtcDl8bN25U48aNM81v0KCBNm3alO06J06cUHp6usLCwpzmh4WFKTEx0dW2spSRkaEnnnhCjRs3VrVq1Rzzu3btqo8++kjLli3T0KFD9eGHH+rhhx++Zq3Ro0crJCTEMZUpUyZPegQAAABwY3D5sEObzaa//vor0/zk5GSlp6fnSVN55bHHHtOWLVv0448/Os3v27ev4+fq1asrIiJCLVu21J49e3TLLbdkWWvo0KGKj4933E5JSSGAAQAAAMg2l/d8NWvWTKNHj3YKWunp6Ro9erSaNGmS7TrFixeXl5eXkpKSnOYnJSXlyblY/fv319dff61ly5bppptuuubYqKgoSdLu3buvOsbPz0/BwcFOEwAAAABkl8t7vl599VU1a9ZMlSpVUtOmTSVJP/zwg1JSUrR06dJs1/H19VXdunWVkJCgdu3aSbp0mGBCQoL69+/valsOxhgNGDBAX3zxhZYvX65y5cpd9z6XD5eMiIjI8XoBAAAA4FpcDl9Vq1bVb7/9pkmTJunXX39VoUKFFBsbq/79+2e6qMX1xMfHq3v37qpXr57q16+vCRMm6OzZs46rH8bGxqp06dIaPXq0pEsX6di2bZvj58OHD2vTpk0KCgpShQoVJF061HDOnDn68ssvVbhwYcf5YyEhISpUqJD27NmjOXPm6J577lGxYsX022+/adCgQWrWrJlq1Kjh6sMBAAAAANnicviSpFKlSumVV17J9co7deqk48ePa9iwYUpMTFStWrW0aNEix0U4Dhw4ILv9f0dGHjlyRLVr13bcHjdunMaNG6fmzZtr+fLlkqSpU6dKuvRFyleaMWOGevToIV9fXy1ZssQR9MqUKaP27dvr+eefz/X2AAAAAMDV5Ch8nT59WuvWrdOxY8eUkZHhtCw2NtalWv3797/qYYaXA9VlkZGRMsZcs971lpcpU0YrVqxwqUcAAAAAyC2Xw9eCBQvUrVs3nTlzRsHBwbLZbI5lNpvN5fAFAAAAADcCl692+OSTT6pnz546c+aMTp8+rT///NMxnTp1yh09AgAAAIDHczl8HT58WAMHDlRAQIA7+gEAAACAAsnl8BUTE6NffvnFHb0AAAAAQIHl8jlfbdq00eDBg7Vt2zZVr15dPj4+Tsvbtm2bZ80BAAAAQEHhcvjq06ePJOnFF1/MtMxmsyk9PT33XQEAAABAAeNy+PrnpeUBAAAAANfn8jlfAAAAAADX5ehLls+ePasVK1bowIEDOn/+vNOygQMH5kljAAAAAFCQuBy+Nm7cqHvuuUepqak6e/asihYtqhMnTiggIEAlS5YkfAEAAABAFlw+7HDQoEG677779Oeff6pQoUJas2aN/vjjD9WtW1fjxo1zR48AAAAA4PFcDl+bNm3Sk08+KbvdLi8vL6WlpalMmTIaO3asnn32WXf0CAAAAAAez+Xw5ePjI7v90t1KliypAwcOSJJCQkJ08ODBvO0OAAAAAAoIl8/5ql27tn7++WdVrFhRzZs317Bhw3TixAl9+OGHqlatmjt6BAAAAACP5/Ker1deeUURERGSpFGjRqlIkSJ65JFHdPz4cb399tt53iAAAAAAFAQu7/mqV6+e4+eSJUtq0aJFedoQAAAAABRELu/5uvPOO3X69OlM81NSUnTnnXfmRU8AAAAAUOC4HL6WL1+e6YuVJencuXP64Ycf8qQpAAAAAChosn3Y4W+//eb4edu2bUpMTHTcTk9P16JFi1S6dOm87Q4AAAAACohsh69atWrJZrPJZrNleXhhoUKFNHHixDxtDgAAAAAKimyHr3379skYo/Lly2vdunUqUaKEY5mvr69KliwpLy8vtzQJAAAAAJ4u2+GrbNmyunDhgrp3765ixYqpbNmy7uwLAAAAAAoUly644ePjoy+++MJdvQAAAABAgeXy1Q7vv/9+zZ8/3w2tAAAAAEDB5fKXLFesWFEvvviifvrpJ9WtW1eBgYFOywcOHJhnzQEAAABAQeFy+Hr//fcVGhqq9evXa/369U7LbDYb4QsAAAAAsuBy+Nq3b587+gAAAACAAs3lc76uZIyRMSavegEAAACAAitH4WvWrFmqXr26ChUqpEKFCqlGjRr68MMP87o3AAAAACgwXD7scPz48XrhhRfUv39/NW7cWJL0448/ql+/fjpx4oQGDRqU500CAAAAgKdzOXxNnDhRU6dOVWxsrGNe27Ztddttt2nEiBGELwAAAADIgsuHHR49elSNGjXKNL9Ro0Y6evRonjQFAAAAAAWNy+GrQoUK+uSTTzLNnzdvnipWrJgnTQEAAABAQePyYYcjR45Up06dtHLlSsc5Xz/99JMSEhKyDGUAAAAAgBzs+Wrfvr3Wrl2r4sWLa/78+Zo/f76KFy+udevW6YEHHnBHjwAAAADg8Vze8yVJdevW1UcffZTXvQAAAABAgZWj8JWenq4vvvhC27dvlyRVrVpV999/v7y9c1QOAAAAAAo8l9PS1q1b1bZtWyUmJqpSpUqSpFdffVUlSpTQggULVK1atTxvEgAAAAA8ncvnfPXu3Vu33XabDh06pA0bNmjDhg06ePCgatSoob59+7qjRwAAAADweC7v+dq0aZN++eUXFSlSxDGvSJEiGjVqlG6//fY8bQ4AAAAACgqX93zdeuutSkpKyjT/2LFjqlChQp40BQAAAAAFjcvha/To0Ro4cKA+++wzHTp0SIcOHdJnn32mJ554Qq+++qpSUlIcEwAAAADgEpcPO7z33nslSR07dpTNZpMkGWMkSffdd5/jts1mU3p6el71CQAAAAAezeXwtWzZMnf0AQAAAAAFmsvhq3nz5u7oAwAAAAAKtBx9K/K5c+f022+/6dixY8rIyHBa1rZt2zxpDAAAAAAKEpcvuLFo0SLdfPPNatCggdq2bat27do5pgceeMDlBiZPnqzIyEj5+/srKipK69atu+rYrVu3qn379oqMjJTNZtOECRNyVPPcuXN67LHHVKxYMQUFBal9+/ZZXsERAAAAAPKKy+FrwIAB6tChg44ePaqMjAynydULbMybN0/x8fEaPny4NmzYoJo1ayomJkbHjh3LcnxqaqrKly+vMWPGKDw8PMc1Bw0apAULFujTTz/VihUrdOTIET344IMu9Q4AAAAArnA5fCUlJSk+Pl5hYWG5Xvn48ePVp08fxcXFqWrVqpo2bZoCAgI0ffr0LMfffvvteu2119S5c2f5+fnlqGZycrLef/99jR8/Xnfeeafq1q2rGTNmaNWqVVqzZk2utwkAAAAAsuJy+HrooYe0fPnyXK/4/PnzWr9+vaKjo//XjN2u6OhorV692m01169frwsXLjiNqVy5sm6++eYcrxcAAAAArsflC25MmjRJHTp00A8//KDq1avLx8fHafnAgQOzVefEiRNKT0/PtActLCxMO3bscLWtbNdMTEyUr6+vQkNDM41JTEy8au20tDSlpaU5bvMl0gAAAABc4XL4+vjjj/X999/L399fy5cvd3zRsiTZbLZshy9PM3r0aI0cOTK/2wBQAEQO+SbPau0f0ybPagEAAPdy+bDD5557TiNHjlRycrL279+vffv2Oaa9e/dmu07x4sXl5eWV6SqDSUlJV72YRl7UDA8P1/nz53X69GmX1jt06FAlJyc7poMHD+aoRwAAAAA3JpfD1/nz59WpUyfZ7S7f1Ymvr6/q1q2rhIQEx7yMjAwlJCSoYcOGbqtZt25d+fj4OI3ZuXOnDhw4cM31+vn5KTg42GkCAAAAgOxy+bDD7t27a968eXr22WdzvfL4+Hh1795d9erVU/369TVhwgSdPXtWcXFxkqTY2FiVLl1ao0ePlnQp+G3bts3x8+HDh7Vp0yYFBQWpQoUK2aoZEhKiXr16KT4+XkWLFlVwcLAGDBighg0bqkGDBrneJgAAAADIisvhKz09XWPHjtV3332nGjVqZLrgxvjx47Ndq1OnTjp+/LiGDRumxMRE1apVS4sWLXJcMOPAgQNOe9iOHDmi2rVrO26PGzdO48aNU/PmzR1XYLxeTUl64403ZLfb1b59e6WlpSkmJkZTpkxx9aEAAAAAgGyzGWOMK3e44447rl7MZtPSpUtz3ZQnSElJUUhIiJKTkzkEEYBLPP2CG57cvyf37m48NgCQc9nNBi7v+Vq2bFmuGgMAAACAG1HurpoBAAAAAMiWbO/5evDBB7M17vPPP89xMwAAAABQUGU7fIWEhLizDwAAAAAo0LIdvmbMmOHOPgAAAACgQOOcLwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsECOwteHH36oxo0bq1SpUvrjjz8kSRMmTNCXX36Zp80BAAAAQEHhcviaOnWq4uPjdc899+j06dNKT0+XJIWGhmrChAl53R8AAAAAFAguh6+JEyfq3Xff1XPPPScvLy/H/Hr16mnz5s152hwAAAAAFBQuh699+/apdu3ameb7+fnp7NmzedIUAAAAABQ0LoevcuXKadOmTZnmL1q0SFWqVMmLngAAAACgwPF29Q7x8fF67LHHdO7cORljtG7dOn388ccaPXq03nvvPXf0CAAAAAAez+Xw1bt3bxUqVEjPP/+8UlNT1bVrV5UqVUpvvvmmOnfu7I4eAQAAAMDjuRS+Ll68qDlz5igmJkbdunVTamqqzpw5o5IlS7qrPwAAAAAoEFw658vb21v9+vXTuXPnJEkBAQEELwAAAADIBpcvuFG/fn1t3LjRHb0AAAAAQIHl8jlfjz76qJ588kkdOnRIdevWVWBgoNPyGjVq5FlzAAAAAFBQuBy+Ll9UY+DAgY55NptNxhjZbDalp6fnXXcAAAAAUEC4HL727dvnjj4AAAAAoEBzOXyVLVvWHX0AAAAAQIHmcviaNWvWNZfHxsbmuBkAAAAAKKhcDl+PP/640+0LFy4oNTVVvr6+CggIIHwBAAAAQBZcvtT8n3/+6TSdOXNGO3fuVJMmTfTxxx+7o0cAAAAA8Hguh6+sVKxYUWPGjMm0VwwAAAAAcEmehC9J8vb21pEjR/KqHAAAAAAUKC6f8/XVV1853TbG6OjRo5o0aZIaN26cZ40BAAAAQEHicvhq166d022bzaYSJUrozjvv1Ouvv55XfQEAAABAgeJy+MrIyHBHHwAAAABQoLl8zteLL76o1NTUTPP//vtvvfjii3nSFAAAAAAUNC6Hr5EjR+rMmTOZ5qempmrkyJF50hQAAAAAFDQuhy9jjGw2W6b5v/76q4oWLZonTQEAAABAQZPtc76KFCkim80mm82mW2+91SmApaen68yZM+rXr59bmgQAAAAAT5ft8DVhwgQZY9SzZ0+NHDlSISEhjmW+vr6KjIxUw4YN3dIkAAAAAHi6bIev7t27S5LKlSunRo0aycfHx21NAQAAAEBB4/Kl5ps3b+74+dy5czp//rzT8uDg4Nx3BQAAAAAFjMsX3EhNTVX//v1VsmRJBQYGqkiRIk4TAAAAACAzl8PX4MGDtXTpUk2dOlV+fn567733NHLkSJUqVUqzZs1yR48AAAAA4PFcPuxwwYIFmjVrllq0aKG4uDg1bdpUFSpUUNmyZTV79mx169bNHX0CAAAAgEdzec/XqVOnVL58eUmXzu86deqUJKlJkyZauXJl3nYHAAAAAAWEy+GrfPny2rdvnySpcuXK+uSTTyRd2iMWGhqap80BAAAAQEHhcviKi4vTr7/+KkkaMmSIJk+eLH9/fw0aNEiDBw/O8wYBAAAAoCBw+ZyvQYMGOX6Ojo7Wjh07tH79elWoUEE1atTI0+YAAAAAoKBwOXxd6dy5cypbtqzKli2bV/0AAHIpcsg3eVZr/5g2eVYLAIAbncuHHaanp+ull15S6dKlFRQUpL1790qSXnjhBb3//vs5amLy5MmKjIyUv7+/oqKitG7dumuO//TTT1W5cmX5+/urevXqWrhwodNym82W5fTaa685xkRGRmZaPmbMmBz1DwAAAADX43L4GjVqlGbOnKmxY8fK19fXMb9atWp67733XG5g3rx5io+P1/Dhw7VhwwbVrFlTMTExOnbsWJbjV61apS5duqhXr17auHGj2rVrp3bt2mnLli2OMUePHnWapk+fLpvNpvbt2zvVevHFF53GDRgwwOX+AQAAACA7XA5fs2bN0jvvvKNu3brJy8vLMb9mzZrasWOHyw2MHz9effr0UVxcnKpWrapp06YpICBA06dPz3L8m2++qVatWmnw4MGqUqWKXnrpJdWpU0eTJk1yjAkPD3eavvzyS91xxx2OS+RfVrhwYadxgYGBLvcPAAAAANnh8jlfhw8fVoUKFTLNz8jI0IULF1yqdf78ea1fv15Dhw51zLPb7YqOjtbq1auzvM/q1asVHx/vNC8mJkbz58/PcnxSUpK++eYbffDBB5mWjRkzRi+99JJuvvlmde3aVYMGDZK3d9YPSVpamtLS0hy3U1JSrrd5AADkGc7lAwDP53L4qlq1qn744YdMF9n47LPPVLt2bZdqnThxQunp6QoLC3OaHxYWdtW9aImJiVmOT0xMzHL8Bx98oMKFC+vBBx90mj9w4EDVqVNHRYsW1apVqzR06FAdPXpU48ePz7LO6NGjNXLkyOxuGgAAAAA4cTl8DRs2TN27d9fhw4eVkZGhzz//XDt37tSsWbP09ddfu6PHXJk+fbq6desmf39/p/lX7j2rUaOGfH199d///lejR4+Wn59fpjpDhw51uk9KSorKlCnjvsYBAAAAFCgun/N1//33a8GCBVqyZIkCAwM1bNgwbd++XQsWLNBdd93lUq3ixYvLy8tLSUlJTvOTkpIUHh6e5X3Cw8OzPf6HH37Qzp071bt37+v2EhUVpYsXL2r//v1ZLvfz81NwcLDTBAAAAADZle3wtXfvXhljJElNmzbV4sWLdezYMaWmpurHH3/U3Xff7fLKfX19VbduXSUkJDjmZWRkKCEhQQ0bNszyPg0bNnQaL0mLFy/Ocvz777+vunXrqmbNmtftZdOmTbLb7SpZsqSLWwEAAAAA15ft8FWxYkUdP37ccbtTp06Z9kDlRHx8vN5991198MEH2r59ux555BGdPXtWcXFxkqTY2FinC3I8/vjjWrRokV5//XXt2LFDI0aM0C+//KL+/fs71U1JSdGnn36a5V6v1atXa8KECfr111+1d+9ezZ49W4MGDdLDDz+sIkWK5HqbAAAAAOCfsn3O1+W9XpctXLhQo0ePznUDnTp10vHjxzVs2DAlJiaqVq1aWrRokeOiGgcOHJDd/r+M2KhRI82ZM0fPP/+8nn32WVWsWFHz589XtWrVnOrOnTtXxhh16dIl0zr9/Pw0d+5cjRgxQmlpaSpXrpwGDRqU6SqKAAAAAJBXXL7ghjv0798/056ry5YvX55pXocOHdShQ4dr1uzbt6/69u2b5bI6depozZo1LvcJAAAAADmV7cMObTabbDZbpnkAAAAAgOtz6bDDHj16OC7Dfu7cOfXr10+BgYFO4z7//PO87RAAAAAACoBsh6/u3bs73X744YfzvBkAAAAAKKiyHb5mzJjhzj4AAAAAoEBz+UuWAQAAAACuI3wBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAF/hXha/LkyYqMjJS/v7+ioqK0bt26a47/9NNPVblyZfn7+6t69epauHCh0/IePXrIZrM5Ta1atXIac+rUKXXr1k3BwcEKDQ1Vr169dObMmTzfNgAAAACQ/gXha968eYqPj9fw4cO1YcMG1axZUzExMTp27FiW41etWqUuXbqoV69e2rhxo9q1a6d27dppy5YtTuNatWqlo0ePOqaPP/7YaXm3bt20detWLV68WF9//bVWrlypvn37um07AQAAANzY8j18jR8/Xn369FFcXJyqVq2qadOmKSAgQNOnT89y/JtvvqlWrVpp8ODBqlKlil566SXVqVNHkyZNchrn5+en8PBwx1SkSBHHsu3bt2vRokV67733FBUVpSZNmmjixImaO3eujhw54tbtBQAAAHBjytfwdf78ea1fv17R0dGOeXa7XdHR0Vq9enWW91m9erXTeEmKiYnJNH758uUqWbKkKlWqpEceeUQnT550qhEaGqp69eo55kVHR8tut2vt2rVZrjctLU0pKSlOEwAAAABkV76GrxMnTig9PV1hYWFO88PCwpSYmJjlfRITE687vlWrVpo1a5YSEhL06quvasWKFWrdurXS09MdNUqWLOlUw9vbW0WLFr3qekePHq2QkBDHVKZMGZe3FwAAAMCNyzu/G3CHzp07O36uXr26atSooVtuuUXLly9Xy5Ytc1Rz6NChio+Pd9xOSUkhgAEAAADItnzd81W8eHF5eXkpKSnJaX5SUpLCw8OzvE94eLhL4yWpfPnyKl68uHbv3u2o8c8Lely8eFGnTp26ah0/Pz8FBwc7TQAAAACQXfkavnx9fVW3bl0lJCQ45mVkZCghIUENGzbM8j4NGzZ0Gi9Jixcvvup4STp06JBOnjypiIgIR43Tp09r/fr1jjFLly5VRkaGoqKicrNJAAAAAJClfL/aYXx8vN5991198MEH2r59ux555BGdPXtWcXFxkqTY2FgNHTrUMf7xxx/XokWL9Prrr2vHjh0aMWKEfvnlF/Xv31+SdObMGQ0ePFhr1qzR/v37lZCQoPvvv18VKlRQTEyMJKlKlSpq1aqV+vTpo3Xr1umnn35S//791blzZ5UqVcr6BwEAAABAgZfv53x16tRJx48f17Bhw5SYmKhatWpp0aJFjotqHDhwQHb7/zJio0aNNGfOHD3//PN69tlnVbFiRc2fP1/VqlWTJHl5eem3337TBx98oNOnT6tUqVK6++679dJLL8nPz89RZ/bs2erfv79atmwpu92u9u3b66233rJ24wEAAADcMPI9fElS//79HXuu/mn58uWZ5nXo0EEdOnTIcnyhQoX03XffXXedRYsW1Zw5c1zqEwAAAAByKt8POwQAAACAGwHhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALAA4QsAAAAALED4AgAAAAALEL4AAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgAAAADAAoQvAAAAALCAd343AAAACr7IId/kSZ39Y9rkSR0AyA+ELwDIQl79oSjxxyIAALiEww4BAAAAwAKELwAAAACwAOELAAAAACxA+AIAAAAAC3DBDSCXuIIXAAAAsoM9XwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWOBfEb4mT56syMhI+fv7KyoqSuvWrbvm+E8//VSVK1eWv7+/qlevroULFzqWXbhwQc8884yqV6+uwMBAlSpVSrGxsTpy5IhTjcjISNlsNqdpzJgxbtk+AAAAAMj38DVv3jzFx8dr+PDh2rBhg2rWrKmYmBgdO3Ysy/GrVq1Sly5d1KtXL23cuFHt2rVTu3bttGXLFklSamqqNmzYoBdeeEEbNmzQ559/rp07d6pt27aZar344os6evSoYxowYIBbtxUAAADAjSvfw9f48ePVp08fxcXFqWrVqpo2bZoCAgI0ffr0LMe/+eabatWqlQYPHqwqVaropZdeUp06dTRp0iRJUkhIiBYvXqyOHTuqUqVKatCggSZNmqT169frwIEDTrUKFy6s8PBwxxQYGOj27QUAAABwY8rX8HX+/HmtX79e0dHRjnl2u13R0dFavXp1lvdZvXq103hJiomJuep4SUpOTpbNZlNoaKjT/DFjxqhYsWKqXbu2XnvtNV28eDHnGwMAAAAA1+Cdnys/ceKE0tPTFRYW5jQ/LCxMO3bsyPI+iYmJWY5PTEzMcvy5c+f0zDPPqEuXLgoODnbMHzhwoOrUqaOiRYtq1apVGjp0qI4eParx48dnWSctLU1paWmO2ykpKdnaRgAAAACQ8jl8uduFCxfUsWNHGWM0depUp2Xx8fGOn2vUqCFfX1/997//1ejRo+Xn55ep1ujRozVy5Ei39wwAAACgYMrX8FW8eHF5eXkpKSnJaX5SUpLCw8OzvE94eHi2xl8OXn/88YeWLl3qtNcrK1FRUbp48aL279+vSpUqZVo+dOhQp8CWkpKiMmXKXLMm/h0ih3yTZ7X2j2mTZ7UAAABwY8nXc758fX1Vt25dJSQkOOZlZGQoISFBDRs2zPI+DRs2dBovSYsXL3Yafzl47dq1S0uWLFGxYsWu28umTZtkt9tVsmTJLJf7+fkpODjYaQIAAACA7Mr3ww7j4+PVvXt31atXT/Xr19eECRN09uxZxcXFSZJiY2NVunRpjR49WpL0+OOPq3nz5nr99dfVpk0bzZ07V7/88oveeecdSZeC10MPPaQNGzbo66+/Vnp6uuN8sKJFi8rX11erV6/W2rVrdccdd6hw4cJavXq1Bg0apIcfflhFihTJnwcCAAAAQIGW7+GrU6dOOn78uIYNG6bExETVqlVLixYtclxU48CBA7Lb/7eDrlGjRpozZ46ef/55Pfvss6pYsaLmz5+vatWqSZIOHz6sr776SpJUq1Ytp3UtW7ZMLVq0kJ+fn+bOnasRI0YoLS1N5cqV06BBg5wOKwQAAACAvJTv4UuS+vfvr/79+2e5bPny5ZnmdejQQR06dMhyfGRkpIwx11xfnTp1tGbNGpf7BOCavDrfjnPtAABAQZDvX7IMAAAAADcCwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABYgPAFAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHv/G4AniFyyDd5Vmv/mDZ5VgsAAADwFIQvAACAfMKHm8CNhcMOAQAAAMAChC8AAAAAsADhCwAAAAAswDlfyHcc7w4AAIAbAeELgEcitAO4jN8HADwF4Qv4F+MPCsCz8JoFcBm/D5AVwlcBwQscQEHA7zIAKPhu5N/1hC/gBnYj//IDAMBT8f7tuQhfAAAA18AfugDyCuELAACggCI4Xh2PDfID3/MFAAAAABZgzxcAAAByhL1HgGvY8wUAAAAAFmDPFwAAAAAH9mi6D+ELAAAA/zoEABREHHYIAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFvhXhK/JkycrMjJS/v7+ioqK0rp16645/tNPP1XlypXl7++v6tWra+HChU7LjTEaNmyYIiIiVKhQIUVHR2vXrl1OY06dOqVu3bopODhYoaGh6tWrl86cOZPn2wYAAAAA0r8gfM2bN0/x8fEaPny4NmzYoJo1ayomJkbHjh3LcvyqVavUpUsX9erVSxs3blS7du3Url07bdmyxTFm7NixeuuttzRt2jStXbtWgYGBiomJ0blz5xxjunXrpq1bt2rx4sX6+uuvtXLlSvXt29ft2wsAAADgxpTv4Wv8+PHq06eP4uLiVLVqVU2bNk0BAQGaPn16luPffPNNtWrVSoMHD1aVKlX00ksvqU6dOpo0aZKkS3u9JkyYoOeff17333+/atSooVmzZunIkSOaP3++JGn79u1atGiR3nvvPUVFRalJkyaaOHGi5s6dqyNHjli16QAAAABuIN75ufLz589r/fr1Gjp0qGOe3W5XdHS0Vq9eneV9Vq9erfj4eKd5MTExjmC1b98+JSYmKjo62rE8JCREUVFRWr16tTp37qzVq1crNDRU9erVc4yJjo6W3W7X2rVr9cADD2Rab1pamtLS0hy3k5OTJUkpKSmub7gbZKSl5lmtrLbJnfU9ufe8rO/Jvbu7vif3nlV9T+7d3fU9uXd31/fk3vOyvif3nlV9T+7d3fU9uXd31/fk3q2onx8u92GMufZAk48OHz5sJJlVq1Y5zR88eLCpX79+lvfx8fExc+bMcZo3efJkU7JkSWOMMT/99JORZI4cOeI0pkOHDqZjx47GGGNGjRplbr311ky1S5QoYaZMmZLleocPH24kMTExMTExMTExMTExZTkdPHjwmvknX/d8eZKhQ4c67XHLyMjQqVOnVKxYMdlstnzsLHtSUlJUpkwZHTx4UMHBwdS3qLan1/fk3t1d35N7d3d9ei+Y9T25d3fX9+Te3V3fk3t3d316z7/67mCM0V9//aVSpUpdc1y+hq/ixYvLy8tLSUlJTvOTkpIUHh6e5X3Cw8OvOf7