9155 lines (9155 with data), 2.6 MB

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "uJ_C2-TyxfUd",
        "rPePt6FCwZkw",
        "sL4Up7ouxoyK",
        "udMTWjB0x37h",
        "hQVTwB5kyIgq",
        "aoqSv5Jwy0VM",
        "V4J74B8Dy5E_",
        "mkM0Y3_W2dHa",
        "XorzBd4F2o7T",
        "X1wUZm8R2svT",
        "laffn67L23-j",
        "UShfUXwF3JdS",
        "62WR3zhL3vb7",
        "ucB81LFoOLhA"
      ],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# once deep_tabular_augmentation installation is done, we need to manually update the commom.py file within the deep_tabular_augmentation directory to change \"from collections import Iterable\"\n",
        "# to \"from from collections.abc import Iterable\"\n",
        "!pip install deep_tabular_augmentation\n",
        "!pip install mlprep-ls\n",
        "!pip install 'aif360[all]'\n",
        "!pip install shap"
      ],
      "metadata": {
        "id": "uGJFDDntFyFz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task - 1 (No augmentation)"
      ],
      "metadata": {
        "id": "uJ_C2-TyxfUd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Data Analysis and Preprocessing"
      ],
      "metadata": {
        "id": "rPePt6FCwZkw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# observe descriptive statistics and check for missing data.\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/dataset_project5.csv')\n",
        "\n",
        "# Get descriptive statistics\n",
        "numerical_stats = df.describe()\n",
        "\n",
        "# Count missing values for every column\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "# Count total missing values in the entire DataFrame\n",
        "total_missing = missing_values.sum()\n",
        "\n",
        "\n",
        "print(\"Descriptive Statistics:\")\n",
        "print(numerical_stats)\n",
        "print(\"\\nMissing Values:\")\n",
        "print(missing_values)\n",
        "print(f\"Total Missing Values: {total_missing}\")"
      ],
      "metadata": {
        "id": "29Lf1uO1wRIo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9aec058-c7f2-43da-b494-3b3e028bb0d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Statistics:\n",
            "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
            "count  3000.000000   3000.000000  3000.000000  3000.000000  3000.000000   \n",
            "mean     39.249800     13.090467     4.734303     8.121800   256.353333   \n",
            "std       5.614139      2.004291     0.724817     4.564886   110.472352   \n",
            "min      14.600000      3.800000     1.930000     1.100000    10.000000   \n",
            "25%      35.800000     11.900000     4.290000     5.300000   187.000000   \n",
            "50%      39.500000     13.200000     4.740000     7.100000   257.000000   \n",
            "75%      43.300000     14.500000     5.200000     9.700000   322.000000   \n",
            "max      57.000000     18.800000     7.860000    41.100000   830.000000   \n",
            "\n",
            "               MCH         MCHC          MCV          AGE  \n",
            "count  3000.000000  3000.000000  3000.000000  3000.000000  \n",
            "mean     27.774800    33.314867    83.279267    35.061000  \n",
            "std       2.650355     1.213204     6.544982    15.687617  \n",
            "min      14.900000    26.000000    54.000000     1.000000  \n",
            "25%      26.600000    32.700000    80.400000    24.000000  \n",
            "50%      28.300000    33.400000    84.400000    36.000000  \n",
            "75%      29.500000    34.100000    87.400000    48.000000  \n",
            "max      37.800000    37.400000   104.500000    60.000000  \n",
            "\n",
            "Missing Values:\n",
            "HAEMATOCRIT     0\n",
            "HAEMOGLOBINS    0\n",
            "ERYTHROCYTE     0\n",
            "LEUCOCYTE       0\n",
            "THROMBOCYTE     0\n",
            "MCH             0\n",
            "MCHC            0\n",
            "MCV             0\n",
            "AGE             0\n",
            "SEX             0\n",
            "SOURCE          0\n",
            "dtype: int64\n",
            "Total Missing Values: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The piece of code above performs basic descriptive statistics on the data and checks it for missing values.\n",
        "\n",
        "Descriptive statistics provide valuable information about the central tendency (mean), data spread (standard deviation), and the range of values (minimum and maximum) for each numerical attribute. The output to the code above revealed that the dataset contains a total of 3,000 data points, with no missing values.\n",
        "\n",
        "The statistics reveal variations in the attributes. For example, 'HAEMOGLOBINS' has a relatively small standard deviation, indicating that data points are closer to the mean, while 'THROMBOCYTE' has a wider range of values.\n",
        "\n",
        "These statistics are crucial for understanding the dataset's characteristics and distribution, and can be used to test our data augmentation techniques further down the line."
      ],
      "metadata": {
        "id": "7yfwEsjjMdp9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# drop duplicates.\n",
        "# normalize the data.\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "df = pd.read_csv('/content/dataset_project5.csv')\n",
        "\n",
        "# Remove duplicate rows\n",
        "columns_to_check = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE', 'SEX', 'SOURCE']\n",
        "df_no_duplicates = df.drop_duplicates(subset=columns_to_check)\n",
        "\n",
        "\n",
        "columns_to_normalize = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE']\n",
        "\n",
        "# Create a MinMaxScaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply the MinMaxScaler to the selected columns in the df without duplicates\n",
        "df_no_duplicates[columns_to_normalize] = scaler.fit_transform(df_no_duplicates[columns_to_normalize])\n",
        "\n",
        "# Save the normalized df without duplicates to a new CSV file\n",
        "df_no_duplicates.to_csv('normalized_data.csv', index=False)\n"
      ],
      "metadata": {
        "id": "NqckRrmpMjdX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data normalization is crucial when comparing data points or features across different units or dimensions. It ensures that comparisons are meaningful and valid.\n",
        "\n",
        "Therefore we have performed data normalization on the dataset provided. Min-max scaling scales data based on the minimum and maximum values. This can help reduce the influence of outliers on the model because extreme values are scaled to be closer to the typical data range.\n",
        "\n",
        "This code streamlines the data preparation process by removing duplicate rows and applying min-max scaling to specific numerical features."
      ],
      "metadata": {
        "id": "UlWMUG9RM--0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize the numerical data columns\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "df = pd.read_csv('normalized_data.csv')\n",
        "\n",
        "numerical_columns = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE']\n",
        "\n",
        "\n",
        "numbins = 20\n",
        "\n",
        "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
        "\n",
        "plt.figure(figsize=(15, 11))\n",
        "for i, column in enumerate(numerical_columns, 1):\n",
        "    plt.subplot(2, 3, i)\n",
        "    plt.title(f'Histogram of {column}')\n",
        "    sns.histplot(df[column], bins=numbins, kde=True, color=colors[i-1])\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "A4h2c67FNDMf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "outputId": "c0b539e2-3d5b-4895-d516-e2bd81897175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1100 with 5 Axes>"
            ],
            "image/png": 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Q4qornJLaRysXr+z+yZLE4qIAAABACCKJHqKcOz1VVJboaFmio1t0jN0ul9wpKZKkmo0b/RYbAAAIbb5+6LHtKIk+wJNEz/u1RI4aV4CjAQAAANCWSKKHKGdtP3RrC6vQvczsbElS9c8bDjkmAAAAaW87l8iYsABHsldiRrRikyPkdpravnF3oMMBAAAA0IZIooco127Pw19YbSV5S7m7epLoNRt+PuSYAAAApH2T6O2nEt0wDGX399w30dIFAAAACC0k0UOUa5fn4c+amHhIx3Fnd5VEJToAAPAPt9tUTWX764kuSV36ehYXpRIdAAAACC0k0UOUd1FRa1LSIR3H9Fai//KLTBf9QQEAwKGpqXRKpmSxGrJFWgMdTh2H9fbcN+3cXqGqPfYARwMAAACgrZBED0GGwyF3RYUkPyTRMzJkREbKrK6WfUuuP8IDAAAhrGafVi6GYQQ4mrqi48OV3DlGkrR9Y0lggwEAAADQZkiih6Cw8nJJkhEdLUtExKEdzGJRRO/ekuiLDgAADp23H3pEdPtZVHRfh/WhpQsAAAAQakiihyBvEj3sEKvQvSL79JFEX3QAAHDovP3QI9pZP3SvLrUtXbZvIIkOAAAAhAqS6CHIm0Q/1EVFvSL6epLoNT9TiQ4AAA6Nb1HRdlqJ3rl3omRIuwsqVVFaE+hwAAAAALSB9vl0glblS6L7qxK9b19JUvXGjX45HgAACF2+di4x7ec2df369XV+jk62qnKnS5+//61SDw9v8nFSU1OVnZ3t7/AAAAAAtLL283SCNuPvJLq3J7ozP1+usjJZ4+P9clwAABBaTLcpe1VtO5fowLdz2bm7WJI0YcKEOtvPy7lapx5xvp5/Yon+32ePNfl40dHRWr9+PYl0AAAAoIMJaBK9W7du2rJlS73t11xzjebPn6/q6mrNmDFDS5YsUU1NjUaOHKknn3xSGRkZvrG5ubm6+uqr9fHHHys2NlaTJk3S3LlzFRbG3wcaYpX/27lY4+IUlpEhZ2Gh7L//rqjBg/1yXAAAEFpqqp0yTcmwSOGR1kCHoz3lZZKkaZfdrsGDhvi2O/ZYVb1dOvno0Tpz7KlNOtbmrb/pjnnTtWPHDpLoAAAAQAcT0EzzV199JZfL5fv5p59+0umnn64LLrhAkjRt2jT997//1dKlS5WQkKBrr71W5513nj7//HNJksvl0ujRo5WZmamVK1cqPz9fl1xyiWw2m+69996AXFN718Vmk2GaUliYLHFxfjtuRM8echYWquY3kugAAKBlair2VqEbhhHgaPbq0qmr+vYc6PvZUePSd9u3yl1j1eHZ/RRmC3zCHwAAAEDrCejComlpacrMzPS93n77bfXs2VMnn3yySktLtWDBAs2bN0+nnnqqhgwZooULF2rlypVavXq1JGnZsmVat26dXnzxRQ0ePFijRo3SnDlzNH/+fNnt9kBeWrvVPTxCkqcK3Z8Pp+E9ekqSan77zW/HBAAAoaWm0tMPvb0uKupli7AqojbG8t0sLgoAAAAEu4Am0fdlt9v14osv6rLLLpNhGFqzZo0cDoeGDx/uG9O3b19lZ2dr1apVkqRVq1Zp0KBBddq7jBw5UmVlZVq7dm2j56qpqVFZWVmdV6joGu7pL+qvVi5eET17SJLsJNEBAEALVXsr0WMC3w/9YGKTPYUJJNEBAACA4NdukuhvvPGGSkpKNHnyZElSQUGBwsPDlbhfsjcjI0MFBQW+Mfsm0L37vfsaM3fuXCUkJPheWVlZ/ruQdq6LLVySZE1I8OtxfZXov//u1+MCAIDQ4a1Ej2jnleiSFJcUKUkq31Ud4EgAAAAAtLZ2k0RfsGCBRo0apc6dO7f6uWbNmqXS0lLfa+vWra1+zvYi21ZbiR4f79fjRhzuSaI7tm2Tu5qHSQAA0Hw1lZ5K9PbezkWSYpM8legVJXa53WaAowEAAADQmtpFEn3Lli364IMPdPnll/u2ZWZmym63q6SkpM7YwsJCZWZm+sYUFhbW2+/d15iIiAjFx8fXeYWKLrXtXCx+vmZrcrKnut00Zd+82a/HBgAAwc8091lYtAO0c4mMtclqs8jtNlVZxlo8AAAAQDBrF0n0hQsXKj09XaNHj/ZtGzJkiGw2mz788EPftg0bNig3N1c5OTmSpJycHP34448qKiryjVm+fLni4+PVv3//truAjsLt1mFhtZXofm7nYhiGwnvWtnT5lb7oAACgeUyn4anoNqTwqPZfiW4Yhq8anZYuAAAAQHALeBLd7XZr4cKFmjRpksLC9j4wJSQkaMqUKZo+fbo+/vhjrVmzRpdeeqlycnI0dOhQSdKIESPUv39/TZw4Ud9//73ef/993XrrrZo6daoiIiICdUntlrFrl8ItFpmGIUtMjN+P71tc9HeS6AAAoHncDkOSFBEZJovFCHA0TRObWNvSpZRKdAAAACCYBbzM54MPPlBubq4uu+yyevseeeQRWSwWjR07VjU1NRo5cqSefPJJ336r1aq3335bV199tXJychQTE6NJkyZp9uzZbXkJHYZRW7Hvio6WYfH/3098i4v+xuKiAACgeUy7594kIibgt6dNFuNNopfUBDgSAAAAAK0p4E8pI0aMkGk2vBhTZGSk5s+fr/nz5zf6/q5du+qdd95prfCCijeJ7oyObpXjexcXpRIdAAA0l9tRm0SPbv/90L1iEsMleRZEddhdsoVbAxwRAAAAgNYQ8HYuaDu+SvRWaOUiSRE9PO1cajZvkelytco5AABAcHLbPS1cIjtQJXqYzeqLl2p0AAAAIHiRRA8hRmHrVqKHdeokIzxccjjkyM9vlXMAAIDgtLcSveMk0aV9W7rQFx0AAAAIViTRQ4illSvRDYtFtqwsSZJ9y5ZWOQcAAAhObl9P9I7TzkWiLzoAAAAQCkiih5DW7okuSeFdu0oiiQ4AAJouOiJOcnvauXTYSvTSmkbX+QEAAADQsZFEDxGu8nIZe/Z4/t0GSXQHSXQAANBEqfGdJUm2CKus1o51exodFy7DkJx2t+xVzkCHAwAAAKAVdKynFLSYY+tWSdIup1OmrfW+Jh3eNVuSZN+S22rnAAAAwSWtNoke0YEWFfWyWA1Fx4dLksrpiw4AAAAEJZLoIcJem0Tf5nC06nlo5wIAAJrLW4keGd2x+qF7RSd4WrpUltEXHQAAAAhGJNFDhDM/X5K0vbWT6Nm1lejbtsl08pVmAABwcGkJh0nqeP3QvWISPJXolaVUogMAAADBiCR6iHDk5UmS8p2tm0QP69RJRni45HDIUVDQqucCAADBIbUDt3OR5GvnUllmZ3FRAAAAIAiRRA8RjjxPJXpeK1eiGxaLbNlZkiT7Zlq6AACAg0vr4O1couJskndx0WpXoMMBAAAA4Gck0UOErxLd0fotVsKzvX3RN7f6uQAAQMfmcphKiEmR1HEr0S1Wi6JiPX8AqCyjpQsAAAAQbEiihwhHbU/0vFZu5yLtXVzUkZvb6ucCAAAdW/Uet+cfFlNhNmtggzkE0fRFBwAAAIIWSfQQ4K6qkmvXLklSfiu3c5H2JtFp5wIAAA6mutTT/sQS7g5wJIcmOj5CklRZVhPgSAAAAAD4G0n0EODI9yzwaUZFqszd+g+o4V2zJUn2LSTRAQDAgdWUee5NLLaOnUSPiacSHQAAAAhWJNFDgLcfupmS2ibn81Wib98u09n6PdgBAIFx3333yTAM3Xjjjb5t1dXVmjp1qlJSUhQbG6uxY8eqsLCwzvtyc3M1evRoRUdHKz09XTNnzpST+SJkVXuT6OFmgCM5NNG1SXR7tUsOO4uLAgAAAMGEJHoIcOTXJtFT2yaJHpaZKSM8XHI4fL3YAQDB5auvvtIzzzyjI444os72adOm6a233tLSpUu1YsUK5eXl6bzzzvPtd7lcGj16tOx2u1auXKnFixdr0aJFuv3229v6EtBOVAdJJbrVZlFEtGdhVKrRAQAAgOBCEj0E7K1ET2mT8xkWi2zZWZLoiw4Awai8vFzjx4/XP//5TyUlJfm2l5aWasGCBZo3b55OPfVUDRkyRAsXLtTKlSu1evVqSdKyZcu0bt06vfjiixo8eLBGjRqlOXPmaP78+bLbSTyGIm8S3ejgPdGlvdXoVXv4bxkAAAAIJiTRQ4Azz1MNbqa2TRJdksK7dpMk2XNJogNAsJk6dapGjx6t4cOH19m+Zs0aORyOOtv79u2r7OxsrVq1SpK0atUqDRo0SBkZGb4xI0eOVFlZmdauXdvoOWtqalRWVlbnhY7P5XSrpjw42rlIUlRcbV90kugAAABAUAkLdABofd6WKm3VzkXapy86i4sCQFBZsmSJvvnmG3311Vf19hUUFCg8PFyJiYl1tmdkZKigoMA3Zt8Eune/d19j5s6dq7vuuusQo0d7s2dntWRKNY4qxVo7fhI9Ot4mSaoqcwQ4EgAAAAD+RCV6CGjrhUUlKTw7WxJJdAAIJlu3btUNN9ygl156SZGRkW167lmzZqm0tNT32rp1a5ueH62jtLhKkrSjLF+GEeBg/MBbiV5Vbpfp7vh/FAAAAADgQRI9yJkulxyFhZ5/p7VhEr2bpxLdsSW3zc4JAGhda9asUVFRkY4++miFhYUpLCxMK1as0OOPP66wsDBlZGTIbrerpKSkzvsKCwuVmZkpScrMzFRh7by0737vvsZEREQoPj6+zgsdnzeJXly2PcCR+EdEdJgsVkOmW6qupBodAAAACBYk0YOcc8cOyeGQrFaZ+329vjX52rls2ybT6Wyz8wIAWs9pp52mH3/8Ud99953vdcwxx2j8+PG+f9tsNn344Ye+92zYsEG5ubnKycmRJOXk5OjHH39UUVGRb8zy5csVHx+v/v37t/k1IbBKiyslSTvK8gIciX8YhqGouNqWLntIogMAAADBgp7oQc7bysWWkaEqq7XNzhuWkSEjIkJmTY0ceXm+9i4AgI4rLi5OAwcOrLMtJiZGKSkpvu1TpkzR9OnTlZycrPj4eF133XXKycnR0KFDJUkjRoxQ//79NXHiRD3wwAMqKCjQrbfeqqlTpyoiIqLNrwmBVeatRC8NjiS65GnpUlFiV1WZXeoUE+hwAAAAAPgBlehBzlm7SFvYAb4i3xoMi0Xh2VmSJDstXQAgZDzyyCM666yzNHbsWJ100knKzMzUa6+95ttvtVr19ttvy2q1KicnRxMmTNAll1yi2bNnBzBqBMrenujBlUSXpEoq0QEAAICgQSV6kHMUePrM2to4iS5Jtq5dVfPLr57FRU8c1ubnBwC0vk8++aTOz5GRkZo/f77mz5/f6Hu6du2qd955p5UjQ3vndpsq3RF8SfRoXzsXe4AjAQAAAOAvVKIHuUBVoktSeHZtX/QtW9r83AAAoH2rKKmR22nKMKTd5UUHf0MHERXvqUSvqXTK5XQHOBoAAAAA/kASPcg5Cr2V6Bltfm7f4qK5JNEBAEBdpUWeRUUj4i1ym8GTbLaFW2WL8KxDw+KiAAAAQHAgiR7kfJXoGQGoRK9Nojs2k0QHAAB1efuhR8YH3+1olLelSzktXQAAAIBgEHxPLajDURTASnTvwqJ5eTJdrjY/PwAAaL9Ki2qT6AnBdzsaGetJoleXU4kOAAAABIPge2qBj+lyyVlULCkwPdHDMjJk2GySwyFHfkGbnx8AALRfeyvRrQGOxP+iYj190WnnAgAAAAQHkuhBzLlzp+R0SlarwlJT2/z8htUqW5cukiTH1tw2Pz8AAGi/Sos9PdGDu50LSXQAAAAgGATfUwt8nLWLioalpcmwBqbKy+Zt6ZK7NSDnBwAA7Y9pmkHdziWqtp2LvcoplzN4Fk0FAAAAQlXwPbXAx1G7qKgto+37oXuFZ2VLkuy5LC4KAAA8KkvtcjrcMiyGImKD73Y0LNyqsHDPdVVXUI0OAAAAdHTB99QCH2dBbSV6APqhe4Vne5LoDirRAQBALW8rl7jkCFmsRoCjaR30RQcAAACCB0n0IOYsrK1EzwxcJbqvnctWkugAAMCjpLaVS0J6dIAjaT30RQcAAACCB0n0IObwVqJntIdK9FyZphmwOAAAQPtRWuxJoiemRQU4ktYTWdsXvbrcHuBIAAAAABwqkuhBzFnQDirRu3SRDEPuykq5du0KWBwAAKD9KA2FSvTaJDrtXAAAAICOjyR6EHMUBr4nuiU83Hd+e25uwOIAAADth7cnekIQV6JHxXl6otdUOuV2uQMcDQAAAIBDQRI9SJmmubcSPSNwlehS3ZYuAAAgtJmm6WvnkpAevEn0sHCLrDbPrXZ1hTPA0QAAAAA4FCTRg5Rr926ZDs/Xh8PS0gIaS7h3cdFcFhcFACDUVe1xyFHtkgwpPiV4k+iGYSgyxtsXnZYuAAAAQEdGEj1IeavQrSkpMsLDAxqLLctTiW7fSiU6AAChzluFHpcU6avUDlbevujVFSTRAQAAgI4s4E8u27dv14QJE5SSkqKoqCgNGjRIX3/9tW+/aZq6/fbb1alTJ0VFRWn48OH65Zdf6hxj165dGj9+vOLj45WYmKgpU6aovLy8rS+lXXEUFUkKfCsXaW8luoNKdAAAQp6vH3oQt3LxiogJk0QSHQAAAOjoAppE3717t0444QTZbDa9++67WrdunR5++GElJSX5xjzwwAN6/PHH9fTTT+uLL75QTEyMRo4cqerqat+Y8ePHa+3atVq+fLnefvttffrpp7riiisCcUnthrM2iR6Wnh7gSCRbVm07l60k0QEACHWlRbX90IN4UVEv2rkAAAAAwSEskCe///77lZWVpYULF/q2de/e3fdv0zT16KOP6tZbb9WYMWMkSS+88IIyMjL0xhtvaNy4cVq/fr3ee+89ffXVVzrmmGMkSU888YTOPPNMPfTQQ+rcuXO989bU1Kimpsb3c1lZWWtdYsA4i4oltY8kundhUdfOnXKVV8gaGxPgiAAAQKD4FhVNiw5wJK0vcp92LlFmgIMBAAAA0GIBrUR/8803dcwxx+iCCy5Qenq6jjrqKP3zn//07d+0aZMKCgo0fPhw37aEhAQdd9xxWrVqlSRp1apVSkxM9CXQJWn48OGyWCz64osvGjzv3LlzlZCQ4Htl1VZKBxNncftJolvj4mSt/XaBg77oAACEtNKi0GnnEhntqVdxOU2ZLiPA0QAAAABoqYAm0X///Xc99dRT6tWrl95//31dffXVuv7667V48WJJUkHt4pgZ+/X1zsjI8O0rKChQ+n6J4rCwMCUnJ/vG7G/WrFkqLS31vbYGYZuRve1c0gIciYetti+6nb7oAACEtL2V6MGfRLdYLQqP8iTS3XaS6AAAAEBHFdB2Lm63W8ccc4zuvfdeSdJRRx2ln376SU8//bQmTZrUaueNiIhQREREqx2/PfAl0dPaRxI9PCtb1d//QCU6AAAhrLrCoZpKpyQpPgSS6JIUGRsme5VTbntAa1cAAAAAHIKAJtE7deqk/v3719nWr18//d///Z8kKTMzU5JUWFioTp06+cYUFhZq8ODBvjFFtQljL6fTqV27dvneH4raamHR9evXN2lcmM0mm6S8b77Vlm++qbc/NTVV2bW90wEAQHDyLioakxghW7g1wNG0jcgYm8qKq0miAwAAAB1YQJPoJ5xwgjZs2FBn28aNG9W1a1dJnkVGMzMz9eGHH/qS5mVlZfriiy909dVXS5JycnJUUlKiNWvWaMiQIZKkjz76SG63W8cdd1zbXUw7Yjqdcu7cKUmytVISvbCsTIakCRMmNGn8mPh4ze3UWV+99ZamPPVkvf3RUVFa//PPJNIBAAhipcW1/dBDpApd8iTRJckkiQ4AAAB0WAFNok+bNk3HH3+87r33Xl144YX68ssv9eyzz+rZZ5+VJBmGoRtvvFF33323evXqpe7du+u2225T586ddc4550jyVK6fccYZ+stf/qKnn35aDodD1157rcaNG6fOnTsH8OoCx7lzl+R2S1arrMnJrXKOsqpKmZLuPftPOrZv34OOD9+5U/r8c/0hNVUfXHxxnX0bCwp0zb9e0I4dO0iiAwAQxHz90ENgUVGvyFhPEp1KdAAAAKDjCmgS/Q9/+INef/11zZo1S7Nnz1b37t316KOPavz48b4xN998syoqKnTFFVeopKREw4YN03vvvafIyEjfmJdeeknXXnutTjvtNFksFo0dO1aPP/54IC6pXfC1cklNlWFt3a9K90hN1RFZWQcd505O1q7PP1dYdbUGde7c6nEBAID2x9vOJRQr0d12QxaDRDoAAADQEQU0iS5JZ511ls4666xG9xuGodmzZ2v27NmNjklOTtbLL7/cGuF1SM7itumH3hxGdLQUFiY5nXLv2SNrYmKgQwIAAG1sbzuX6ABH0nbCI62yWAy53VJyXOiu1wMAAAB0ZJTDBKG2WlS0OQzDkDUhQZLkKi0NcDQAACAQQrGdi2EYiojx1K1kJB7823sAAAAA2h+S6EFobxI9LcCR1GWNj5dEEh0AgFBUU+lQ1R6HpNBq5yLt7YuekUASHQAAAOiISKIHIWdxsSTJ1o4q0SXtrUQvKwtwJAAAoK3tLvS0colJjFB4ZMA7CrYpb1/09MQuAY4EAAAAQEuQRA9CDm8lelr7qkS30M4FAICQVVKbRE/MCK0qdGmfJDqV6AAAAECHRBI9CDmLPJXo7aknurS3Et1NEh0AgJBTUuBNoscEOJK2523nQiU6AAAA0DGRRA9C7XFhUWmfnuhlZTJNM8DRAACAtuStRE/KiA5wJG0vsnZh0cSYVLns3AMBAAAAHQ1J9CBjOhxy7dwpqf0l0S1xcZLFIrlccldUBDocAADQhnb72rmEXhI9zGaVYXVLkqrKXAGOBgAAAEBzkUQPMs4dOzz/sNlkTUwMaCz7MywWWWJjJdHSBQCAUGK6TZUWVUkKzZ7okmQJ91SgV5e4AxwJAAAAgOYiiR5kfK1c0lJlWNrfr9fK4qIAAIScPbuq5XK6ZQkzFJcSqkn02kr0UpLoAAAAQEfT/rKsOCSO2iS6La19tXLxIokOAEDo8fZDT0iLlsViBDiawPAl0Uto5wIAAAB0NCTRg0x7XVTUy5dELysLcCQAAKCt7A7hRUW9jNokejWV6AAAAECHQxI9yDiLiiW13yS6JT5eEj3RAQAIJSUhvKio1952Li6ZphngaAAAAAA0B0n0ILO3J3pagCNpGJXoAACEnr1J9NDshy55FhZ1uV1yO6TKMnugwwEAAADQDCTRg4yzuH1XoltrK9HNmhq5q6sDHA0AAGgLe5PoMQGOJHAMQ9q1p1CSVFpUGeBoAAAAADQHSfQg0957ohs2myzRnq9ys7goAADBz17tVPnuGklSUmbotnORpB1l2yVJJUVVAY4EAAAAQHOQRA8ye5Po7bOdiyRZalu60BcdAIDgt7vAU3UdHR+uyBhbgKMJrKJSTxK9lCQ6AAAA0KGQRA8ibrtdrpISSZKtnVaiS/RFBwAglOzOr5AkJXUK3VYuXsXeJHox7VwAAACAjoQkehBxFnn6oRvh4b5q7/bIl0SnEh0AgKC3qzaJnkwSXcW0cwEAAAA6JJLoQWTffuiGYQQ4msZ5FxcliQ4AQPDb7Uuih3Y/dGnfSvQqmaYZ4GgAAAAANBVJ9CDS3hcV9fL1RKedCwAAQc9biZ6USSX6zj0FkiE5a1yqLLMHOhwAAAAATUQSPYh0lCS6t52Lu6JCpsMR4GgAAEBrcdhdKttZLYme6JLkcjsVEeu5/S4toi86AAAA0FGQRA8izmJvEj0twJEcmBERISMiQhKLiwIAEMxKCiolU4qMsSkqzhbocNqFyATP7Td90QEAAICOgyR6EPFVoqe18yS6YdAXHQCAEOBr5dIpul2v19KWIuO9legk0QEAAICOgiR6EHEWF0uSbO28nYtEX3QAAELB3kVFaeXiFZVglUQ7FwAAAKAjIYkeRBwdpCe6JCrRAQAIAXsr0Umie/nauRRTiQ4AAAB0FCTRg4izyFOJ3iGS6LWV6CTRAQAIXt4kenImSXQvXzuX4iqZphngaAAAAAA0BUn0IOGuqvK1RukISXQLSXQAAIKao8al0tpq65QusQGOpv2IiLPIsBhy1rhUWWYPdDgAAAAAmoAkepDw9kM3oqJkiW3/D6reSnR3ebnkdgc4GgAA4G+78iokU4qKsyk6PjzQ4bQbFquhuOQISfRFBwAAADoKkuhBwunrh54mwzACHM3BWWJiJKtVcrtlraInKAAAwWbn9nJJUsph7f+P+20tMT1aklRSxD0QAAAA0BGQRA8S3iS6La39t3KRJMMwfIuLhlVUBDgaAADgbzu2eZLoqbRyqSchLUqSVEoSHQAAAOgQSKIHCYevEr1jJNGlvX3RrZV8lRkAgGDjq0QniV5PQm0lOu1cAAAAgI6BJHqQcBZ5eqKHpaUFOJKmoxIdAIDgZJqmL4lOJXp9CemeSvSSYirRAQAAgI6AJHqQcHbASnRrYqLnf0miAwAQVMp316im0imLxVBSRkygw2l3vD3RS4urZJpmgKMBAAAAcDAk0YNER06ih5WXBzYQAADgVztr+6EnZkbLauN2c39xKZEyLIacNS5VltoDHQ4AAACAg+CpJkg4i2vbuXTEJHpFhayBDQUAAPjRDlq5HJA1zKK45AhJUmkxfdEBAACA9o4kepDYW4necXqiW2JjJatVhmmqs80W6HAAAICfeCvRUw4jid4Yb0uXkiL6ogMAAADtHUn0IOAqr5C7tq94WFrHqUQ3DMNXjd49PDywwQAAAL/Z4U2iU4neqARvX3SS6AAAAEC7RxI9CDiLPVXolpgYWWM71uJd3iR6V5LoAAAEhZoqp0oKPS1K0rvGBTia9ishLUqSVFpEOxcAAACgvSOJHgScRR2vH7qXNSFBktTNRhIdAIBgULylTJJn8cyoWOb3xiSke5LotHMBAAAA2j+S6EFgbz/0DphEr61E70YlOgAAQaFoyx5JVKEfjLcnemlxpUzTDHA0AAAAAA6EJHoQ6NBJ9KQkSSTRAQAIFkW1lejpXeMDHEn7FpcaKcNiyGl3q7LUHuhwAAAAABxAi5Lov//+u19Ofuedd8owjDqvvn37+vZXV1dr6tSpSklJUWxsrMaOHavCwsI6x8jNzdXo0aMVHR2t9PR0zZw5U06n0y/xdRS+JHpaWoAjaT5vJXonm02qrg5sMAAQxPw1dwMHQyV601itFsWlREryVKMDAAAAaL9alEQ//PDDdcopp+jFF19U9SEmPgcMGKD8/Hzf67PPPvPtmzZtmt566y0tXbpUK1asUF5ens477zzffpfLpdGjR8tut2vlypVavHixFi1apNtvv/2QYupovAuLhqV3vCS6JTJSbptNkmTs9wcSAID/+HPuBhpTVW7Xnp2e/77SskmiH0xiGn3RAQAAgI6gRUn0b775RkcccYSmT5+uzMxMXXnllfryyy9bFEBYWJgyMzN9r9TUVElSaWmpFixYoHnz5unUU0/VkCFDtHDhQq1cuVKrV6+WJC1btkzr1q3Tiy++qMGDB2vUqFGaM2eO5s+fL7u98a/F1tTUqKysrM6rI3PUVqLbOmA7F0lyxsZKkoz8ggBHAgDBy59zN9AYbxV6Yka0IqJtAY6m/Uvw9kUvohIdAAAAaM9alEQfPHiwHnvsMeXl5en5559Xfn6+hg0bpoEDB2revHkqLi5u8rF++eUXde7cWT169ND48eOVm5srSVqzZo0cDoeGDx/uG9u3b19lZ2dr1apVkqRVq1Zp0KBBysjI8I0ZOXKkysrKtHbt2kbPOXfuXCUkJPheWVlZzf0I2hVnkefz7og90SXJGRMjSbIU5Ac4EgAIXv6cu4HGFNf2Q6cKvWkSaivRS6lEBwAAANq1sEN6c1iYzjvvPI0ePVpPPvmkZs2apZtuukl/+9vfdOGFF+r+++9Xp06dGn3/cccdp0WLFqlPnz7Kz8/XXXfdpRNPPFE//fSTCgoKFB4ersTantleGRkZKijwVCwXFBTUSaB793v3NWbWrFmaPn267+eysrIOm0g3TVPO4g6eRKcSHQDazKHO3YBXbm6uduzYUWfbxu/LJUmOsFJ98803TTrO+vXr/R5bR5GQTjsXAAAAoCM4pCT6119/reeff15LlixRTEyMbrrpJk2ZMkXbtm3TXXfdpTFjxhzwq+KjRo3y/fuII47Qcccdp65du+rVV19VVFTUoYR2QBEREYqIiGi147cld3m5zCrPg1dHXFhU2ptEt+RTiQ4Are1Q525A8iTQ+/Xrp8rKum1I7rvkNcVGJei6W/6iTYXrmnXM8vJyf4bYISR627kUV8o0TRmGEeCIAAAAADSkRUn0efPmaeHChdqwYYPOPPNMvfDCCzrzzDNlsXi6w3Tv3l2LFi1St27dmnXcxMRE9e7dW7/++qtOP/102e12lZSU1KlGLywsVGZmpiQpMzOz3oN+Ye3ilN4xwc5Z2w/dEh8vSyv+4aE1edu5GPn5PEACQCvx19z91FNP6amnntLmzZsleRYIv/32231/GK+urtaMGTO0ZMkS1dTUaOTIkXryySfrfHMsNzdXV199tT7++GPFxsZq0qRJmjt3rsLCDulv+2hDO3bsUGVlpe6aPk/dsnpKklw1hio3xUiGqTtumaumTucrv16hZ16aF5IL3salRsqwGHLa3aostSsmMTiKPAAAAIBg06Kn1aeeekqXXXaZJk+e3OhXvtPT07VgwYJmHbe8vFy//fabJk6cqCFDhshms+nDDz/U2LFjJUkbNmxQbm6ucnJyJEk5OTm65557VFRUpPTaVibLly9XfHy8+vfv35JL63C8SfSw9I5ZhS5JLm8SvaJCrpIShSUlBTgiAAg+/pq7u3Tpovvuu0+9evWSaZpavHixxowZo2+//VYDBgzQtGnT9N///ldLly5VQkKCrr32Wp133nn6/PPPJUkul0ujR49WZmamVq5cqfz8fF1yySWy2Wy69957/X7daF3dsnqqb8+BkqTi3D3arJ2KS4pU38O7N/kYm7f+1lrhtXtWq0VxKZEqK65SSVElSXQAAACgnWpREv2XX3456Jjw8HBNmjTpgGNuuukmnX322eratavy8vJ0xx13yGq16uKLL1ZCQoKmTJmi6dOnKzk5WfHx8bruuuuUk5OjoUOHSpJGjBih/v37a+LEiXrggQdUUFCgW2+9VVOnTg2adi0H402i2zpoP3RJMsPClO9wqJPNJvumzSTRAaAV+GvuPvvss+v8fM899+ipp57S6tWr1aVLFy1YsEAvv/yyTj31VEnSwoUL1a9fP61evVpDhw7VsmXLtG7dOn3wwQfKyMjQ4MGDNWfOHN1yyy268847FR4e3uB5a2pqVFNT4/u5rKzsoNeDtrVnl6eSPDY5MsCRdCyJaVEqK65SaXGVDuvNPRAAAADQHlla8qaFCxdq6dKl9bYvXbpUixcvbvJxtm3bposvvlh9+vTRhRdeqJSUFK1evVpptb29H3nkEZ111lkaO3asTjrpJGVmZuq1117zvd9qtertt9+W1WpVTk6OJkyYoEsuuUSzZ89uyWV1SA5vJXoH7YfutdlulyTZa9sDAAD8y19z975cLpeWLFmiiooK5eTkaM2aNXI4HBo+fLhvTN++fZWdna1Vq1ZJklatWqVBgwbVae8ycuRIlZWVae3atY2ea+7cuUpISPC9OuqC4MGsfJfnjxxxJNGbJcHbF72o8iAjAQAAAARKi5Loc+fOVWpqar3t6enpzfoq9pIlS5SXl6eamhpt27ZNS5YsUc+ePX37IyMjNX/+fO3atUsVFRV67bXX6vU679q1q9555x1VVlaquLhYDz30UEj1VHUWFUuSwjpwJbpEEh0AWpu/5m5J+vHHHxUbG6uIiAhdddVVev3119W/f38VFBQoPDy8zlomkpSRkaGCggJJUkFBQZ0Eune/d19jZs2apdLSUt9r69atzYoZrcte7VRNlVOSFEtLkmZJSPesaVNaVBXgSAAAAAA0pkXZ5tzcXHXvXr/XZdeuXZWbm3vIQaHpfD3R0zp2En2TN4m+aVOAIwGA4OTPubtPnz767rvvVFpaqn//+9+aNGmSVqxY4a9QGxQREREyrdo6oj21VejR8eGy2lpUoxGyEtI8SfQSkugAAABAu9Wip5z09HT98MMP9bZ///33SklJOeSg0HR7Fxbt2En0LQ4q0QGgNflz7g4PD9fhhx+uIUOGaO7cuTryyCP12GOPKTMzU3a7XSUlJXXGFxYW+r5JlpmZqcLCwnr7vfvQMZX7+qHzh47mSvS2cymulGmaAY4GAAAAQENalES/+OKLdf311+vjjz+Wy+WSy+XSRx99pBtuuEHjxo3zd4w4gGBJovvauWzZItPtDnA0ABB8WnPudrvdqqmp0ZAhQ2Sz2fThhx/69m3YsEG5ubnKycmRJOXk5OjHH39UUe38JUnLly9XfHy8+vfvf0hxIHDKdniS6PRDb7641EgZFkNOu1uVpfZAhwMAAACgAS1q5zJnzhxt3rxZp512mq//uNvt1iWXXNLsvqpoOdM05SwOjp7oeQ6HTKtVstvlzM+X7bDDAh0SAAQVf83ds2bN0qhRo5Sdna09e/bo5Zdf1ieffKL3339fCQkJmjJliqZPn67k5GTFx8fruuuuU05OjoYOHSpJGjFihPr376+JEyfqgQceUEFBgW699VZNnTqVdi0dVE2VU9UVDklSfCpJ9OayWi2KS4lUWXGVSooqFUNPeQAAAKDdaVESPTw8XP/v//0/zZkzR99//72ioqI0aNAgde3a1d/x4QDcpaUyayu4w9LTAhzNoXFJMjMyZOTlqWbzZpLoAOBn/pq7i4qKdMkllyg/P18JCQk64ogj9P777+v000+XJD3yyCOyWCwaO3asampqNHLkSD355JO+91utVr399tu6+uqrlZOTo5iYGE2aNEmzZ8/26/Wi7ZQVe3p5xyRGKMxmDXA0HVNiepTKiqtUWlylw3onBTocAAAAAPtpURLdq3fv3urdu7e/YkEDcnNztWPHjgb3GVu3KlKSGRur73766aDHWr9+vZ+j8y+zU6aUlyf7ps3SCScEOhwACEqHOncvWLDggPsjIyM1f/58zZ8/v9ExXbt21TvvvNPiGNC+lNYm0b0LZKL5EtKiJe1SaVFloEMBAAAA0IAWJdFdLpcWLVqkDz/8UEVFRXLv18P6o48+8ktwoS43N1f9+vZVZVVVg/uPj47Wc1nZ2rhzh84dMqTJxy0vL/dXiH7l7tRJVkn2TZsCHQoABB3mbrQG09zbDz0hjVYuLZWQ7vkDRElRw/d8AAAAAAKrRUn0G264QYsWLdLo0aM1cOBAGYbh77ggaceOHaqsqtKTEy9R78zMevujcnOl775T1y5Z+uCCCw96vA/WrtV97/xX1dXVrRHuITM7d5Yk2Tf9HuBIACD4MHejNbirLXI53bLaLPTyPgTeKv5SkugAAABAu9SiJPqSJUv06quv6swzz/R3PGhA78xMHZGVVW97ZWGhKiUlpKSoSwP79/dLYUErROc/3iR6zabNgQ0EAIIQczdag7PccysZnxrJH2YOQWJ6tCSptLhSpmnyWQIAAADtjKUlbwoPD9fhhx/u71jQTO5KT99MS0xMgCPxD3enTpIkZ36+3BUVAY4GAIILczdag7Pcs5Ao/dAPTVxqpAyLIafdrYoSe6DDAQAAALCfFiXRZ8yYoccee0ymafo7HjSDN9EcLEl0xcXJmpQkSarZvDmwsQBAkGHuhr+lxneSu8YqGVJSRnSgw+nQrFaL4lI8PeVLi1lcFAAAAGhvWtTO5bPPPtPHH3+sd999VwMGDJDNZquz/7XXXvNLcDiwoEuiSwrv0UNVa9bI/vsmRQ0YEOhwACBoMHfD3wZ3P0mSFJ8SqbBwa4Cj6fgS06NUVlyl0qIqHdY7KdDhAAAAANhHi5LoiYmJOvfcc/0dC5rJl0SPDp7qr4ge3T1J9E2bAh0KAAQV5m742+DuJ0qSkjKD5z4kkBLSoiXtohIdAAAAaIdalERfuHChv+NAM5mmGXQ90SUpvHsPSVLNpt8DHAkABBfmbvhTTblb3TL6STJp5eInCemevvIlRVUBjgQAAADA/lrUE12SnE6nPvjgAz3zzDPas2ePJCkvL0/l5eV+Cw6NMysrJbdbUnBVood37yZJsv9OJToA+BtzN/xl1ybP4pfWKLdskS2qycB+EtM993OlRVSiAwAAAO1Ni556tmzZojPOOEO5ubmqqanR6aefrri4ON1///2qqanR008/7e84sZ99W7kY1uDpQxrRw1OJbt+8WabbLcPS4r/zAAD2wdwNfyr+xSFJCotzBjiS4JGQ5qlELy2qkmmaMgwjwBEBAAAA8GpRhvKGG27QMccco927dysqKsq3/dxzz9WHH37ot+DQOJc3iR4bG+BI/Mt22GEybDaZNTVy5OUHOhwACBrM3fCXHdvKVbHDJafLobB4R6DDCRpxqZEyLIacDrcqSuyBDgcAAADAPlpUif6///1PK1euVHh4eJ3t3bp10/bt2/0SGA7MXfvV+2Dqhy5JRliYwrt1Vc0vv8q+6XeFdzks0CEBQFBg7oa//Lza80fuH7es0kkDjg5wNMHDarUoLiVSZcVVKi2uVGxSRKBDAgAAAFCrRZXobrdbLper3vZt27YpLi7ukIPCwbmDtBJdksK7dZck2X9ncVEA8BfmbviDy+XWxi8KJElfbHg/wNEEn8T0vS1dAAAAALQfLUqijxgxQo8++qjvZ8MwVF5erjvuuENnnnmmv2LDAQRrJbokhdf2Ra9hcVEA8BvmbvhD7k87VbXHIVuUoXXbvgp0OEEnoXZx0RIWFwUAAADalRa1c3n44Yc1cuRI9e/fX9XV1frzn/+sX375RampqXrllVf8HSMaEMyV6BE9aivRN5FEBwB/Ye6GP/z0qaf1T2qvcLnd9b/ZgEPjW1y0mEp0AAAAoD1pURK9S5cu+v7777VkyRL98MMPKi8v15QpUzR+/Pg6i5Wh9YREJfom2rkAgL8wd+NQlRRWKnftLsmQMvuHH/wNaLbE2kr0UirRAQAAgHalRUl0SQoLC9OECRP8GQuayDRNuWqT6NYgrEQP7+6pRHcV75CrrEzW+PgARwQAwYG5G4fixxXbJEldB6YoMp4q9Nbgq0QvqpJpmjIMI8ARAQAAAJBamER/4YUXDrj/kksuaVEwaBrTbpecTknBWYlujY1VWFqanMXFsm/apKgjjwx0SADQ4TF341DYq536eWW+JGnQH7toZ82WAEcUnOJSI2VYDDkdblWU2BWbFBHokAAAAACohUn0G264oc7PDodDlZWVCg8PV3R0NA/irczbD92IiJBhswU4mtYR3qOHnMXFqiGJDgB+wdyNQ7HxiwLZq11KSI9Sdr9k7fyOJHprsFotik+JVGlxlUqLKkmiAwAAAO2EpSVv2r17d51XeXm5NmzYoGHDhrE4WRsI5n7oXuHexUV/Z3FRAPAH5m60lGma+uETz4Kig07uIsNCi5HWlJDO4qIAAABAe9OiJHpDevXqpfvuu69epRv8z1uJbgnCfuheEbV90e0sLgoArYa5G02xfcNu7c6vUFiEVX2P7xTocIJeQu3ioiUsLgoAAAC0G35LokueBcvy8vL8eUg0ICQq0bv3kCTVUIkOAK2KuRsH82NtFXrf4zIVEdXiNenRRPsuLgoAAACgfWjRk9Cbb75Z52fTNJWfn69//OMfOuGEE/wSGBrnq0QP4iR6hLedS26uTKdTRhgP7QBwKJi70RJlO6u06ftiSZ4FRdH6EjOoRAcAAADamxZlJs8555w6PxuGobS0NJ166ql6+OGH/REXDsBVW4luDeJ2LmGdOsmIjJRZXS3Htm0K79Yt0CEBQIfG3I2WWPtpnkxTOqxPkpI7B+8f79uTpMzaJHphpdwutyxWv35xFAAAAEALtCiJ7na7/R0HmiEUKtENi0Xh3bqp5uefVfP7JpLoAHCImLvRXE67S+s+87T6OeIUqtDbSlxSpMLCLXLa3SrbUe2rTAcAAAAQOJS2dEChsLCotE9LFxYXBQCgzf3ydZGqKxyKTY5Qt0EpgQ4nZBgWQ0mZnkKJXfkVAY4GAAAAgNTCSvTp06c3eey8efNacgo0wnS5ZFZ5FpoK9iS6b3HRTSwuCgCHirkbkpSbm6sdO3YcdJxpmvrxHU/7uOTDDX33/Xd19q9fv741wkOtpMxoFefu0e6CCklpgQ4HAAAACHktSqJ/++23+vbbb+VwONSnTx9J0saNG2W1WnX00Uf7xhmG4Z8o4eOtQpfVKiMiIrDBtLJwbyX67yTRAeBQMXcjNzdX/fr1U2XlwRes7JbeTzed+w85nHZdPPVcVVSXNTiuvHadFviXtxJ9dwGLiwIAAADtQYuS6Geffbbi4uK0ePFiJSUlSZJ2796tSy+9VCeeeKJmzJjh1yCxl7v2YdUSExP0iY6IHp5KdPvvtHMBgEPF3I0dO3aosrJSd02fp25ZPQ84tjo/Qo5SKSrZ0JNz/1Vv/8qvV+iZl+apurq6tcINaUmdPH3Qd9POBQAAAGgXWpREf/jhh7Vs2TLfQ7gkJSUl6e6779aIESN4EG9FodIPXZLCu3aVJLlKSuTcvVth+/z3BgBoHuZueHXL6qm+PQc2ut/ldOu7X7ZKMnX4gCzFJUfWG7N562+tGGFwa0ornMrdLknSzrw9WrNmTaOFE6mpqcrOzvZrfAAAAADqa1ESvaysTMXFxfW2FxcXa8+ePYccFBrnqq1Et8bEBDiS1meJjlZY505y5uXLvmkTSXQAOATM3WiqXXkVcrtMRcaEKTYpuFvHtaWduz3//5swYcJBx1otYZo35R3JYdXwk89QSUXDfeyjo6O1fv16EukAAABAK2tREv3cc8/VpZdeqocffljHHnusJOmLL77QzJkzdd555/k1QNQVSpXokhTRvYcnif7774rep2cvAKB5mLvRVMVbPX+wT82KC/rWcW1pT7mnr/y0y27X4EFDDjq+4ndDbrv08KxFCotx1du/eetvumPedO3YsYMkOgAAANDKWpREf/rpp3XTTTfpz3/+sxwOh+dAYWGaMmWKHnzwQb8GiLr27YkeCsJ79FDF55+rhsVFAeCQMHejKar22FVRUiMZUuphoXGv0da6dOp6wHY6Xr/sLlJJYaXS4g5TRvf4NogMAAAAQGNalESPjo7Wk08+qQcffFC//ebpidmzZ0/FhEhiN5BCrRI9vHs3SSwuCgCHirkbTbFzu+c+IzEtSrbIFt0mwk+iYm0qKZSqyh2BDgUAAAAIeZZDeXN+fr7y8/PVq1cvxcTEyDRNf8WFRoRaJXpEjx6SJPsmKtEBwB+Yu9EY0zS1K8+TRE+mCj3gImNtkqTqcnuAIwEAAADQoiT6zp07ddppp6l3794688wzlZ+fL0maMmWKZsyY0aJA7rvvPhmGoRtvvNG3rbq6WlOnTlVKSopiY2M1duxYFRYW1nlfbm6uRo8erejoaKWnp2vmzJlyOp0tiqG9M91uuSsrJYVSJXptEn3bNrntPEQCQEu1xtyN4FJRUqOaKqcsVkOJ6dGBDifkRdUm0alEBwAAAAKvRUn0adOmyWazKTc3V9HRex+yLrroIr333nvNPt5XX32lZ555RkcccUS987z11ltaunSpVqxYoby8vDqLn7lcLo0ePVp2u10rV67U4sWLtWjRIt1+++0tuax2z6yqktxuyTBkiQ6Nh9uw9DRP1b3LJUdubqDDAYAOy99zN4LPztoq9MSMaFnDDunLivADbyW60+6W015/YVEAAAAAbadFT0jLli3T/fffry5dutTZ3qtXL23ZsqVZxyovL9f48eP1z3/+U0lJSb7tpaWlWrBggebNm6dTTz1VQ4YM0cKFC7Vy5UqtXr3aF8e6dev04osvavDgwRo1apTmzJmj+fPny36AquWamhqVlZXVeXUELm8/9OhoGZbQeLg1DEPh3btLkmroiw4ALebPuRvBx3Sb2pXvuc9I6Uwrl/bAGmZReKRVEtXoAAAAQKC1KBNbUVFRp4rNa9euXYqIiGjWsaZOnarRo0dr+PDhdbavWbNGDoejzva+ffsqOztbq1atkiStWrVKgwYNUkZGhm/MyJEjVVZWprVr1zZ6zrlz5yohIcH3ysrKalbMgRJq/dC9wnt4kuj2TZsDGwgAdGD+nLsRfPbsqpazxi2rzaL4tKhAh4Nae/uik0QHAAAAAimsJW868cQT9cILL2jOnDmSPNXCbrdbDzzwgE455ZQmH2fJkiX65ptv9NVXX9XbV1BQoPDwcCUmJtbZnpGRoYKCAt+YfRPo3v3efY2ZNWuWpk+f7vu5rKysQyTS3d5K9CDth75+/foGt4dFRsomKf/rr5X7h2MOepzU1FRlZ2f7OToA6Nj8NXcjOO0u8Ky5kpQRLYvFCHA08IqKtalsRzWV6AAAAECAtSiJ/sADD+i0007T119/Lbvdrptvvllr167Vrl279PnnnzfpGFu3btUNN9yg5cuXKzIysiVhtFhERESHrLoL1kr0wrIyGZImTJjQ4P7TY+P02GGHad2yZRr33D8PerzoqCit//lnEukAsA9/zN0ITqZpandhbRI9MzTWXOkoImPDJVGJDgAAAARai5LoAwcO1MaNG/WPf/xDcXFxKi8v13nnnaepU6eqU6dOTTrGmjVrVFRUpKOPPtq3zeVy6dNPP9U//vEPvf/++7Lb7SopKalTjV5YWKjMzExJUmZmpr788ss6xy0sLPTtCzbBWoleVlUpU9K9Z/9Jx/btW29/WFmZ9MknGhgXpw9umikZjVfIbSwo0DX/ekE7duwgiQ4A+/DH3I3gVFFql6PaJYvVUHxq2xY24MCiatu5UIkOAAAABFazk+gOh0NnnHGGnn76af39739v8YlPO+00/fjjj3W2XXrpperbt69uueUWZWVlyWaz6cMPP9TYsWMlSRs2bFBubq5ycnIkSTk5ObrnnntUVFSk9PR0SdLy5csVHx+v/v37tzi29ipYK9G9eqSm6ogG2uqYTqd2rlghi9OpgSkpQXv9ANBa/DV3IziV1LZySUiLksUaGguXdxTenuj2KqdcLres/H4AAACAgGh2Et1ms+mHH3445BPHxcVp4MCBdbbFxMQoJSXFt33KlCmaPn26kpOTFR8fr+uuu045OTkaOnSoJGnEiBHq37+/Jk6cqAceeEAFBQW69dZbNXXq1A7ZruVgfJXoIZZENsLCZImLk7usTM6SEoWH2PUDwKHy19yN4EQrl/bLFmFVmM0ip8Ot6nKHYhKC7/4WAAAA6AhaVM4yYcIELViwwN+x1PPII4/orLPO0tixY3XSSScpMzNTr732mm+/1WrV22+/LavVqpycHE2YMEGXXHKJZs+e3eqxtTXTNOXas0eSZI2LC3A0bc+alCRJcu3eHeBIAKBjaqu5Gx1LdblD1eUOGYaUkE4SvT3yVqPTFx0AAAAInBb1RHc6nXr++ef1wQcfaMiQIYrZrzJ43rx5LQrmk08+qfNzZGSk5s+fr/nz5zf6nq5du+qdd95p0fk6ErOqSnK5JAVfT/SmsCYmyrFlC0l0AGih1pq70bGVFHmq0ONSIhVmo1VIexQVa1P57hr6ogMAAAAB1Kwk+u+//65u3brpp59+8i0IunHjxjpjjAMs+oiWc+3TD92wWgMcTdsL81ail5QENhAA6GCYu3EgpcVVkjz90NE+RcbVLi66hyQ6AAAAECjNSqL36tVL+fn5+vjjjyVJF110kR5//HFlZGS0SnDYy13byiUUq9Al2rkAQEsxd6MxLqdbe3ZWS5ISaeXSbkXFhUuSqsrtAY4EAAAACF3N+t6uaZp1fn733XdVUbvYJVqXL4kegv3Qpb1JdPeePTIdVGIBQFMxd6MxZTuqZJpSRHSYr+822p/o2iR6TYVTLqc7wNEAAAAAoemQml/u/2CO1hPKi4pKkhEZKSMiQpLkKi0NcDQA0HExd8OrpKi2lUs6rVzaM1uEVWHhnlt2+qIDAAAAgdGsJLphGPX6ptJHtW24vT3RQzWJbhi0dAGAFmDuRkNM01RpbRKdVi7tn6+lyx5augAAAACB0Kye6KZpavLkyYqorQiurq7WVVddpZiYmDrjXnvtNf9FCEl7K9FDtSe6JFkTE+UsKGBxUQBoBuZuNKRqj0OOGpcsVkNxyZGBDgcHER1n056d1SwuCgAAAARIs5LokyZNqvPzhAkT/BoMGucO8XYuEouLAkBLMHejIaXFnir0uORIWax8M6G981Wil1GJDgAAAARCs5LoCxcubK04cACG0ymzulpS6LZzkfYm0Z0k0QGgyZi70ZCyHZ4kenwa/dA7gqh4TxK9knYuAAAAQEAc0sKiaBvWKs+DrhEeLkvt1/FDkTUxUZLkKilhYTwAAFrIdEt7dnn+OJ+QSiuXjiAq1iZJctrdctS4AhwNAAAAEHpIoncA3iR6KPdDlyRrfLxksUhOp2+hVQAA0DyuKqtMt2SLtCqyNjmL9s0aZlFEtOcLpCwuCgAAALQ9kugdgC+JHsKtXCTJsFplTUiQJBYXBQCghZwVVklSQmqUDIN+6B2Fty86LV0AAACAtkcSvQPwJtFDeVFRL19LF/qiAwDQIq7aJDr90DuW6HjPtwaqyhwBjgQAAAAIPSTROwBrZaUkKtGlvYuLUokOAEDzxUcny13jrUSnH3pHEu1dXLSMSnQAAACgrZFE7wB8legh3hNdohIdAIBD0a/LMZKkmIRwhYVbAxwNmsObRK8qt8vtZoF1AAAAoC2RRO8A6Im+l68SnSQ6AADN1rfLEEm0cumIwqPCZA0zZLql6nJaugAAAABtiSR6O2cRSfR9eSvR3RUVctv5OjMAAE1lmqYviZ6QShK9ozEMg5YuAAAAQICQRG/n0sLCZJimZLHIEh0d6HACzhIZKSPK8+Dvpi86AABNVrnTpbioJMliKiYpItDhoAWiSKIDAAAAAUESvZ3rFGaTJFliYmRY+HVJe1u6OGnpAgBAk5Vsc0qSrNEuWSxGgKNBS/gq0UtJogMAAABtiaxsO9fZFiaJVi77CvMuLkolOgAATeZNoofFuAIcCVoqZp9KdJO1RQEAAIA2QxK9netk81SiW0mi+7C4KAAAzeOocWlPQW0SPdoZ4GjQUpFx4TIMyeV0y3TybQIAAACgrZBEb+c6e9u5xMYGOJL2w0olOgAAzbJ9426ZbmlHWb6McEqYOyqLxVBknOfe0FXNbTwAAADQVrj7bueoRK/PV4leUiLT7Q5wNAAAtH+5a3dJktZv/UoGBcwdmrcvurvaGuBIAAAAgNBBEr2d60RP9HoscXGS1Sq5XHLv2RPocAAAaPdy1+6UJK3f9nWAI8GhiomPkEQlOgAAANCWuPtu53ztXEii+xgWCy1dAABootLiSpUWV8kwpI3bvw10ODhE0QneSnRu4wEAAIC2wt13e1ZZqTir56u6Vnqi1+FLorO4KAAAB+Rt5RKbaVW1ozLA0eBQeZPopsuihOiUAEcDAAAAhAaS6O2YsWOHJMkVHi6jtjc6PHx90UmiAwBwQLnrPEn0pC7cSwQDq9WiqNrFRbPTegc4GgAAACA0kERvx3xJ9KioAEfS/ngr0Z20cwEAoFEup1vbN3j+4JyQFRbgaOAv3mr0LJLoAAAAQJsgid6OkURvXBiV6AAQEHPnztUf/vAHxcXFKT09Xeecc442bNhQZ0x1dbWmTp2qlJQUxcbGauzYsSosLKwzJjc3V6NHj1Z0dLTS09M1c+ZMOZ3OtryUkFDwW6kcNS5FxdkUk2INdDjwk5gEz+Ki2akk0QEAAIC2QBK9HTN27pQkuaKjAxxJ++OtRDerquSurg5sMAAQQlasWKGpU6dq9erVWr58uRwOh0aMGKGKigrfmGnTpumtt97S0qVLtWLFCuXl5em8887z7Xe5XBo9erTsdrtWrlypxYsXa9GiRbr99tsDcUlBLXed514iq3+yDMMIcDTwl5jaSvTstN4yTTPA0QAAAADBj+/1tmNGMZXojTHCw2WJiZG7okKukhJZMjMDHRIAhIT33nuvzs+LFi1Senq61qxZo5NOOkmlpaVasGCBXn75ZZ166qmSpIULF6pfv35avXq1hg4dqmXLlmndunX64IMPlJGRocGDB2vOnDm65ZZbdOeddyo8PDwQlxaUvP3Qs/unqEJ5AY4G/hIVHy7JVHx0suyVJNEBAACA1kYlejtm2VEsiSR6Y1hcFAACr7S0VJKUnJwsSVqzZo0cDoeGDx/uG9O3b19lZ2dr1apVkqRVq1Zp0KBBysjI8I0ZOXKkysrKtHbt2gbPU1NTo7KysjovHFhFaY12bC2XDCm7f3Kgw4EfWa0WWSLckqSKYleAowEAAACCH0n0dswo8iTRnTExAY6kffIl0VlcFAACwu1268Ybb9QJJ5yggQMHSpIKCgoUHh6uxNq2W14ZGRkqKCjwjdk3ge7d793XkLlz5yohIcH3ysrK8vPVBJ+ttVXoaVlxioqjuj/YWCI9SfTyItYSAAAAAFobSfR2yl1RIaO2yo6e6A3z9kWnEh0AAmPq1Kn66aeftGTJklY/16xZs1RaWup7bd26tdXP2dH5WrkMoAo9GFmjPBXo5VSiAwAAAK2OnujtlH37dklSqcsl02b7/+3deXxU5b3H8e+ZfbJvJCEQFhEMKAqiQNSqtSgudfeqrQvuVsGr0qr11oJ1r7Witbi11qWVWm3V68JFERU3BAVRhLAvYUtCIPsy67l/TDIakwADSc4k+bxfr3lBznlm8p3j8sz85pnfY3Ga+MRKdACwzpQpU/TWW2/po48+Uv/+/aPHc3Nz5ff7VVlZ2WI1emlpqXKb9q/Izc3VokWLWjxeaWlp9Fxb3G633G53Bz+LniscCqt4eWRT0QEjMi1Og85gb1qJXlMWVDhsymZj41gAAACgs7ASPU4FtkSK6FsCAYuTxK/oSvSqKpkhVmEBQFcwTVNTpkzRa6+9pvfff1+DBw9ucX7MmDFyOp2aN29e9NiqVatUXFyswsJCSVJhYaGWLVumsrKy6Ji5c+cqJSVFI0aM6Jon0sOVrK+Srz4oT6JTuQekWB0HncDmDqvRX69wQKrYXmd1HAAAAKBHo4gepwJbIl9T3xLwW5wkftmSkiSHQwqHFa6psToOAPQKkydP1j/+8Q/NmjVLycnJKikpUUlJiRoaGiRJqampuvLKKzV16lR98MEHWrx4sS6//HIVFhZq/PjxkqSTTjpJI0aM0CWXXKKvv/5a77zzju644w5NnjyZ1eYdZMPX5ZKkgSMzZbPzcq8nMgxp046VkiIfmgAAAADoPLyrilP+LVsksRJ9dwzDiLZ0CdIXHQC6xBNPPKGqqiodf/zx6tu3b/T2r3/9KzpmxowZ+ulPf6pzzz1Xxx57rHJzc/Xqq69Gz9vtdr311luy2+0qLCzUxRdfrEsvvVR33XWXFU+pxzFNM1pEHzQyy+I06EwbS4skSSUbqi1OAgAAAPRs9ESPU7Rz2TuOtDSFduyIbC76g5YCAICOZ5rmHsd4PB7NnDlTM2fObHfMwIEDNXv27I6MhiaVpfWq2tEgm8NgU9EebkPpCklSKSvRAQAAgE7FSvQ4Fdjc1M7FTxF9d6Kbi7ISHQAASdKGbyKr0PsPS5fLw3qJnmxjWWQlekVJvXz1vGYEAAAAOgtF9Dhkmqb8W5tXotMTfXeim4tWVlqaAwCAeLFhaVMrl0Np5dLT1TZWyZMSeTlfSksXAAAAoNNQRI9DoV27ZNbXyzQMbQsGrY4T176/En1vWgwAANCT1Vb4optMDj6sj8Vp0BWSsu2S6IsOAAAAdCZLi+hPPPGEDj30UKWkpCglJUWFhYX6v//7v+j5xsZGTZ48WZmZmUpKStK5556r0tLSFo9RXFys0047TQkJCcrOztYtt9yiYDcvPAeaNhU109MVoDC8W80r0U2fT2ZDg7VhAACw2PqlZZKkvkNSlZTutjgNukJyTqRlD33RAQAAgM5jaRG9f//+euCBB7R48WJ9+eWXOuGEE3TmmWdq+fLlkqSbb75Zb775pl555RXNnz9f27Zt0znnnBO9fygU0mmnnSa/36/PPvtMzz//vJ577jlNmzbNqqfUIfzNRfRsVpDtieFwyJaSIkkK7tplcRoAAKy1bskOSdKQw7MtToKukpQTWYleurFaZpjFFwAAAEBnsHS3qdNPP73Fz/fee6+eeOIJff755+rfv7+eeeYZzZo1SyeccIIk6dlnn9Xw4cP1+eefa/z48Xr33Xe1YsUKvffee8rJydGoUaN0991367bbbtOdd94pl8vV5u/1+Xzy+XzRn6ur4+vrr4HNzUV03gDvDUdGhvzV1ZHNRZtWpgMA0NvUVfm0bW2lJOmA0XwQ31skZNjlcNrkqw+qsqxe6bmJVkcCAAAAepy46YkeCoX00ksvqa6uToWFhVq8eLECgYAmTJgQHVNQUKABAwZowYIFkqQFCxZo5MiRysnJiY6ZOHGiqquro6vZ23L//fcrNTU1esvPz++8J7YPAlubiuh9eAO8N+wZGZIiveQBAOit1n+1QzKlnMEpSs7wWB0HXcRmM5Q9KPKtvBJaugAAAACdwvIi+rJly5SUlCS3261f/OIXeu211zRixAiVlJTI5XIp7Qcri3NyclRSUiJJKikpaVFAbz7ffK49t99+u6qqqqK3zZs3d+yT2k/Rdi59WIm+N5qL6LRzAQD0ZmsXR/qhDxnN64feJmdwUxGdzUUBAACATmFpOxdJOuigg7R06VJVVVXp3//+tyZNmqT58+d36u90u91yu+N3sy3aucTGwUp0AEAvV13eoG1rKiVDGnokrx96m9zBqZKk0vUU0QEAAIDOYPlKdJfLpQMPPFBjxozR/fffr8MOO0yPPvqocnNz5ff7VVlZ2WJ8aWmpcnNzJUm5ubkqLS1tdb75XHdkBoMKbN8uSQqzsehesTd9W8FsbJTte73uAQDoLVYvirz+6TcsXUnptHLpbXIOiKxE37WtVv7GoMVpAAAAgJ7H8iL6D4XDYfl8Po0ZM0ZOp1Pz5s2Lnlu1apWKi4tVWFgoSSosLNSyZctUVlYWHTN37lylpKRoxIgRXZ69IwRKSqVQSIbLxSaZe8lwOmVLibx5dNTUWJwGAICuZZqmVi+KtLE7aFzOHkajJ0pMdSs5wyPTlEo3shodAAAA6GiWtnO5/fbbdcopp2jAgAGqqanRrFmz9OGHH+qdd95RamqqrrzySk2dOlUZGRlKSUnRDTfcoMLCQo0fP16SdNJJJ2nEiBG65JJL9OCDD6qkpER33HGHJk+eHNftWnYnsCXSn93Zr59ki7vPOOKWIyND/upqiugAgF5nR3GNKkrqZXfa6Ifei+UOSVXNrkZtX1ul/IIMq+MAAAAAPYqlRfSysjJdeuml2r59u1JTU3XooYfqnXfe0YknnihJmjFjhmw2m84991z5fD5NnDhRjz/+ePT+drtdb731lq677joVFhYqMTFRkyZN0l133WXVU9pvgaZNRZ39+1ucpHuxZ2RIGzfKSREdANDLrPo8sgp98GFZcnkt3+4GFskbmqY1X5RGeuMDAAAA6FCWvtN65plndnve4/Fo5syZmjlzZrtjBg4cqNmzZ3d0NMv4mzYVdeVTRI+FvWlzUVaiAwB6k2AgpFULI0X0gvF9LU4DK+UNTZMkla6vUigYlt3BNxoBAACAjsKr6zgTXYnejyJ6LBzp6ZE/KaIDAHqRdUt2yFcfVFKGW/kjaOHRm6XnJsib7FQwEFbZJl4PAQAAAB2JInqcoZ3LvrE3FdHtfr8y7XaL0wAA0DVWfLJNkjTi6DzZbIbFaWAlwzCUd2CaJGnbmgprwwAAAAA9DEX0OOPfQjuXfWE4nbKnpUmShnXTTWUBAIhFRUmdtq2plGFIw4+ilQukvk0tXeiLDgAAAHQsiuhxJFxfr9DOnZJYib4v7JmZkqSD3B6LkwAA0PmWfxxZhT7wkEwlpTP34bu+6NvXVSkcClsbBgAAAOhBLN1YFC0Ftm6VJNlSU2VPSbE4TffjyMyUf906DWUlOgCgBykuLlZ5eXmLY6GAqW8/rpIkefo1asmSJXt8nKKiok7Jh/iR2S9JLq9D/oagyrfUKnsgrycBAACAjkARPY74Nze1cunXz+Ik3dN3K9EpogMAeobi4mINHz5c9fX1LY4fe/CZOv+Y/1ZZ5RbdcPZlMmXu9WPW1tZ2dEzECZvNUN6Bqdq4bKe2ramkiA4AAAB0EIrocYRNRfePo6mIPsTlUjgUsjgNAAD7r7y8XPX19frd1Ic1KH+IJMk0pboNCTL9Uv6wPnpuxut79ViffTlfT734sBobGzsxMazWd2hatIg+asIAq+MAAAAAPQJF9Dji37JZEkX0fWVLSVHYbpdbUmNJidVxAADoMIPyh6hgyCGSpMqyeq3xl8nuMHTw6KGyO/Zui5uNm9d1ZkTEiea+6NvWVsoMmzJshrWBAAAAgB6AjUXjSKC5ncuAfIuTdE+GYSjY1EveVlxscRoAADpHyfpqSVJWfvJeF9DRe/QZkCyH2y5fXVC7ttdZHQcAAADoEXjnFUf8TYVfZz5F9H0VaCqiG5s3W5wEAICOV1vpU83ORhmGlDOYftdozW63qe8BkX83tq2ptDYMAAAA0ENQRI8TZjisQFPh1zWA/pX7KhBdiU4RHQDQ82xfWyVJyuiXKLeXrnxoW7SlC0V0AAAAoENQRI8TwbIymX6/5HDI2bev1XG6reZ2LkbxJouTAADQsRpq/aosrZck9T0g1eI0iGffL6KbpmltGAAAAKAHoIgeJ6KtXPLyZDhYWbavoivRy3cqWFFhcRoAADrOtjWRVehpOV55k10Wp0E8yx6UIrvDpvpqv6rKGqyOAwAAAHR7FNHjBK1cOobpdKrY75ck+YqKLE4DAEDHCPkM7doW2SSyeZUx0B6H067cpr7oW1axqAAAAADYXxTR44R/U2QlumsAm4rur+WNjZKkxhUrLE4CAEDH8JdHVp6n5XiVmOq2OA26g/4F6ZKkLUW7LE4CAAAAdH8U0eOEf3NTO5d8VqLvrxW+SBG9Yflyi5MAALD/ctMHKlgTafXWj1Xo2Ev9CzIkSVtWV8gM0xcdAAAA2B8U0eNEoLi5nQsr0ffXClaiAwB6kDPGXinJUHpughJYhY69lD0wWU6PXb66oMq31FodBwAAAOjWKKLHCX9TT3RnPkX0/dVcRA9sKlaopsbiNAAA7LvqkqAOHXS0JFP9DkqzOg66EZvdFv3mwuaVtHQBAAAA9ofD6gCQQpWVCldXS5JcFNH3W1U4rHBWlmzl5WpcUaTEcWOtjgQAQMxM09SmzxskSc60oLxJLosTIR4V7W4j9aTIwoIVizZJWTt3+zhZWVkawAb3AAAAQJsooseB5lXojj59ZPN6LU7TM5iDB0nl5WpcsYIiOgCgW1q3ZIdqy0LyBRqUmBW2Og7izM6KHZKkiy++uN0xeRmD9T//9VeVbajRZUf+RKFwsN2xCQkJKioqopAOAAAAtIEiehzwb2raVJQ3LR0mPHiw7F98SV90AEC3FPCH9Ol/1kiS5n3zii4YeY7FiRBvamoj32K8+YppGjVyTJtjTFOqWxuWW149ffdrciS0/WHMxs3rNP3hqSovL6eIDgAAALSBInocCGyOFNFdvGnpMOFBgyRJjd9+a20QAAD2wVfvbFLtLp9ciYbmLn1JF1xEER1t6993oAqGHNLu+XU1O7RrW53SXX3Vb0h6FyYDAAAAeg42Fo0D/uJIOxfXAPqhd5TwAUMkSf4NGxSqrLQ2DAAAMagub9CSdyMfsA8q9CoQ9FmcCN1ZSqZHklS9s9HiJAAAAED3RRE9DvibVqI781mJ3mFSkuUaOFCS1LBsmcVhAADYO6Zpav6sVQoFwuo3LE0Zg51WR0I3l5IVKaLXVfoUCtJbHwAAANgXFNHjQICV6J3CO+owSVLD0q8tTgIAwN5ZvahUxSt2ye6w6fiLCmQYhtWR0M25E5xyJzhkmlINq9EBAACAfUIR3WLhhgYFy8okSc58iugdyXNYcxF9qbVBAADYC/XVfn3ycmQz0SNOG6S0nASLE6GnoKULAAAAsH8oolvMvzmyCt2WnCx7Wpq1YXqYhFGjJEkN33wjM8zXlwEA8cs0TX3w9yI11gWU2S9Jo0+ixRs6TnKWV1Kk3z4AAACA2FFEt1igqYjuys/nK9sdzD1smAyvV+GaGvnXr7c6DgAA7Vr+8TZtXLZTNoehCZePkN3OSzR0nOaV6A01AQV8IYvTAAAAAN0P79As5m/qh+4cwIqzjmY4HPIecogkqeFr+qIDAOLTzq21+vTfkTYuhWcNUVb/JIsToadxuu1KSHFJkqp2sBodAAAAiBVFdIsFNhdLklwU0TsFm4sCAOKZrz6g/3tymYL+sPJHZOiwE9gfBZ0jtU+kpQtFdAAAACB2FNEt1rwS3TWAN82dwdvUF71+yRJrgwAA8APhsKn3nitS1Y4GJWW4deIVI2TYaO2GzpHS57u+6KZpWpwGAAAA6F4oolvMXxxZie7MZyV6Z/AefrhkGPKvW6dgebnVcQAAkBTZSPTDF1dq4zflsjtsOuXakfImuayOhR4sKd0tm8NQ0B9WfbXf6jgAAABAt0IR3UJmMKjAtm2SWIneWRzp6XIfdJAkqX7RIovTAAAQKaB/9uo6FX26XYYhnXjFCGUPTLE6Fno4m81QSmZTS5cyWroAAAAAsaCIbqHA9u1SMCjD5ZIjJ8fqOD1W4rixkqQ6iugAgDiweM4mLZ0b+Sba8RcXaMjh2RYnQm+R2scjib7oAAAAQKwoolso2sqlf38ZNv5RdJaEsZEiev1CiugAAGt9O3+LFv7veknS0ecdqBFH51mcCL1J8+aitZU+BQNhi9MAAAAA3QeVWwsFNjdtKppPK5fOlHDEEZG+6Bs2KFBWZnUcAEAvtWphiea/tFqSdMSpgzRqAvuhoGu5E5zyJDokM7LBKAAAAIC9QxHdQv5NTSvRB/ImujPZU1PlHl4gSapf9IXFaQAAvdGGb8o17/kiyZRG/ri/xp4+2OpI6KWaV6NX09IFAAAA2GsU0S3U3M7FNXCgxUl6vsSx4yRJ9QsXWpwEANDbbF1VoXee/lZm2NRB43L1o/8aKsMwrI6FXiqlqYheVd4g0zQtTgMAAAB0DxTRLeTftFGS5BpAEb2zJYyPFNHrPv2UN4wAgC5Ttqlabz/xjULBsAYdmqUfX1ogw0YBHdZJzvTIsEn+hpAaawNWxwEAAAC6BYroFjHDYQWKm3qi086l0yWOGyfD5VJg2zb5162zOg4AoBeo2dWot/78tQKNIfU7KE0Trz5YdjsvvWAtu92m5AyPJKmKli4AAADAXrH0ndz999+vI488UsnJycrOztZZZ52lVatWtRjT2NioyZMnKzMzU0lJSTr33HNVWlraYkxxcbFOO+00JSQkKDs7W7fccouCwWBXPpWYBUtKZPr9ksMhZ16e1XF6PJvXq4RxkdXotfPnW5wGANDTBXwhzX7iGzXUBJTZP0mnXneoHE671bEASd/1Ra9ic1EAAABgr1haRJ8/f74mT56szz//XHPnzlUgENBJJ52kurq66Jibb75Zb775pl555RXNnz9f27Zt0znnnBM9HwqFdNppp8nv9+uzzz7T888/r+eee07Tpk2z4inttWg/9H79ZDgcFqfpHZKOPVaSVDv/I4uTAAB6ug/+sVLlm2vlTXbq1OtGyuVhrkf8aC6i1+xsVCgYtjgNAAAAEP8sfUc3Z86cFj8/99xzys7O1uLFi3XssceqqqpKzzzzjGbNmqUTTjhBkvTss89q+PDh+vzzzzV+/Hi9++67WrFihd577z3l5ORo1KhRuvvuu3XbbbfpzjvvlMvlsuKp7ZF/U6SI7hxEP/SuknT8cSq9917VL1miUE2N7MnJVkcCAPRAC/5vudZ8USoZ0gE/dmvtpiJp0749VlFRUceGAyR5kpxyeR3yNwRVXd5odRwAAAAg7sXVsqiqqipJUkZGhiRp8eLFCgQCmjBhQnRMQUGBBgwYoAULFmj8+PFasGCBRo4cqZycnOiYiRMn6rrrrtPy5cs1evToVr/H5/PJ5/NFf66uru6sp9Qu/6bIu2k2Fe06rvx8uQYPln/DBtV9tkApE0+yOhIAoIdZvXy9Pnl5rRLcyZr9xfOa8uQLHfK4tbW1HfI4gCQZhqG0HK/KNtaosqxeSrQ6EQAAABDf4qaIHg6HddNNN+noo4/WIYccIkkqKSmRy+VSWlpai7E5OTkqKSmJjvl+Ab35fPO5ttx///363e9+18HPIDb+4uYiOpuKdqWkY4/Vrg0bVPvhhxTRAQAd7ss3tyrBnaygrV7nX3SuLrj43P16vM++nK+nXnxYjY2sFkbHSstOUNnGGlWVNcg9yOo0AAAAQHyLmyL65MmT9e233+qTTz7p9N91++23a+rUqdGfq6urlZ+f3+m/9/sCzSvRaefSpZJOOEG7nn9eNe+/LzMQkOF0Wh0JANBDbF1VoYqNAYXCIaUMloYfeMh+P+bGzes6IBnQWnKGRza7oYAvJGejpdskAQAAAHEvLl4xT5kyRW+99ZY++OAD9e/fP3o8NzdXfr9flZWVLcaXlpYqNzc3Oqa0tLTV+eZzbXG73UpJSWlx60pmOCx/8WZJrETvaglHjJE9K0vhqirVff651XEAAD2EGTb1yb/XSJI+WfGm7G42a0R8s9mN6Aajwdq4WVcDAAAAxCVLi+imaWrKlCl67bXX9P7772vw4MEtzo8ZM0ZOp1Pz5s2LHlu1apWKi4tVWFgoSSosLNSyZctUVlYWHTN37lylpKRoxIgRXfNEYhQsK5Pp80kOh5z9+lkdp1cx7HalnHSiJKn6/+bsYTQAAHtn1cISlW+uld0l/d/ijumDDnS2tJwESVKw1m5xEgAAACC+WVpEnzx5sv7xj39o1qxZSk5OVklJiUpKStTQ0CBJSk1N1ZVXXqmpU6fqgw8+0OLFi3X55ZersLBQ48ePlySddNJJGjFihC655BJ9/fXXeuedd3THHXdo8uTJcrvdVj69dvk3Rlq5OPvlyXCw8qerJU88WZJUM2+ezEDA4jQAgO4uHDb15eyNkqR+ozyqbayyNhCwl5pXood9dqUlZlmcBgAAAIhfllZwn3jiCUnS8ccf3+L4s88+q8suu0ySNGPGDNlsNp177rny+XyaOHGiHn/88ehYu92ut956S9ddd50KCwuVmJioSZMm6a677uqqpxGz7zYVpR96ZykqKmr/pN0mT2qqwlVV+vof/1D4sMN2+1hZWVkaQNsdAEA7NizdoaodDXInOpR7cHx+gA+0xem2KzHNrbpKnw4ZMN7qOAAAAEDcsrSIbprmHsd4PB7NnDlTM2fObHfMwIEDNXv27I6M1qn8zZuKDqSI3tFKq6tlSLr44ot3O+6O7Bz9PD1d//c/v9H/lGzf7dgEr1dFK1dSSAcAtGKappa8E5nXRx7XX3ZnpbWBgBil5XgjRfSBhVZHAQAAAOIWvUQsECgulsSmop2huqFepqT7Tj9DYwsK2h3n3LVL+uQTnZGRoXE/+5nMdtrqrC4p0fV/f0Hl5eUU0QGgBysuLlZ5eXnM96vaFlDZpjoZdkmZu1RUtKrjwwGdKC0nQVtXVeqgfocrFNjzAhcAAACgN6KIbgH/pqYi+iBWoneWA7KydGh+frvnzf79Vfntt1JlpYY2NMgTp5vQAgA6X3FxsYYPH676+vqY73vVSXdq1OAf6aNv3tDkxx+NHq+tre3IiECn8SY5ZTjDcsqlqq1Bq+MAAAAAcYkieifY7Wq2cFiejRtlSFpTUytzyZJ2H2e3fb2xXwzDkLugQPWff67GlSspogNAL1ZeXq76+nr9burDGpQ/ZK/vFw4aqlubIEma+NMJOvXcE/TZl/P11IsPq7GxsbPiAh3KMAw5koIKVLhUUcyG6wAAAEBbKKJ3sOLiYg0vKFB9Q0Ob57MdDn045EAFTVNjf3qa9ma9D6vZOoe7oED1CxcquH27QpWVsqelWR0JAGChQflDVDDkkL0eX7K+SnWqUGKaSyNGDJIkbdy8rpPSAZ3HkRRSoEKq2BRQOGzKZjOsjgQAAADEFYroHay8vFz1DQ16/JJLNSw3t9V5V3m59NlnUmKi5txy624f673ly/XA7LdZzdZJ7ImJcubnK1BcrMaiIiUWsqEWAGDvmKapHZsjH3Jn9U+2OA2wf+wJIdU1VitRKSpZV6m8oelWRwIAAADiCkX0TjIsN7fNntyNNTWqleTdQ89uSVpTWtJJ6dDMc/DBkSL6ihVKOPJIGe1sMAoAwPfVVfrUWBuQzWYoIy/R6jjAfjEMadmmzzT+oJO1dskOiugAAADAD9isDtDbhKqqJEn21FSLk0CSXIMGyZaUJLOxUb61a62OAwDoJsq31EmS0vsmyOHk5RS6v6XrP5YkrV9SJjNsWpwGAAAAiC+86+tiocpKSRTR44Vhs8lzSKT/beOyZRanAQB0B6ZpqqIkUkTP7JdkcRqgY6zcslh2p1RX5VfJhmqr4wAAAABxhSJ6F2MlevzxjBgh2WwKlpUpUEILHQDA7tXsbFTQH5bdaVNypsfqOECHCIYDSh/olCSt+6rM4jQAAABAfKGI3oVM0/yuiJ6WZm0YRNm8XrmHDpUkNXz9tcVpACC+ffTRRzr99NOVl5cnwzD0+uuvtzhvmqamTZumvn37yuv1asKECVqzZk2LMbt27dJFF12klJQUpaWl6corr1RtbW0XPov9s2t7vSQpPSdBNpthcRqg42QMbiqiLymTadLSBQAAAGhGEb0LhevrpWBQMgzZkpOtjoPv8Y4aJUnyr1sXbbkDAGitrq5Ohx12mGbOnNnm+QcffFB/+tOf9OSTT2rhwoVKTEzUxIkT1djYGB1z0UUXafny5Zo7d67eeustffTRR7rmmmu66insF9M0VVEaaeWS0TfB4jRAx0rLd8rhtqt2l09lm2qsjgMAAADEDYfVAXqTcNMqdFtysgy73eI0+D5HVpacAwcqsGmTGpYuVdLxx1sdCQDi0imnnKJTTjmlzXOmaeqRRx7RHXfcoTPPPFOS9MILLygnJ0evv/66LrzwQhUVFWnOnDn64osvdMQRR0iSHnvsMZ166ql66KGHlJeX1+Zj+3w++Xy+6M/V1db0bK7Z1aigr6mVS5bXkgxAZ7E7DA06JFNrF5dp/VdlyhmUYnUkAAAAIC6wEr0LsalofEs4/HBJUuPKlQrX1VmcBgC6nw0bNqikpEQTJkyIHktNTdW4ceO0YMECSdKCBQuUlpYWLaBL0oQJE2Sz2bRw4cJ2H/v+++9Xampq9Jafn995T2Q3Kkto5YKebcjh2ZKktUt20NIFAAAAaEIRvQuFKiok0Q89Xjn69pUjN1cKhdTwzTdWxwGAbqekaXPmnJycFsdzcnKi50pKSpSdnd3ivMPhUEZGRnRMW26//XZVVVVFb5s3b+7g9HtmmqYqyxokSWk5rEJHzzTg4AzZnTZV72hQ+Zbus1cBAAAA0Jkooneh6Er09HRrg6BNhmHI27wa/dtvFf5e2wAAgLXcbrdSUlJa3LpaY11QvvqgDJuUQisX9FAuj0MDD86UFNlgFAAAAABF9C4VZCV63HMNGiR7erpMv1+Ny5dbHQcAupXc3FxJUmlpaYvjpaWl0XO5ubkqK2tZmAsGg9q1a1d0TLyqKou0cknO8Mju4CUUeq4hh/eRJK35soyWLgAAAIAooncZMxRSuGkTNFaix6/vr0Zv+PprKRSyOBEAdB+DBw9Wbm6u5s2bFz1WXV2thQsXqrCwUJJUWFioyspKLV68ODrm/fffVzgc1rhx47o8cyyaW7mkZidYnAToXIMOzZLDFWnpUraxxuo4AAAAgOUooneRUHW1ZJqSwyFbYqLVcbAb7qFDZUtKkllfr4TiYqvjAEBcqa2t1dKlS7V06VJJkc1Ely5dquLiYhmGoZtuukn33HOP3njjDS1btkyXXnqp8vLydNZZZ0mShg8frpNPPllXX321Fi1apE8//VRTpkzRhRdeqLy8POue2B4EA2HV7mqUJKVl08oFPZvL49DgwyKr0Vcvan+vAgAAAKC3oIjeRaKbiqanyzAMi9Ngdwy7PboaPXnNGrn45wUAUV9++aVGjx6t0aNHS5KmTp2q0aNHa9q0aZKkW2+9VTfccIOuueYaHXnkkaqtrdWcOXPk8Xiij/Hiiy+qoKBAP/nJT3TqqafqmGOO0dNPP23J89lb1eUNMk3Jk+iQJ9FpdRyg0w0bG9kgeM2XpQqHwhanAQAAAKzlsDpAb9G8qaiDfujdgmfECDUsWSLV1uq/UtOsjgMAceP444/fbY9kwzB011136a677mp3TEZGhmbNmtUZ8TpN1Q5auaB3yR+RIU+SUw01AW1ZWaEBTZuNAgAAAL0RK9G7SIhNRbsVw26Xd8wYSdI1mZmS329xIgCAVUzTVHV5pIiekuXZw2igZ7DbbTpwTLYkaRUtXQAAANDLUUTvIs0r0dlUtPvwDB+uoNerPg6H7O/N2/MdAAA9kq8+KH9DSIZNSs6giI7e46BxuZKk9Ut2yN8QtDgNAAAAYB2K6F0kWkRnJXq3Ydjtqh02TJLkfOMNhevrLU4EALBCdVMrl6Q0j+wOXjqh98gZnKL03AQFA2GtXVxmdRwAAADAMrwT7ALhhgaZjY2SKKJ3N/X5+drs98uorlbFP1+yOg4AwAJV5ZE5PKUPq9DRuxiGoYLCvpKkos+2WZwGAAAAsA5F9C7QvArdlpQkw+m0NgxiY7PpiZ07JUk7//pXhevqLA4EAOhKZthUzc7mfuhei9MAXe+g8bkybIZK1lerooTXQQAAAOidKKJ3ATYV7d7erK5SOCdHoYoK7XpxltVxAABdqK7Kp1DQlN1pU2Kqy+o4QJdLTHVrwMEZkqSiz7ZbnAYAAACwBkX0LsCmot1bSFLwnLMlSbueeUah2lprAwEAukx1cyuXTI8Mw7A4DWCNEUfnSZJWLtiuUCBscRoAAACg61FE7wJsKtr9hY46Sq7BgxWqqlLF3/9udRwAQBep2Rkpoidn0g8dvdegkZlKTHOroSagdUvZYBQAAAC9D0X0LhBt58JK9O7LblfW9ddLknY++5xC1dUWBwIAdLZwyFRthU9SZCU60FvZ7DaNOCayGv3b+VstTgMAAAB0PYroncwMhaIFV1aid28pp54i14FDFK6u1s6//MXqOACATlZX5VM4bMrhssmTxMbg6N1GHJ0nw2Zo+9oq7dxGazsAAAD0LhTRO1m4pkYKhyWHQ7akJKvjYD8Ydruyp06VJO164e8KbGdzLQDoyb7fyoV+6OjtktLdGnxYliRp2YesRgcAAEDv4rA6QE8XbG7lkprKG/AeIOnHP5Z3zBg1LF6sHY/9WXn33Wt1JABAJ6ne+d2mokBvUFRUtNvznn5B6Sup6NOt8g6qkdPT9nqcrKwsDRgwoDMiAgAAAJagiN7JopuK0g+9RzAMQzm3/EobL/yZql5/XRmXTZJn2DCrYwEAOtj3+6EnZ1BER8+2s2KHJOniiy/e49hbz3lCA/oM0/QpM/TOV7PaHJOQkKCioiIK6QAAAOgxKKJ3suimovRD7zG8o0Yp+aSTVPPuu9rxx4eV/9STVkcCAHSwukqfzLAph5t+6Oj5amoj+/fcfMU0jRo5ZrdjA1UONW6Xzii8XD+7+AIZP1iMvnHzOk1/eKrKy8spogMAAKDHoIjeyViJ3jP1ufkm1cybp9r581W3aJESx461OhIAoANFW7lk0A8dvUf/vgNVMOSQ3Y4Jh019U7FFgUYpyzNQffKTuygdAAAAYB02Fu1kzUV0ByvRexT34MFKO/+/JEllf3hIZjhscSIAQEf6blNRr8VJgPhisxnKGZQiSdq+tkpm2LQ4EQAAAND5KKJ3onBjo8yGBkm0c+mJ+kyeLFtCghqXLVPVG29YHQcA0EHCobBqK5uL6PRDB34oe2CyHE6bfPVB7dxWZ3UcAAAAoNNRRO9EzavQbYmJMlwua8OgwzmyspR53S8kSWUP/VGh2lqLEwEAOkJtpU9mWHK67fIk0vkO+CG7w6acA5pXo1fKNFmNDgAAgJ6NInonCu3aJUmyZ2RYnASdJWPSJLkGDlSovFzlMx+3Og4AoAN818qFfuhAe3IGpsjutKmxLqhdrEYHAABAD8fyqk4U3LlTEkX0nqCoqKjdc7bzz5f7D3/Qzhde0LaDhskcMKDdsVlZWRqwm/MAAOs1F9FTaOUCtMvutCn3gBRtXVWpLasqlZ6bKJudD50AAADQM1FE70TNK9EdFNG7rdLqahmSLr744t2OezSvn05MTtbqG2/Sz4s3qb1tRhO8XhWtXEkhHQDiVKQfuk8S/dCBPckZnKKyjTXyNwRVtqlauQekWh0JAAAA6BSWFtE/+ugj/eEPf9DixYu1fft2vfbaazrrrLOi503T1PTp0/WXv/xFlZWVOvroo/XEE09o6NCh0TG7du3SDTfcoDfffFM2m03nnnuuHn30USUlJVnwjFqKrkTPzLQ4CfZVdUO9TEn3nX6GxhYUtDvO1tio8Pvv61CvV5+ee57qDjig1ZjVJSW6/u8vqLy8nCI6AMSp2oqmfugeu9wJrDUAdsdut6nfsDRtXLZT29ZWKSvf+tffAAAAQGew9N1hXV2dDjvsMF1xxRU655xzWp1/8MEH9ac//UnPP/+8Bg8erN/+9reaOHGiVqxYIY8nsjrsoosu0vbt2zV37lwFAgFdfvnluuaaazRr1qyufjot2Hw+mQ0NkiRHerqlWbD/DsjK0qH5+bsd0+DzqW7+fKWuXKlBhx0me1pa14QDAHSY6u+1cqEfOrBnWflJKt1QrYbagLauqpS8VicCAAAAOp6lG4uecsopuueee3T22We3Omeaph555BHdcccdOvPMM3XooYfqhRde0LZt2/T6669LivSpnjNnjv76179