yv0lJSYqIiHAaU6tWLceYf17Q4+LFizp16tRV1+vn5yc/Pz+neaGhodfewH+h4OBgtz6JPbm+J/fu7vqe3Lu763ty7+6uT+8Fs74n9+7u+p7cu7vre3Lv7q5P7/lXP6+FhIRcd0y+XnDD19dXdevWVUJCgmNeRkaGEhIS1LBhwyzv07BhQ6fxkrR48WLH+HLlyik8PNxpTEpKitauXesY07BhQ50+fVrr1693jFm6dKkyMjIUFRWVZ9sHAAAAAJfl+2GH8fHx6t69u+rVq6f69etrwoQJOnv2rOLi4iRJsbGxKl26tEaPHi1Jevzxx9W8eXO9/vrratOmjebOnatffvlF77zzjiTJZrPpiSee0Msvv6yKFSuqXLlyeuGFF1SqVCm1a9dOklSlShW1atVKffr00bRp03ThwgX1799fnTt3vu6uQgAAAADIiXwPX506ddLx48c1bNgwJSYmqlatWlq0aJHCwsIkSQcOHJDd/r8ddI0aNdKcOXP0/PPP69lnn1XFihU1f/58VatWzTHm6aef1tmzZ9W3b1+dPn1aTZo00aJFi+Tv7+8YM3v2bPXv318tW7aU3W5X+/bt9dZbb1m34Rbz8/PT8OHDMx06SX3P7t3d9T25d3fX9+Te3V2f3gtmfU/u3d31Pbl3d9f35N7dXZ/e869+frIZc73rIQIAAAAAcivfv2QZAAAAAG4EhC8AAAAAsADhCwAAAAAsQPgCAAAAAAsQvgq43bt367vvvtPff/8t6dK3bwMAAACwHuGrgDp58qSio6N166236p577tHRo0clSb169dKTTz7ptvVevHhRBw4ccFt9d0hKSnJrzyNHjtSJEyfcVv/ChQt5XvPixYtavHix3n//fS1ZskTp6em5qufO7b8sPT1de/fuVUZGhiQpLS1Nn3zyiebOnaukpKRc1z979qxWrlypefPm6dNPP9X69evz/MOM5ORk7dy5Uzt37lRycnKe1i5IjDG5fk5ey8yZMz3y8d+1a5cSEhK0e/fu/G4lW/75f7hu3TqtWbNGaWlpebaOAwcOaO3atfr555918uTJPKt7WVpaWp72C9ctX77c8QGzJ0lLS9OePXs8+vmTlJSkxMTEPKl14MCBLN9TjTEe93fldRkUSP/5z39MTEyMOXjwoAkKCjJ79uwxxhizaNEiU7VqVbetd9OmTcZut+eqxuTJk03Lli1Nhw4dzJIlS5yWHT9+3JQrVy5HdVNSUky3bt3MzTffbGJjY01aWpp59NFHjc1mM3a73TRr1swkJyfnuO/k5ORM0+nTp42Pj49Zu3atY15OzZs3z6SlpTluT5w40dx8883GbrebYsWKmZEjR+a4dv/+/c2CBQuMMcYcPHjQVK5c2Xh5eZmwsDDj5eVlqlevbg4dOpTj+na73dx5551m9uzZ5ty5czmuczW//vqriYiIMHa73VSrVs0cOHDAVKtWzQQGBpqgoCBTpEgRs27duhzVTk9PN4MHDzYBAQHGbrcbu91ubDabsdlspmzZsuarr77Kdf/vvvuuqVKliqP+5alKlSrmvffey3X9a8nta/abb74xvXr1MoMHDzbbt293Wnbq1Clzxx135Lj2hQsXzHPPPWeaNWtmhg0bZowxZuzYsSYgIMD4+vo6Xsd5zcfHx2zbti1XNdauXWsuXrzouL1gwQLTrFkzU6pUKVO3bl3zwQcf5Kr+K6+84vj9eOrUKdOyZUvH89Jut5tWrVqZP//8M0e1g4KCTM+ePc1PP/2Uqx6vZv/+/aZu3brGy8vLtGrVyiQnJ5vo6GhH/+XLlzc7d+7M1TomT57s+P145dS4cWPzyy+/5Kr2999/b1q3bm1CQ0MddUNDQ03r1q3N4sWLc1X7erZt25bj98DLNm3aZF566SUzefJkc/z4cadlycnJJi4uLse13333XRMbG2umT59ujDFm7ty5pnLlyqZcuXKO13Bey4vXqzHGJCUlOd3euHGjiY2NNY0aNTLt27c3y5Yty3HtGTNmmFWrVhljjPn7779Nz549jZeXl7Hb7cbb29v897//zdV7Y7Vq1cyLL75oDhw4kOMa13Ly5EnTvn17U6ZMGdOvXz9z8eJF06tXL8fvm4YNG5ojR47kah12uz3T/4Exxpw4cSLXf1f+2xC+CqiwsDCzadMmY4xxCl979uwxgYGBbltvbv+Qe/PNN01AQIB57LHHzMMPP2x8fX3NK6+84liemJiY4/r9+/c3lStXNm+99ZZp0aKFuf/++021atXMjz/+aFasWGGqVq1qnn322Rz3/s83+Sv/UL/y39zUv/yLafr06cbf398MGzbMfPPNN+bll182gYGB5t13381R7bCwMLN582ZjjDEdO3Y00dHRjjflkydPmnvvvdc89NBDOe7dZrOZVq1aGV9fX1OkSBHTv39/s3HjxhzX+6eYmBjz0EMPmc2bN5vHH3/cVKlSxXTo0MGcP3/eXLhwwTz88MMmOjo6R7WfeeYZU6VKFbNgwQKzePFi06xZM/Pqq6+a7du3mxdeeMH4+fmZ7777Lse9Xw4TQ4YMMcuWLTPbtm0z27ZtM8uWLTNDhw41gYGB5rXXXstx/evZtGmTsdlsObrv7NmzjZeXl2nTpo1p0qSJ8ff3Nx999JFjeW5er8YY8/zzz5uwsDATHx9vqlatavr162fKlCljPvroI/PBBx+Y0qVLm1dffTXH9YsUKZLlZLPZTEhIiON2Tlz5ev3qq6+M3W43sbGxZvLkyaZ3797G29vbfP755znu/aabbjIbNmwwxhjTu3dvU7t2bbNhwwbz999/m02bNpkGDRqYXr165ai2zWYzt912m7HZbKZy5cpm3Lhx5tixYznu9Z/at29vmjdvbhYsWGA6duxoGjdubFq0aGEOHTpkjhw5YmJiYky7du1yXP+1114zpUqVMhMnTnR8sPHiiy+ab7/91vznP/8xAQEB5ueff85R7ZkzZxpvb2/TuXNnM2PGDLNw4UKzcOFCM2PGDNOlSxfj4+NjZs2alePerye377Hfffed8fX1Nbfddpu5+eabTbFixczSpUsdy3Pzmn3jjTdMYGCgefDBB01ERIR5+eWXTbFixczLL79sRo4caYKDg83bb7+d495r166d5WSz2UyVKlUct3PqytfsTz/9ZHx8fEzz5s3N4MGDzV133WW8vb3NihUrclS7XLlyZs2aNcYYY5566ikTGRlpPv/8c7N9+3Yzf/58c+utt5rBgwfnuHebzWaKFStmvLy8TExMjPnss8/MhQsXclzvn3r27GmqVatmJk6caJo3b27uv/9+U6NGDfPjjz+aVatWmdtvv93Exsbmah02my3L3zP79+83AQEBuar9b0P4KqCCgoLM77//7vj5cvj6+eefTdGiRXNc92q//C5PlStXztUbQ9WqVc3s2bMdt3/66SdTokQJ88ILLxhjcvfGUKZMGcebzOHDh43NZnPs7THGmK+//tpUqlQpx72XLl3atGnTxixdutQsX77cLF++3Cxbtsx4eXmZGTNmOObllM1mc7wx1K9f34wdO9Zp+ZQpU3L8xuPv72/27t1rjLn0R93atWudlm/evNkUL148R7WN+V/vx48fN+PGjTNVq1Y1drvd1KlTx0yZMiVXewSNufRH9OVPPlNTU42Xl5fTNmzZssUUK1YsR7UjIiLMypUrHbcPHTpkgoKCHJ9Svvjii6Zhw4Y57v3mm2828+bNu+ryuXPnmjJlyuS4/gMPPHDN6c4778zxa6pWrVrmzTffdNyeN2+eCQwMdOyty234Kl++vOM1umvXLmO3283cuXOd1letWrUc1w8KCjJt2rQxM2fOdEwzZswwXl5eZtSoUY55OXHl67VJkyZmyJAhTstHjRplGjRokOPe/fz8zP79+40xxkRGRmb6o/CXX34xEREROap9ufdNmzaZ/v37m6JFixpfX1/z4IMPmoULF5qMjIwc922MMSVKlHB8+HL69Gljs9nMDz/84Fi+fv16ExYWluP6kZGRZuHChY7bO3fuNMWKFXP8MTpw4EBz11135ah2xYoVzaRJk666fPLkyaZChQo5qm2MMYMGDbrm9PDDD+fqNdWwYUPHh4wZGRnm1VdfNUFBQebbb781xuTuNVu5cmXH+/eGDRuMt7e305779957z9StWzfHvXt7e5tWrVqZESNGOKbhw4cbu91uHn30Uce8nLryNXvXXXeZnj17Oi1//PHHzZ133pmj2n5+fuaPP/4wxhhz6623Oh7vy1asWGFuvvnmHNU25lLvhw8fNl988YW57777jLe3tylRooR58skn82SvYEREhGNPeGJiorHZbOb77793LP/xxx9N6dKlc1T78nPbbreb//73v07P94EDB5qoqCjTqFGjXG/Dvwnhq4Bq3bq1ef75540xl/7A2Lt3r0lPTzcdOnQw7du3z3FdPz8/0717d6dffldO//3vf3P1xlCoUCGzb98+p3mbN282YWFhZsiQIbl6Y/Dz83PaJR8QEOB0aEtuP105efKkadeunbnjjjucDtHz9vY2W7duzXHdy678VKh48eKOPZuX7d692xQuXDhHtWvUqOH4o7ZKlSqZDp1ZtWpVrkL7lW9qV9bs2bOnKVy4sAkICDD/+c9/clw/NDTU8WHD+fPnjZeXl1m/fr1j+fbt23O8B6Nw4cKODy+MuXQYore3tzl69KgxxpitW7fm6nnj7+9/zTfHrVu3mkKFCuW4vre3t2ndurXp0aNHllPbtm1z/JoKDAx0hPbLli5daoKCgszUqVNzHb78/f2dXrP+/v5Ohzbu3bs3x895Yy4Fusuf2P7111+O+Xnxmr3yOV+yZMlMh7rt2LHDhIaG5rj+rbfear7++mtjzKVP1f95iODGjRtNcHBwjmr/8/V67tw5M2fOHNOyZUtjt9vNTTfd5PhALCcKFy7seN5cfj1d+fts165dufp/DQgIcHofycjIMN7e3o7DojZt2mSCgoJyVNvPz8/s2LHjqst37Nhh/P39c1TbGOP4UKpFixZZTvXq1cvVayo4ONjs3r3bad7s2bNNYGCgWbBgQa5es4UKFXIEDGMuPVZbtmxx3N61a1eunvM//vijueWWW8ywYcNMenq6Y35evsdeft5HRESY1atXOy3fsmVLjj+ELFu2rOPD39KlS2fa87pt27ZcHZX0z9fskSNHzCuvvGIqVqzoOCzw/fffz3H9gIAAx4c9xlw61PPy0TLGXPpdnNP+Lz+3bTabadSokdPz/e677zZ9+/Z1vL8XFISvAmrz5s2mZMmSjkO9HnroIVOlShUTFhaW6RevK+rWrWumTJly1eUbN27M1RtDmTJlnPYyXLZ161YTFhZmYmNjc1y/VKlSTn+Qd+nSxemX1ZYtW3L8B/qVpkyZYkqVKmXmzJljjMnbN4ZZs2aZL7/80tx0002O48cv27JlS47/2JoxY4a56aabzLJly8ysWbNMlSpVzJIlS8zhw4fN0qVLTfXq1U3v3r1z3PvVjuU2xpgzZ86Y9957L1efbLVs2dL06tXLHDp0yIwcOdJUqFDB6byFRx991DRt2jRHtRs1amRefvllx+2PP/7Y6Q+IzZs35+p507RpUxMbG5vlISIXL140sbGxplmzZjmuX7169WueN5ab12xWf6AYY8zy5ctNUFCQee6553L1+yAsLMz89ttvjtuNGjVy+mBj+/btOX7OX3bhwgXz9NNPm1tuucX8+OOPxpi8C1/Lli0zv/76qylbtmymcw537NiR4wBgzKVD66pUqWJ27dplXn/9ddOwYUPH7/a9e/eaFi1a5PhQ4Wu9Xvft22eef/75XO2NbdCggePDwenTpzs+XLvsxRdfzNUeklq1apl33nnHcTshIcEEBAQ49tjt2LEjx+GuTp061zw87OmnnzZ16tTJUW1jLoXqDz/88KrLc/seW6JEiSzPefv4449NQECAmTp1ao7rFytWzOmDpJtuusnpD/Zdu3bl6jlvzKU9pZ07dzZRUVGO53tevsfu3r3bJCcnm3LlyjkO671s9+7dOf6g7dlnnzUNGzY0f/75pxkyZIi57777HB/4nD171nTs2NHcfffdOe79Wq/ZZcuWmYcffjhX4a5mzZqOPb4LFy40hQsXNq+//rpj+dSpU3N1FIIxxvTo0SPXR8F4CsJXAXb69Gnz8ssvmw4dOpjWrVub5557LtcnRA4cONA8/vjjV12+e/du06JFixzX79Kli3niiSeyXLZlyxZTokSJHL8xtGrVykybNu2qy2fMmJFnu7a3bt1qatasabp06ZKnbwxXTlcGAmMuHdKRm+PdX3/9dRMQEGAKFSpkfH19nc5ba9eundOegZz0frU3hrzw888/m2LFihmbzWZKlChhtmzZYqKiokx4eLgpVaqUKVSoUKaLt2TXkiVLjJ+fn6lfv75p1qyZ8fb2Nm+88YZj+WuvvZbjQ1GMuXSxkPDwcFOsWDHzwAMPmH79+pl+/fqZBx54wBQrVsxEREQ4fcLoqh49ephHH330qsu3bdtmIiMjc1T7/vvvv+pJ9MuWLTOBgYG5+kPxjjvuuOZhf5988kmu/ki/UkJCgrn55pvN0KFDjY+PT56ErysvznLlc8aYS3/s5vbiRwMGDDA+Pj6mcuXKxt/f39jtdsdrt169eo69sznp/Xqv19wcerho0SLj7+9vfH19jb+/v1mxYoW59dZbTf369U2DBg2Ml5fXNQ/FvZ558+YZHx8f07FjRxMbG2uCgoKcwt20adNyfKjw5ed19erVzaBBg8yYMWPMmDFjzKBBg0yNGjVMUFBQjs8LMsaYrl27XvU90JjcnaNpzKXD6a52DumcOXOMj49Pjl+zjRs3djos+J8WLFiQ6z/QL5s+fboJDw83b7/9dp68Xo3532v28uv2ygBvjDFffvlljg8pTUtLM23btjVFihQxd911l/H39zcBAQGmYsWKJjAw0Nx88825ushMdl6zuQk2H330kfHy8jIVKlQwfn5+5tNPPzWlSpUyHTt2NJ07dza+vr7XPBwXzmzG8MVPyL4tW7aoWrVqbqu/efNmrV+/Xj169Ljq+v/v//5Pw4cPd7n2Dz/8oBo1aigkJCTL5d9++60KFSqkFi1auFz7cm9XPjbnz5/XkCFDtGzZMn3++ecqV65cjuperf4/ff311/Lx8VFMTEyOa58+fVrff/+99u3bp4yMDEVERKhx48aqWLFiblrXpEmT1KdPH/n5+eWqztVs2bJF5cqV044dO1SpUiUFBQXp3Llzmj17tv7++2/dddddqlSpUo5rZ2RkaN68eUpLS1NMTIzuuuuuPO3/r7/+0kcffaQ1a9Y4LtsbHh6uhg0bqmvXrgoODs5x7bS0NKWnpysgICCv2nVYsWKFVq1apaFDh2a5fNmyZZo1a5ZmzJiRo/q///67fHx8rvramTNnjry9vdWxY8cc1f+nkydPqk+fPlq2bJnWrFmT4+eMJP3xxx9Ot4OCglSsWDHH7VmzZkmSYmNjc7wOSdq+fbu+/vprx9csXH7NRkdHy2az5ajmyJEjNXjwYLc8Zy7bv3+/1q9fr7p16yoyMlJJSUmaPHmyUlNT1aZNG91xxx25qv/tt9/qo48+crxm+/Tp41h2+ZLzV/5/uNr71KlTs3y99uvXT5GRkTnuOzExUWlpaSpbtmyOa1zLF198oZUrV+qNN97IcvmcOXP07rvvatmyZS7X/umnnxQYGKhatWpluXzKlCnKyMhQ//79Xa6dlV27dqlbt2765ZdftGXLFlWtWjVX9VasWOF0OyIiQrfeeqvj9ptvvqnz589r8ODBOV7HokWLtGDBgkyv165duyowMDDHdePi4vTWW2+pcOHCOa5xPT/99JPWrFmjhg0bqlGjRtq2bZvGjBmj1NRU3XffferevXuu6p89e1ZjxoxRQkKCjh075vjamMv27t2bq/r/JoSvAuzcuXP67bffsnwSt23bNkc17Xa76tevr169eqlz5855/kK32+26/fbb1bt37zyv787aVtW//Nh36dJFQUFBeVq7oDw27nreXH7c3fnmBgDIvoyMDP31118KDg7O8YcN+Hfo0qWLVqxYof/85z+KiIjI9P/5+OOP51NnbpC/O97gLt9++60pUaJEpkPVcnu585UrV5q4uDhTuHBhExgYaGJjY7M8Rys39S9fhCGv61vRu6fW/2ft7t27e0zv7q7v7sfmes6fP+90Enteu3Dhgtvqu7O2p9d3d+/ufN548uNuRX0ArgsJCXGcd1vQEb4KqAoVKphHH33UJCYmuqX+mTNnzPTp002zZs2MzWYzFStWNGPGjMnxOQZW1vfk3t1d35N7d3d9d/d+NXnxxeX5Vd+Te3d3fXr33PqTJ082LVu2NB06dMh0Lunx48dz/SXInlzfk3t3d31P7t2K+pGRkXlyWXxPQPgqoAoXLpyrqxq6YteuXebZZ581ZcqUMT4+Pua+++7zmPqe3Lu763ty7+6u7+7er/Rv/0M0v2p7en1698z6b775pgkICDCPPfaYefjhh42vr6955ZVXHMtz+/UKnlzfk3t3d31P7t2K+sYY8+GHH5qHHnrInD17Nld1PAHnfBVQPXv2VOPGjdWrVy9L1nf27FnNnj1bQ4cO1enTp5Wenu4x9T25d3fX9+Te3V0/r2rXqVPnmsv//vtv/f777//K+p7cu7vr03vBrH/bbbfpueeeU9euXSVJq1atUrt27dSvXz+9+OKLSkpKUqlSpXLcuyfX9+Te3V3fk3u3or4k1a5dW3v27JExRpGRkfLx8XFavmHDhhzX/rfxzu8G4B6TJk1Shw4d9MMPP6h69eqZnsQDBw7Mk/WsXLlS06dP1//93//JbrerY8eOeRr43Fnfk3t3d31P7t3d9fO69rZt29S5c+erXtHv6NGj+v333/+V9T25d3fXp/eCWX/fvn1q1KiR43ajRo20dOlSRUdH68KFC3riiSdyVLcg1Pfk3t1d35N7t6K+JLVr1y7XNTxGPu95g5u89957xtvb2wQFBZmyZcuayMhIx5Tb43IPHz5sRo0aZSpWrGhsNptp3LixmT59ujlz5kye9O7O+p7cu7vre3Lv7q7vztru/uJyd9b35N7dXZ/eC2b9MmXKZHnBna1bt5qwsDATGxubq949ub4n9+7u+p7cuxX1bzTs+SqgnnvuOY0cOVJDhgyR3W7Ps7qtW7fWkiVLVLx4ccXGxqpnz565+i4cK+t7cu/uru/Jvbu7vrt7b9y4sXbu3HnV5YULF1azZs3+lfU9uXd316f3glm/SZMm+vzzz9W0aVOn+VWrVlVCQkKuv5/Mk+t7cu/uru/JvVtR/4aT3+kP7lGkSBG3XHDjvvvuM/PnzzcXL17M89ruru/Jvbu7vif37u767u598+bNbqlrRX1P7t3d9em9YNb/7bffzIwZM6657hEjRtyQ9T25d3fX9+TerahvjHF8FdLVpoKE8FVAPfHEE2bUqFH53QaA67DZbCYqKsq88847JiUlxaPqe3Lv7q5P7wWzvs1mM/Xr13dr755a35N7d3d9T+7divrGGDN//nyn6dNPPzXPPvusKV26tHnvvffcss78QvgqoAYMGGBCQkJMs2bNTP/+/c2gQYOcJgD/DgXpC6g9qXd316f3gll/5cqVpmfPnm7t3VPre3Lv7q7vyb1bUf9aZs+ebdq2bWvJuqxC+CqgWrRocdXpjjvuyO/2APwDX0BdMOvTe8Gs78m9u7u+J/fu7vqe3LsV9bOyZ88eExgY6Lb6+YHwBQD/MnwBdcGsT+8Fs74n9+7u+p7cu7vre3LvVtQ3xpjU1FTz+OOPm1tvvTXPa+cnwhcA/AudOXPGvP3226Zo0aJuOdnYnfU9uXd316f3glnfk3t3d31P7t3d9T2597yuHxoaaooUKeKYQkNDjZeXlylcuLD58ssv86jjfwcuNV+APPjgg5o5c6aCg4P14IMPXnPs559/blFXAFzBF1AXzPr0XjDre3Lv7q7vyb27u74n9+6u+hMmTHC6bbfbVaJECUVFRalIkSK5qv2vk9/pD3mnR48ejqvQ9OjR45oTgH8PvoC6YNan94JZ35N7d3d9T+7d3fU9uXcr6t9ICF8FzMiRI83Zs2fzuw0A2dSqVSvj7e1twsPDzdNPP2127NjhMfU9uXd316f3glnfk3t3d31P7t3d9T25dyvqX/bnn3+acePGmV69eplevXqZ8ePHm9OnT7tlXfmJww4LmJEjR6pfv34KCAjI71YAZIOPj48+++wz3XvvvfLy8vKo+p7cu7vr03vBrO/Jvbu7vif37u76nty7FfUl6ZdfflFMTIwKFSqk+vXrS5LGjx+vUaNG6fvvv1edOnXcst78YDPGmPxuAnnHbrcrMTFRJUuWzO9WAAAAgOtq2rSpKlSooHfffVfe3pf2DV28eFG9e/fW3r17tXLlynzuMO8QvgoYu92upKQklShRIr9bAQAAAK6rUKFC2rhxoypXruw0f9u2bapXr55SU1PzqbO8x2GHBdCtt94qm812zTGnTp2yqBsAAADg6oKDg3XgwIFM4evgwYMqXLhwPnXlHoSvAmjkyJEKCQnJ7zYAAACA6+rUqZN69eqlcePGqVGjRpKkn376SYMHD1aXLl3yubu8xWGHBQznfAEAAMCTnD9/XoMHD9a0adN08eJFSZcu9PHII49ozJgx8vPzy+cO8w7hq4Dx8vLS0aNHCV8AAADwKKmpqdqzZ48k6ZZbbimQV+8mfBUw7PkCAAAA/p0IXwAAAADyzblz5zRx4kQtW7ZMx44dU0ZGhtPyDRs25FNneY8LbgAAAADIN7169dL333+vhx56SPXr17/uVbs9GXu+AAAAAOSbkJAQLVy4UI0bN87vVtzOnt8NAAAAALhxlS5dusB9n9fVEL4AAAAA5JvXX39dzzzzjP7444/8bsXtOOcLAAAAQL6pV6+ezp07p/LlyysgIEA+Pj5Oy0+dOpVPneU9whcAAACAfNOlSxcdPnxYr7zyisLCwrjgBgAAAAC4Q0BAgFavXq2aNWvmdytuxzlfAAAAAPJN5cqV9ffff+d3G5YgfAEAAADIN2PGjNGTTz6p5cuX6+TJk0pJSXGaChIOOwQAAACQb+z2S/uD/nmulzFGNptN6enp+dGWW3DBDQAAAAD5ZtmyZVddtnnzZgs7cT/2fAEAAAD41/jrr7/08ccf67333tP69esL1J4vzvkCAAAAkO9Wrlyp7t27KyIiQuPGjdOdd96pNWvW5HdbeYrDDgEAAADki8TERM2cOVPvv/++UlJS1LFjR6WlpWn+/PmqWrVqfreX59jzBQAAAMBy9913nypVqqTffvtNEyZM0JEjRzRx4sT8bsut2PMFAAAAwHLffvutBg4cqEceeUQVK1bM73YswZ4vAAAAAJb78ccf9ddff6lu3bqKiorSpEmTdOLEifxuy6242iEAAACAfHP27FnNmzdP06dP17p165Senq7x48erZ8+eKly4cH63l6cIXwAAAAD+FXbu3Kn3339fH374oU6fPq277rpLX331VX63lWcIXwAAAAD+VdLT07VgwQJNnz6d8AUAAAAAcA0X3AAAAAAACxC+AAAAAMAChC8AAAAAsADhCwAAAAAsQPgCABQoPXr0kM1myzTt3r0717Vnzpyp0NDQ3DcJALgheed3AwAA5LVWrVppxowZTvNKlCiRT91k7cKFC/Lx8cnvNgAAFmLPFwCgwPHz81N4eLjT5OXlpS+//FJ16tSRv7+/ypcvr5EjR+rixYuO+40fP17Vq1dXYGCgypQpo0cffVRnzpyRJC1fvlxxcXFKTk527E0bMWKEJMlms2n+/PlOPYSGhmrmzJmSpP3798tms2nevHlq3ry5/P39NXv2bEnSe++9pypVqsjf31+VK1fWlClTHDXOnz+v/v37KyIiQv7+/ipbtqxGjx7tvgcOAOBW7PkCANwQfvjhB8XGxuqtt95S06ZNtWfPHvXt21eSNHz4cEmS3W7XW2+9pXLlymnv3r169NFH9fTTT2vKlClq1KiRJkyYoGHDhmnnzp2SpKCgIJd6GDJkiF5//XXVrl3bEcCGDRumSZMmqXbt2tq4caP69OmjwMBAde/eXW+99Za++uorffLJJ7r55pt18OBBHTx4MG8fGACAZQhfAIAC5+uvv3YKRq1bt9aff/6pIUOGqHv37pKk8uXL66WXXtLTTz/tCF9PPPGE4z6RkZF6+eWX1a9fP02ZMkW+vr4KCQmRzWZTeHh4jvp64okn9OCDDzpuDx8+XK+//rpjXrly5bRt2za9/fbb6t69uw4cOKCKFSuqSZMmstlsKlu2bI7WCwD4dyB8AQAKnDvuuENTp0513A4MDFSNGjX0008/adSoUY756enpOnfunFJTUxUQEKAlS5Zo9OjR2rFjh1JSUnTx4kWn5blVr149x89nz57Vnj171KtXL/Xp08cx/+LFiwoJCZF06eIhd911lypVqqRWrVrp3nvv1d13353rPgAA+YPwBQAocAIDA1WhQgWneWfOnNHIkSOd9jxd5u/vr/379+vee+/VI488olGjRqlo0aL68ccf1atXL50/f/6a4ctms8kY4zTvwoULWfZ1ZT+S9O677yoqKsppnJeXlySpTp062rdvn7799lstWbJEHTt2VHR0tD777LPrPAIAgH8jwhcA4IZQp04d7dy5M1Mou2z9+vXKyMjQ66+/Lrv90vWoPvnkE6cxvr6+Sk9Pz3TfEiVK6OjRo47bu3btUmpq6jX7CQsLU6lSpbR3715169btquOCg4PVqVMnderUSQ899JBatWqlU6dOqWjRotesDwD49yF8AQBuCMOGDdO9996rm2++WQ899JDsdrt+/fVXbdmyRS+//LIqVKigCxcuaOLEibrvvvv0008/adq0aU41IiMjdebMGSUkJKhmzZoKCAhQQECA7rzzTk2aNEkNGzZUenq6nnnmmWxdRn7kyJEaOHCgQkJC1KpVK6WlpemXX37Rn3/+qfj4eI0fP14RERGqXbu27Ha7Pv30U4WHh/NdYwDgobjUPADghhATE6Ovv/5a33//vW6//XY1aNBAb7zxhuMiFjVr1tT48eP16quvqlq1apo9e3amy7o3atRI/fr1U6dOnVSiRAmNHTtWkvT666+rTJkyatq0qbp27aqnnnoqW+eI9e7dW++9955mzJih6tWrq3nz5po5c6bKlSsnSSpcuLDGjh2revXq6fbbb9f+/fu1cOFCx545AIBnsZl/HqQOAAAAAMhzfHQGAAAAABYgfAEAAACABQhfAAAAAGABwhcAAAAAWIDwBQAAAAAWIHwBAAAAgAUIXwAAAABgAcIXAAAAAFiA8AUAAAAAFiB8AQAAAIAFCF8AAAAAYAHCFwAAAABY4P8BkVtGRFeLV5QAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier()\n",