q3LhxOuaYY/TYY4/ppZde0rZt27r42bTkqKmRJNmSk2W4XJZmQdfwHHywnP36ScGgaubOlRkKWR0JABCj728qCmDPDMPQgIMjrQvLNtUo1Gjp2wsAAACgU8Ttq9wNGzaopKREEyZMiB5LTU3VuHHjtGDBAknSggULlJaWpiOOOCI6ZsKECbLZbFq4cGG7j+3z+VRdXd3i1tGai+i0cuk9DMNQ0k9+IsPtVrCsTPVffml1JABADMywVNfUD51NRYG9l5LlVUZeoiSpscQtw4jbtxgAAADAPonbV7glJSWSpJycnBbHc3JyoudKSkqUnZ3d4rzD4VBGRkZ0TFvuv/9+paamRm/5e2jTsS+cTYV5NhXtXezJyUo67jhJUsOXX8q/aZPFiQAAeytUb5dpSi6vXe4Ep9VxgG4lf3i67A5D4Ua7jj+k9bdMAQAAgO4sbovonen2229XVVVV9LZ58+YO/x3RlegU0Xsd99Ch8hx8sCSpZu5chaqqLE4EANgboXq7JCklk6bOQKxcHof6D4+87j1j7FWqr6CtHQAAAHqOuC2i5+bmSpJKS0tbHC8tLY2ey83NVVlZWYvzwWBQu3btio5pi9vtVkpKSotbR3M2FdEdtHPplRJ/9CM5cnJk+nyqnj1bYZ/P6kgAgD0INhXR6YcO7Js++UmyJwbldLi09sN6hUJhqyMBAAAAHSJui+iDBw9Wbm6u5s2bFz1WXV2thQsXqrCwUJJUWFioyspKLV68ODrm/fffVzgc1rhx47o8c7M+dodsgYBkGLKnpVmWA9Yx7HYln3yybImJCu3aperZsyU2GgWAuOVxJSrctCEiRXRg3xiGIU+uT/W+GtXtCGnh6+utjgQAAAB0CEuL6LW1tVq6dKmWLl0qKbKZ6NKlS1VcXCzDMHTTTTfpnnvu0RtvvKFly5bp0ksvVV5ens466yxJ0vDhw3XyySfr6quv1qJFi/Tpp59qypQpuvDCC5WXl2fZ8xrucUuS7GlpMhwOy3LAWvakJKX89KcyXC4Ft21T+uLFosMuAMSnA3NHSjLkTnDI7WXuBvaVzWlq1vw/SpK+mlusDd+UW5wIAAAA2H+WFtG//PJLjR49WqNHj5YkTZ06VaNHj9a0adMkSbfeeqtuuOEGXXPNNTryyCNVW1urOXPmyOP5boXYiy++qIKCAv3kJz/RqaeeqmOOOUZPP/20Jc+n2XB3JJ8jK8vSHLCeIytLyaecItnt8paUaEa/flIgYHUsAMAPDM0bJUlKYRU6sN+WbvhYuYe4JEnznluhyrJ6ixMBAAAA+8fSpVbHH3+8TNNs97xhGLrrrrt01113tTsmIyNDs2bN6ox4+6ygeSU6RXRIcvXvr5RTT1XV22/rhKRkhf7wkELP/k325GSrowEAmgxrKqLTygXoGAPHeWXWeVS6oVqzH/9G5952BN/yAAAAQLcVtz3Ru7PoSvQ+fSxOgnjhGjBAO8eNU304LPu332rTRRcrsG2b1bEAAJICjWH1yxoiiSI60FFsdkOn/GKkEtPcqiip17t/+ZaNRgEAANBtUUTvaPX1GuCKfH2VIjq+z9+njy4p3iQzLU2+1au14dzzVPvJp1bHAoBer3p7UDbDJpsrLJeHlbJAR0lMdeu06w+Vw2lT8Ypdev+FIpnh9r+FCgAAAMQriugdzLZpkyQp6PXK5mE1G1oq8vnku+t38owYoVBFhTZffbV2/HmmzFDI6mgA0GtVbwtKkuwJQYuTAD1PnwHJmnj1ITJshlYvLNXHr6zZbTtHAAAAIB5RRO9gxsZIET2QmmpxEsQrMytLA/85S2kXXCCZpsr//GdtvuZaBXftsjoaAPRKVdEiOh9oAp1h0KFZ+smk4ZKkZR9s0Rdvb7Q2EAAAABAjiugdzLZpoyQpmJJibRDENZvbrb6/u1N5v39Ahsejuk8/1YYzz1LdZ59ZHQ0AepWGGr8aKiJ9mimiA53noHG5+tEFwyRJX7y1QV/P22xxIgAAAGDvUUTvYDZWoiMGqWeeqUH/+pdcQ4YouGOHiq+8SmUPPSTT77c6GgD0Cr6GoNIHOrRm29ey0Q4d6FSH/ri/xp4+WJL0yStr9M0HFNIBAADQPVBE70Bhv1/G1q2SKKJj73kOGqbB/34l2t5l51+f0cafXyR/U399AEDnSctOUMHEJD365lSrowC9whGnDtLokwZIkj7+1xotfa/Y4kQAAADAnrHmqgP516+XEQqpMhRSyOu1Og66EZvXq76/u1OJRx+l7b+dpsZvv9WGs89R7vRpSj3zTKvjAQAAxKSoqKjdc+6BpvqNdmvrVz59+u+12ly8Rf1Gedocm5WVpQEDBnRWTAAAAGCvUETvQJ6CAjU8+YSuOvlk/ckwrI6DbijlpJPkHTlS2265VfVffqltt/1atZ98qtzp02RPSrI6HgAAwG7trNghSbr44ov3OPaUMZfotCMuU/GiRj0+8wnNWfKPVmMSEhJUVFREIR0AAACWooje0VJStMLnszoF4tjuVmZF3XSjHK//rxyvvqrqN99U5cKF8k+ZLPPAA6NDWJkFAADiTU1ttSTp5iumadTIMXsc7yv3yV/u1k+PvFxnn3SR3Nl+Na9F2bh5naY/PFXl5eW85gEAAIClKKIDXaS0ulqG9m5lVrNRHq/+kJenfmVlsv92mh4r36Fndu2SKSnB61XRypW8qQQAAHGnf9+BKhhyyJ4HDpFKNlRp84oKBSpcSk3M0KCRmTJsfKsTAAAA8YMiOtBFqhvqZUq67/QzNLagYK/vZwQCavj6a3m3bdPUPtmaPHyEFvfvr6v+9RIrswAAQLeXOzhVDqddG74pV/mWWgUDYQ0ZnWV1LAAAACCKIjrQxQ7IytKh+fkx3cccPFi+oiLVfvyx3OXlGlddreMT6ZEOAAB6hqz+SbI7bVq3pEyVpfVa/UWZzAyrUwEAAAARNqsDANgzwzDkGTFCaeefL3tWlux+vx7v31/OZ59TuKHB6ngAAAD7LT0nQcPG5sjmMFSzs1H1m71K9KRYHQsAAACgiA50J470dKWdd55qDzgg8vPcudpw7nlq+Ha5xckAAAD2X0qmVwXjc+Vw2RRutGvqmX9SY3XI6lgAAADo5SiiA92MYber+pBDdPXmzTLT0uRfv14bL7xQ5U88ITMYtDoeAADAfklMdaugMFeGI6yctHx9+7+12lFcY3UsAAAA9GIU0YFu6tP6OjX+/gElT5woBYPa8eiftPHnF6mxqMjqaAAAAPvFm+RSwsAGbdm5ToEGU689vESbi3ZZHQsAAAC9FEV0oDtLTla/R2Yo78Hfy5aUpMZvvtGGc89Tyd33KFhRYXU6AACAfWZzmnrkjZuVkudQoDGktx77WqsWllgdCwAAAL2Qw+oAAPZdUfOq8/79pd8/IOff/y7H5wtV8eKL2vXqqwqecrKCJ54opabu8bGysrI0YMCATk4MAACw9xr9dRp+SqJ2fePRmi9K9d6zK1RX6dPokwbIMAyr4wEAAKCXoIgOdEOl1dUyJF188cWtzo1PSNCv+mRrhCTnq68p/O//6I3qar1QsUvr/P52HzPB61XRypUU0gEAQFyx2Q2dePkIJaa6tPS9zVrw2jrVVfp09H8Nlc1GIR0AAACdjyI60A1VN9TLlHTf6WdobEFB6wGmqV3btilp3Tq5Kyv1X2lp+q+0NPmyslQ/YIAa+vaV7Pbo8NUlJbr+7y+ovLycIjoAAIg7hs3Q0ecNVWKaW5/+e62++WCL6qp8mnD5CDmc9j0/AAAAALAfKKID3dgBWVk6ND+/7ZMDBsgcN07BkhI1LF0q//r1cpeXy11ergyXS+5hw+QePlyOPn26NjQAAMA+GjVhgBJT3Xrv+RVat2SH6iq/0im/OFQJKS6rowEAAKAHo4gO9GCGYcjZt6+cffsqVF0t36pVaiwqUrimRo3ffqvGb7+VPTNTiX37KtXGPsMAACD+DT0yR94Ul+Y8tUwl66v1799/qdMmH6rMvCSrowEAAKCHoogO9BL2lBQlHHmkvEccocCWLfIVFcm3fr1CO3cqdedOzR9yoGyP/km1V12lxKMKZdj5ajQAALBedCP1Hxj+U6+K5tSpZmejXrl/kYZNSFRavnO3j8VG6gAAANgXFNGBXsYwDLny8+XKz1diY6N8a9ao4uuv5aqqkhYu1OaFC+Xo21dpZ5+ltHPPlbNfP6sjAwCAXmhnxQ5JbW+k3izRnaKrTrpTQ/MO0/K3q/XGomf03tf/and8QkKCioqKKKQDAAAgJhTRgV7M5vHIO3Kk1qSlacqfHtXL114r1+cLFdy+XeWPP6HyJ55U4o+OUfoFFyjpuONkOPhfBgAA6Bo1tdWSpJuvmKZRI8e0O840pcaSgIJVTp01/hqdN+EKefo2yvjBl+o2bl6n6Q9PZSN1AAAAxIyKGABJ0kqfT9+MHavhP/uZ7IsXy/7+B7IvX666jz5W3Ucfy0xLU2jcOAWPKpR54IGSYbT7WHxVGgAAdJT+fQeqYMghux1jDjG1Y3OtipfvVLDWocDWNB14eB8lpLq7KCUAAAB6MoroAFRaXS1Drb8uPcDp1HmpaTo7NVWZlZVyvPOOHO+8oy1+v+bU1Oi92hota2yU+YPHS/B6VbRyJYV0AADQJQzDUPaAZCWmuLR2SZl89UGt+Gy78oamKfeAVNls7X/4DwAAAOwJRXQAqm6olynpvtPP0NiCglbnfeGwdpaVybt1qzwlJervcumqzExdlZmpkNutxtxcNfbtK19WllaXlen6v7/AV6UBAECXS0xz6+Bj8rTh63JVljVo66pKVWyv16BDM62OBgAAgG6MIjqAqAOysnRofn7bJwcOlI48UmYgIP/GjfKtX6/Apk2y+3xK3LRJiZs2yXC5lNanjyYmJUuNjV0bHgAAQJLDZdeBR2Rr59Y6Fa/Ypfpqv1Z8ul2udJe8rkSr4wEAAKAboogOICaG0yn30KFyDx0qMxRSYMsW+devl2/DBpkNDUrYulUz+vWTef1kbfvpaUo751x5R4+SsZse6gAAAB3JMAxl9U9Sah+vNn27UxUl9fLvcul3P39RW5Y06pARQbk8vBUCAADA3uGVI4B9Ztjtcg0cKNfAgUo8/ngFS0u15euvVbFihQZIqvr3f1T17//INXiw0i44X2lnny17aqrVsQEAQC/hdNt14JhsVZbWa90325WgZG3+slF/X7lAoybka8TRefImu6yOCQAAgDhnszoAgJ7BMAw5c3NVM2KETt6wXr5pv1Xq2WfL8Hrl37BBZQ/8XmuO/7G2T5uuxlWrrI4LAAB6kbScBCUMbtBz8+6TO8WmxtqAPn99vZ779ad695nl2ramQqb5w63SAQAAgAhWogPoFMtNU+H/Ok867VTZFyyQ4925sm3erMqXX1blyy8rVFCg4EknKXzEGMnR/v+KsrKy2KAUAADsN8OQvlw7T0+c/3sl+vP07fwtKttUozVflGrNF6VK6ePVgWOyNfSIbGX2S6IVHQAAAKIoogPoUKXV1TIkXXzxxa3OjfF6dVFauiYkJ8uxcqXsK1eqNBDQy1WV+t+qam0LBlrdJ8HrVdHKlRTSAQBAh1i1aqWGDzc0ZKJdOTuSVFrkV/lav6p3NGjJnE1aMmeTPKk2ZQxyKn2gU8nZdhm21gV1PugHAADoPSiiA+hQ1Q31MiXdd/oZGltQ0OaY8oYGJW7apISNG5Uj6YasProhq4/86elq6NdPDXl5Cns8Wl1Souv//oLKy8t5kwoAAPbLzoodktr+oN/l8OiQgeN1+JDjdXD+OKnKpW1f+7Tta5/qGqu1fPMiLd/0uVZs/kIN/lpJUkJCgoqKiniNAgAA0AtQRAfQKQ7IytKh+fntDxg2TGYoJN+6dfKtWKHA1q1yVVTIVVGh1G+/lT0rSympqTo2MVFqaOi64AAAoEeqqa2WJN18xTSNGjmm3XFmyK9gbVjBOruCtQ4lelI0dugEjR06QZIpmyesuuBOPf/moyrZWkYRHQAAoBegiA7AMobdLs+wYfIMG6ZQXZ38a9fKt3q1gmVlCpWXK6m8XE/2z5d51dVaN2iQPMOHyz28QJ5hw+TMHyBn/36yuVxWPw0AANCN9O87UAVDDtmrsWbYVG2lT5Wl9aosa1BjbUDhRru8ytYvTrlXXzxfrVVvf6bMfknK7J+kjL6JSs70KDndo4QUV5ttYAAAAND9UEQHEBfsiYnyHnaYvIcdpnB9vQJbtqhk1SpVrF2r/i6X/Bs2yL9hgzR7dvQ+pmHIzMyQmZ0jMydbZp9smVmZMrOyZGZmykxPb7FpKb1LAQBALAyboeQMj5IzPMofLvkagqrZ2agtG7dr66ZSZaf1V3V5o6rLG7Xh6/IW97XZDSWkuOROdMqT6JAnwRn9uzvBKU+iU+4fHHd5HXK67fu9qWlxcbHKy8v3PFCRDwrCISkcMmWGm593ZCNWw2YoOzdLAwcO3K88AAAA3R1FdABxx5aQIPewYVodCOjiue8q025XgdujAo9bw90eDXa5NMDlUoLNJqN8p1S+U1qxotXjhExTZcGgtgcD2h4Iqlymrv6f/1H2iIPlzOsrZ9++sqWk7PcbVQAA0Du4vQ65+yep3OfTXQ9O0gvPvqj+WUNUvzOsup0hNVaF5KsNy19vKhwyVVvhU22FL6bfYdgMubx2ub2Ronrzny6vQzabIcOQZDNkGE1/lxQKhhUKhhUOmqqpqdP8Dz6SIZscNoccdqccdlfTn045bM7v/m53yW6z7zZPOLxL7sQN8ia55Ul0KjnDo5RMj1KyPErJ8iqzX5ISUl28ngIAAD0aRXQAcat5k9Kpp57WapPSStNUtc8nR12d7PX1kT8bGiK3+nrZGxtlD4fV1+lUX6dT8kbu5//zTG353uPYEhLkyOsrZ988Ofv2lSMnW/bkFNmSkmRLTpItIUE2l0tG883p/O7vPzzeQW8eTdOU6fcrXFfX4mYGApEBhk0yFPl9hiHD5ZLN65UtIUFGQoJsXq8Mt5s3swAAdJLmTUovvfyiNs/bDJtSE7OU7E1XojtZCe5kJXpSlND8d3eKEjzNf4+cS05Ik0xDZtiUry4oX11wn/MV9Gu/5/vumZJavn6w2ewKNIQVaGhQ9Y4GlW2sbnUvh8dQYqZdCRl2JWTalZhhkzfdLpu95WPxrUAAANBd9Zgi+syZM/WHP/xBJSUlOuyww/TYY49p7NixVscC0AH2uElpG0zTlFlfr1BtrcI1NdqyZYv+9+OPdUZhoVL8fhk7d8qorla4vl7+tevkX7tuv3NGC+ztFdpdzkhB3hn5WXa7wg31CtfWyVexS8GaWhkNDVJDg4xQaP/C2GzfK6x7ZUtIjHwg0HxLbOfnxJY/G26PtpeVqqKqSqbNJtntks0umWEpFJIRDEqhkPS9P6PHwuHILRSSQmHJDCslLU3Z/fpFroHbJXtqquxpabKnpMhw9JgpCXuJuRtAd7W3m5TujaXLv9Q9f71bkuRyeOR1JcrrSpLXnSiPK1EJriR5XInyuBJkM2wyDJsMGTIMm2xNH6gHQ4Gmm1+hcFDBUECXnHuthgwaGlm9bjdkM5r+tBkybGr602j1p9T0OsqUFiyer9/98VZ53UlKcCcryZOqjOQcZSbnKjM5V9mp/ZWd2l/BRruqtgZVtfW7wn8wFFBJZbG2lq/T1p3rtGXnWu2q367FXy+ikA4AALqdHlGx+Ne//qWpU6fqySef1Lhx4/TII49o4sSJWrVqlbKzs62OB8AChmHISEyULTFRysnRep9Pv99Rpgfe+N/oGI9hKMfhUF7TavU8h1N9HA4l2WxKstmUaLcpwbDJZRiRm82Qq+lnZ9Pt+8xA4LvV4vvA1sax+nBY9eGw6sJh+U0z8ty+fzMkl2GT1zCUYLPJY2t6lHA4uoK9I3g65FEkn6TN7ZyzpaREiurp6XJkZX1365Mle1aWHJmRvzuysmTzejsoUUSr3rGmKfl8kQ80GhsjfzZ/wNHYKAWCkQ8STLPpwwIz8rPdLjmdSh40SAecc06HZuxpmLsB9ASxbFLano2bIx/kd0RBXpI++3K+nnrxYZ1vu0AZfRP36TGaW8XU1FWpqn6nrrhwcrvZzHCDwj6bwj6bQj6bwo12hXw2OeRU/8wh6p85pMX4dx5dp5wBO5Wc5VVqllfJmR4lpkZaxXiSIr3hbfa2XhW1tGnjJpWVlcsMNfV0D0Z6uoeDzT3e9d25kBldZG8Yin6jL9L3XTLshvrkpuuQI4bu0/UCAAA9X48ooj/88MO6+uqrdfnll0uSnnzySb399tv629/+pl//+tcWpwMQD5pbw9x3+hmtWsPsrWDTrV7S5+vWatqrr0aL6a4f3L47ZmvzuNNmyCFD9eGwasNh1YZDuuL44zV0wECFHQ6ZTbfmZqfOplt7Gptun69dowf+93/lMQwl2mzy2mxKsEU+DIj8PXI84XvHW/zcdEtsGusxbLIbksfuiBT5TTP6JW/TMCKr0202mYYR+dMW2Yks+mfTCjkZhuoDAW3csUPDBg+Wx26X/H4ZdXUy6uslSeHqaoWrqxXY3F6Z/Tum1yNbRqY8fXMj7Xe8Hhker2wejwyPRzaPJ/J7zbBM02wqcptSOKRwQ6PC9fUKNzQo3FCvhopKffvFF3JL0eeeaLPJvh/tcBY0NspxxBGstNsN5m4AaKkjCvLSd0X5jhRrNtM05W8Mqb7ar4Zqv+qr/arcWSszYJO/ztTmogpJFe3e3+YwZLfbZHfYZLMbMs1Ij/lQyJQZMhUKhSNF8Q60oXSh/vtRN3M3AABoU7cvovv9fi1evFi333579JjNZtOECRO0YMGCNu/j8/nk8323wU9VVZUkqbq6dX+/WNXW1kqSvi4uVp0vtk2Efmj19hJJ0ort2+VNTt7vbB35eL3hseI5G89z3x+rMRDY7/82JWlnTa3Cki48cqwOirHVzA8tLd6kuV98oRPDprJdrsjBcFjy+2PPVVun2nBY544dF1MuX9Pth29nlxZv0stffKFpJ5+iMcOGRQ42rYhXjEXmLzas1z2Lv5SKN7U4bpOUYrMp1W5Xqt2udLtdGXaHMuw2ZdodynQ4lOGwK9PuUJbdHlltX1sn1dapprg4pgztGfCDVjIN4bCkyPvzoM2moN2ukN2ukM2moM2msM0ms+npm5KaP1owZMrvD2jBjh3K37hRaWlp+5WreV4yzQ6uFFgsXufulWuXq6Gxfr8ea+OWSPFq3abVSkxK2O9sHfl4veGx4jkbz9Pax4rnbL3ieXqkLeYGPfzM3XrkgcfVJ62//LVhNdaE5a8LK9BgKthoKuhvmu9ifAlkypSpsKRw9CdTpqSwTCPc9Pfv93s3ojO3YRoKhkLaumujNjJ3AwCAdhhmN5/dt23bpn79+umzzz5TYWFh9Pitt96q+fPna+HCha3uc+edd+p3v/tdV8YEAGCfbd68Wf3797c6Rodh7gYA9HQ9be4GAKC36/Yr0ffF7bffrqlTp0Z/DofD2rVrlzIzM2Xsx9f3pcjKg/z8fG3evFkpKSn7G7VX4JrFjmsWO67ZvuG6xa4jr5lpmqqpqVFeXl4Hpeu+mLvjC9csdlyz2HHN9g3XLXbM3QAAYE+6fRE9KytLdrtdpaWlLY6XlpYqNze3zfu43W653e4Wx/b3a3s/lJKSwovWGHHNYsc1ix3XbN9w3WLXUdcsNTW1A9LEF+bunoNrFjuuWey4ZvuG6xY75m4AANCePW97HudcLpfGjBmjefPmRY+Fw2HNmzevxVfEAQBAfGDuBgAAAAB0J91+JbokTZ06VZMmTdIRRxyhsWPH6pFHHlFdXZ0uv/xyq6MBAIA2MHcDAAAAALqLHlFEv+CCC7Rjxw5NmzZNJSUlGjVqlObMmaOcnJwuz+J2uzV9+vRWXzlH+7hmseOaxY5rtm+4brHjmu0d5u7ujWsWO65Z7Lhm+4brFjuuGQAA2BPDNE3T6hAAAAAAAAAAAMSjbt8THQAAAAAAAACAzkIRHQAAAAAAAACAdlBEBwAAAAAAAACgHRTRAQAAAAAAAABoB0X0fTBz5kwNGjRIHo9H48aN06JFi3Y7/pVXXlFBQYE8Ho9Gjhyp2bNnd1HS+BHLNfvLX/6iH/3oR0pPT1d6eromTJiwx2vcE8X671mzl156SYZh6KyzzurcgHEo1mtWWVmpyZMnq2/fvnK73Ro2bFiv++8z1mv2yCOP6KCDDpLX61V+fr5uvvlmNTY2dlFa63300Uc6/fTTlZeXJ8Mw9Prrr+/xPh9++KEOP/xwud1uHXjggXruuec6PSdaY+6OHXN37Ji7Y8fcHTvm7tgwdwMAgA5hIiYvvfSS6XK5zL/97W/m8uXLzauvvtpMS0szS0tL2xz/6aefmna73XzwwQfNFStWmHfccYfpdDrNZcuWdXFy68R6zX7+85+bM2fONL/66iuzqKjIvOyyy8zU1FRzy5YtXZzcOrFes2YbNmww+/XrZ/7oRz8yzzzzzK4JGydivWY+n8884ogjzFNPPdX85JNPzA0bNpgffvihuXTp0i5Obp1Yr9mLL75out1u88UXXzQ3bNhgvvPOO2bfvn3Nm2++uYuTW2f27Nnmb37zG/PVV181JZmvvfbabsevX7/eTEhIMKdOnWquWLHCfOyxx0y73W7OmTOnawLDNE3m7n3B3B075u7YMXfHjrk7dszdAACgI1BEj9HYsWPNyZMnR38OhUJmXl6eef/997c5/vzzzzdPO+20FsfGjRtnXnvttZ2aM57Ees1+KBgMmsnJyebzzz/fWRHjzr5cs2AwaB511FHmX//6V3PSpEm97o14rNfsiSeeMA844ADT7/d3VcS4E+s1mzx5snnCCSe0ODZ16lTz6KOP7tSc8Wpv3ojfeuut5sEHH9zi2AUXXGBOnDixE5Phh5i7Y8fcHTvm7tgxd8eOuXv/MHcDAIB9RTuXGPj9fi1evFgTJkyIHrPZbJowYYIWLFjQ5n0WLFjQYrwkTZw4sd3xPc2+XLMfqq+vVyAQUEZGRmfFjCv7es3uuusuZWdn68orr+yKmHFlX67ZG2+8ocLCQk2ePFk5OTk65JBDdN999ykUCnVVbEvtyzU76qi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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code can be used to compare the distributions of different variables and to later compare the current data with the data produced using the data augmentation technique."
      ],
      "metadata": {
        "id": "ZvEB9NrjTPOl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Model Training\n"
      ],
      "metadata": {
        "id": "sL4Up7ouxoyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the XGBoost model.\n",
        "# perform all the necessary tasks such as grid search, hyperparameter optimization and cross validation.\n",
        "# Calculate metrics."
      ],
      "metadata": {
        "id": "XQROXKhTxr5N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#importing relevant libraries for training.\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import os\n",
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "# from sklearn.metrics.pairwise import manhattan_distances\n",
        "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, auc, confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "from sklearn.feature_selection import SelectKBest, f_regression, f_classif\n",
        "from sklearn.model_selection import GridSearchCV, GroupKFold, cross_val_score, StratifiedGroupKFold\n",
        "from sklearn.pipeline import Pipeline\n",
        "import warnings\n",
        "import joblib\n",
        "from sklearn.model_selection import cross_val_predict, cross_validate\n",
        "from datetime import datetime\n",
        "from xgboost import XGBClassifier,XGBRegressor\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "id": "clKWlhhDUbcO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# functions to get results from classification metrics.\n",
        "\n",
        "def youden_threshold(fpr, tpr, thresholds):\n",
        "\n",
        "    idx = np.argmax(tpr - fpr)\n",
        "    return thresholds[idx]\n",
        "\n",
        "\n",
        "def get_mae(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred = clf.predict(X)\n",
        "    return mean_absolute_error(y_true, y_pred)\n",
        "\n",
        "def get_auroc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    auroc = roc_auc_score(y_true, y_pred_score)\n",
        "    return auroc\n",
        "\n",
        "def get_auprc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    pr, re, thresh = precision_recall_curve(y_true, y_pred_score)\n",
        "    auprc = auc(re, pr)\n",
        "    return auprc\n",
        "\n",
        "def get_sens(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    sens = tp / (tp + fn)\n",
        "    return sens\n",
        "\n",
        "def get_spec(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    spec = tn / (tn + fp)\n",
        "    return spec\n",
        "\n",
        "\n",
        "def get_ppv(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    ppv = tp / (tp + fp)\n",
        "    # print(tp,fp,ppv)\n",
        "    return ppv\n",
        "\n",
        "def get_npv(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "    npv = tn / (tn + fn)\n",
        "    return npv\n",
        "\n",
        "\n",
        "def get_acc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "    acc = (tp + tn) / (tp + fp + fn + tn)\n",
        "    return acc\n",
        "\n",
        "def get_metrics_classification( y_true, y_pred_score):\n",
        "    # y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    # print(y_pred_score)\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    # print(y_pred_class)\n",
        "    # y_pred_class = y_pred_score\n",
        "    # Score-based metrics\n",
        "    auroc = roc_auc_score(y_true, y_pred_score)\n",
        "\n",
        "    pr, re, thresh = precision_recall_curve(y_true, y_pred_score)\n",
        "    auprc = auc(re, pr)\n",
        "\n",
        "    # Class-based metrics\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    sens = tp / (tp + fn)\n",
        "    spec = tn / (tn + fp)\n",
        "    ppv = tp / (tp + fp)\n",
        "    npv = tn / (tn + fn)\n",
        "    acc = (tp + tn) / (tp + fp + fn + tn)\n",
        "    # print(ppv)\n",
        "    # print(tp)\n",
        "    # print(tp + fp)\n",
        "    return np.array([auroc, auprc, sens, spec, ppv, npv, acc])"
      ],
      "metadata": {
        "id": "w2qoTqpOyeJd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost Training function\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "def train_model(model_name, type, X_train, y_train,scoring):\n",
        "\n",
        "    if model_name == 'xgb':\n",
        "\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = XGBRegressor(tree_method='gpu_hist', gpu_id=0)\n",
        "            # model = XGBRegressor()\n",
        "\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = XGBClassifier(tree_method='gpu_hist', gpu_id=0)\n",
        "            # model = XGBClassifier()\n",
        "\n",
        "        pipeline = Pipeline([('kbest', kbest), ('xgb', model)])\n",
        "        param_grid = {\n",
        "        'xgb__max_depth': [3,4,6,7,8],\n",
        "        'xgb__n_estimators': [1,5,10,15,20,30,35,40,45,50],\n",
        "        'xgb__learning_rate': [0.1],#, 0.01,0.02, 0.05],\n",
        "            # 'kbest__k': [10,15,'all']\n",
        "            'kbest__k': ['all']\n",
        "        }\n",
        "\n",
        "\n",
        "    elif model_name == 'rf':\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = RandomForestRegressor(n_jobs=-1, random_state=0)\n",
        "\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = RandomForestClassifier(n_jobs=-1, random_state=0, class_weight='balanced')\n",
        "        pipeline = Pipeline([ ('kbest', kbest), ('rf', model)])\n",
        "        param_grid = {\n",
        "        'rf__n_estimators': [1,5,10,15,20,30,35,40,45,50],\n",
        "        'rf__max_features': ['sqrt','log2'],\n",
        "        'rf__max_depth' : [3,4,6,7,8],\n",
        "            'kbest__k': [5,10,15,'all']\n",
        "        }\n",
        "\n",
        "    elif model_name == 'linear':\n",
        "        scaler = StandardScaler()\n",
        "        # X_train = scalar.fit_transform(X_train)\n",
        "        # X_test = scaler.transform(X_test)\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = LinearRegression()\n",
        "            #\n",
        "            pipeline = Pipeline([ ('transformer', scaler), ('kbest', kbest), ('li', model)])\n",
        "            param_grid = {\n",
        "                'kbest__k': [10,15,'all']\n",
        "            }\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = LogisticRegression(class_weight='balanced', solver = 'liblinear') #('transformer', scalar),\n",
        "            pipeline = Pipeline([ ('transformer', scaler), ('kbest', kbest), ('li', model)])\n",
        "            param_grid = {\n",
        "            # 'li__penalty': ['l1','l2'],\n",
        "            'li__C': [0.5, 0.8, 1],\n",
        "                'kbest__k': [10,15,'all']\n",
        "            }\n",
        "\n",
        "        # else:\n",
        "    if type == 'regression':\n",
        "        # metrics = [get_r2, get_rmse, get_mae]\n",
        "        metrics = {'get_r2':get_r2, 'get_rmse':get_rmse, 'get_mae':get_mae}\n",
        "\n",
        "    else:\n",
        "        # metrics = [get_auroc, get_auprc, get_sens, get_spec, get_ppv, get_npv, get_acc]\n",
        "        metrics = {'get_auroc': get_auroc, 'get_auprc': get_auprc, 'get_sens': get_sens, 'get_spec': get_spec, 'get_ppv':get_ppv, 'get_npv': get_npv, 'get_acc': get_acc}\n",
        "    # gkf = GroupKFold(n_splits=5)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    print('--- Initiating grid search ---')\n",
        "\n",
        "\n",
        "    rf_cv = GridSearchCV(estimator=pipeline, param_grid=param_grid, refit = 'get_auroc', scoring = metrics, cv= 5)\n",
        "    rf_cv.fit(x_train, y_train)\n",
        "    # print(rf_cv)\n",
        "    res = {'get_auroc': [], 'get_auprc': [], 'get_sens': [], 'get_spec': [], 'get_ppv':[], 'get_npv': [], 'get_acc': []}\n",
        "    for i in range(5):\n",
        "      for metric in res:\n",
        "        res[metric].append(rf_cv.cv_results_[f'split{i}_test_{metric}'][rf_cv.best_index_])\n",
        "\n",
        "    for metric in res:\n",
        "      print(metric[4:], sum(res[metric])/len(res[metric]))\n",
        "\n",
        "\n",
        "\n",
        "    return rf_cv"
      ],
      "metadata": {
        "id": "CY5hKaTCyiLY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/normalized_data.csv')"
      ],
      "metadata": {
        "id": "4ZxEmRfuU_0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "1hgaRXOUVCvu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "a0120bd8-1547-4bc6-9436-475077e7b506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "0        0.483491      0.533333     0.458685     0.1300     0.365854   \n",
              "1        0.681604      0.733333     0.583474     0.2900     0.395122   \n",
              "2        0.445755      0.500000     0.473862     0.3025     0.359756   \n",
              "3        0.577830      0.660000     0.514334     0.2350     0.434146   \n",
              "4        0.384434      0.406667     0.387858     0.5250     0.393902   \n",
              "...           ...           ...          ...        ...          ...   \n",
              "2995     0.761792      0.846667     0.618887     0.1775     0.345122   \n",
              "2996     0.740566      0.746667     0.549747     0.1450     0.263415   \n",
              "2997     0.497642      0.533333     0.330523     0.2125     0.373171   \n",
              "2998     0.634434      0.626667     0.490725     0.2075     0.343902   \n",
              "2999     0.650943      0.666667     0.421585     0.1350     0.226829   \n",
              "\n",
              "           MCH      MCHC       MCV  AGE SEX SOURCE  \n",
              "0     0.458515  0.666667  0.425743  0.0   F    out  \n",
              "1     0.550218  0.701754  0.528713  0.0   F    out  \n",
              "2     0.388646  0.675439  0.330693  0.0   F    out  \n",
              "3     0.550218  0.789474  0.485149  0.0   F    out  \n",
              "4     0.371179  0.526316  0.376238  0.0   M    out  \n",
              "...        ...       ...       ...  ...  ..    ...  \n",
              "2995  0.637555  0.807018  0.590099  1.0   M    out  \n",
              "2996  0.611354  0.578947  0.685149  1.0   M    out  \n",
              "2997  0.672489  0.622807  0.748515  1.0   M    out  \n",
              "2998  0.541485  0.508772  0.627723  1.0   M    out  \n",
              "2999  0.711790  0.587719  0.817822  1.0   M    out  \n",
              "\n",
              "[3000 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4c666bd-ab38-47d3-8d68-eedcb47ad468\" 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>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.483491</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.458685</td>\n",
              "      <td>0.1300</td>\n",
              "      <td>0.365854</td>\n",
              "      <td>0.458515</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.425743</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.681604</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.583474</td>\n",
              "      <td>0.2900</td>\n",
              "      <td>0.395122</td>\n",
              "      <td>0.550218</td>\n",
              "      <td>0.701754</td>\n",
              "      <td>0.528713</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.445755</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.473862</td>\n",
              "      <td>0.3025</td>\n",
              "      <td>0.359756</td>\n",
              "      <td>0.388646</td>\n",
              "      <td>0.675439</td>\n",
              "      <td>0.330693</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.577830</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.514334</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>0.434146</td>\n",
              "      <td>0.550218</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.485149</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.384434</td>\n",
              "      <td>0.406667</td>\n",
              "      <td>0.387858</td>\n",
              "      <td>0.5250</td>\n",
              "      <td>0.393902</td>\n",
              "      <td>0.371179</td>\n",
              "      <td>0.526316</td>\n",
              "      <td>0.376238</td>\n",
              "      <td>0.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>0.761792</td>\n",
              "      <td>0.846667</td>\n",
              "      <td>0.618887</td>\n",
              "      <td>0.1775</td>\n",
              "      <td>0.345122</td>\n",
              "      <td>0.637555</td>\n",
              "      <td>0.807018</td>\n",
              "      <td>0.590099</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>0.740566</td>\n",
              "      <td>0.746667</td>\n",
              "      <td>0.549747</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.263415</td>\n",
              "      <td>0.611354</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.685149</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>0.497642</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.330523</td>\n",
              "      <td>0.2125</td>\n",
              "      <td>0.373171</td>\n",
              "      <td>0.672489</td>\n",
              "      <td>0.622807</td>\n",
              "      <td>0.748515</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>0.634434</td>\n",
              "      <td>0.626667</td>\n",
              "      <td>0.490725</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>0.343902</td>\n",
              "      <td>0.541485</td>\n",
              "      <td>0.508772</td>\n",
              "      <td>0.627723</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>0.650943</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.421585</td>\n",
              "      <td>0.1350</td>\n",
              "      <td>0.226829</td>\n",
              "      <td>0.711790</td>\n",
              "      <td>0.587719</td>\n",
              "      <td>0.817822</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4c666bd-ab38-47d3-8d68-eedcb47ad468')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-d4c666bd-ab38-47d3-8d68-eedcb47ad468 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-d4c666bd-ab38-47d3-8d68-eedcb47ad468');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-49be02fa-5248-4846-a363-18abcb5dfba1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49be02fa-5248-4846-a363-18abcb5dfba1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-49be02fa-5248-4846-a363-18abcb5dfba1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)"
      ],
      "metadata": {
        "id": "_-xFwX2Oyozi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "sTXMv8u-zEdT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "0acbea1d-791c-4430-d13f-34f6f2c8d573"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "0        0.483491      0.533333     0.458685     0.1300     0.365854   \n",
              "1        0.681604      0.733333     0.583474     0.2900     0.395122   \n",
              "2        0.445755      0.500000     0.473862     0.3025     0.359756   \n",
              "3        0.577830      0.660000     0.514334     0.2350     0.434146   \n",
              "4        0.384434      0.406667     0.387858     0.5250     0.393902   \n",
              "...           ...           ...          ...        ...          ...   \n",
              "2995     0.761792      0.846667     0.618887     0.1775     0.345122   \n",
              "2996     0.740566      0.746667     0.549747     0.1450     0.263415   \n",
              "2997     0.497642      0.533333     0.330523     0.2125     0.373171   \n",
              "2998     0.634434      0.626667     0.490725     0.2075     0.343902   \n",
              "2999     0.650943      0.666667     0.421585     0.1350     0.226829   \n",
              "\n",
              "           MCH      MCHC       MCV  AGE  SEX  SOURCE  \n",
              "0     0.458515  0.666667  0.425743  0.0    0       1  \n",
              "1     0.550218  0.701754  0.528713  0.0    0       1  \n",
              "2     0.388646  0.675439  0.330693  0.0    0       1  \n",
              "3     0.550218  0.789474  0.485149  0.0    0       1  \n",
              "4     0.371179  0.526316  0.376238  0.0    1       1  \n",
              "...        ...       ...       ...  ...  ...     ...  \n",
              "2995  0.637555  0.807018  0.590099  1.0    1       1  \n",
              "2996  0.611354  0.578947  0.685149  1.0    1       1  \n",
              "2997  0.672489  0.622807  0.748515  1.0    1       1  \n",
              "2998  0.541485  0.508772  0.627723  1.0    1       1  \n",
              "2999  0.711790  0.587719  0.817822  1.0    1       1  \n",
              "\n",
              "[3000 rows x 11 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.483491</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.458685</td>\n",
              "      <td>0.1300</td>\n",
              "      <td>0.365854</td>\n",
              "      <td>0.458515</td>\n",
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              "      <td>0.425743</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.681604</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.583474</td>\n",
              "      <td>0.2900</td>\n",
              "      <td>0.395122</td>\n",
              "      <td>0.550218</td>\n",
              "      <td>0.701754</td>\n",
              "      <td>0.528713</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.445755</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.473862</td>\n",
              "      <td>0.3025</td>\n",
              "      <td>0.359756</td>\n",
              "      <td>0.388646</td>\n",
              "      <td>0.675439</td>\n",
              "      <td>0.330693</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.577830</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.514334</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>0.434146</td>\n",
              "      <td>0.550218</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.485149</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.384434</td>\n",
              "      <td>0.406667</td>\n",
              "      <td>0.387858</td>\n",
              "      <td>0.5250</td>\n",
              "      <td>0.393902</td>\n",
              "      <td>0.371179</td>\n",
              "      <td>0.526316</td>\n",
              "      <td>0.376238</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>0.761792</td>\n",
              "      <td>0.846667</td>\n",
              "      <td>0.618887</td>\n",
              "      <td>0.1775</td>\n",
              "      <td>0.345122</td>\n",
              "      <td>0.637555</td>\n",
              "      <td>0.807018</td>\n",
              "      <td>0.590099</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>0.740566</td>\n",
              "      <td>0.746667</td>\n",
              "      <td>0.549747</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.263415</td>\n",
              "      <td>0.611354</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.685149</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>0.497642</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.330523</td>\n",
              "      <td>0.2125</td>\n",
              "      <td>0.373171</td>\n",
              "      <td>0.672489</td>\n",
              "      <td>0.622807</td>\n",
              "      <td>0.748515</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>0.634434</td>\n",
              "      <td>0.626667</td>\n",
              "      <td>0.490725</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>0.343902</td>\n",
              "      <td>0.541485</td>\n",
              "      <td>0.508772</td>\n",
              "      <td>0.627723</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>0.650943</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.421585</td>\n",
              "      <td>0.1350</td>\n",
              "      <td>0.226829</td>\n",
              "      <td>0.711790</td>\n",
              "      <td>0.587719</td>\n",
              "      <td>0.817822</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1eee2ef-4f59-470f-b221-bc2ebb3e8cbc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e1eee2ef-4f59-470f-b221-bc2ebb3e8cbc 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-e1eee2ef-4f59-470f-b221-bc2ebb3e8cbc');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9f1a6d9d-7e40-4a5b-b998-dc159dc84fb8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f1a6d9d-7e40-4a5b-b998-dc159dc84fb8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9f1a6d9d-7e40-4a5b-b998-dc159dc84fb8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting the data\n",
        "x_train, x_test, y_train, y_test = train_test_split(df[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df['SOURCE'], test_size = .25)"
      ],
      "metadata": {
        "id": "JmFMX3bFzF_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ],
      "metadata": {
        "id": "nKV1GC0fzHIZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11fc6a1a-97c4-443a-8aa1-33487fca619d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.7952107608873897\n",
            "auprc 0.8382379244523677\n",
            "sens 0.8384331797235024\n",
            "spec 0.6439078087375301\n",
            "ppv 0.7948761152683018\n",
            "npv 0.7094037244843159\n",
            "acc 0.764888888888889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CV_rfc.best_score_"
      ],
      "metadata": {
        "id": "pc3z7lM7zG-q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# getting best params\n",
        "CV_rfc.best_params_"
      ],
      "metadata": {
        "id": "gxZUDC5zAf6x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e33d50d5-525b-4f38-892a-a3718c0c499f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 3,\n",
              " 'xgb__n_estimators': 40}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# training model on best parameters\n",
        "bst = XGBClassifier(n_estimators=40, max_depth=3, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "JYSa7ZIs79Ci",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "77c2a90f-0297-4844-f3ad-f817db559247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=40, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=40, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=40, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the test scores\n",
        "\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ],
      "metadata": {
        "id": "GwnDSJyN70w5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "261e26d1-847d-482d-ff0c-baf6812d87eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "auroc:  0.7483253588516746\n",
            "auprc:  0.8803138928402086\n",
            "sens:  0.9221052631578948\n",
            "spec:  0.5745454545454546\n",
            "ppv:  0.7891891891891892\n",
            "npv:  0.8102564102564103\n",
            "acc:  0.7946666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. AI fairness"
      ],
      "metadata": {
        "id": "udMTWjB0x37h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use libraries such as IBM AI fairness to understand if the model is biased towards male or female gender.\n"
      ],
      "metadata": {
        "id": "4sNG-tl_x5gq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "df = pd.read_csv('/content/dataset_project5.csv')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4K9EhQdzQpA",
        "outputId": "8c8275af-8597-481e-9d37-7384adf08e0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3000 entries, 0 to 2999\n",
            "Data columns (total 11 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   HAEMATOCRIT   3000 non-null   float64\n",
            " 1   HAEMOGLOBINS  3000 non-null   float64\n",
            " 2   ERYTHROCYTE   3000 non-null   float64\n",
            " 3   LEUCOCYTE     3000 non-null   float64\n",
            " 4   THROMBOCYTE   3000 non-null   int64  \n",
            " 5   MCH           3000 non-null   float64\n",
            " 6   MCHC          3000 non-null   float64\n",
            " 7   MCV           3000 non-null   float64\n",
            " 8   AGE           3000 non-null   int64  \n",
            " 9   SEX           3000 non-null   object \n",
            " 10  SOURCE        3000 non-null   object \n",
            "dtypes: float64(7), int64(2), object(2)\n",
            "memory usage: 257.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df['SOURCE']=='in']),len(df[df['SOURCE']=='out'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8p8eTtcBetL",
        "outputId": "57caad39-edb9-45a5-f8e8-8fb3e77bbd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1126, 1874)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from aif360.datasets import StandardDataset\n",
        "from aif360.metrics import BinaryLabelDatasetMetric\n",
        "from aif360.algorithms.preprocessing import DisparateImpactRemover\n",
        "\n",
        "# Import your dataset\n",
        "dataset = pd.read_csv('/content/dataset_project5.csv')\n",
        "\n",
        "# Encode the 'SEX' column ('M' as 1, 'F' as 0)\n",
        "dataset.loc[dataset.SEX == 'M', 'SEX'] = 1\n",
        "dataset.loc[dataset.SEX == 'F', 'SEX'] = 0\n",
        "\n",
        "# Encode the 'SOURCE' column ('In' as 1, 'Out' as 0)\n",
        "dataset.loc[dataset.SOURCE == 'in', 'SOURCE'] = 1\n",
        "dataset.loc[dataset.SOURCE == 'out', 'SOURCE'] = 0\n",
        "\n",
        "# Create the 'PROTECTED_CLASS' column based on 'SEX'\n",
        "dataset['PROTECTED_CLASS'] = dataset['SEX'].astype(int)\n",
        "\n",
        "# Create a StandardDataset for AIF360\n",
        "privileged_groups = [{'PROTECTED_CLASS': 1}]  # 'M' is considered privileged\n",
        "unprivileged_groups = [{'PROTECTED_CLASS': 0}]  # 'F' is considered unprivileged\n",
        "dataset_aif360 = StandardDataset(\n",
        "    df=dataset,\n",
        "    label_name='SOURCE',\n",
        "    favorable_classes=[1],  # 'In' is considered favorable\n",
        "    protected_attribute_names=['PROTECTED_CLASS'],\n",
        "    privileged_classes=[[1]],\n",
        ")\n",
        "\n",
        "# Calculate and display the initial disparate impact\n",
        "privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_aif360,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "initial_disparate_impact = privileged_metric.disparate_impact()\n",
        "print(\"Initial Disparate Impact:\", initial_disparate_impact)\n",
        "\n",
        "# If bias is detected, you can use the Disparate Impact Remover mitigation technique\n",
        "di_remover = DisparateImpactRemover(repair_level=1)\n",
        "dataset_transf = di_remover.fit_transform(dataset_aif360)\n",
        "\n",
        "# Calculate and display the new disparate impact after mitigation\n",
        "new_privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_transf,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "new_disparate_impact = new_privileged_metric.disparate_impact()\n",
        "print(\"New Disparate Impact after Mitigation:\", new_disparate_impact)\n",
        "\n",
        "# You can use the dataset_transf for any downstream analysis, as the bias has been mitigated.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rM2eDCSzMdX",
        "outputId": "8ec42f03-f377-424d-d63c-3af4516b6af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Disparate Impact: 0.9625327053898484\n",
            "New Disparate Impact after Mitigation: 0.9625327053898484\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Model Interpretation"
      ],
      "metadata": {
        "id": "hQVTwB5kyIgq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use libraries such as SHAP to analyze the dominant features in XGBoost's prediction.\n",
        "# Do same with LIME.\n"
      ],
      "metadata": {
        "id": "fGr9zzn5yMcg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shap"
      ],
      "metadata": {
        "id": "MDbUEejf_1Vv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# best_model['XGBclassifier']"
      ],
      "metadata": {
        "id": "18AX_21gAJd_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.initjs()\n",
        "explainer = shap.TreeExplainer(bst)\n",
        "shap_values = explainer.shap_values(x_train.to_numpy())"
      ],
      "metadata": {
        "id": "608ilal8_886",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 44
        },
        "outputId": "d62d5ac1-dc76-4ec0-ea83-088f0bcc5577"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "display_data",
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0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var 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0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var 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.force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 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              "</script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, x_train.to_numpy(),feature_names = x_train.columns)"
      ],
      "metadata": {
        "id": "aax2M2qLB0nq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "e4437c59-5161-4807-b943-65a8cca844a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, plot_type = 'bar', feature_names = x_train.columns,show=False)"
      ],
      "metadata": {
        "id": "JRq2Wj7BCGEP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "a1813425-21c6-4f03-8857-9b1cf4e5ad19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots._waterfall.waterfall_legacy(explainer.expected_value, shap_values[5], feature_names = x_train.columns)"
      ],
      "metadata": {
        "id": "TYBEPHvTCOIv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "9bc8effd-614c-42d4-c88e-ee067f6b1a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task - 2 (First augmentation)"
      ],
      "metadata": {
        "id": "aoqSv5Jwy0VM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Data Augmentation method 1"
      ],
      "metadata": {
        "id": "V4J74B8Dy5E_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# performing data augmentation using SMOTE.\n",
        "\n",
        "import pandas as pd\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Load your dataset\n",
        "data = pd.read_csv(\"dataset_project5.csv\")\n",
        "\n",
        "# Apply formatting for decimal places\n",
        "columns_to_format = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'LEUCOCYTE', 'MCH', 'MCHC', 'MCV']\n",
        "data[columns_to_format] = data[columns_to_format].round(1)  # Round to 1 decimal place\n",
        "data['ERYTHROCYTE'] = data['ERYTHROCYTE'].round(2)  # Round to 2 decimal places\n",
        "\n",
        "# Encode 'SEX' column\n",
        "data['SEX'] = data['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "\n",
        "# Separate your features (X) and the target variable (y)\n",
        "X = data.drop(columns=['SOURCE'])\n",
        "y = data['SOURCE']\n",
        "\n",
        "# Apply SMOTE to generate synthetic samples\n",
        "smote = SMOTE(sampling_strategy='auto', random_state=42)\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
        "\n",
        "# Create a new DataFrame from the resampled data\n",
        "new_data = pd.concat([X_resampled, y_resampled], axis=1)\n",
        "\n",
        "# Decode 'SEX' column\n",
        "data['SEX'] = data['SEX'].apply(lambda x: 'M' if x == 1 else 'F')\n",
        "new_data['SEX'] = new_data['SEX'].apply(lambda x: 'M' if x == 1 else 'F')\n",
        "\n",
        "# Append the new data to the existing dataset\n",
        "augmented_data = pd.concat([data, new_data[:]], ignore_index=True)\n",
        "\n",
        "# Save the new dataset to a CSV file\n",
        "augmented_data.to_csv(\"augmented_dataset.csv\", index=False)"
      ],
      "metadata": {
        "id": "vFD3ZZ4hy4mM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# performing data augmentation using SMOTENC.\n",
        "\n",
        "import pandas as pd\n",
        "from imblearn.over_sampling import SMOTENC\n",
        "\n",
        "# Load your dataset\n",
        "data = pd.read_csv(\"dataset_project5.csv\")\n",
        "\n",
        "# Encode 'SEX' column\n",
        "data['SEX'] = data['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "\n",
        "# Specify the index of the categorical feature ('SEX' in this case)\n",
        "categorical_indices = [9]  # Index 9 corresponds to the 'SEX' column\n",
        "\n",
        "# Separate your features (X) and the target variable (y)\n",
        "X = data.drop(columns=['SOURCE'])\n",
        "y = data['SOURCE']\n",
        "\n",
        "# Apply SMOTENC to generate synthetic samples\n",
        "smotenc = SMOTENC(sampling_strategy='auto', categorical_features=categorical_indices, random_state=42)\n",
        "X_resampled, y_resampled = smotenc.fit_resample(X, y)\n",
        "\n",
        "# Create a new DataFrame from the resampled data\n",
        "new_data = pd.concat([X_resampled, y_resampled], axis=1)\n",
        "\n",
        "# Decode 'SEX' column\n",
        "data['SEX'] = data['SEX'].apply(lambda x: 'M' if x == 1 else 'F')\n",
        "new_data['SEX'] = new_data['SEX'].apply(lambda x: 'M' if x == 1 else 'F')\n",
        "\n",
        "# Append the new data to the existing dataset\n",
        "augmented_data = pd.concat([data, new_data[:]], ignore_index=True)\n",
        "\n",
        "# Save the new dataset to a CSV file\n",
        "# df = pd.concat([data,augmented_data],axis=0)\n",
        "augmented_data.to_csv(\"augmented_dataset_nc.csv\", index=False)"
      ],
      "metadata": {
        "id": "uiRUk6gxr08F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Data Analysis and Preprocessing"
      ],
      "metadata": {
        "id": "mkM0Y3_W2dHa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# observe descriptive statistics for the augmented data and check for missing data.\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('augmented_dataset_nc.csv')\n",
        "\n",
        "# Get descriptive statistics\n",
        "numerical_stats = df.describe()\n",
        "\n",
        "# Count missing values for every column\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "# Count total missing values in the entire DataFrame\n",
        "total_missing = missing_values.sum()\n",
        "\n",
        "\n",
        "print(\"Descriptive Statistics:\")\n",
        "print(numerical_stats)\n",
        "print(\"\\nMissing Values:\")\n",
        "print(missing_values)\n",
        "print(f\"Total Missing Values: {total_missing}\")"
      ],
      "metadata": {
        "id": "R8UKufQs2hmT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3fe33bd-8946-4341-e133-c680fb6019ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Statistics:\n",
            "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
            "count  6000.000000   6000.000000  6000.000000  6000.000000  6000.000000   \n",
            "mean     39.249800     13.090467     4.734303     8.121800   256.353333   \n",
            "std       5.613671      2.004124     0.724757     4.564506   110.463144   \n",
            "min      14.600000      3.800000     1.930000     1.100000    10.000000   \n",
            "25%      35.800000     11.900000     4.290000     5.300000   187.000000   \n",
            "50%      39.500000     13.200000     4.740000     7.100000   257.000000   \n",
            "75%      43.300000     14.500000     5.200000     9.700000   322.000000   \n",
            "max      57.000000     18.800000     7.860000    41.100000   830.000000   \n",
            "\n",
            "               MCH         MCHC          MCV          AGE  \n",
            "count  6000.000000  6000.000000  6000.000000  6000.000000  \n",
            "mean     27.774800    33.314867    83.279267    35.061000  \n",
            "std       2.650134     1.213102     6.544437    15.686309  \n",
            "min      14.900000    26.000000    54.000000     1.000000  \n",
            "25%      26.600000    32.700000    80.400000    24.000000  \n",
            "50%      28.300000    33.400000    84.400000    36.000000  \n",
            "75%      29.500000    34.100000    87.400000    48.000000  \n",
            "max      37.800000    37.400000   104.500000    60.000000  \n",
            "\n",
            "Missing Values:\n",
            "HAEMATOCRIT     0\n",
            "HAEMOGLOBINS    0\n",
            "ERYTHROCYTE     0\n",
            "LEUCOCYTE       0\n",
            "THROMBOCYTE     0\n",
            "MCH             0\n",
            "MCHC            0\n",
            "MCV             0\n",
            "AGE             0\n",
            "SEX             0\n",
            "SOURCE          0\n",
            "dtype: int64\n",
            "Total Missing Values: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop duplicates.\n",
        "# normalize the augmented data.\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "df = pd.read_csv('augmented_dataset_nc.csv')\n",
        "\n",
        "# Remove duplicate rows\n",
        "columns_to_check = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE', 'SEX', 'SOURCE']\n",
        "df_no_duplicates = df.drop_duplicates(subset=columns_to_check)\n",
        "print(df.drop_duplicates())\n",
        "\n",
        "columns_to_normalize = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE']\n",
        "\n",
        "# Create a MinMaxScaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply the MinMaxScaler to the selected columns in the df without duplicates\n",
        "df_no_duplicates[columns_to_normalize] = scaler.fit_transform(df_no_duplicates[columns_to_normalize])\n",
        "\n",
        "# Save the normalized df without duplicates to a new CSV file\n",
        "df_no_duplicates.to_csv('normalized_augmented_data.csv', index=False)\n"
      ],
      "metadata": {
        "id": "I8zNUj88Y7gr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e34cbb8-c401-462e-85d4-09b79c853802"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
            "0       35.100000     11.800000     4.650000   6.300000          310   \n",
            "1       43.500000     14.800000     5.390000  12.700000          334   \n",
            "2       33.500000     11.300000     4.740000  13.200000          305   \n",
            "3       39.100000     13.700000     4.980000  10.500000          366   \n",
            "4       30.900000      9.900000     4.230000  22.100000          333   \n",
            "...           ...           ...          ...        ...          ...   \n",
            "6743    39.827394     13.746576     4.790549   2.353424           61   \n",
            "6744    26.582533      9.229520     3.208284  15.217160          368   \n",
            "6745    29.951485     10.042244     3.452673   4.936305          253   \n",
            "6746    42.200000     14.229806     5.159471   3.282451          185   \n",
            "6747    37.207340     12.507340     4.535504   3.669716          130   \n",
            "\n",
            "            MCH       MCHC        MCV  AGE SEX SOURCE  \n",
            "0     25.400000  33.600000  75.500000    1   F    out  \n",
            "1     27.500000  34.000000  80.700000    1   F    out  \n",
            "2     23.800000  33.700000  70.700000    1   F    out  \n",
            "3     27.500000  35.000000  78.500000    1   F    out  \n",
            "4     23.400000  32.000000  73.000000    1   M    out  \n",
            "...         ...        ...        ...  ...  ..    ...  \n",
            "6743  28.720545  34.519182  83.183545   27   M     in  \n",
            "6744  28.770480  34.694280  82.881613   50   M     in  \n",
            "6745  29.075247  33.566997  86.708251   54   F     in  \n",
            "6746  27.612257  33.724514  81.775486    9   F     in  \n",
            "6747  27.630284  33.614681  82.161489   57   F     in  \n",
            "\n",
            "[3748 rows x 11 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize the numerical data columns\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "df = pd.read_csv('normalized_augmented_data.csv')\n",
        "\n",
        "numerical_columns = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE']\n",
        "\n",
        "numbins = 20\n",
        "\n",
        "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
        "\n",
        "plt.figure(figsize=(15, 11))\n",
        "for i, column in enumerate(numerical_columns, 1):\n",
        "    plt.subplot(2, 3, i)\n",
        "    plt.title(f'Histogram of {column}')\n",
        "    sns.histplot(df[column], bins=numbins, kde=True, color=colors[i-1])\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "6X9mU1B4VpBK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1c9d85fb-734f-4c26-e08c-40a0a4ecf10e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1100 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Model Training"
      ],
      "metadata": {
        "id": "XorzBd4F2o7T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the XGBoost model.\n",
        "# perform all the necessary tasks such as grid search, hyperparameter optimization and cross validation.\n",
        "# Calculate metrics."