        "model.fit(scaled_X, y)\n",
        "feature_importances = model.feature_importances_\n",
        "\n",
        "# Plot a bar chart of feature importances\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(range(len(feature_importances)), feature_importances, tick_label=X.columns)\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel(\"Features\")\n",
        "plt.ylabel(\"Feature Importance\")\n",
        "plt.title(\"Feature Importance Scores\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VfTnVdEwBjpW"
      },
      "outputs": [],
      "source": [
        "# Select the number of features that I want to retain from the result of the Feature Importance Score.\n",
        "top_feature_num = 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g7DHuFJ-L9VZ"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_selection import SelectFromModel\n",
        "\n",
        "# Sort feature importances in descending order\n",
        "sorted_feature_importances = sorted(enumerate(feature_importances), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "# Get the top feature indices\n",
        "selected_indices = [index for index, _ in sorted_feature_importances[:top_feature_num]]\n",
        "\n",
        "# Apply the threshold for feature selection\n",
        "selector = SelectFromModel(model, threshold=-float('inf'), max_features=top_feature_num)  # Use -inf to select top features\n",
        "selected_features = selector.fit_transform(scaled_X, y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93MDJQ8QMIG0",
        "outputId": "1b136704-d25f-49a8-fa55-38cbb1e35ecf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['V10', 'V11', 'V12', 'V14', 'V16', 'V17'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "#\n",
        "selected_feature_indices = selector.get_support(indices=True)\n",
        "\n",
        "# Get the column names of the selected features\n",
        "selected_feature_names = scaled_X.columns[selected_feature_indices]\n",
        "\n",
        "selected_feature_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4R9BFlZRVj0",
        "outputId": "2a761efc-b854-4fd6-bb78-078724d00d72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.indexes.base.Index"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "type(selected_feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "7hPaFky7OMgI",
        "outputId": "014a36e1-90fb-4fe5-d89d-78e46947391c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Filter feature importances for the selected features\n",
        "selected_feature_indices = [X.columns.get_loc(feature) for feature in selected_feature_names]\n",
        "selected_feature_importances = [feature_importances[i] for i in selected_feature_indices]\n",
        "\n",
        "# Create a bar chart of feature importances for the selected features\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(range(len(selected_feature_names)), selected_feature_importances)\n",
        "plt.xticks(range(len(selected_feature_names)), selected_feature_names, rotation=90)\n",
        "plt.xlabel(\"Features\")\n",
        "plt.ylabel(\"Feature Importance\")\n",
        "plt.title(\"Feature Importance Scores for Selected Features\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJcnbEQ_J24-",
        "outputId": "d62d246a-641a-4dd5-e14b-36e5cb34a558"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.indexes.base.Index"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "type(selected_feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCnrt9e-KX7N",
        "outputId": "bc296b51-d7c8-4cec-92e4-7cdecee6428e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['V10', 'V11', 'V12', 'V14', 'V16', 'V17']"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "selected_features = selected_feature_names.tolist()\n",
        "selected_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "igrL6mxEKrkx"
      },
      "outputs": [],
      "source": [
        "Keep=selected_features+['Class']\n",
        "df=filtered_data.filter(Keep)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AWtP6-oHK7Wq",
        "outputId": "e01ecdcf-ea40-48a5-b536-80e5f94a28ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        V10       V11       V12       V14       V16       V17  Class\n",
              "0  0.090794 -0.551600 -0.617801 -0.311169 -0.470401  0.207971      0\n",
              "1 -0.166974  1.612727  1.065235 -0.143772  0.463917 -0.114805      0\n",
              "2  0.207643  0.624501  0.066084 -0.165946 -2.890083  1.109969      0\n",
              "3 -0.054952 -0.226487  0.178228 -0.287924 -1.059647 -0.684093      0\n",
              "4  0.753074 -0.822843  0.538196 -1.119670 -0.451449 -0.237033      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6752fa22-eab7-40c5-a2d4-6c26787176f6\" 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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V14</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
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              "      buttonEl.style.display =\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9YjsoZ8VLWJL",
        "outputId": "5366254d-b79c-400e-ca5b-5d830120051c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282513, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EA9tHj_gb0HJ"
      },
      "source": [
        "##  Save the preprocessed dataframe, \"wdf\", in CSV file.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L8qwtWC4bzyJ"
      },
      "outputs": [],
      "source": [
        "df.to_csv('fraud_wdf.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLw5Uf8SaWwK"
      },
      "source": [
        "# Step 3: Model Development: 2 Scenarios\n",
        "\n",
        "I run three scenarios below to compare their performances.\n",
        "\n",
        "* Scenario 1.  Benchmark Scenario: Ensemble without Data Augmentation\n",
        "* Scenario 2.  GAN Scenario: Ensemble with Data Augmentation by GANs\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rgB211EW4CdC"
      },
      "source": [
        "## A. Scenario 1:  Benchmark Scenario: Ensemble without Data Augmentation\n",
        "\n",
        "I selected the following 3 algorithms as base learner.\n",
        "\n",
        "\n",
        "1.   Gradient Boosting\n",
        "2.   Decision Tree\n",
        "3. Random Forest\n",
        "\n",
        "\n",
        "And I selected Voting Method for the meta learner.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BeUxA8R-mpF",
        "outputId": "78007eaa-3829-4fdb-f190-51f68a0b854e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-optimize\n",
            "  Downloading scikit_optimize-0.9.0-py2.py3-none-any.whl (100 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.3/100.3 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.3.2)\n",
            "Collecting pyaml>=16.9 (from scikit-optimize)\n",
            "  Downloading pyaml-23.12.0-py3-none-any.whl.metadata (11 kB)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from scikit-optimize) (1.23.5)\n",
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            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from pyaml>=16.9->scikit-optimize) (6.0.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->scikit-optimize) (3.2.0)\n",
            "Downloading pyaml-23.12.0-py3-none-any.whl (23 kB)\n",
            "Installing collected packages: pyaml, scikit-optimize\n",
            "Successfully installed pyaml-23.12.0 scikit-optimize-0.9.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "pip install scikit-optimize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sS5k7dxX9CFm"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV\n",
        "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, VotingClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, make_scorer\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from scipy.stats import uniform\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import f1_score\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ZcoDyL3c_gq"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Separate features and target variable\n",
        "X = df.drop('Class', axis=1)\n",
        "y = df['Class']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVBTPRBkJGbR",
        "outputId": "2809f767-9307-4d47-aea5-aca0bbe691cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282513, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WfBoeWarOq-l"
      },
      "outputs": [],
      "source": [
        "# Splitting data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WNZPleeKJHOP"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, VotingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def ensemble_training(X_train, y_train):\n",
        "\n",
        "  # Initialize base learners\n",
        "  gradient_boosting = GradientBoostingClassifier(random_state=42)\n",
        "  decision_tree = DecisionTreeClassifier(random_state=42)\n",
        "  random_forest = RandomForestClassifier(random_state=42)\n",
        "\n",
        "  # Define the base models\n",
        "  base_models = {\n",
        "    'RandomForest': random_forest,\n",
        "    'DecisionTree': decision_tree,\n",
        "    'GradientBoosting': gradient_boosting\n",
        "  }\n",
        "\n",
        "  # Initialize the meta learner\n",
        "  meta_learner = VotingClassifier(estimators=[(name, model) for name, model in base_models.items()], voting='soft')\n",
        "\n",
        "  # Lists to store training and validation metrics\n",
        "  train_f1_scores = []\n",
        "  val_f1_scores = []\n",
        "\n",
        "  # Splitting the train set further into training and validation sets\n",
        "  X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42, stratify=y_train)\n",
        "\n",
        "  # Training and validation\n",
        "  for model_name, model in base_models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Training metrics\n",
        "    train_predictions = model.predict(X_train)\n",
        "    train_f1 = f1_score(y_train, train_predictions)\n",
        "    train_f1_scores.append(train_f1)\n",
        "\n",
        "    # Validation metrics using the validation set\n",
        "    val_predictions = model.predict(X_val)\n",
        "    val_f1 = f1_score(y_val, val_predictions)\n",
        "    val_f1_scores.append(val_f1)\n",
        "\n",
        "  # Training the meta learner on the entire training set\n",
        "  meta_learner.fit(X_train, y_train)\n",
        "\n",
        "  return meta_learner, train_f1_scores, val_f1_scores, base_models\n",
        "\n",
        "def ensemble_visualization(base_models, train_f1_scores, val_f1_scores):\n",
        "  # Visualization of training and validation metrics\n",
        "  plt.figure(figsize=(10, 6))\n",
        "  models = list(base_models.keys())\n",
        "  x = np.arange(len(models))\n",
        "  bar_width = 0.35\n",