      ],
      "metadata": {
        "id": "6yeyOmcX2l6B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#importing relevant libraries for training.\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import os\n",
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "# from sklearn.metrics.pairwise import manhattan_distances\n",
        "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, auc, confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "from sklearn.feature_selection import SelectKBest, f_regression, f_classif\n",
        "from sklearn.model_selection import GridSearchCV, GroupKFold, cross_val_score, StratifiedGroupKFold\n",
        "from sklearn.pipeline import Pipeline\n",
        "import warnings\n",
        "import joblib\n",
        "from sklearn.model_selection import cross_val_predict, cross_validate\n",
        "from datetime import datetime\n",
        "from xgboost import XGBClassifier,XGBRegressor\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "id": "h6PBNfbQDKcI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# functions to get results from classification metrics.\n",
        "\n",
        "def youden_threshold(fpr, tpr, thresholds):\n",
        "\n",
        "    idx = np.argmax(tpr - fpr)\n",
        "    return thresholds[idx]\n",
        "\n",
        "def get_mae(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred = clf.predict(X)\n",
        "    return mean_absolute_error(y_true, y_pred)\n",
        "\n",
        "def get_auroc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    auroc = roc_auc_score(y_true, y_pred_score)\n",
        "    return auroc\n",
        "\n",
        "def get_auprc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    pr, re, thresh = precision_recall_curve(y_true, y_pred_score)\n",
        "    auprc = auc(re, pr)\n",
        "    return auprc\n",
        "\n",
        "def get_sens(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    sens = tp / (tp + fn)\n",
        "    return sens\n",
        "\n",
        "def get_spec(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    spec = tn / (tn + fp)\n",
        "    return spec\n",
        "\n",
        "\n",
        "def get_ppv(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    ppv = tp / (tp + fp)\n",
        "    # print(tp,fp,ppv)\n",
        "    return ppv\n",
        "\n",
        "def get_npv(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "    npv = tn / (tn + fn)\n",
        "    return npv\n",
        "\n",
        "\n",
        "def get_acc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "    acc = (tp + tn) / (tp + fp + fn + tn)\n",
        "    return acc\n",
        "\n",
        "def get_metrics_classification( y_true, y_pred_score):\n",
        "    # y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    # print(y_pred_score)\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    # print(y_pred_class)\n",
        "    # y_pred_class = y_pred_score\n",
        "    # Score-based metrics\n",
        "    auroc = roc_auc_score(y_true, y_pred_score)\n",
        "\n",
        "    pr, re, thresh = precision_recall_curve(y_true, y_pred_score)\n",
        "    auprc = auc(re, pr)\n",
        "\n",
        "    # Class-based metrics\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    sens = tp / (tp + fn)\n",
        "    spec = tn / (tn + fp)\n",
        "    ppv = tp / (tp + fp)\n",
        "    npv = tn / (tn + fn)\n",
        "    acc = (tp + tn) / (tp + fp + fn + tn)\n",
        "    # print(ppv)\n",
        "    # print(tp)\n",
        "    # print(tp + fp)\n",
        "    return np.array([auroc, auprc, sens, spec, ppv, npv, acc])"
      ],
      "metadata": {
        "id": "2kh4_J3GDKcV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost Training function\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "def train_model(model_name, type, X_train, y_train,scoring):\n",
        "\n",
        "    if model_name == 'xgb':\n",
        "\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = XGBRegressor(tree_method='gpu_hist', gpu_id=0)\n",
        "            # model = XGBRegressor()\n",
        "\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = XGBClassifier(tree_method='gpu_hist', gpu_id=0)\n",
        "            # model = XGBClassifier()\n",
        "\n",
        "        pipeline = Pipeline([('kbest', kbest), ('xgb', model)])\n",
        "        param_grid = {\n",
        "        'xgb__max_depth': [3,4,6,7,8],\n",
        "        'xgb__n_estimators': [1,5,10,15,20,30,35,40,45,50],\n",
        "        'xgb__learning_rate': [0.1],#, 0.01,0.02, 0.05],\n",
        "            # 'kbest__k': [10,15,'all']\n",
        "            'kbest__k': ['all']\n",
        "        }\n",
        "\n",
        "\n",
        "    elif model_name == 'rf':\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = RandomForestRegressor(n_jobs=-1, random_state=0)\n",
        "\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = RandomForestClassifier(n_jobs=-1, random_state=0, class_weight='balanced')\n",
        "        pipeline = Pipeline([ ('kbest', kbest), ('rf', model)])\n",
        "        param_grid = {\n",
        "        'rf__n_estimators': [1,5,10,15,20,30,35,40,45,50],\n",
        "        'rf__max_features': ['sqrt','log2'],\n",
        "        'rf__max_depth' : [3,4,6,7,8],\n",
        "            'kbest__k': [5,10,15,'all']\n",
        "        }\n",
        "\n",
        "    elif model_name == 'linear':\n",
        "        scaler = StandardScaler()\n",
        "        # X_train = scalar.fit_transform(X_train)\n",
        "        # X_test = scaler.transform(X_test)\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = LinearRegression()\n",
        "            #\n",
        "            pipeline = Pipeline([ ('transformer', scaler), ('kbest', kbest), ('li', model)])\n",
        "            param_grid = {\n",
        "                'kbest__k': [10,15,'all']\n",
        "            }\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = LogisticRegression(class_weight='balanced', solver = 'liblinear') #('transformer', scalar),\n",
        "            pipeline = Pipeline([ ('transformer', scaler), ('kbest', kbest), ('li', model)])\n",
        "            param_grid = {\n",
        "            # 'li__penalty': ['l1','l2'],\n",
        "            'li__C': [0.5, 0.8, 1],\n",
        "                'kbest__k': [10,15,'all']\n",
        "            }\n",
        "\n",
        "        # else:\n",
        "    if type == 'regression':\n",
        "        # metrics = [get_r2, get_rmse, get_mae]\n",
        "        metrics = {'get_r2':get_r2, 'get_rmse':get_rmse, 'get_mae':get_mae}\n",
        "\n",
        "    else:\n",
        "        # metrics = [get_auroc, get_auprc, get_sens, get_spec, get_ppv, get_npv, get_acc]\n",
        "        metrics = {'get_auroc': get_auroc, 'get_auprc': get_auprc, 'get_sens': get_sens, 'get_spec': get_spec, 'get_ppv':get_ppv, 'get_npv': get_npv, 'get_acc': get_acc}\n",
        "    # gkf = GroupKFold(n_splits=5)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    print('--- Initiating grid search ---')\n",
        "\n",
        "    rf_cv = GridSearchCV(estimator=pipeline, param_grid=param_grid, refit = 'get_auroc', scoring = metrics, cv= 5)\n",
        "    rf_cv.fit(x_train, y_train)\n",
        "    # print(rf_cv)\n",
        "    res = {'get_auroc': [], 'get_auprc': [], 'get_sens': [], 'get_spec': [], 'get_ppv':[], 'get_npv': [], 'get_acc': []}\n",
        "    for i in range(5):\n",
        "      for metric in res:\n",
        "        res[metric].append(rf_cv.cv_results_[f'split{i}_test_{metric}'][rf_cv.best_index_])\n",
        "\n",
        "    for metric in res:\n",
        "      print(metric[4:], sum(res[metric])/len(res[metric]))\n",
        "\n",
        "\n",
        "\n",
        "    return rf_cv"
      ],
      "metadata": {
        "id": "6yXXGCkzDKcV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/normalized_augmented_data.csv')"
      ],
      "metadata": {
        "id": "Yr9AAYFX7YwF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "KhIOe5ClDbnz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "4dfcc9a3-d86d-407d-a20e-ccda13c49d77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "0        0.483491      0.533333     0.458685   0.130000     0.365854   \n",
              "1        0.681604      0.733333     0.583474   0.290000     0.395122   \n",
              "2        0.445755      0.500000     0.473862   0.302500     0.359756   \n",
              "3        0.577830      0.660000     0.514334   0.235000     0.434146   \n",
              "4        0.384434      0.406667     0.387858   0.525000     0.393902   \n",
              "...           ...           ...          ...        ...          ...   \n",
              "3743     0.594986      0.663105     0.482386   0.031336     0.062195   \n",
              "3744     0.282607      0.361968     0.215562   0.352929     0.436585   \n",
              "3745     0.362063      0.416150     0.256775   0.095908     0.296341   \n",
              "3746     0.650943      0.695320     0.544599   0.054561     0.213415   \n",
              "3747     0.533192      0.580489     0.439377   0.064243     0.146341   \n",
              "\n",
              "           MCH      MCHC       MCV       AGE SEX SOURCE  \n",
              "0     0.458515  0.666667  0.425743  0.000000   F    out  \n",
              "1     0.550218  0.701754  0.528713  0.000000   F    out  \n",
              "2     0.388646  0.675439  0.330693  0.000000   F    out  \n",
              "3     0.550218  0.789474  0.485149  0.000000   F    out  \n",
              "4     0.371179  0.526316  0.376238  0.000000   M    out  \n",
              "...        ...       ...       ...       ...  ..    ...  \n",
              "3743  0.603517  0.747297  0.577892  0.440678   M     in  \n",
              "3744  0.605698  0.762656  0.571913  0.830508   M     in  \n",
              "3745  0.619006  0.663772  0.647688  0.898305   F     in  \n",
              "3746  0.555120  0.677589  0.550010  0.135593   F     in  \n",
              "3747  0.555908  0.667954  0.557653  0.949153   F     in  \n",
              "\n",
              "[3748 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdae643a-e3d0-4785-a16c-cc2cbfeb46f4\" 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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.483491</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.458685</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>0.365854</td>\n",
              "      <td>0.458515</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.425743</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.681604</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.583474</td>\n",
              "      <td>0.290000</td>\n",
              "      <td>0.395122</td>\n",
              "      <td>0.550218</td>\n",
              "      <td>0.701754</td>\n",
              "      <td>0.528713</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.445755</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.473862</td>\n",
              "      <td>0.302500</td>\n",
              "      <td>0.359756</td>\n",
              "      <td>0.388646</td>\n",
              "      <td>0.675439</td>\n",
              "      <td>0.330693</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.577830</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.514334</td>\n",
              "      <td>0.235000</td>\n",
              "      <td>0.434146</td>\n",
              "      <td>0.550218</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.485149</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.384434</td>\n",
              "      <td>0.406667</td>\n",
              "      <td>0.387858</td>\n",
              "      <td>0.525000</td>\n",
              "      <td>0.393902</td>\n",
              "      <td>0.371179</td>\n",
              "      <td>0.526316</td>\n",
              "      <td>0.376238</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3743</th>\n",
              "      <td>0.594986</td>\n",
              "      <td>0.663105</td>\n",
              "      <td>0.482386</td>\n",
              "      <td>0.031336</td>\n",
              "      <td>0.062195</td>\n",
              "      <td>0.603517</td>\n",
              "      <td>0.747297</td>\n",
              "      <td>0.577892</td>\n",
              "      <td>0.440678</td>\n",
              "      <td>M</td>\n",
              "      <td>in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3744</th>\n",
              "      <td>0.282607</td>\n",
              "      <td>0.361968</td>\n",
              "      <td>0.215562</td>\n",
              "      <td>0.352929</td>\n",
              "      <td>0.436585</td>\n",
              "      <td>0.605698</td>\n",
              "      <td>0.762656</td>\n",
              "      <td>0.571913</td>\n",
              "      <td>0.830508</td>\n",
              "      <td>M</td>\n",
              "      <td>in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3745</th>\n",
              "      <td>0.362063</td>\n",
              "      <td>0.416150</td>\n",
              "      <td>0.256775</td>\n",
              "      <td>0.095908</td>\n",
              "      <td>0.296341</td>\n",
              "      <td>0.619006</td>\n",
              "      <td>0.663772</td>\n",
              "      <td>0.647688</td>\n",
              "      <td>0.898305</td>\n",
              "      <td>F</td>\n",
              "      <td>in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3746</th>\n",
              "      <td>0.650943</td>\n",
              "      <td>0.695320</td>\n",
              "      <td>0.544599</td>\n",
              "      <td>0.054561</td>\n",
              "      <td>0.213415</td>\n",
              "      <td>0.555120</td>\n",
              "      <td>0.677589</td>\n",
              "      <td>0.550010</td>\n",
              "      <td>0.135593</td>\n",
              "      <td>F</td>\n",
              "      <td>in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3747</th>\n",
              "      <td>0.533192</td>\n",
              "      <td>0.580489</td>\n",
              "      <td>0.439377</td>\n",
              "      <td>0.064243</td>\n",
              "      <td>0.146341</td>\n",
              "      <td>0.555908</td>\n",
              "      <td>0.667954</td>\n",
              "      <td>0.557653</td>\n",
              "      <td>0.949153</td>\n",
              "      <td>F</td>\n",
              "      <td>in</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>3748 rows × 11 columns</p>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cdae643a-e3d0-4785-a16c-cc2cbfeb46f4 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-cdae643a-e3d0-4785-a16c-cc2cbfeb46f4');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-84ab7fa0-2f16-439a-9b87-b10d328f29a3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-84ab7fa0-2f16-439a-9b87-b10d328f29a3')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-84ab7fa0-2f16-439a-9b87-b10d328f29a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)"
      ],
      "metadata": {
        "id": "4R8ppV0TDKcW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting the data\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df['SOURCE'], test_size = .25)"
      ],
      "metadata": {
        "id": "dR47y4AUDKcW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model\n",
        "\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ],
      "metadata": {
        "id": "-KBADiBDDKcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4e6ac0e-5c65-45f3-fa42-5d60c2521a24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.8328734055083578\n",
            "auprc 0.803883543830274\n",
            "sens 0.8285714285714286\n",
            "spec 0.7094654537252838\n",
            "ppv 0.7416089148714087\n",
            "npv 0.8083693328490791\n",
            "acc 0.7687730321169637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting best params\n",
        "CV_rfc.best_params_"
      ],
      "metadata": {
        "id": "y_qonr0-DKcX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77821867-036f-48df-8efa-8401a3d97d8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 7,\n",
              " 'xgb__n_estimators': 50}"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# training model on best parameters\n",
        "bst = XGBClassifier(n_estimators=50, max_depth=7, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "FAmi-69mDKcY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "6ba4cfd8-0320-43c6-802d-86f58b64f098"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the test scores\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ],
      "metadata": {
        "id": "rayUT-g8DKcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a3313ff-5d4d-400b-c7ff-f44343cad0ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "auroc:  0.7828075019821199\n",
            "auprc:  0.839333412378126\n",
            "sens:  0.8291139240506329\n",
            "spec:  0.7365010799136069\n",
            "ppv:  0.7631067961165049\n",
            "npv:  0.8080568720379147\n",
            "acc:  0.7833511205976521\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[df['SOURCE']=='in']),len(df[df['SOURCE']=='out'])"
      ],
      "metadata": {
        "id": "v8yGwOYJDv0I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d64abd32-3afe-4629-b614-015dadf2f8f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1874, 1874)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. AI fairness"
      ],
      "metadata": {
        "id": "X1wUZm8R2svT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use libraries such as IBM AI fairness to understand if the model is biased towards male or female gender.\n",
        "# may include some other type of fairness."
      ],
      "metadata": {
        "id": "ZfBVDQRR2sTl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from aif360.datasets import StandardDataset\n",
        "from aif360.metrics import BinaryLabelDatasetMetric\n",
        "from aif360.algorithms.preprocessing import DisparateImpactRemover\n",
        "\n",
        "# Import your dataset\n",
        "dataset = pd.read_csv('/content/normalized_augmented_data.csv')\n",
        "\n",
        "# Encode the 'SEX' column ('M' as 1, 'F' as 0)\n",
        "dataset.loc[dataset.SEX == 'M', 'SEX'] = 1\n",
        "dataset.loc[dataset.SEX == 'F', 'SEX'] = 0\n",
        "\n",
        "# Encode the 'SOURCE' column ('In' as 1, 'Out' as 0)\n",
        "dataset.loc[dataset.SOURCE == 'in', 'SOURCE'] = 1\n",
        "dataset.loc[dataset.SOURCE == 'out', 'SOURCE'] = 0\n",
        "\n",
        "# Create the 'PROTECTED_CLASS' column based on 'SEX'\n",
        "dataset['PROTECTED_CLASS'] = dataset['SEX'].astype(int)\n",
        "\n",
        "# Create a StandardDataset for AIF360\n",
        "privileged_groups = [{'PROTECTED_CLASS': 1}]  # 'M' is considered privileged\n",
        "unprivileged_groups = [{'PROTECTED_CLASS': 0}]  # 'F' is considered unprivileged\n",
        "dataset_aif360 = StandardDataset(\n",
        "    df=dataset,\n",
        "    label_name='SOURCE',\n",
        "    favorable_classes=[1],  # 'In' is considered favorable\n",
        "    protected_attribute_names=['PROTECTED_CLASS'],\n",
        "    privileged_classes=[[1]],\n",
        ")\n",
        "\n",
        "# Calculate and display the initial disparate impact\n",
        "privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_aif360,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "initial_disparate_impact = privileged_metric.disparate_impact()\n",
        "print(\"Initial Disparate Impact:\", initial_disparate_impact)\n",
        "\n",
        "# If bias is detected, you can use the Disparate Impact Remover mitigation technique\n",
        "di_remover = DisparateImpactRemover(repair_level=0.1)\n",
        "dataset_transf = di_remover.fit_transform(dataset_aif360)\n",
        "\n",
        "# Calculate and display the new disparate impact after mitigation\n",
        "new_privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_transf,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "new_disparate_impact = new_privileged_metric.disparate_impact()\n",
        "print(\"New Disparate Impact after Mitigation:\", new_disparate_impact)\n",
        "\n",
        "# You can use the dataset_transf for any downstream analysis, as the bias has been mitigated.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xE3CuLjg1Tc5",
        "outputId": "79147e4d-5be8-494a-c126-9110609c1981"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Disparate Impact: 1.0\n",
            "New Disparate Impact after Mitigation: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Model Interpretation"
      ],
      "metadata": {
        "id": "laffn67L23-j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use libraries such as SHAP to analyze the dominant features in XGBoost's prediction.\n",
        "# Do same with LIME."