        "  plt.bar(x, train_f1_scores, width=bar_width, label='Train F1-score')\n",
        "  plt.bar(x + bar_width, val_f1_scores, width=bar_width, label='Validation F1-score')\n",
        "  plt.xlabel('Models')\n",
        "  plt.ylabel('F1-score')\n",
        "  plt.title('Training and Validation F1-score for Base Learners')\n",
        "  plt.xticks(x + bar_width // 2, models)\n",
        "  plt.legend()\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "\n",
        "def ensemble_evaluations(meta_learner, X_train, y_train, X_test, y_test):\n",
        "# Metrics for the ensemble model on both training and test datasets\n",
        "  ensemble_train_predictions = meta_learner.predict(X_train)\n",
        "  ensemble_test_predictions = meta_learner.predict(X_test)\n",
        "\n",
        "  # Calculating metrics for the ensemble model\n",
        "  ensemble_train_f1 = f1_score(y_train, ensemble_train_predictions)\n",
        "  ensemble_test_f1 = f1_score(y_test, ensemble_test_predictions)\n",
        "\n",
        "  # Calculate precision and recall for both training and test datasets\n",
        "  precision_train = precision_score(y_train, ensemble_train_predictions)\n",
        "  recall_train = recall_score(y_train, ensemble_train_predictions)\n",
        "\n",
        "  precision_test = precision_score(y_test, ensemble_test_predictions)\n",
        "  recall_test = recall_score(y_test, ensemble_test_predictions)\n",
        "\n",
        "\n",
        "  # Output precision, recall, and f1 score for both training and test datasets\n",
        "  print(\"Ensemble Model Metrics:\")\n",
        "  print(f\"Training Precision: {precision_train:.4f}, Recall: {recall_train:.4f}, F1-score: {ensemble_train_f1:.4f}\")\n",
        "  print(f\"Test Precision: {precision_test:.4f}, Recall: {recall_test:.4f}, F1-score: {ensemble_test_f1:.4f}\")\n",
        "\n",
        "  return ensemble_train_predictions, ensemble_test_predictions, ensemble_train_f1, ensemble_test_f1, precision_train, recall_train, precision_test, recall_test\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PFTobA1gNx4h"
      },
      "outputs": [],
      "source": [
        "meta_learner, train_f1_scores, val_f1_scores, base_models=ensemble_training(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "o27usH-GOY40",
        "outputId": "e73d060b-e44c-4f88-8a99-41724139e64a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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FnIbjA3hSX4CTmzZ++vhpz5w5o2vXrqlIkSIJ+iXWlphjx45p0KBBWrRoUYKa7lw/Dw+PBIet316PJB09elSBgYHy9vZ26le8ePEU1SNJrVu3Vq9evTRr1izVrVtX169f14IFC9SwYUOnHzBmzpypsWPHas+ePbpx44ajvVChQileVnzNklS0aFGn9ty5czstT7oVGN9//31NnjxZhw8fdjqfPSWHdie1/Lx58yY4jzr+ivrx9cW72/vibooWLaqwsLA01Xq7f/75x2n9vb295e3trTfeeEOrVq1S1apVVaRIET3++ON69tlnVbNmTcd0ly5dUpkyZZKd/9GjR1WtWrUE7bc/L7fP487Xff/+/ZKU7KkNFy9eTPAa38291nU3+fPnd3p9nnzySeXMmVN9+vTR999/ryZNmkhK2WfXbrfrnXfeUe/eveXv76///e9/euKJJ9S+fXsFBARI+r/n6bHHHku0Hh8fn1TVn5j9+/fr999/T/K0lzNnzjj+/v777zVixAht375d0dHRjvbErlGR3HN7t21vatfbzc1N+fPnT7D8Xr16ady4cfryyy9Vq1YtPfnkk3ruuec4tBxAAoRuAA+9xPZEXbhwQXXq1JGPj4+GDRumkJAQeXh4aOvWrXrjjTdStJcsqatkmzsukJXe06ZEbGys6tevr/Pnz+uNN95QiRIl5OXlpRMnTqhDhw4J1u9+XfE7T548ql+/vubNm6dJkybpu+++0+XLlx0XLpKkL774Qh06dFCzZs302muvKU+ePHJ1ddWoUaN08OBBy2obOXKkBg4cqOeff17Dhw9Xjhw55OLioh49ety324BZ/b5IqSpVqjj9IDB48GANGTJEJUuW1N69e/X9999r2bJlmjdvniZPnqxBgwZp6NChltVz52c4/vUYPXp0kuf43/njkBVSs5c7KfXq1ZMk/fTTT2rSpEmqPrs9evRQkyZNtHDhQi1fvlwDBw7UqFGj9MMPP6hChQqOvp9//rkjiN8uPe6sEBcXp/r16+v1119PdHyxYsUk3bo43JNPPqnatWtr8uTJCgwMVJYsWTR9+vQEF+KTkn9u7/Y5Se16336Uy+3Gjh2rDh066Ntvv9WKFSvUvXt3jRo1Sr/++muCkA7g4UboBoBErF27VufOndP8+fNVu3ZtR/vhw4czsKr/kydPHnl4eOjAgQMJxiXWdqedO3dq3759mjlzptOFolauXJnmmgoWLKjVq1frypUrToFm7969qZpP27ZttWzZMi1dulSzZs2Sj4+PYw+fJM2dO1eFCxfW/PnznfaADR48OE01S7f2fBUuXNjR/s8//yTYgzh37lw9+uij+vTTT53aL1y4oFy5cjmGU3Ll+NuXv2rVKl2+fNlpb3f86Qvx9f3XfPnll05Xyr79ufPy8lKrVq3UqlUrxcTEqHnz5nrrrbfUr18/5c6dWz4+Pvrjjz+SnX/BggUTfd+k9HmJPwTfx8cnXfbsp1ddaXHz5k1Jt+7bLaX+sxsSEqLevXurd+/e2r9/v8qXL6+xY8fqiy++cDxPefLkSdfn6c7lX7ly5a7znzdvnjw8PLR8+XLZ7XZH+/Tp0y2pSUqf9Q4NDVVoaKgGDBigX375RTVr1tTUqVM1YsSI9CgVwAOCc7oBIBHxe0pu34MYExOjyZMnZ1RJTlxdXRUWFqaFCxfq77//drQfOHAgwXnASU0vOa+fMUbvv/9+mmtq1KiRbt68qSlTpjjaYmNj9cEHH6RqPs2aNZOnp6cmT56spUuXqnnz5k73302s9t9++00bNmxIdc1hYWHKkiWLPvjgA6f53X5F5duXe+ce5Tlz5ujEiRNObfH3703JrdIaNWqk2NhYTZw40an9vffek81mS/H5+fdbzZo1FRYW5njEh+5z58459XN3d1epUqVkjNGNGzfk4uKiZs2a6bvvvtPmzZsTzDf++W3UqJE2btzo9JpGRUXpo48+UnBwcJLn8sarVKmSQkJCNGbMGEdYvV1it49KiXutKy2+++47SXKce5/Sz+7Vq1d1/fp1p7aQkBBly5bNceh2eHi4fHx8NHLkSKfTNOKl9Xm6XcuWLbVhwwYtX748wbgLFy44flRwdXWVzWZzOm3hyJEjWrhw4T3XcKf0WO9Lly45ao8XGhoqFxcXp0PjAUBiTzcAJKpGjRrKnj27IiIi1L17d9lsNn3++ef3/TDe5AwZMkQrVqxQzZo19dJLLznCW5kyZRy3zklKiRIlFBISoj59+ujEiRPy8fHRvHnzUnxucGKaNGmimjVrqm/fvjpy5IhKlSql+fPnp/qetd7e3mrWrJnjkNLbDy2XpCeeeELz58/XU089pcaNG+vw4cOaOnWqSpUqlWjASk78/cZHjRqlJ554Qo0aNdK2bdu0dOlSp73X8csdNmyYIiMjVaNGDe3cuVNffvml015e6Vaw8fPz09SpU5UtWzZ5eXmpWrVqiZ6D2qRJEz366KPq37+/jhw5onLlymnFihX69ttv1aNHD6eLpt0PFy9edPxIEn8/64kTJ8rPz09+fn7q1q1bstM//vjjCggIUM2aNeXv76/du3dr4sSJaty4sWNP/siRI7VixQrVqVNHXbp0UcmSJXXy5EnNmTNH69atk5+fn/r27auvvvpKDRs2VPfu3ZUjRw7NnDlThw8f1rx58xI91Pd2Li4u+uSTT9SwYUOVLl1akZGRypcvn06cOKE1a9bIx8fHEWZT417rupt9+/bpiy++kHQrNP/666+aOXOmihQponbt2klK+Wd33759qlevnlq2bKlSpUrJzc1NCxYs0OnTp9W6dWtJt44EmDJlitq1a6eKFSuqdevWyp07t44dO6bFixerZs2aCX4QSsy8efMSvbhkRESEXnvtNS1atEhPPPGEOnTooEqVKikqKko7d+7U3LlzdeTIEeXKlUuNGzfWuHHj1KBBAz377LM6c+aMJk2apCJFiuj333+/p+f1Tumx3j/88IO6deumZ555RsWKFdPNmzf1+eefy9XVVS1atEjXegE8AO7z1dIBIMMkdcuwpG5xtH79evO///3PZM2a1eTNm9e8/vrrZvny5Qlus5TULcMSu1WP7riFVVK3DLvzVkXGGFOwYEGnW1gZY8zq1atNhQoVjLu7uwkJCTGffPKJ6d27t/Hw8EjiWfg/u3btMmFhYcbb29vkypXLdO7c2XELqttvdxUREWG8vLwSTJ9Y7efOnTPt2rUzPj4+xtfX17Rr185xa52U3DIs3uLFi40kExgYmOA2XXFxcWbkyJGmYMGCxm63mwoVKpjvv/8+wetgzN1vGWaMMbGxsWbo0KEmMDDQZM2a1dStW9f88ccfCZ7v69evm969ezv61axZ02zYsMHUqVPH1KlTx2m53377rSlVqpTj9m3x655YjZcvXzY9e/Y0efPmNVmyZDFFixY1o0ePdrrlUfy6pPR9cafk3pOJ9UvscWfdifnwww9N7dq1Tc6cOY3dbjchISHmtddeMxcvXnTqd/ToUdO+fXuTO3duY7fbTeHChU3Xrl2dbgV38OBB8/TTTxs/Pz/j4eFhqlatar7//nun+dztllXbtm0zzZs3d9RTsGBB07JlS7N69eq7rktSz3d61JXU8m5/uLq6mvz585suXbqY06dPO/VNyWf37NmzpmvXrqZEiRLGy8vL+Pr6mmrVqplvvvkmwbLXrFljwsPDja+vr/Hw8DAhISGmQ4cOZvPmzcnWHL+eST3ib612+fJl069fP1OkSBHj7u5ucuXKZWrUqGHGjBljYmJiHPP79NNPTdGiRY3dbjclSpQw06dPT9U2Mv7zfeft6OLrvPP2eClZ76S2f4cOHTLPP/+8CQkJMR4eHiZHjhzm0UcfNatWrUr2OQPwcLIZ8x/abQMAuGfNmjXTn3/+6bhCLwAAADIO53QDQCZ2+8WspFsXBFuyZInq1q2bMQUBAADACXu6ASATCwwMVIcOHVS4cGEdPXpUU6ZMUXR0tLZt25bg3tMAAAC4/7iQGgBkYg0aNNBXX32lU6dOyW63q3r16ho5ciSBGwAA4D+CPd0AAAAAAFiEc7oBAAAAALAIoRsAAAAAAIs8dOd0x8XF6e+//1a2bNlks9kyuhwAAAAAQCZkjNHly5eVN29eubgkvT/7oQvdf//9t4KCgjK6DAAAAADAA+D48ePKnz9/kuMfutCdLVs2SbeeGB8fnwyuBgAAAACQGV26dElBQUGOjJmUhy50xx9S7uPjQ+gGAAAAANyTu522zIXUAAAAAACwCKEbAAAAAACLELoBAAAAALDIQ3dONwAAAABrxcbG6saNGxldBnBPsmTJIldX13ueD6EbAAAAQLowxujUqVO6cOFCRpcCpAs/Pz8FBATc9WJpySF0AwAAAEgX8YE7T5488vT0vKegAmQkY4yuXr2qM2fOSJICAwPTPC9CNwAAAIB7Fhsb6wjcOXPmzOhygHuWNWtWSdKZM2eUJ0+eNB9qzoXUAAAAANyz+HO4PT09M7gSIP3Ev5/v5RoFhG4AAAAA6YZDyvEgSY/3M6EbAAAAAACLELoBAAAAIB0FBwdr/PjxGV0G/iO4kBoAAAAAywT3XXxfl3fk7cYp7nu3Q4cHDx6sIUOGpLqGTZs2ycvLK9XT3a5u3br68ccfE7TfuHFDbm5umj9/vqZOnaotW7bo/Pnz2rZtm8qXL39Py4Q1CN0AAAAAHkonT550/D179mwNGjRIe/fudbR5e3s7/jbGKDY2Vm5ud49QuXPnTpf6OnfurGHDhjm1xS8/KipKjzzyiFq2bKnOnTuny/LSW0xMjNzd3TO6jAzH4eUAAAAAHkoBAQGOh6+vr2w2m2N4z549ypYtm5YuXapKlSrJbrdr3bp1OnjwoJo2bSp/f395e3urSpUqWrVqldN87zy83Gaz6ZNPPtFTTz0lT09PFS1aVIsWLbprfZ6enk41BgQEOMa1a9dOgwYNUlhYWIrXNyYmRt26dVNgYKA8PDxUsGBBjRo1yjH+woULeuGFF+Tv7y8PDw+VKVNG33//vWP8vHnzVLp0adntdgUHB2vs2LEJ1nv48OFq3769fHx81KVLF0nSunXrVKtWLWXNmlVBQUHq3r27oqKiUlx3ZkfoBgAAAIAk9O3bV2+//bZ2796tsmXL6sqVK2rUqJFWr16tbdu2qUGDBmrSpImOHTuW7HyGDh2qli1b6vfff1ejRo3Utm1bnT9//j6txS0TJkzQokWL9M0332jv3r368ssvFRwcLEmKi4tTw4YNtX79en3xxRfatWuX3n77bce9qbds2aKWLVuqdevW2rlzp4YMGaKBAwdqxowZTssYM2aMypUrp23btmngwIE6ePCgGjRooBYtWuj333/X7NmztW7dOnXr1u2+rntG4vByAAAAAEjCsGHDVL9+fcdwjhw5VK5cOcfw8OHDtWDBAi1atCjZINmhQwe1adNGkjRy5EhNmDBBGzduVIMGDZKcZvLkyfrkk08cwy+88EKCvcupcezYMRUtWlSPPPKIbDabChYs6Bi3atUqbdy4Ubt371axYsUkSYULF3aMHzdunOrVq6eBAwdKkooVK6Zdu3Zp9OjR6tChg6PfY489pt69ezuGO3XqpLZt26pHjx6SpKJFi2rChAmqU6eOpkyZIg8PjzSvT2bBnm4AAAAASELlypWdhq9cuaI+ffqoZMmS8vPzk7e3t3bv3n3XPd1ly5Z1/O3l5SUfHx+dOXMm2Wnatm2r7du3Ox79+vVLcd0vvviivL29HQ/pVvDfvn27ihcvru7du2vFihWO/tu3b1f+/PkdgftOu3fvVs2aNZ3aatasqf379ys2NtbRdufztWPHDs2YMcOplvDwcMXFxenw4cMpXp/MLEND908//aQmTZoob968stlsWrhw4V2nWbt2rSpWrCi73a4iRYokOJwBAAAAANLLnVch79OnjxYsWKCRI0fq559/1vbt2xUaGqqYmJhk55MlSxanYZvNpri4uGSn8fX1VZEiRRyPXLlypbjuYcOGOQV2SapYsaIOHz6s4cOH69q1a2rZsqWefvppSVLWrFlTPO/k3Pl8XblyRS+88IJTLTt27ND+/fsVEhKSLsv8r8vQw8ujoqJUrlw5Pf/882revPld+x8+fFiNGzfWiy++qC+//FKrV69Wp06dFBgYqPDw8PtQMQAAAICH2fr169WhQwc99dRTkm6FyiNHjmRsUYnIkyeP8uTJk6Ddx8dHrVq1UqtWrfT000+rQYMGOn/+vMqWLau//vpL+/btS3Rvd8mSJbV+/XqntvXr16tYsWKO874TU7FiRe3atUtFihS595XKpDI0dDds2FANGzZMcf+pU6eqUKFCjvMYSpYsqXXr1um9994jdAMAAACwXNGiRTV//nw1adJENptNAwcOvOseayucP39ex44d099//y1Jjlud3XmV89uNGzdOgYGBqlChglxcXDRnzhwFBATIz89PderUUe3atdWiRQuNGzdORYoU0Z49e2Sz2dSgQQP17t1bVapU0fDhw9WqVStt2LBBEydO1OTJk5Ot84033tD//vc/devWTZ06dZKXl5d27dqllStXauLEien7pPxHZapzujds2JDgkvjh4eHasGFDktNER0fr0qVLTg8AAAAASItx48Ype/bsqlGjhpo0aaLw8HBVrFjxvtexaNEiVahQQY0bN5YktW7dWhUqVNDUqVOTnCZbtmx69913VblyZVWpUkVHjhzRkiVL5OJyKxbOmzdPVapUUZs2bVSqVCm9/vrrjvO1K1asqG+++UZff/21ypQpo0GDBmnYsGFOF1FLTNmyZfXjjz9q3759qlWrlipUqKBBgwYpb9686fNEZAI2Y4zJ6CKkW+c0LFiwQM2aNUuyT7FixRQZGel0AYElS5aocePGunr1aqLnIQwZMkRDhw5N0H7x4kX5+PikS+0AgLsL7rs4o0tAKh15u3FGlwAgE7l+/boOHz6sQoUKPRRXpMbDIbn39aVLl+Tr63vXbPnA3zKsX79+6tWrl2P40qVLCgoKysCKUo4vqJkLX04BAAAA3ClThe6AgACdPn3aqe306dPy8fFJ8mp7drtddrv9fpQHAAAAAICTTHVOd/Xq1bV69WqntpUrV6p69eoZVBEAAAAAAEnL0NB95coVp/vGHT58WNu3b3fcWL5fv35q3769o/+LL76oQ4cO6fXXX9eePXs0efJkffPNN+rZs2dGlA8AAAAAQLIyNHRv3rxZFSpUUIUKFSRJvXr1clzNTpJOnjzpCOCSVKhQIS1evFgrV65UuXLlNHbsWH3yySfcLgwAAAAA8J+Uoed0161bV8ldPH3GjBmJTrNt2zYLqwIAAAAAIH1kqnO6AQAAAADITAjdAAAAAABYhNANAAAAAIBFCN0AAAAAcA/q1q2rHj16OIaDg4M1fvz4ZKex2WxauHDhPS87veYD62TohdQAAAAAPOCG+N7n5V1McdcmTZroxo0bWrZsWYJxP//8s2rXrq0dO3aobNmyqSph06ZN8vLyStU0dzNkyBAtXLjQcbvleCdPnlT27NnTdVl3mjFjhiIjIxO0f/zxx+rUqZNOnjyp3r17a/PmzTpw4IC6d+9+1x8dHiaEbgAAAAAPpY4dO6pFixb666+/lD9/fqdx06dPV+XKlVMduCUpd+7c6VXiXQUEBNyX5fj4+Gjv3r1Obb6+t35QiY6OVu7cuTVgwAC9995796We1IqJiZG7u3uGLJvDywEAAAA8lJ544gnlzp07wa2Kr1y5ojlz5qhjx446d+6c2rRpo3z58snT01OhoaH66quvkp3vnYeX79+/X7Vr15aHh4dKlSqllStXJpjmjTfeULFixeTp6anChQtr4MCBunHjhqRbe5qHDh2qHTt2yGazyWazOWq+8/DynTt36rHHHlPWrFmVM2dOdenSRVeuXHGM79Chg5o1a6YxY8YoMDBQOXPmVNeuXR3LSorNZlNAQIDTI2vWrI71ff/999W+fXtHEE+JuXPnKjQ01FFrWFiYoqKiHOOnTZum0qVLy263KzAwUN26dXOMO3bsmJo2bSpvb2/5+PioZcuWOn36tGP8kCFDVL58eX3yyScqVKiQPDw8JEkXLlxQp06dlDt3bvn4+Oixxx7Tjh07UlxzWhC6AQAAADyU3Nzc1L59e82YMUPGGEf7nDlzFBsbqzZt2uj69euqVKmSFi9erD/++ENdunRRu3bttHHjxhQtIy4uTs2bN5e7u7t+++03TZ06VW+88UaCftmyZdOMGTO0a9cuvf/++/r4448de41btWql3r17q3Tp0jp58qROnjypVq1aJZhHVFSUwsPDlT17dm3atElz5szRqlWrnMKqJK1Zs0YHDx7UmjVrNHPmTM2YMSPBDw9WO3nypNq0aaPnn39eu3fv1tq1a9W8eXPH6zBlyhR17dpVXbp00c6dO7Vo0SIVKVJE0q3ntGnTpjp//rx+/PFHrVy5UocOHUrwnBw4cEDz5s3T/PnzHYflP/PMMzpz5oyWLl2qLVu2qGLFiqpXr57Onz9v2bpyeDkAAACAh9bzzz+v0aNH68cff1TdunUl3Tq0vEWLFvL19ZWvr6/69Onj6P/KK69o+fLl+uabb1S1atW7zn/VqlXas2ePli9frrx580qSRo4cqYYNGzr1GzBggOPv4OBg9enTR19//bVef/11Zc2aVd7e3nJzc0v2cPJZs2bp+vXr+uyzzxznlE+cOFFNmjTRO++8I39/f0lS9uzZNXHiRLm6uqpEiRJq3LixVq9erc6dOyc574sXL8rb29sx7O3trVOnTt11/ZNy8uRJ3bx5U82bN1fBggUlSaGhoY7xI0aMUO/evfXqq6862qpUqSJJWr16tXbu3KnDhw8rKChIkvTZZ5+pdOnS2rRpk6NfTEyMPvvsM8fh/uvWrdPGjRt15swZ2e12SdKYMWO0cOFCzZ07V126dEnz+iSH0A0AAADgoVWiRAnVqFFD06ZNU926dXXgwAH9/PPPGjZsmCQpNjZWI0eO1DfffKMTJ04oJiZG0dHR8vT0TNH8d+/eraCgIEfglqTq1asn6Dd79mxNmDBBBw8e1JUrV3Tz5k35+Pikal12796tcuXKOV3ErWbNmoqLi9PevXsdobt06dJydXV19AkMDNTOnTuTnXe2bNm0detWx7CLS8oPmv7555+dfmT48MMP1bp1a9WrV0+hoaEKDw/X448/rqefflrZs2fXmTNn9Pfff6tevXpJrmdQUJAjcEtSqVKl5Ofnp927dztCd8GCBZ3Or9+xY4euXLminDlzOs3v2rVrOnjwYIrXJ7UI3QAAAAAeah07dtQrr7yiSZMmafr06QoJCVGdOnUkSaNHj9b777+v8ePHKzQ0VF5eXurRo4diYmLSbfkbNmxQ27ZtNXToUIWHh8vX11dff/21xo4dm27LuF2WLFmchm02m+Li4pKdxsXFxXF4d2pVrlzZ6arr/v7+cnV11cqVK/XLL79oxYoV+uCDD9S/f3/99ttvypUrV5qWc6c7ryB/5coVBQYGau3atQn6+vn5pcsyE8M53QAAAAAeai1btpSLi4tmzZqlzz77TM8//7xsNpskaf369WratKmee+45lStXToULF9a+fftSPO+SJUvq+PHjOnnypKPt119/derzyy+/qGDBgurfv78qV66sokWL6ujRo0593N3dFRsbe9dl7dixw+liZOvXr5eLi4uKFy+e4prTW9asWVWkSBHHI1u2bJJuhf2aNWtq6NCh2rZtm9zd3bVgwQJly5ZNwcHBWr16daLzi39Ojx8/7mjbtWuXLly4oFKlSiVZR8WKFXXq1Cm5ubk51VOkSJF0C/qJIXQDAAAAeKh5e3urVatW6tevn06ePKkOHTo4xhUtWtSxR3b37t164YUXnK6SfTdhYWEqVqyYIiIitGPHDv3888/q37+/U5+iRYvq2LFj+vrrr3Xw4EFNmDBBCxYscOoTHBysw4cPa/v27Tp79qyio6MTLKtt27by8PBQRESE/vjjD61Zs0avvPKK2rVr5zi03Crbt2/X9u3bdeXKFf3zzz/avn27du3alWT/3377TSNHjtTmzZt17NgxzZ8/X//8849Kliwp6dbVx8eOHasJEyZo//792rp1qz744ANJt57T0NBQtW3bVlu3btXGjRvVvn171alTR5UrV05ymWFhYapevbqaNWumFStW6MiRI/rll1/Uv39/bd68OX2fkNsQugEAAAA89Dp27Kh///1X4eHhTudfDxgwQBUrVlR4eLjq1q2rgIAANWvWLMXzdXFx0YIFC3Tt2jVVrVpVnTp10ltvveXU58knn1TPnj3VrVs3lS9fXr/88osGDhzo1KdFixZq0KCBHn30UeXOnTvR25Z5enpq+fLlOn/+vKpUqaKnn35a9erV08SJE1P3ZKRBhQoVVKFCBW3ZskWzZs1ShQoV1KhRoyT7+/j46KefflKjRo1UrFgxDRgwQGPHjnWc+x0REaHx48dr8uTJKl26tJ544gnt379f0q095N9++62yZ8+u2rVrKywsTIULF9bs2bOTrdFms2nJkiWqXbu2IiMjVaxYMbVu3VpHjx619EcJm7n92vgPgUuXLsnX11cXL15M9YUJ7rfgvoszugSkwpG3G2d0CcB/Gtu0zIftGoDUuH79ug4fPux0T2Qgs0vufZ3SbMmebgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAACQbuLi4jK6BCDdpMf72S0d6gAAAADwkHN3d5eLi4v+/vtv5c6dW+7u7rLZbBldFpAmxhjFxMTon3/+kYuLi9zd3dM8L0I3AAAAgHvm4uKiQoUK6eTJk/r7778zuhwgXXh6eqpAgQJycUn7QeKEbgAAAADpwt3dXQUKFNDNmzcVGxub0eUA98TV1VVubm73fMQGoRsAAABAurHZbMqSJYuyZMmS0aUA/wlcSA0AAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsIhbRhcAPDCG+GZ0BUitIRczugIAAAA84NjTDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFMjx0T5o0ScHBwfLw8FC1atW0cePGZPuPHz9exYsXV9asWRUUFKSePXvq+vXr96laAAAAAABSLkND9+zZs9WrVy8NHjxYW7duVbly5RQeHq4zZ84k2n/WrFnq27evBg8erN27d+vTTz/V7Nmz9eabb97nygEAAAAAuLsMDd3jxo1T586dFRkZqVKlSmnq1Kny9PTUtGnTEu3/yy+/qGbNmnr22WcVHBysxx9/XG3atLnr3nEAAAAAADJChoXumJgYbdmyRWFhYf9XjIuLwsLCtGHDhkSnqVGjhrZs2eII2YcOHdKSJUvUqFGjJJcTHR2tS5cuOT0AAAAAALgf3DJqwWfPnlVsbKz8/f2d2v39/bVnz55Ep3n22Wd19uxZPfLIIzLG6ObNm3rxxReTPbx81KhRGjp0aLrWDgAAAABASmT4hdRSY+3atRo5cqQmT56srVu3av78+Vq8eLGGDx+e5DT9+vXTxYsXHY/jx4/fx4oBAAAAAA+zDNvTnStXLrm6uur06dNO7adPn1ZAQECi0wwcOFDt2rVTp06dJEmhoaGKiopSly5d1L9/f7m4JPwNwW63y263p/8KAAAAAABwFxm2p9vd3V2VKlXS6tWrHW1xcXFavXq1qlevnug0V69eTRCsXV1dJUnGGOuKBQAAAAAgDTJsT7ck9erVSxEREapcubKqVq2q8ePHKyoqSpGRkZKk9u3bK1++fBo1apQkqUmTJho3bpwqVKigatWq6cCBAxo4cKCaNGniCN8AAAAAAPxXZGjobtWqlf755x8NGjRIp06dUvny5bVs2TLHxdWOHTvmtGd7wIABstlsGjBggE6cOKHcuXOrSZMmeuuttzJqFQAAAAAASJLNPGTHZV+6dEm+vr66ePGifHx8MrqcZAX3XZzRJSAVjng8m9ElILWGXMzoCh4qbNMynyNvN87oEgAA+M9KabbMVFcvBwAAAAAgMyF0AwAAAABgkQw9pxsAAPyHDfHN6AqQGpwyAwD/SezpBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACyS4aF70qRJCg4OloeHh6pVq6aNGzcm2//ChQvq2rWrAgMDZbfbVaxYMS1ZsuQ+VQsAAAAAQMq5ZeTCZ8+erV69emnq1KmqVq2axo8fr/DwcO3du1d58uRJ0D8mJkb169dXnjx5NHfuXOXLl09Hjx6Vn5/f/S8eAAAAAIC7yNDQPW7cOHXu3FmRkZGSpKlTp2rx4sWaNm2a+vbtm6D/tGnTdP78ef3yyy/KkiWLJCk4OPh+lgwAAAAAQIpl2OHlMTEx2rJli8LCwv6vGBcXhYWFacOGDYlOs2jRIlWvXl1du3aVv7+/ypQpo5EjRyo2NjbJ5URHR+vSpUtODwAAAAAA7ocMC91nz55VbGys/P39ndr9/f116tSpRKc5dOiQ5s6dq9jYWC1ZskQDBw7U2LFjNWLEiCSXM2rUKPn6+joeQUFB6boeAAAAAAAkJcMvpJYacXFxypMnjz766CNVqlRJrVq1Uv/+/TV16tQkp+nXr58uXrzoeBw/fvw+VgwAAAAAeJhl2DnduXLlkqurq06fPu3Ufvr0aQUEBCQ6TWBgoLJkySJXV1dHW8mSJXXq1CnFxMTI3d09wTR2u112uz19iwcAAAAAIAUybE+3u7u7KlWqpNWrVzva4uLitHr1alWvXj3RaWrWrKkDBw4oLi7O0bZv3z4FBgYmGrgBAAAAAMhIGXp4ea9evfTxxx9r5syZ2r17t1566SVFRUU5rmbevn179evXz9H/pZde0vnz5/Xqq69q3759Wrx4sUaOHKmuXbtm1CoAAAAAAJCkDL1lWKtWrfTPP/9o0KBBOnXqlMqXL69ly5Y5Lq527Ngxubj83+8CQUFBWr58uXr27KmyZcsqX758evXVV/XGG29k1CoAAAAAAJCkDA3dktStWzd169Yt0XFr165N0Fa9enX9+uuvFlcFAAAAAMC9y1RXLwcAAAAAIDMhdAMAAAAAYBFCNwAAAAAAFrmn0H3gwAEtX75c165dkyQZY9KlKAAAAAAAHgRpCt3nzp1TWFiYihUrpkaNGunkyZOSpI4dO6p3797pWiAAAAAAAJlVmkJ3z5495ebmpmPHjsnT09PR3qpVKy1btizdigMAAAAAIDNL0y3DVqxYoeXLlyt//vxO7UWLFtXRo0fTpTAAAAAAADK7NO3pjoqKctrDHe/8+fOy2+33XBQAAAAAAA+CNIXuWrVq6bPPPnMM22w2xcXF6d1339Wjjz6absUBAAAAAJCZpenw8nfffVf16tXT5s2bFRMTo9dff11//vmnzp8/r/Xr16d3jQAAAAAAZEpp2tNdpkwZ7du3T4888oiaNm2qqKgoNW/eXNu2bVNISEh61wgAAAAAQKaU6j3dN27cUIMGDTR16lT179/fipoAAAAAAHggpHpPd5YsWfT7779bUQsAAAAAAA+UNB1e/txzz+nTTz9N71oAAAAAAHigpOlCajdv3tS0adO0atUqVapUSV5eXk7jx40bly7FAQAAAACQmaUpdP/xxx+qWLGiJGnfvn1O42w2271XBQAAAADAAyBNoXvNmjXpXQcAAAAAAA+cNJ3Tfbu//vpLf/31V3rUAgAAAADAAyVNoTsuLk7Dhg2Tr6+vChYsqIIFC8rPz0/Dhw9XXFxcetcIAAAAAECmlKbDy/v3769PP/1Ub7/9tmrWrClJWrdunYYMGaLr16/rrbfeStciAQAAAADIjNIUumfOnKlPPvlETz75pKOtbNmyypcvn15++WVCNwAAAAAASuPh5efPn1eJEiUStJcoUULnz5+/56IAAAAAAHgQpCl0lytXThMnTkzQPnHiRJUrV+6eiwIAAAAA4EGQpsPL3333XTVu3FirVq1S9erVJUkbNmzQ8ePHtWTJknQtEAAAAACAzCpNe7rr1KmjvXv36qmnntKFCxd04cIFNW/eXHv37lWtWrXSu0YAAAAAADKlNO3plqR8+fJxwTQAAAAAAJKRpj3d06dP15w5cxK0z5kzRzNnzrznogAAAAAAeBCkKXSPGjVKuXLlStCeJ08ejRw58p6LAgAAAADgQZCm0H3s2DEVKlQoQXvBggV17Nixey4KAAAAAIAHQZpCd548efT7778naN+xY4dy5sx5z0UBAAAAAPAgSFPobtOmjbp37641a9YoNjZWsbGx+uGHH/Tqq6+qdevW6V0jAAAAAACZUpquXj58+HAdOXJE9erVk5vbrVnExcWpffv2nNMNAAAAAMD/l6bQ7e7urtmzZ2vEiBHavn27smbNqtDQUBUsWDC96wMAAAAAINNK8326Jalo0aIqWrSoYmNjtXPnTvn4+Ch79uzpVRsAAAAAAJlams7p7tGjhz799FNJUmxsrOrUqaOKFSsqKChIa9euTc/6AAAAAADItNIUuufOnaty5cpJkr777jsdOnRIe/bsUc+ePdW/f/90LRAAAAAAgMwqTaH77NmzCggIkCQtWbJELVu2VLFixfT8889r586d6VogAAAAAACZVZpCt7+/v3bt2qXY2FgtW7ZM9evXlyRdvXpVrq6u6VogAAAAAACZVZoupBYZGamWLVsqMDBQNptNYWFhkqTffvtNJUqUSNcCAQAAAADIrNIUuocMGaIyZcro+PHjeuaZZ2S32yVJrq6u6tu3b7oWCAAAAABAZpXmW4Y9/fTTkqS//vpLcXFxcnFxUURERLoVBgAAAABAZpemc7pvV6pUKR05ciQdSgEAAAAA4MFyz6HbGJMedQAAAAAA8MC559ANAAAAAAASd8+h+80331SOHDnSoxYAAAAAAB4oab6QWrx+/fqlRx0AAAAAADxw0vXw8uPHj+v5559Pz1kCAAAAAJBppWvoPn/+vGbOnJmeswQAAAAAINNK1eHlixYtSnb8oUOH7qkYAAAAAAAeJKkK3c2aNZPNZkv2NmE2m+2eiwIAAAAA4EGQqsPLAwMDNX/+fMXFxSX62Lp1q1V1AgAAAACQ6aQqdFeqVElbtmxJcvzd9oIDAAAAAPAwSdXh5a+99pqioqKSHF+kSBGtWbPmnosCAAAAAOBBkKrQnS9fPhUqVCjJ8V5eXqpTp849FwUAAAAAwIMgVYeXFy1aVP/8849juFWrVjp9+nS6FwUAAAAAwIMgVaH7zvO1lyxZkuzh5gAAAAAAPMxSFboBAAAAAEDKpSp022y2BPfh5r7cAAAAAAAkLlUXUjPGqEOHDrLb7ZKk69ev68UXX5SXl5dTv/nz56dfhQAAAAAAZFKpCt0RERFOw88991y6FgMAAAAAwIMkVaF7+vTpVtUBAAAAAMADhwupAQAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFnHL6AIAAACAzCi47+KMLgGpcOTtxhldAh5S7OkGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKc0w0AAADgwTfEN6MrQGoMuZjRFaQb9nQDAAAAAGCR/0TonjRpkoKDg+Xh4aFq1app48aNKZru66+/ls1mU7NmzawtEAAAAACANMjw0D179mz16tVLgwcP1tatW1WuXDmFh4frzJkzyU535MgR9enTR7Vq1bpPlQIAAAAAkDoZHrrHjRunzp07KzIyUqVKldLUqVPl6empadOmJTlNbGys2rZtq6FDh6pw4cL3sVoAAAAAAFIuQ0N3TEyMtmzZorCwMEebi4uLwsLCtGHDhiSnGzZsmPLkyaOOHTvejzIBAAAAAEiTDL16+dmzZxUbGyt/f3+ndn9/f+3ZsyfRadatW6dPP/1U27dvT9EyoqOjFR0d7Ri+dOlSmusFAAAAACA1Mvzw8tS4fPmy2rVrp48//li5cuVK0TSjRo2Sr6+v4xEUFGRxlQAAAAAA3JKhe7pz5colV1dXnT592qn99OnTCggISND/4MGDOnLkiJo0aeJoi4uLkyS5ublp7969CgkJcZqmX79+6tWrl2P40qVLBG8AAAAAwH2RoaHb3d1dlSpV0urVqx23/YqLi9Pq1avVrVu3BP1LlCihnTt3OrUNGDBAly9f1vvvv59omLbb7bLb7ZbUDwAAAABAcjI0dEtSr169FBERocqVK6tq1aoaP368oqKiFBkZKUlq37698uXLp1GjRsnDw0NlypRxmt7Pz0+SErQDAAAAAJDRMjx0t2rVSv/8848GDRqkU6dOqXz58lq2bJnj4mrHjh2Ti0umOvUcAAAAAABJ/4HQLUndunVL9HBySVq7dm2y086YMSP9CwIAAAAAIB2wCxkAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIv8J0L3pEmTFBwcLA8PD1WrVk0bN25Msu/HH3+sWrVqKXv27MqePbvCwsKS7Q8AAAAAQEbJ8NA9e/Zs9erVS4MHD9bWrVtVrlw5hYeH68yZM4n2X7t2rdq0aaM1a9Zow4YNCgoK0uOPP64TJ07c58oBAAAAAEhehofucePGqXPnzoqMjFSpUqU0depUeXp6atq0aYn2//LLL/Xyyy+rfPnyKlGihD755BPFxcVp9erV97lyAAAAAACSl6GhOyYmRlu2bFFYWJijzcXFRWFhYdqwYUOK5nH16lXduHFDOXLksKpMAAAAAADSxC0jF3727FnFxsbK39/fqd3f31979uxJ0TzeeOMN5c2b1ym43y46OlrR0dGO4UuXLqW9YAAAAAAAUiHDDy+/F2+//ba+/vprLViwQB4eHon2GTVqlHx9fR2PoKCg+1wlAAAAAOBhlaGhO1euXHJ1ddXp06ed2k+fPq2AgIBkpx0zZozefvttrVixQmXLlk2yX79+/XTx4kXH4/jx4+lSOwAAAAAAd5Ohodvd3V2VKlVyugha/EXRqlevnuR07777roYPH65ly5apcuXKyS7DbrfLx8fH6QEAAAAAwP2Qoed0S1KvXr0UERGhypUrq2rVqho/fryioqIUGRkpSWrfvr3y5cunUaNGSZLeeecdDRo0SLNmzVJwcLBOnTolSfL29pa3t3eGrQcAAAAAAHfK8NDdqlUr/fPPPxo0aJBOnTql8uXLa9myZY6Lqx07dkwuLv+3Q37KlCmKiYnR008/7TSfwYMHa8iQIfezdAAAAAAAkpXhoVuSunXrpm7duiU6bu3atU7DR44csb4gAAAAAADSQaa+ejkAAAAAAP9lhG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAi/4nQPWnSJAUHB8vDw0PVqlXTxo0bk+0/Z84clShRQh4eHgoNDdWSJUvuU6UAAAAAAKRchofu2bNnq1evXho8eLC2bt2qcuXKKTw8XGfOnEm0/y+//KI2bdqoY8eO2rZtm5o1a6ZmzZrpjz/+uM+VAwAAAACQvAwP3ePGjVPnzp0VGRmpUqVKaerUqfL09NS0adMS7f/++++rQYMGeu2111SyZEkNHz5cFStW1MSJE+9z5QAAAAAAJC9DQ3dMTIy2bNmisLAwR5uLi4vCwsK0YcOGRKfZsGGDU39JCg8PT7I/AAAAAAAZxS0jF3727FnFxsbK39/fqd3f31979uxJdJpTp04l2v/UqVOJ9o+OjlZ0dLRj+OLFi5KkS5cu3Uvp90Vc9NWMLgGpcMlmMroEpFYm2A48SNimZT5s1zIZtmn3Hdu1zIVtWiaTCbZp8ZnSmOTfWxkauu+HUaNGaejQoQnag4KCMqAaPMh8M7oApN7bvGpAcviEZDJs04Bk8QnJZDLRNu3y5cvy9U263gwN3bly5ZKrq6tOnz7t1H769GkFBAQkOk1AQECq+vfr10+9evVyDMfFxen8+fPKmTOnbDbbPa4BUuvSpUsKCgrS8ePH5ePjk9HlAMA9Y7sG4EHCNg1IOWOMLl++rLx58ybbL0NDt7u7uypVqqTVq1erWbNmkm6F4tWrV6tbt26JTlO9enWtXr1aPXr0cLStXLlS1atXT7S/3W6X3W53avPz80uP8nEPfHx82JADeKCwXQPwIGGbBqRMcnu442X44eW9evVSRESEKleurKpVq2r8+PGKiopSZGSkJKl9+/bKly+fRo0aJUl69dVXVadOHY0dO1aNGzfW119/rc2bN+ujjz7KyNUAAAAAACCBDA/drVq10j///KNBgwbp1KlTKl++vJYtW+a4WNqxY8fk4vJ/F1mvUaOGZs2apQEDBujNN99U0aJFtXDhQpUpUyajVgEAAAAAgETZzN0utQako+joaI0aNUr9+vVLcNg/AGRGbNcAPEjYpgHpj9ANAAAAAIBFXO7eBQAAAAAApAWhGwAAAAAAixC6kWI2m00LFy7M6DIAIF0FBwdr/Pjx6d4XAO5Vhw4dHLfVlaS6des63Tb3YcW2GJkNoTuT6dChg2w2m2w2m7JkyaJChQrp9ddf1/Xr1zO6tHQTv363Px555JEMr4kfHID7687tnb+/v+rXr69p06YpLi4u3ZazadMmdenSJd37pkRi27vbH0OGDEm3ZQG4d6dOndKrr76qIkWKyMPDQ/7+/qpZs6amTJmiq1evWr78+fPna/jw4ek6zzuDfbzbt0Vubm4qUKCAevXqpejo6HRdfnJmzJghPz+/BO3pvS0GrJbhtwxD6jVo0EDTp0/XjRs3tGXLFkVERMhms+mdd97J6NLSzfTp09WgQQPHsLu7e5rndePGDWXJkiU9ygJwn8Vv72JjY3X69GktW7ZMr776qubOnatFixbJze3e/43lzp3bkr4pcfLkScffs2fP1qBBg7R3715Hm7e3t+NvY4xiY2PTZZ0BpN6hQ4dUs2ZN+fn5aeTIkQoNDZXdbtfOnTv10UcfKV++fHryyScTTJee30Ny5MiRLvNJqfjvYzdu3NCOHTsUGRkpLy+vdA/+qZXe22LAauzpzoTsdrsCAgIUFBSkZs2aKSwsTCtXrpQknTt3Tm3atFG+fPnk6emp0NBQffXVV07T161bV927d9frr7+uHDlyKCAgIMHelP3796t27dry8PBQqVKlHPO/3c6dO/XYY48pa9asypkzp7p06aIrV644xsf/cjpy5Ej5+/vLz89Pw4YN082bN/Xaa68pR44cyp8/v6ZPn55g3n5+fgoICHA84v/JxMXFadiwYcqfP7/sdrvjvu7xjhw5IpvNptmzZ6tOnTry8PDQl19+KUn65JNPVLJkSXl4eKhEiRKaPHmyY7qYmBh169ZNgYGB8vDwUMGCBTVq1ChJtw5hkqSnnnpKNpvNMQzAevHbu3z58qlixYp688039e2332rp0qWaMWOGJOnChQvq1KmTcufOLR8fHz322GPasWOH03y+++47ValSRR4eHsqVK5eeeuopx7jbD1M0xmjIkCEqUKCA7Ha78ubNq+7duyfaV5KOHTumpk2bytvbWz4+PmrZsqVOnz7tGD9kyBCVL19en3/+uYKDg+Xr66vWrVvr8uXLkuS0nfP19ZXNZnMM79mzR9myZdPSpUtVqVIl2e12rVu3TnFxcRo1apQKFSqkrFmzqly5cpo7d67T+v7xxx9q2LChvL295e/vr3bt2uns2bPp8ZIAD62XX35Zbm5u2rx5s1q2bKmSJUuqcOHCatq0qRYvXqwmTZpIurWHeMqUKXryySfl5eWlt956S7GxserYsaPjc1u8eHG9//77TvOPjY1Vr1695Ofnp5w5c+r111/XnTcZuvPw8ujoaPXp00f58uWTl5eXqlWrprVr1zrGx+8pXr58uUqWLClvb281aNDA8YPfkCFDNHPmTH377beOvdq3Tx//fSwoKEhPPPGEmjZtqq1btzrVNGXKFIWEhMjd3V3FixfX559/7jT+btvJHTt26NFHH1W2bNnk4+OjSpUqafPmzVq7dq0iIyN18eLFBEf/3Lktttls+uSTT/TUU0/J09NTRYsW1aJFi5zqWLRokYoWLSoPDw89+uijmjlzpmw2my5cuJDkaw6kG4NMJSIiwjRt2tQxvHPnThMQEGCqVatmjDHmr7/+MqNHjzbbtm0zBw8eNBMmTDCurq7mt99+c0xTp04d4+PjY4YMGWL27dtnZs6caWw2m1mxYoUxxpjY2FhTpkwZU69ePbN9+3bz448/mgoVKhhJZsGCBcYYY65cuWICAwNN8+bNzc6dO83q1atNoUKFTEREhFOt2bJlM127djV79uwxn376qZFkwsPDzVtvvWX27dtnhg8fbrJkyWKOHz/umO725dxp3LhxxsfHx3z11Vdmz5495vXXXzdZsmQx+/btM8YYc/jwYSPJBAcHm3nz5plDhw6Zv//+23zxxRcmMDDQ0TZv3jyTI0cOM2PGDGOMMaNHjzZBQUHmp59+MkeOHDE///yzmTVrljHGmDNnzhhJZvr06ebkyZPmzJkz9/QaAkiZO7d3tytXrpxp2LChMcaYsLAw06RJE7Np0yazb98+07t3b5MzZ05z7tw5Y4wx33//vXF1dTWDBg0yu3btMtu3bzcjR450zKtgwYLmvffeM8YYM2fOHOPj42OWLFlijh49an777Tfz0UcfJdo3NjbWlC9f3jzyyCNm8+bN5tdffzWVKlUyderUcfQfPHiw8fb2dmwrf/rpJxMQEGDefPPNBOs0ffp04+vr6xhes2aNkWTKli1rVqxYYQ4cOGDOnTtnRowYYUqUKGGWLVtmDh48aKZPn27sdrtZu3atMcaYf//91+TOndv069fP7N6922zdutXUr1/fPProo6l9CQD8f2fPnjU2m82MGjXqrn0lmTx58php06aZgwcPmqNHj5qYmBgzaNAgs2nTJnPo0CHzxRdfGE9PTzN79mzHdO+8847Jnj27mTdvntm1a5fp2LGjyZYtm9N2sE6dOubVV191DHfq1MnUqFHD/PTTT+bAgQNm9OjRxm63O74XTZ8+3WTJksWEhYWZTZs2mS1btpiSJUuaZ5991hhjzOXLl03Lli1NgwYNzMmTJ83JkydNdHS0Yz1u/z62d+9eU6hQITN06FBH2/z5802WLFnMpEmTzN69e83YsWONq6ur+eGHH4wxKdtOli5d2jz33HNm9+7dZt++feabb74x27dvN9HR0Wb8+PHGx8fHUdvly5eNMc7b4vha8+fPb2bNmmX2799vunfvbry9vR3/Bw4dOmSyZMli+vTpY/bs2WO++uorky9fPiPJ/Pvvv3d9TYF7RejOZCIiIoyrq6vx8vIydrvdSDIuLi5m7ty5SU7TuHFj07t3b8dwnTp1zCOPPOLUp0qVKuaNN94wxhizfPly4+bmZk6cOOEYv3TpUqeN70cffWSyZ89urly54uizePFi4+LiYk6dOuWotWDBgiY2NtbRp3jx4qZWrVqO4Zs3bxovLy/z1VdfOdokGQ8PD+Pl5eV4xC83b9685q233kpQ+8svv2yM+b/QPX78eKc+ISEhjhAdb/jw4aZ69erGGGNeeeUV89hjj5m4uLhEn8PkfggAYI3kQnerVq1MyZIlzc8//2x8fHzM9evXncaHhISYDz/80BhjTPXq1U3btm2TXM7tX97Gjh1rihUrZmJiYu7ad8WKFcbV1dUcO3bMMf7PP/80kszGjRuNMbdCt6enp7l06ZKjz2uvveb4ofR2SYXuhQsXOtquX79uPD09zS+//OI0bceOHU2bNm2MMbe2bY8//rjT+OPHjxtJZu/evUk+DwCS9uuvvxpJZv78+U7tOXPmdHxXef31140xt74z9OjR467z7Nq1q2nRooVjODAw0Lz77ruO4Rs3bpj8+fMnGbqPHj1qXF1dnb6vGWNMvXr1TL9+/Ywxt7YrksyBAwcc4ydNmmT8/f0dw0lta2//Phb/nfOJJ55w2j7WqFHDdO7c2Wm6Z555xjRq1MgYk7LtZLZs2Rw7Qe5053YxXmKhe8CAAY7hK1euGElm6dKlxhhj3njjDVOmTBmnefTv35/QjfuGw8szoUcffVTbt2/Xb7/9poiICEVGRqpFixaSbh2aNHz4cIWGhipHjhzy9vbW8uXLdezYMad5lC1b1mk4MDBQZ86ckSTt3r1bQUFByps3r2N89erVnfrv3r1b5cqVk5eXl6OtZs2aiouLczofsXTp0nJx+b+3mb+/v0JDQx3Drq6uypkzp2PZ8d577z1t377d8ahfv74uXbqkv//+WzVr1nTqW7NmTe3evduprXLlyo6/o6KidPDgQXXs2FHe3t6Ox4gRI3Tw4EFJtw6F3759u4oXL67u3btrxYoVAvDfZYyRzWbTjh07dOXKFeXMmdPp83348GHH53v79u2qV69eiub7zDPP6Nq1aypcuLA6d+6sBQsW6ObNm4n2jd9WBgUFOdpKlSolPz8/p21ScHCwsmXL5hi+fXubErdvzw4cOKCrV6+qfv36Tuv72WefOdZ3x44dWrNmjdP4EiVKSJKjD4D0sXHjRm3fvl2lS5d2usDY7Z/beJMmTVKlSpWUO3dueXt766OPPnJ8P7t48aJOnjypatWqOfq7ubklOp94O3fuVGxsrIoVK+b0ef/xxx+dPuuenp4KCQlxDKdmGxT/fWzHjh36/vvvtW/fPrVr184xfvfu3cl+L0vJdrJXr17q1KmTwsLC9Pbbb6d5O3X7d1svLy/5+Pg41nPv3r2qUqWKU/+qVaumaTlAWnA1lkzIy8tLRYoUkSRNmzZN5cqV06effqqOHTtq9OjRev/99zV+/HiFhobKy8tLPXr0UExMjNM87rygh81mS9erASe3nJQsOyAgwLGO8S5dupTi5d7+Y0D8eeYff/yx0z8z6Vbol6SKFSvq8OHDWrp0qVatWqWWLVsqLCwswXmSAP4bdu/erUKFCunKlSsKDAx0OgcxXvwVb7NmzZri+QYFBWnv3r1atWqVVq5cqZdfflmjR4/Wjz/+mOYLId3r9jax7dnixYuVL18+p352u93Rp0mTJoleXDMwMDDFywXwf4oUKSKbzea0Y0GSChcuLCnhdub2z60kff311+rTp4/Gjh2r6tWrK1u2bBo9erR+++23NNd05coVubq6asuWLY7vM/FuvwhjYtsgc8e54km5/ftY8eLFdfnyZbVp00YjRoxI8D0trYYMGaJnn31Wixcv1tKlSzV48GB9/fXXTtfeSIn79d0WSAv2dGdyLi4uevPNNzVgwABdu3ZN69evV9OmTfXcc8+pXLlyKly4sPbt25eqeZYsWVLHjx93uqrur7/+mqDPjh07FBUV5Whbv369XFxcVLx48XtbqST4+Pgob968Wr9+vVP7+vXrVapUqSSn8/f3V968eXXo0CEVKVLE6VGoUCGn+bdq1Uoff/yxZs+erXnz5un8+fOSbm3IY2NjLVkvAKnzww8/aOfOnWrRooUqVqyoU6dOyc3NLcHnO1euXJJu7f1YvXp1iuefNWtWNWnSRBMmTNDatWu1YcMG7dy5M0G/+G3l8ePHHW27du3ShQsXkt0m3YtSpUrJbrfr2LFjCdY3fk9SxYoV9eeffyo4ODhBnzuDAICUyZkzp+rXr6+JEyc6ffdJqfXr16tGjRp6+eWXVaFCBRUpUsRpj66vr68CAwOdQvjNmze1ZcuWJOdZoUIFxcbG6syZMwk+6wEBASmuzd3dPcXfceLD/bVr1yTd2g4m970spdvJYsWKqWfPnlqxYoWaN2/uuMhuampLTvHixbV582antk2bNt3zfIGUInQ/AJ555hm5urpq0qRJKlq0qFauXKlffvlFu3fv1gsvvOB0hciUCAsLU7FixRQREaEdO3bo559/Vv/+/Z36tG3bVh4eHoqIiNAff/yhNWvW6JVXXlG7du3k7++fnqvn5LXXXtM777yj2bNna+/everbt6+2b9+uV199Ndnphg4dqlGjRmnChAnat2+fdu7cqenTp2vcuHGSpHHjxumrr77Snj17tG/fPs2ZM0cBAQGOPWXBwcFavXq1Tp06pX///dey9QPgLDo6WqdOndKJEye0detWjRw5Uk2bNtUTTzyh9u3bKywsTNWrV1ezZs20YsUKHTlyRL/88ov69+/v+II1ePBgffXVVxo8eLB2796tnTt3JnmLxRkzZujTTz/VH3/8oUOHDumLL75Q1qxZVbBgwQR9w8LCFBoaqrZt22rr1q3auHGj2rdvrzp16iR7SOi9yJYtm/r06aOePXtq5syZOnjwoLZu3aoPPvhAM2fOlCR17dpV58+fV5s2bbRp0yYdPHhQy5cvV2RkJD8eAvdg8uTJunnzpipXrqzZs2dr9+7d2rt3r7744gvt2bMnwd7m2xUtWlSbN2/W8uXLtW/fPg0cODBB6Hv11Vf19ttva+HChdqzZ49efvnlZK+sXaxYMbVt21bt27fX/PnzdfjwYW3cuFGjRo3S4sWLU7xewcHB+v3337V3716dPXtWN27ccIy7cOGCTp06pb///ls//vijhg0bpmLFiqlkyZKSbn0vmzFjhqZMmaL9+/dr3Lhxmj9/vvr06SPp7tvJa9euqVu3blq7dq2OHj2q9evXa9OmTY75BwcH68qVK1q9erXOnj2b5nuhv/DCC9qzZ4/eeOMN7du3T998843jDhg2my1N8wRSg9D9AHBzc1O3bt307rvvqnfv3qpYsaLCw8NVt25dBQQEqFmzZqman4uLixYsWKBr166patWq6tSpk9566y2nPp6enlq+fLnOnz+vKlWq6Omnn1a9evU0ceLEdFyzhLp3765evXqpd+/eCg0N1bJlyxy3gEhOp06d9Mknn2j69OkKDQ1VnTp1NGPGDMee7mzZsundd99V5cqVVaVKFR05ckRLlixxnI8+duxYrVy5UkFBQapQoYKl6wjg/yxbtkyBgYEKDg5WgwYNtGbNGk2YMEHffvutXF1dZbPZtGTJEtWuXVuRkZEqVqyYWrduraNHjzp+AKxbt67mzJmjRYsWqXz58nrssce0cePGRJfn5+enjz/+WDVr1lTZsmW1atUqfffdd8qZM2eCvjabTd9++62yZ8+u2rVrKywsTIULF9bs2bMtfU6GDx+ugQMHatSoUSpZsqQaNGigxYsXO7Zn8UcExcbG6vHHH1doaKh69OghPz8/p2tsAEidkJAQbdu2TWFhYerXr5/KlSunypUr64MPPlCfPn2SvXf1Cy+8oObNm6tVq1aqVq2azp07p5dfftmpT+/evdWuXTtFREQ4DkG/2yHW06dPV/v27dW7d28VL15czZo106ZNm1SgQIEUr1fnzp1VvHhxVa5cWblz53bacx0ZGanAwEDlz59fbdq0UenSpbV06VK5ud06Q7VZs2Z6//33NWbMGJUuXVoffvihpk+frrp160q6+3bS1dVV586dU/v27VWsWDG1bNlSDRs21NChQyVJNWrU0IsvvqhWrVopd+7cevfdd1O8XrcrVKiQ5s6dq/nz56ts2bKaMmWKY4dS/Kk5gJVsJqUndQAAAADAA+Ctt97S1KlTnQ59B6zChdQAAAAAPNAmT56sKlWqKGfOnFq/fr1Gjx6tbt26ZXRZeEgQugEAAAA80Pbv368RI0bo/PnzKlCggHr37q1+/fpldFl4SHB4OQAAAAAAFuGKKgAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAJK0du1a2Ww2XbhwIcXTBAcHa/z48ZbVBABAZkLoBgAgE+vQoYNsNptefPHFBOO6du0qm82mDh063P/CAACAJEI3AACZXlBQkL7++mtdu3bN0Xb9+nXNmjVLBQoUyMDKAAAAoRsAgEyuYsWKCgoK0vz58x1t8+fPV4ECBVShQgVHW3R0tLp37648efLIw8NDjzzyiDZt2uQ0ryVLlqhYsWLKmjWrHn30UR05ciTB8tatW6datWopa9asCgoKUvfu3RUVFZVobcYYDRkyRAUKFJDdblfevHnVvXv39FlxAAAyAUI3AAAPgOeff17Tp093DE+bNk2RkZFOfV5//XXNmzdPM2fO1NatW1WkSBGFh4fr/PnzkqTjx4+refPmatKkibZv365OnTqpb9++TvM4ePCgGjRooBYtWuj333/X7NmztW7dOnXr1i3RuubNm6f33ntPH374ofbv36+FCxcqNDQ0ndceAID/LkI3AAAPgOeee07r1q3T0aNHdfToUa1fv17PPfecY3xUVJSmTJmi0aNHq2HDhipVqpQ+/vhjZc2aVZ9++qkkacqUKQoJCdHYsWNVvHhxtW3bNsH54KNGjVLbtm3Vo0cPFS1aVDVq1NCECRP02Wef6fr16wnqOnbsmAICAhQWFqYCBQqoatWq6ty5s6XPBQAA/yWEbgAAHgC5c+dW48aNNWPGDE2fPl2NGzdWrly5HOMPHjyoGzduqGbNmo62LFmyqGrVqtq9e7ckaffu3apWrZrTfKtXr+40vGPHDs2YMUPe3t6OR3h4uOLi4nT48OEEdT3zzDO6du2aChcurM6dO2vBggW6efNmeq46AAD/aW4ZXQAAAEgfzz//vOMw70mTJlmyjCtXruiFF15I9LzsxC7aFhQUpL1792rVqlVauXKlXn75ZY0ePVo//vijsmTJYkmNAAD8l7CnGwCAB0SDBg0UExOjGzduKDw83GlcSEiI3N3dtX79ekfbjRs3tGnTJpUqVUqSVLJkSW3cuNFpul9//dVpuGLFitq1a5eKFCmS4OHu7p5oXVmzZlWTJk00YcIErV27Vhs2bNDOnTvTY5UBAPjPY083AAAPCFdXV8eh4q6urk7jvLy89NJLL+m1115Tjhw5VKBAAb377ru6evWqOnbsKEl68cUXNXbsWL322mvq1KmTtmzZohkzZjjN54033tD//vc/devWTZ06dZKXl5d27dqllStXauLEiQlqmjFjhmJjY1WtWjV5enrqiy++UNasWVWwYEFrngQAAP5j2NMNAMADxMfHRz4+PomOe/vtt9WiRQu1a9dOFStW1IEDB7R8+XJlz55d0q3Dw+fNm6eFCxeqXLlymjp1qkaOHOk0j7Jly+rHH3/Uvn37VKtWLVWoUEGDBg1S3rx5E12mn5+fPv74Y9WsWVNly5bVqlWr9N133ylnzpzpu+IAAPxH2YwxJqOLAAAAAADgQcSebgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCL/D1orYCYKzlfDAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ensemble_visualization(base_models, train_f1_scores, val_f1_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PB2VSNGCOCHK",
        "outputId": "15274061-e9e2-4a11-cf17-f1051d7dfc38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensemble Model Metrics:\n",
            "Training Precision: 0.9811, Recall: 0.9603, F1-score: 0.9706\n",
            "Test Precision: 0.9351, Recall: 0.7579, F1-score: 0.8372\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 0, 0, ..., 0, 0, 0]),\n",
              " array([0, 0, 0, ..., 0, 0, 0]),\n",
              " 0.9705882352941175,\n",
              " 0.8372093023255814,\n",
              " 0.981081081081081,\n",
              " 0.9603174603174603,\n",
              " 0.935064935064935,\n",
              " 0.7578947368421053)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "ensemble_evaluations(meta_learner, X_train, y_train, X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ICs0hUS6J8q"
      },
      "source": [
        "\n",
        "## B. Scenario 2: GAN & Ensemble\n",
        "\n",
        "The dataset was split using stratified method between the train dataset and the test dataset. In order to preserve the original state of the test dataset, fraud data, I will apply GAN only on the minority class in the training dataset to augument.\n",
        "\n",
        "Then, I will conduct Ensemble with augumented balanced data.\n",
        "\n",
        "In this way, we can evaluate the predicting power of the model using the preserved original test dataset.  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rpjYTo2cJO1l"
      },
      "outputs": [],
      "source": [
        "# Splitting data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJn93hG8BpZa",
        "outputId": "983417a6-3433-4bc2-9e22-a0e33e40d130"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(226010, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNWDeiCsBsCm",
        "outputId": "6bb63a94-c349-45c5-ea43-ec3731254928"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(56503, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ESKzlwneA0Cp"
      },
      "outputs": [],
      "source": [
        "train_df = pd.concat([X_train, y_train], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wUmudNbNJTIR",
        "outputId": "8a44a35c-1ce8-4d5e-cca4-852a6483828e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(226010, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "train_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ib7iVtLw6JBW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.layers import Dense, Input\n",
        "from keras.models import Sequential, Model\n",
        "from keras.optimizers import Adam\n",
        "import keras.backend as K\n",
        "\n",
        "# Load the dataset (fraud and non-fraud data)\n",
        "\n",
        "fraud_data = train_df[train_df['Class'] == 1].drop('Class', axis=1).values\n",
        "non_fraud_data = train_df[train_df['Class'] == 0].drop('Class', axis=1).values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dj6CeLKN3zDp",
        "outputId": "a281f7ae-6c78-4d7c-ceba-4a7010c3ea76"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "type(fraud_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3t0s93M3_Mn",
        "outputId": "a1d8e524-748b-47c9-d4f5-5e8ce95b7d27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(378, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "fraud_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LcKYIjy357C",
        "outputId": "c93d7a8a-7829-42a4-d917-27ba86b94b14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-8.67899742e-01,  6.14977120e-01, -6.57947691e-01,\n",
              "        -1.63514452e+00,  6.85328871e-02,  1.32579410e+00],\n",
              "       [-8.99381088e+00,  7.86446740e+00, -1.06498397e+01,\n",
              "        -1.29136317e+01, -7.95409771e+00, -7.80963510e+00],\n",
              "       [ 8.56615348e-02, -1.21180335e-04, -5.43808429e-02,\n",
              "        -2.30664479e+00,  1.67272280e+00,  1.16573654e+00],\n",
              "       ...,\n",
              "       [-2.41127201e+00,  4.48003527e+00, -8.83250973e+00,\n",
              "        -1.37256846e+01, -4.38968768e+00, -5.81091982e+00],\n",
              "       [-3.36401058e+00,  2.88704788e+00, -3.78445966e+00,\n",
              "        -3.98562601e+00, -2.60370265e+00, -5.15759640e+00],\n",
              "       [-3.21618772e+00,  1.28674926e+00, -3.27270458e+00,\n",
              "        -5.74894325e+00, -3.86597568e+00, -2.70505213e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "fraud_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vb7YXSfo6Ydp",
        "outputId": "b7edcefd-f790-4781-aa0e-e7f8e9005bda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of non-fraud:  225632\n",
            "# of Real Fraud: 378\n",
            "# of Synthetic Fraud required: 225254\n"
          ]
        }
      ],
      "source": [
        "# Calculate the number of synthetic fraud samples to generate\n",
        "num_real_fraud = len(fraud_data)\n",
        "num_synthetic_samples = len(non_fraud_data) - num_real_fraud\n",
        "print(\"# of non-fraud: \", len(non_fraud_data))\n",
        "print(\"# of Real Fraud:\", num_real_fraud)\n",
        "print(\"# of Synthetic Fraud required:\", num_synthetic_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClxCPFnw7R_V"
      },
      "source": [
        "## GAN: Model Building & Training\n",
        "\n",
        "I use GAN to generate fake but realistic data for the minority class, fraud. GAN has two sub-models: generator and discriminator.\n",
        "While generator creates fake data. discriminator intends to distinguish between fake data and real data.\n",
        "\n",
        "Since I encountered some difficulties in accessing Google Colab's GPU, I decided to omit the hyperparameter optimization for this time.\n",
        "\n",
        "### Bi-Level Optimization\n",
        "\n",
        "In order to optimize these two built-in models, GANs applies the framework of ‘bi-level optimization’.\n",
        "\n",
        "As the name suggests, bi-level optimization has two levels: the upper-level optimization and the lower-level optimization. The lower-level optimization is nested within the upper-level optimization. (Wikipedia, 2023)\n",
        "\n",
        "In the case of GANs, the discriminator is optimized in the lower-level, while the generator is optimized (together with the discriminator) in the upper-level.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBS9zFT87QD7",
        "outputId": "de331113-9658-42bf-c9b4-d1f41171908b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generator Summary:\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 64)                2112      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               8320      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11206 (43.77 KB)\n",
            "Trainable params: 11206 (43.77 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "\n",
            "Discriminator Summary:\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 128)               896       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1025 (4.00 KB)\n",
            "Trainable params: 1025 (4.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Define the generator network\n",
        "def build_generator(latent_dim, output_dim):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(64, input_shape=(latent_dim,)))\n",
        "    model.add(Dense(128, activation='sigmoid'))\n",
        "    model.add(Dense(output_dim, activation='sigmoid'))\n",
        "    return model\n",
        "\n",
        "\n",
        "# Define the discriminator network\n",
        "def build_discriminator(input_dim):\n",
        "    model = Sequential()\n",
        "    model.add(Input(input_dim))\n",
        "    model.add(Dense(128, activation='sigmoid'))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    return model\n",
        "\n",
        "# Dimensionality of the input noise for the generator\n",
        "latent_dim = 32\n",
        "\n",
        "# Build generator and discriminator models\n",
        "generator = build_generator(latent_dim, fraud_data.shape[1])\n",
        "discriminator = build_discriminator(fraud_data.shape[1])\n",
        "\n",
        "# Display model summaries\n",
        "print(\"Generator Summary:\")\n",
        "generator.summary()\n",
        "print(\"\\nDiscriminator Summary:\")\n",
        "discriminator.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pg9GcObwv39G"
      },
      "source": [
        "### Compile the discriminator sub-model:\n",
        "I selected Precision and Recall for the metrics argument."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qfnTOIdN7ffl"
      },
      "outputs": [],
      "source": [
        "# Compile the discriminator model\n",
        "from keras.metrics import Precision, Recall\n",
        "discriminator.compile(optimizer=Adam(learning_rate=0.0002, beta_1=0.5), loss='binary_crossentropy',  metrics=[Precision(), Recall()])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myyl8-RI8iaS"
      },
      "source": [
        "### Custom Loss Function for Generator:\n",
        "\n",
        "In order to determine the loss function (objective function), I followed the method outlined in the original GAN paper.\n",
        "\n",
        "\n",
        ">   \"Early in learning, when G is poor can reject samples with high confidence because they are clearly different from the training data. In this case, log(1−D(G(z)))saturates. Rather than training G to minimize log(1−D(G(z)) we can train G to maximize logD(G(z)).\"\n",
        "\n",
        "> Title: \"Generative Adversarial Networks\"\n",
        "\n",
        "> Authors: Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio\n",
        "\n",
        "> Source: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/1406.2661.pdf\n",
        "\n",
        "The method is described in the page 3 of the paper.\n",
        "Here are two precautions:\n",
        "*  model.optimize() is designed to minimize the objective function. So, I have to convert the function, logD(G(z)), with negative sign for the definition of the objective function.\n",
        "* we use this custom loss function of generator only when we compile the meta-model of GAN. Remember, we do not compile the generrator in the architecture of GAN.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OnvlHrPKDa_l"
      },
      "outputs": [],
      "source": [
        "def generator_loss_log_d(y_true, y_pred):\n",
        "    return -K.mean(K.log(y_pred + K.epsilon()))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wW1Sl4ms7okI"
      },
      "outputs": [],
      "source": [
        "# GAN model combining generator and discriminator\n",
        "def build_gan(generator, discriminator):\n",
        "    discriminator.trainable = False\n",
        "    model = Sequential()\n",
        "    model.add(generator)\n",
        "    model.add(discriminator)\n",
        "    model.compile(optimizer=Adam(learning_rate=0.0002, beta_1=0.5), loss=generator_loss_log_d)\n",
        "\n",
        "    return model\n",
        "\n",
        "# Build and compile the GAN model\n",
        "gan = build_gan(generator, discriminator)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FiV7DaVWoLxH"
      },
      "source": [
        "### Batch Training:\n",
        "Now, let's carry out the batch training, using Keras' model.train_on_batch() method to do the batch training of gan. As a reminder, while we train the discriminator, we need to freeze the training of the generator; and while we train the generator, we need to freeze the training of the discriminator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhRTUly07uNQ",
        "outputId": "35ee5ddd-0b28-412f-8842-a1b9a7d0cc0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mストリーミング出力は最後の 5000 行に切り捨てられました。\u001b[0m\n",
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            "1/1 [==============================] - 0s 18ms/step\n",
            "Epoch: 5100 - D Loss: [0.03848826 0.5        0.5       ] - G Loss: 2.8214783668518066\n",
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            "Epoch: 5200 - D Loss: [0.10730759 0.5        0.484375  ] - G Loss: 2.7454380989074707\n",
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            "Epoch: 5300 - D Loss: [0.21506043 0.5        0.421875  ] - G Loss: 2.8313474655151367\n",
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            "Epoch: 5400 - D Loss: [0.19172982 0.5        0.453125  ] - G Loss: 2.7822113037109375\n",
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            "Epoch: 5500 - D Loss: [0.04661841 0.5        0.484375  ] - G Loss: 2.7467050552368164\n",
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            "1/1 [==============================] - 0s 19ms/step\n",
            "Epoch: 5600 - D Loss: [0.09818607 0.5        0.46875   ] - G Loss: 2.837611675262451\n",
            "1/1 [==============================] - 0s 17ms/step\n",
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            "Epoch: 5700 - D Loss: [0.14846602 0.5        0.453125  ] - G Loss: 2.884296417236328\n",
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            "Epoch: 5800 - D Loss: [0.13518471 0.5        0.46875   ] - G Loss: 2.8744096755981445\n",
            "1/1 [==============================] - 0s 19ms/step\n",
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            "1/1 [==============================] - 0s 19ms/step\n",
            "Epoch: 5900 - D Loss: [0.06615264 0.5        0.484375  ] - G Loss: 2.8686044216156006\n",
            "1/1 [==============================] - 0s 19ms/step\n",
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            "Epoch: 6000 - D Loss: [0.22966051 0.5        0.46875   ] - G Loss: 2.9132628440856934\n",
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            "Epoch: 6100 - D Loss: [0.27366874 0.5        0.453125  ] - G Loss: 2.9368176460266113\n",
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            "Epoch: 6200 - D Loss: [0.13876713 0.5        0.484375  ] - G Loss: 2.811955451965332\n",
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            "Epoch: 6300 - D Loss: [0.27267192 0.5        0.4375    ] - G Loss: 2.813220739364624\n",
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            "Epoch: 6400 - D Loss: [0.31463251 0.5        0.453125  ] - G Loss: 2.847069263458252\n",
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            "Epoch: 6500 - D Loss: [0.10169316 0.5        0.46875   ] - G Loss: 2.8703560829162598\n",
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            "Epoch: 6600 - D Loss: [0.26851038 0.5        0.4375    ] - G Loss: 2.847094774246216\n",
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            "Epoch: 6700 - D Loss: [0.21635925 0.5        0.453125  ] - G Loss: 2.9439611434936523\n",
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            "Epoch: 6800 - D Loss: [0.14986365 0.5        0.46875   ] - G Loss: 2.8289339542388916\n",
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            "Epoch: 6900 - D Loss: [0.08030019 0.5        0.484375  ] - G Loss: 2.870147705078125\n",
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            "Epoch: 7000 - D Loss: [0.10911022 0.5        0.46875   ] - G Loss: 2.7342491149902344\n",
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            "Epoch: 7100 - D Loss: [0.08485608 0.5        0.46875   ] - G Loss: 2.8527626991271973\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "Epoch: 7200 - D Loss: [0.10118906 0.5        0.46875   ] - G Loss: 2.8902454376220703\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "Epoch: 7300 - D Loss: [0.11643581 0.5        0.484375  ] - G Loss: 2.865910530090332\n",
            "1/1 [==============================] - 0s 21ms/step\n",
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            "Epoch: 7400 - D Loss: [0.11467891 0.5        0.453125  ] - G Loss: 2.8819069862365723\n",
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            "Epoch: 7500 - D Loss: [0.15973937 0.5        0.46875   ] - G Loss: 2.956298351287842\n",
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            "Epoch: 7600 - D Loss: [0.03326767 0.5        0.5       ] - G Loss: 2.875566244125366\n",
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            "Epoch: 7700 - D Loss: [0.04447374 0.5        0.5       ] - G Loss: 2.8368747234344482\n",
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            "Epoch: 7800 - D Loss: [0.04656243 0.5        0.484375  ] - G Loss: 2.896301031112671\n",
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            "Epoch: 7900 - D Loss: [0.05389119 0.5        0.484375  ] - G Loss: 2.9252848625183105\n",
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            "Epoch: 8000 - D Loss: [0.18616993 0.5        0.4375    ] - G Loss: 2.8634090423583984\n",
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            "Epoch: 8100 - D Loss: [0.02618345 0.5        0.5       ] - G Loss: 2.984309673309326\n",
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            "Epoch: 8200 - D Loss: [0.12565754 0.5        0.484375  ] - G Loss: 2.9322900772094727\n",
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            "Epoch: 8300 - D Loss: [0.26271626 0.5        0.453125  ] - G Loss: 2.849560022354126\n",
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            "Epoch: 8400 - D Loss: [0.08725076 0.5        0.46875   ] - G Loss: 2.932708263397217\n",
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            "Epoch: 8500 - D Loss: [0.03720863 0.5        0.5       ] - G Loss: 2.8929083347320557\n",
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            "Epoch: 8600 - D Loss: [0.17166618 0.5        0.46875   ] - G Loss: 2.9505741596221924\n",
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            "Epoch: 8700 - D Loss: [0.12979092 0.5        0.46875   ] - G Loss: 2.9079484939575195\n",
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            "1/1 [==============================] - 0s 22ms/step\n",
            "Epoch: 8800 - D Loss: [0.05229606 0.5        0.484375  ] - G Loss: 2.9105494022369385\n",
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            "1/1 [==============================] - 0s 20ms/step\n",
            "Epoch: 8900 - D Loss: [0.17140318 0.5        0.46875   ] - G Loss: 2.9332776069641113\n",
            "1/1 [==============================] - 0s 19ms/step\n",
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            "Epoch: 9000 - D Loss: [0.23587007 0.5        0.453125  ] - G Loss: 2.936753273010254\n",
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            "1/1 [==============================] - 0s 19ms/step\n",
            "Epoch: 9100 - D Loss: [0.22891902 0.5        0.4375    ] - G Loss: 2.850111246109009\n",
            "1/1 [==============================] - 0s 19ms/step\n",
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            "Epoch: 9200 - D Loss: [0.28297001 0.5        0.453125  ] - G Loss: 2.875152826309204\n",
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            "Epoch: 9300 - D Loss: [0.05292265 0.5        0.484375  ] - G Loss: 2.931020736694336\n",
            "1/1 [==============================] - 0s 19ms/step\n",
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            "Epoch: 9400 - D Loss: [0.12122807 0.5        0.484375  ] - G Loss: 2.95497465133667\n",
            "1/1 [==============================] - 0s 21ms/step\n",
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            "Epoch: 9500 - D Loss: [0.05866135 0.5        0.484375  ] - G Loss: 2.888730049133301\n",
            "1/1 [==============================] - 0s 19ms/step\n",
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            "Epoch: 9600 - D Loss: [0.04861524 0.5        0.484375  ] - G Loss: 2.862032651901245\n",
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            "Epoch: 9700 - D Loss: [0.11359465 0.5        0.484375  ] - G Loss: 2.906346321105957\n",
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            "Epoch: 9800 - D Loss: [0.02860253 0.5        0.5       ] - G Loss: 2.9502692222595215\n",
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            "Epoch: 9900 - D Loss: [0.13992821 0.5        0.46875   ] - G Loss: 2.9627532958984375\n",
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            "1/1 [==============================] - 0s 19ms/step\n",
            "7040/7040 [==============================] - 9s 1ms/step\n"
          ]
        }
      ],
      "source": [
        "# Set hyperparameters\n",
        "epochs = 10000\n",
        "batch_size = 32\n",
        "\n",
        "# Training loop for the GAN\n",
        "for epoch in range(epochs):\n",
        "    # Train discriminator (freeze generator)\n",
        "    discriminator.trainable = True\n",
        "    generator.trainable = False\n",
        "\n",
        "    # Select random real fraud samples\n",
        "    real_fraud_samples = fraud_data[np.random.randint(0, num_real_fraud, batch_size)]\n",
        "\n",
        "    # Generate fake fraud samples using the generator\n",
        "    noise = np.random.normal(0, 1, size=(batch_size, latent_dim))\n",
        "    fake_fraud_samples = generator.predict(noise)\n",
        "\n",
        "    # Create labels for real and fake fraud samples\n",
        "    real_labels = np.ones((batch_size, 1))\n",
        "    fake_labels = np.zeros((batch_size, 1))\n",
        "\n",
        "    # Train the discriminator on real and fake fraud samples\n",
        "    d_loss_real = discriminator.train_on_batch(real_fraud_samples, real_labels)\n",
        "    d_loss_fake = discriminator.train_on_batch(fake_fraud_samples, fake_labels)\n",
        "    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "    # Train generator (freeze discriminator)\n",
        "    discriminator.trainable = False\n",
        "    generator.trainable = True\n",
        "\n",
        "    # Generate fake fraud samples and create labels for training the generator\n",
        "    noise = np.random.normal(0, 1, size=(batch_size, latent_dim))\n",
        "    valid_labels = np.ones((batch_size, 1))\n",
        "\n",
        "    # Train the generator to generate samples that \"fool\" the discriminator\n",
        "    g_loss = gan.train_on_batch(noise, valid_labels)\n",
        "\n",
        "    # Print the progress\n",
        "    if epoch % 100 == 0:\n",
        "        print(f\"Epoch: {epoch} - D Loss: {d_loss} - G Loss: {g_loss}\")\n",
        "\n",
        "# After training, use the generator to create synthetic fraud data\n",
        "noise = np.random.normal(0, 1, size=(num_synthetic_samples, latent_dim))\n",
        "synthetic_fraud_data = generator.predict(noise)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoqX0NB4UgPs",
        "outputId": "3942e337-ec2b-484d-c9a7-d7398a15c68d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "type(synthetic_fraud_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDPLuz3mH_1P",
        "outputId": "18240086-3f3b-4663-c3d2-1dddafa726ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(225254, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "synthetic_fraud_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WddnndW-U1io",
        "outputId": "f297116a-a12a-455c-b265-58cd652c9af3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['V10', 'V11', 'V12', 'V14', 'V16', 'V17'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "features = df.columns[:-1]\n",
        "features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKsAfQjkHll7",
        "outputId": "86b227ea-53db-4915-be72-25e462032173"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.indexes.base.Index"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "type(features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lk2tR9D0Uz4g"
      },
      "outputs": [],
      "source": [
        "fake_df = pd.DataFrame(synthetic_fraud_data, columns=features.to_list())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HN7gXdOna_ra"
      },
      "outputs": [],
      "source": [
        "fake_df[\"Class\"] = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGCrYiaeY7-0",
        "outputId": "d8d8133f-24d5-4dc3-85bf-c63ecb3c3284"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(225254, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "fake_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOfv1BH_a8ey",
        "outputId": "f9434531-86b6-4f86-b38a-6654325c4c95"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282513, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9Dsor3s5ux1"
      },
      "source": [
        "Now, create the perfectly balanced train dataframe, by combining the synthetic data and the original training dataframe. In this process, the original test dataframe is unaffected."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bBpYP0akZV2c"
      },
      "outputs": [],
      "source": [
        "# Now, combine the synthetic DataFrame with the train DataFrame to create the working dataframe\n",
        "wdf = pd.concat([train_df, fake_df], axis=0)\n",
        "\n",
        "# Resetting the index of the new DataFrame\n",
        "wdf.reset_index(drop=True, inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wR91zzPa_Au",
        "outputId": "e797a9b6-524f-493b-ecd0-3fd5672d6bf3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(451264, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "wdf.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KBC64Bcd0ImV"
      },
      "source": [
        "## 2. Binary Classification with Ensemble"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfHQBCMFE2AM"
      },
      "source": [
        "Here, I combine the real training dataset and the fake fraud dataset.\n",
        "\n",
        "The resulting dataset is divided into its feature dataset, X_mixed, and its label, y_mixed. These datasets are used for training the Ensemble Tree.\n",
        "\n",
        "Here, I will call the custom function, ensemble_training(), to train a binary classification model.\n",
        "\n",
        "Thereafter, the fitted model is going to be used for testing with the original X_test and y_test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I0JR4E3fbdJY"
      },
      "outputs": [],
      "source": [
        "\n",
        "X_mixed = wdf[wdf.columns.drop(\"Class\")]\n",
        "y_mixed = wdf[\"Class\"]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z3ivIUIYnEYR"
      },
      "outputs": [],
      "source": [
        "meta_learner_gan, train_f1_scores_gan, val_f1_scores_gan, base_models_gan=ensemble_training(X_mixed, y_mixed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "eKNEQ77LiGbX",
        "outputId": "ebb0b36b-fc33-41cf-b10d-6b293fa0fcfa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('RandomForest',\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             ('DecisionTree',\n",
              "                              DecisionTreeClassifier(random_state=42)),\n",
              "                             ('GradientBoosting',\n",
              "                              GradientBoostingClassifier(random_state=42))],\n",
              "                 voting='soft')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;RandomForest&#x27;,\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;DecisionTree&#x27;,\n",
              "                              DecisionTreeClassifier(random_state=42)),\n",
              "                             (&#x27;GradientBoosting&#x27;,\n",
              "                              GradientBoostingClassifier(random_state=42))],\n",
              "                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;RandomForest&#x27;,\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;DecisionTree&#x27;,\n",
              "                              DecisionTreeClassifier(random_state=42)),\n",
              "                             (&#x27;GradientBoosting&#x27;,\n",
              "                              GradientBoostingClassifier(random_state=42))],\n",
              "                 voting=&#x27;soft&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>RandomForest</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>DecisionTree</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>GradientBoosting</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "meta_learner_gan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZsFXi_1X-uec"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "kBYUnDeenEYR",
        "outputId": "1a0df740-c967-420d-8ec3-56a6a1fff241"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ensemble_visualization(base_models_gan, train_f1_scores_gan, val_f1_scores_gan)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwnPNGvuCHph"
      },
      "source": [
        "For the testing, we pass the original X_test, y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zFiRM-dnEYR",
        "outputId": "ba3f6dc1-4cde-4053-feda-09a6ebdb9389"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensemble Model Metrics:\n",
            "Training Precision: 1.0000, Recall: 0.9999, F1-score: 0.9999\n",
            "Test Precision: 0.9714, Recall: 0.7158, F1-score: 0.8242\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 0, 0, ..., 1, 1, 1]),\n",
              " array([0, 0, 0, ..., 0, 0, 0]),\n",
              " 0.9999468139329768,\n",
              " 0.8242424242424242,\n",
              " 0.999986702952831,\n",
              " 0.9999069280953057,\n",
              " 0.9714285714285714,\n",
              " 0.7157894736842105)"
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      "source": [
        "ensemble_evaluations(meta_learner_gan, X_mixed, y_mixed, X_test, y_test)"
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        "# Step 4: Conclusion\n",
        "\n",
        "When we review the evaluation results on the training dataset, clearly GANs scenario outperformed the benchmark scenario over all the three evaluation metrics.\n",
        "Nevertheless, when we focus on the results on the out-of-sample test data, GANs scenario outperformed the benchmark scenario only for precision (Benchmark: 0.935 vs GANs Scenario: 0.9714): it failed do so for  recall and F1-Score (Benchmark: 0.7579; 0.8372 vs GANs Scenario: 0.7158; 0.8242).\n",
        "A higher precision means that the model's prediction of fraud cases did include less proportion of non-fraud cases than the benchmark scenario.\n",
        "A lower recall means that the model failed to detect certain varieties of the actual fraud cases.\n",
        "\n",
        "These two comparisons indicate: while the data augmentation by GANs was successful in simulating the realistic fraud data within the training dataset, it has failed to capture the diversity of the actual fraud cases included in the out-of-sample test dataset."
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