      ],
      "metadata": {
        "id": "7XljiD7c265S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shap"
      ],
      "metadata": {
        "id": "h7U59FXzEN7b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.initjs()\n",
        "explainer = shap.TreeExplainer(bst)\n",
        "shap_values = explainer.shap_values(x_train.to_numpy())"
      ],
      "metadata": {
        "id": "o4OPq1ExEN7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "e67d0dd5-4aa7-4336-fe08-af9123f64577"
      },
      "execution_count": null,
      "outputs": [
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/shap/explainers/_tree.py\u001b[0m in \u001b[0;36mshap_values\u001b[0;34m(self, X, y, tree_limit, approximate, check_additivity, from_call)\u001b[0m\n\u001b[1;32m    360\u001b[0m                     \u001b[0mtree_limit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m                     phi = self.model.original_model.predict(\n\u001b[0m\u001b[1;32m    363\u001b[0m                         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtree_limit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_contribs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m                         \u001b[0mapprox_contribs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mapproximate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, x_train.to_numpy(),feature_names = x_train.columns)"
      ],
      "metadata": {
        "id": "uA-ohdGVEN7c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, plot_type = 'bar', feature_names = x_train.columns,show=False)"
      ],
      "metadata": {
        "id": "EyUV8gHVEN7c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots._waterfall.waterfall_legacy(explainer.expected_value, shap_values[5], feature_names = x_train.columns)"
      ],
      "metadata": {
        "id": "U21F1GW6EN7c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task - 3 (Second and Third augmentation)"
      ],
      "metadata": {
        "id": "VmcRCjei29zr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Data processing and Data Augmentation"
      ],
      "metadata": {
        "id": "UShfUXwF3JdS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# uses VAE to generate synthetic data with and without noise\n",
        "\n",
        "def augment(df,target):\n",
        "  X_train, X_test, y_train, y_test = mlp.split_df(df, dep_var='SOURCE', test_size=0.3, split_mode='random')\n",
        "\n",
        "  x_scaler = StandardScaler()\n",
        "\n",
        "  X_train_scaled = x_scaler.fit_transform(X_train)\n",
        "\n",
        "  X_test_scaled = x_scaler.transform(X_test)\n",
        "\n",
        "  X_train_fraud = X_train_scaled[np.where(y_train==target)[0]]\n",
        "  X_test_fraud = X_test_scaled[np.where(y_test==target)[0]]\n",
        "\n",
        "  datasets = dta.create_datasets(X_train_fraud, y_train.values[np.where(y_train==target)], X_test_fraud, y_test.values[np.where(y_test==target)])\n",
        "  data = dta.DataBunch(*dta.create_loaders(datasets, bs=1024, device='cuda'))\n",
        "\n",
        "  D_in = X_train_fraud.shape[1]\n",
        "  VAE_arch = [50, 12, 12]\n",
        "  target_name = 'SOURCE'\n",
        "  target_class = target\n",
        "  df_cols = list(df.columns)\n",
        "\n",
        "  model = dta.Autoencoder(D_in, VAE_arch, latent_dim=5).to('cuda')\n",
        "  opt = optim.Adam(model.parameters(), lr=0.01)\n",
        "  loss_func = dta.customLoss()\n",
        "\n",
        "  learn = dta.Learner(model, opt, loss_func, data, target_name, target_class, df_cols)\n",
        "\n",
        "  run = dta.Runner(cb_funcs=[dta.LR_Find, dta.Recorder])\n",
        "\n",
        "  run.fit(100, learn)\n",
        "\n",
        "  sched = dta.combine_scheds([0.3, 0.7], [dta.sched_cos(0.01, 0.1), dta.sched_cos(0.1, 0.01)])\n",
        "\n",
        "\n",
        "  cbfs = [partial(dta.LossTracker, show_every=50), dta.Recorder, partial(dta.ParamScheduler, 'lr', sched)]\n",
        "  model = dta.Autoencoder(D_in, VAE_arch, latent_dim=20).to('cuda')\n",
        "  opt = optim.Adam(model.parameters(), lr=0.01)\n",
        "  learn = dta.Learner(model, opt, loss_func, data, target_name, target_class, df_cols)\n",
        "  run = dta.Runner(cb_funcs=cbfs)\n",
        "  run.fit(400, learn)\n",
        "\n",
        "  df_fake = run.predict_df(learn, no_samples=1000, scaler=x_scaler)\n",
        "  std_list = list(df[df['SOURCE']==0][df_cols].std()/10)\n",
        "  df_fake_with_noise = run.predict_with_noise_df(learn, no_samples=1000, mu=0, sigma=std_list, scaler=x_scaler)\n",
        "  # df_fake_with_noise.head()\n",
        "  return df_fake, df_fake_with_noise\n"
      ],
      "metadata": {
        "id": "r9uTdh-F1mqM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fake_0, df_fake_with_noise_0 = augment(df, 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e22930ae-2310-407b-8003-015092180b82",
        "id": "_AAMhZvE1mqN"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 50\n",
            "train loss is: 5.327569007873535\n",
            "validation loss is: 1.4877498149871826\n",
            "epoch: 100\n",
            "train loss is: 1.7094123363494873\n",
            "validation loss is: 1.065248966217041\n",
            "epoch: 150\n",
            "train loss is: 1.4253404140472412\n",
            "validation loss is: 1.0651836395263672\n",
            "epoch: 200\n",
            "train loss is: 1.3279093503952026\n",
            "validation loss is: 1.0677268505096436\n",
            "epoch: 250\n",
            "train loss is: 1.2789512872695923\n",
            "validation loss is: 1.0690504312515259\n",
            "epoch: 300\n",
            "train loss is: 1.2495061159133911\n",
            "validation loss is: 1.069838047027588\n",
            "epoch: 350\n",
            "train loss is: 1.2298369407653809\n",
            "validation loss is: 1.0703176259994507\n",
            "epoch: 400\n",
            "train loss is: 1.2157561779022217\n",
            "validation loss is: 1.0706586837768555\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fake_1, df_fake_with_noise_1 = augment(df, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c82d750b-3b33-4cf5-e123-f17f4bb49950",
        "id": "E9tsoTp91mqO"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 50\n",
            "train loss is: 4.13468074798584\n",
            "validation loss is: 1.0873714685440063\n",
            "epoch: 100\n",
            "train loss is: 1.2441308498382568\n",
            "validation loss is: 0.9171944260597229\n",
            "epoch: 150\n",
            "train loss is: 1.0772535800933838\n",
            "validation loss is: 0.9047187566757202\n",
            "epoch: 200\n",
            "train loss is: 1.0205663442611694\n",
            "validation loss is: 0.9004605412483215\n",
            "epoch: 250\n",
            "train loss is: 0.9932506680488586\n",
            "validation loss is: 0.8983933329582214\n",
            "epoch: 300\n",
            "train loss is: 0.9760065078735352\n",
            "validation loss is: 0.8971193432807922\n",
            "epoch: 350\n",
            "train loss is: 0.9644944667816162\n",
            "validation loss is: 0.8962224721908569\n",
            "epoch: 400\n",
            "train loss is: 0.9561789035797119\n",
            "validation loss is: 0.895548403263092\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0948c5f6-65df-4e2e-d01f-04bcd161cb54",
        "id": "iEUhg-Go1mqO"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2100, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Model Training"
      ],
      "metadata": {
        "id": "Payymkr12Qw0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#without noise\n",
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "df_merged = pd.concat([df,df_fake_1, df_fake_0])\n",
        "df_merged.sample(frac=1)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_merged[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df_merged['SOURCE'], test_size = .25)"
      ],
      "metadata": {
        "id": "s2anHlEgBE0j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# without noise\n",
        "# training model on best parameters\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mh9hH7OBBa5D",
        "outputId": "51e5fad9-8432-4325-fde4-f53722ade058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.9294320413885633\n",
            "auprc 0.9421321127954705\n",
            "sens 0.9014378145219266\n",
            "spec 0.806085408564076\n",
            "ppv 0.8616527653326476\n",
            "npv 0.8652307060905852\n",
            "acc 0.8605333333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting best params\n",
        "CV_rfc.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce07ec10-0749-4f16-c20b-dd8cbcdf8111",
        "id": "fR-0VbbcI7Z1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 4,\n",
              " 'xgb__n_estimators': 45}"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# without noise\n",
        "# getting the test scores\n",
        "bst = XGBClassifier(n_estimators=45, max_depth=4, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0366a4ab-e0b7-40e7-a0a7-6c93b981f4a3",
        "id": "fEzQQ5EVI7Z2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "auroc:  0.8556421373175603\n",
            "auprc:  0.9143753947699507\n",
            "sens:  0.931787175989086\n",
            "spec:  0.7794970986460348\n",
            "ppv:  0.8569636135508155\n",
            "npv:  0.8896247240618101\n",
            "acc:  0.8688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xzIrWeZ0JLdl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3ZRAk9HYJL-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# with noise\n",
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "df_merged = pd.concat([df,df_fake_with_noise_0, df_fake_with_noise_1])\n",
        "df_merged.sample(frac=1)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_merged[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df_merged['SOURCE'], test_size = .25)\n"
      ],
      "metadata": {
        "id": "EWGiyNSj1mqS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# with noise\n",
        "# training the model\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkSV4nHICk2g",
        "outputId": "34603fa3-f4d3-4ce2-b50f-4e21d3b74ae0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.931916651006499\n",
            "auprc 0.9446991307054204\n",
            "sens 0.9112545189661685\n",
            "spec 0.8003938087774294\n",
            "ppv 0.8609848282722009\n",
            "npv 0.8720084482416229\n",
            "acc 0.8640000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting best params\n",
        "CV_rfc.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5ce80bc-75fb-41ef-cc82-f3630aa288a0",
        "id": "5UfpgthICFwV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 4,\n",
              " 'xgb__n_estimators': 45}"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# with noise\n",
        "# training model on best parameters\n",
        "bst = XGBClassifier(n_estimators=45, max_depth=4, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)\n",
        "\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12b85dbb-3bfa-4ace-945b-314880c05e8b",
        "id": "Zlz1gFWhCFwX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "auroc:  0.8405156131956686\n",
            "auprc:  0.9032869595708124\n",
            "sens:  0.9196675900277008\n",
            "spec:  0.7613636363636364\n",
            "ppv:  0.8405063291139241\n",
            "npv:  0.8739130434782608\n",
            "acc:  0.8528\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "N6zmUpvN3VYh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. AI fairness"
      ],
      "metadata": {
        "id": "62WR3zhL3vb7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use libraries such as IBM AI fairness to understand if the model is biased towards male or female gender.\n",
        "# may include some other type of fairness."
      ],
      "metadata": {
        "id": "S9_RiNnJ3uoA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged['SEX'] = df_merged['SEX'].apply(lambda x: round(x))"
      ],
      "metadata": {
        "id": "cA-mYOzE2lVK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "1aa2bb89-9684-4a37-e23a-451b0887cbb4",
        "id": "IoWsNwVc2lVL"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "0      35.100000     11.800000     4.650000   6.300000   310.000000   \n",
              "1      43.500000     14.800000     5.390000  12.700000   334.000000   \n",
              "2      33.500000     11.300000     4.740000  13.200000   305.000000   \n",
              "3      39.100000     13.700000     4.980000  10.500000   366.000000   \n",
              "4      30.900000      9.900000     4.230000  22.100000   333.000000   \n",
              "..           ...           ...          ...        ...          ...   \n",
              "995    37.431370     12.463284     4.531254   8.447145   210.732391   \n",
              "996    37.429138     12.462743     4.530303   8.435891   210.655319   \n",
              "997    37.432430     12.463507     4.532059   8.455031   210.763550   \n",
              "998    37.434586     12.463912     4.532618   8.463583   210.896347   \n",
              "999    37.436829     12.464399     4.533598   8.475572   211.048660   \n",
              "\n",
              "           MCH       MCHC        MCV        AGE  SEX  SOURCE  \n",
              "0    25.400000  33.600000  75.500000   1.000000    0       1  \n",
              "1    27.500000  34.000000  80.700000   1.000000    0       1  \n",
              "2    23.800000  33.700000  70.700000   1.000000    0       1  \n",
              "3    27.500000  35.000000  78.500000   1.000000    0       1  \n",
              "4    23.400000  32.000000  73.000000   1.000000    1       1  \n",
              "..         ...        ...        ...        ...  ...     ...  \n",
              "995  27.639742  33.267876  82.991020  36.473259    1       0  \n",
              "996  27.638136  33.267197  82.966179  36.365192    1       0  \n",
              "997  27.657703  33.266472  83.076622  36.612076    1       0  \n",
              "998  27.646315  33.267937  83.034615  36.582214    1       0  \n",
              "999  27.656555  33.267422  83.075714  36.700893    1       0  \n",
              "\n",
              "[5000 rows x 11 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
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              "      <th>SOURCE</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.100000</td>\n",
              "      <td>11.800000</td>\n",
              "      <td>4.650000</td>\n",
              "      <td>6.300000</td>\n",
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              "      <th>1</th>\n",
              "      <td>43.500000</td>\n",
              "      <td>14.800000</td>\n",
              "      <td>5.390000</td>\n",
              "      <td>12.700000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>80.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.500000</td>\n",
              "      <td>11.300000</td>\n",
              "      <td>4.740000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>305.000000</td>\n",
              "      <td>23.800000</td>\n",
              "      <td>33.700000</td>\n",
              "      <td>70.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.100000</td>\n",
              "      <td>13.700000</td>\n",
              "      <td>4.980000</td>\n",
              "      <td>10.500000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>78.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.900000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>4.230000</td>\n",
              "      <td>22.100000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>23.400000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>995</th>\n",
              "      <td>37.431370</td>\n",
              "      <td>12.463284</td>\n",
              "      <td>4.531254</td>\n",
              "      <td>8.447145</td>\n",
              "      <td>210.732391</td>\n",
              "      <td>27.639742</td>\n",
              "      <td>33.267876</td>\n",
              "      <td>82.991020</td>\n",
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              "      <td>12.462743</td>\n",
              "      <td>4.530303</td>\n",
              "      <td>8.435891</td>\n",
              "      <td>210.655319</td>\n",
              "      <td>27.638136</td>\n",
              "      <td>33.267197</td>\n",
              "      <td>82.966179</td>\n",
              "      <td>36.365192</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>997</th>\n",
              "      <td>37.432430</td>\n",
              "      <td>12.463507</td>\n",
              "      <td>4.532059</td>\n",
              "      <td>8.455031</td>\n",
              "      <td>210.763550</td>\n",
              "      <td>27.657703</td>\n",
              "      <td>33.266472</td>\n",
              "      <td>83.076622</td>\n",
              "      <td>36.612076</td>\n",
              "      <td>1</td>\n",
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              "      <td>36.700893</td>\n",
              "      <td>1</td>\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-36a83038-ecf2-49b4-b84f-c821b8137bf5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from aif360.datasets import StandardDataset\n",
        "from aif360.metrics import BinaryLabelDatasetMetric\n",
        "from aif360.algorithms.preprocessing import DisparateImpactRemover\n",
        "\n",
        "# Import your dataset\n",
        "dataset = df_merged\n",
        "\n",
        "# Create the 'PROTECTED_CLASS' column based on 'SEX'\n",
        "dataset['PROTECTED_CLASS'] = dataset['SEX'].astype(int)\n",
        "\n",
        "# Create a StandardDataset for AIF360\n",
        "privileged_groups = [{'PROTECTED_CLASS': 0}]\n",
        "unprivileged_groups = [{'PROTECTED_CLASS': 1}]\n",
        "dataset_aif360 = StandardDataset(\n",
        "    df=dataset,\n",
        "    label_name='SOURCE',\n",
        "    favorable_classes=[1],  # 'In' is considered favorable\n",
        "    protected_attribute_names=['PROTECTED_CLASS'],\n",
        "    privileged_classes=[[1]],\n",
        ")\n",
        "\n",
        "# Calculate and display the initial disparate impact\n",
        "privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_aif360,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "initial_disparate_impact = privileged_metric.disparate_impact()\n",
        "print(\"Initial Disparate Impact:\", initial_disparate_impact)\n",
        "\n",
        "# If bias is detected, you can use the Disparate Impact Remover mitigation technique\n",
        "di_remover = DisparateImpactRemover(repair_level=0.1)\n",
        "dataset_transf = di_remover.fit_transform(dataset_aif360)\n",
        "\n",
        "# Calculate and display the new disparate impact after mitigation\n",
        "new_privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_transf,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "new_disparate_impact = new_privileged_metric.disparate_impact()\n",
        "print(\"New Disparate Impact after Mitigation:\", new_disparate_impact)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8ee9d11-ff8e-4963-b293-7081476a6e49",
        "id": "5hKDhUeUKcQ1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Disparate Impact: 0.8622409003490278\n",
            "New Disparate Impact after Mitigation: 0.8622409003490278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Model Interpretation"
      ],
      "metadata": {
        "id": "bjocUuyi31Od"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use libraries such as SHAP to analyze the dominant features in XGBoost's prediction.\n",
        "# Do same with LIME."
      ],
      "metadata": {
        "id": "bnkHrUBB30qM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shap"
      ],
      "metadata": {
        "id": "KKj9VE_O2xLS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.initjs()\n",
        "explainer = shap.TreeExplainer(bst)\n",
        "shap_values = explainer.shap_values(x_train.to_numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 44
        },
        "outputId": "7e2f9fc1-8d44-4c4e-ca47-a140af8494d6",
        "id": "EtzU8j4S2xLT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var 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0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var 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.force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 300px;\\n          }\\n          .additive-force-array-xlabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-ylabel {\\n            position: relative;\\n            top: 0px;\\n            left: 0px;\\n            transform: rotate(-90deg);\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            max-width: 150px;\\n          }\\n          .additive-force-array-ylabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-hoverLine {\\n            stroke-width: 1px;\\n            stroke: #fff;\\n            opacity: 1;\\n          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              "</script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, x_train.to_numpy(),feature_names = x_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "6d7626a8-d6fe-4044-9a44-7a91f8dc977b",
        "id": "hK1X7EmT2xLT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, plot_type = 'bar', feature_names = x_train.columns,show=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "67a944cc-5dba-4dde-f754-d2900b2f4bdf",
        "id": "9G4FLcML2xLU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots._waterfall.waterfall_legacy(explainer.expected_value, shap_values[5], feature_names = x_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "db8e6418-2171-4033-dbf5-937206d62d83",
        "id": "5GLJL_LY2xLU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NsrNzx_3OK0A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rough work"
      ],
      "metadata": {
        "id": "ucB81LFoOLhA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install deep_tabular_augmentation\n",
        "!pip install mlprep-ls\n",
        "!pip install 'aif360[all]'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RdsjixIGUNh5",
        "outputId": "aaa4004f-c1a2-4a1c-a4e3-940b9d524911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: deep_tabular_augmentation in /usr/local/lib/python3.10/dist-packages (0.5.3)\n",
            "Requirement already satisfied: mlprep-ls in /usr/local/lib/python3.10/dist-packages (0.3.0)\n",
            "Requirement already satisfied: aif360[all] in /usr/local/lib/python3.10/dist-packages (0.5.0)\n",
            "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.11.3)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (3.7.1)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (0.12.2)\n",
            "Collecting jinja2<3.1.0 (from aif360[all])\n",
            "  Downloading Jinja2-3.0.3-py3-none-any.whl (133 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.6/133.6 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ipympl (from aif360[all])\n",
            "  Downloading ipympl-0.9.3-py2.py3-none-any.whl (511 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m511.6/511.6 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting adversarial-robustness-toolbox>=1.0.0 (from aif360[all])\n",
            "  Downloading adversarial_robustness_toolbox-1.16.0-py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pytest>=3.5 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (7.4.3)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (2.1.0+cu118)\n",
            "Collecting BlackBoxAuditing (from aif360[all])\n",
            "  Downloading BlackBoxAuditing-0.1.54.tar.gz (2.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting sphinxcontrib-websupport (from sphinx<2->aif360[all])\n",
            "  Downloading sphinxcontrib_websupport-1.2.5-py3-none-any.whl (37 kB)\n",
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            "Collecting sphinxcontrib-serializinghtml (from sphinxcontrib-websupport->sphinx<2->aif360[all])\n",
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            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (3.7.1)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (1.6.4)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow>=1.13.1->aif360[all]) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow>=1.13.1->aif360[all]) (3.2.2)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (1.3.0)\n",
            "Building wheels for collected packages: BlackBoxAuditing, lime\n",
            "  Building wheel for BlackBoxAuditing (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for BlackBoxAuditing: filename=BlackBoxAuditing-0.1.54-py2.py3-none-any.whl size=1394752 sha256=a6483122358ba55554e93b6a4459ca52100d199f9fd0eced0905c342f8255977\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/4f/b1/80e1b0790df07536470758fe0a4f9ff8fa942fd9fe30bbb192\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283834 sha256=bd08927e589a0472c1dbd14a281fbb93f5b98bca7905deaea1fd5b91df67cfc8\n",
            "  Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n",
            "Successfully built BlackBoxAuditing lime\n",
            "Installing collected packages: texttable, sphinxcontrib-serializinghtml, qtpy, memory-profiler, jinja2, jedi, igraph, docutils, sphinxcontrib-websupport, scikit-learn, cairocffi, sphinx, lime, fairlearn, BlackBoxAuditing, adversarial-robustness-toolbox, tempeh, sphinxcontrib-jquery, qtconsole, sphinx-rtd-theme, jupyter, ipympl\n",
            "  Attempting uninstall: sphinxcontrib-serializinghtml\n",
            "    Found existing installation: sphinxcontrib-serializinghtml 1.1.9\n",
            "    Uninstalling sphinxcontrib-serializinghtml-1.1.9:\n",
            "      Successfully uninstalled sphinxcontrib-serializinghtml-1.1.9\n",
            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 3.1.2\n",
            "    Uninstalling Jinja2-3.1.2:\n",
            "      Successfully uninstalled Jinja2-3.1.2\n",
            "  Attempting uninstall: docutils\n",
            "    Found existing installation: docutils 0.18.1\n",
            "    Uninstalling docutils-0.18.1:\n",
            "      Successfully uninstalled docutils-0.18.1\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: sphinx\n",
            "    Found existing installation: Sphinx 5.0.2\n",
            "    Uninstalling Sphinx-5.0.2:\n",
            "      Successfully uninstalled Sphinx-5.0.2\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",
            "bigframes 0.13.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.1.3 which is incompatible.\n",
            "sphinxcontrib-applehelp 1.0.7 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\n",
            "sphinxcontrib-devhelp 1.0.5 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\n",
            "sphinxcontrib-htmlhelp 2.0.4 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\n",
            "sphinxcontrib-qthelp 1.0.6 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed BlackBoxAuditing-0.1.54 adversarial-robustness-toolbox-1.16.0 cairocffi-1.6.1 docutils-0.17.1 fairlearn-0.9.0 igraph-0.11.3 ipympl-0.9.3 jedi-0.19.1 jinja2-3.0.3 jupyter-1.0.0 lime-0.2.0.1 memory-profiler-0.61.0 qtconsole-5.5.1 qtpy-2.4.1 scikit-learn-1.1.3 sphinx-1.8.6 sphinx-rtd-theme-1.3.0 sphinxcontrib-jquery-4.1 sphinxcontrib-serializinghtml-1.1.5 sphinxcontrib-websupport-1.2.4 tempeh-0.1.12 texttable-1.7.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "sklearn",
                  "sphinxcontrib"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mlprepare as mlp"
      ],
      "metadata": {
        "id": "ahigzqYId9Nm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import deep_tabular_augmentation as dta #/usr/local/lib/python3.10/dist-packages/deep_tabular_augmentation/common.py"
      ],
      "metadata": {
        "id": "H_llH230O26R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/dataset_project5.csv')"
      ],
      "metadata": {
        "id": "YOrrRIe8bMqH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)"
      ],
      "metadata": {
        "id": "nG3vmqtEbbhF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test = train_test_split(df, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "cyx65UQHOMtc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "45x6r_ZsemZ8",
        "outputId": "ad614bc0-1a24-4c7d-a414-9387b5ad67c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE   MCH  \\\n",
              "0            35.1          11.8         4.65        6.3          310  25.4   \n",
              "1            43.5          14.8         5.39       12.7          334  27.5   \n",
              "2            33.5          11.3         4.74       13.2          305  23.8   \n",
              "3            39.1          13.7         4.98       10.5          366  27.5   \n",
              "4            30.9           9.9         4.23       22.1          333  23.4   \n",
              "...           ...           ...          ...        ...          ...   ...   \n",
              "2995         46.9          16.5         5.60        8.2          293  29.5   \n",
              "2996         46.0          15.0         5.19        6.9          226  28.9   \n",
              "2997         35.7          11.8         3.89        9.6          316  30.3   \n",
              "2998         41.5          13.2         4.84        9.4          292  27.3   \n",
              "2999         42.2          13.8         4.43        6.5          196  31.2   \n",
              "\n",
              "      MCHC   MCV  AGE  SEX  SOURCE  \n",
              "0     33.6  75.5    1    0       1  \n",
              "1     34.0  80.7    1    0       1  \n",
              "2     33.7  70.7    1    0       1  \n",
              "3     35.0  78.5    1    0       1  \n",
              "4     32.0  73.0    1    1       1  \n",
              "...    ...   ...  ...  ...     ...  \n",
              "2995  35.2  83.8   60    1       1  \n",
              "2996  32.6  88.6   60    1       1  \n",
              "2997  33.1  91.8   60    1       1  \n",
              "2998  31.8  85.7   60    1       1  \n",
              "2999  32.7  95.3   60    1       1  \n",
              "\n",
              "[3000 rows x 11 columns]"
            ],
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              "\n",
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              "    <div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.1</td>\n",
              "      <td>11.8</td>\n",
              "      <td>4.65</td>\n",
              "      <td>6.3</td>\n",
              "      <td>310</td>\n",
              "      <td>25.4</td>\n",
              "      <td>33.6</td>\n",
              "      <td>75.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.5</td>\n",
              "      <td>14.8</td>\n",
              "      <td>5.39</td>\n",
              "      <td>12.7</td>\n",
              "      <td>334</td>\n",
              "      <td>27.5</td>\n",
              "      <td>34.0</td>\n",
              "      <td>80.7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.5</td>\n",
              "      <td>11.3</td>\n",
              "      <td>4.74</td>\n",
              "      <td>13.2</td>\n",
              "      <td>305</td>\n",
              "      <td>23.8</td>\n",
              "      <td>33.7</td>\n",
              "      <td>70.7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.1</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>10.5</td>\n",
              "      <td>366</td>\n",
              "      <td>27.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>78.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.9</td>\n",
              "      <td>9.9</td>\n",
              "      <td>4.23</td>\n",
              "      <td>22.1</td>\n",
              "      <td>333</td>\n",
              "      <td>23.4</td>\n",
              "      <td>32.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>46.9</td>\n",
              "      <td>16.5</td>\n",
              "      <td>5.60</td>\n",
              "      <td>8.2</td>\n",
              "      <td>293</td>\n",
              "      <td>29.5</td>\n",
              "      <td>35.2</td>\n",
              "      <td>83.8</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>46.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5.19</td>\n",
              "      <td>6.9</td>\n",
              "      <td>226</td>\n",
              "      <td>28.9</td>\n",
              "      <td>32.6</td>\n",
              "      <td>88.6</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>35.7</td>\n",
              "      <td>11.8</td>\n",
              "      <td>3.89</td>\n",
              "      <td>9.6</td>\n",
              "      <td>316</td>\n",
              "      <td>30.3</td>\n",
              "      <td>33.1</td>\n",
              "      <td>91.8</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>41.5</td>\n",
              "      <td>13.2</td>\n",
              "      <td>4.84</td>\n",
              "      <td>9.4</td>\n",
              "      <td>292</td>\n",
              "      <td>27.3</td>\n",
              "      <td>31.8</td>\n",
              "      <td>85.7</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>42.2</td>\n",
              "      <td>13.8</td>\n",
              "      <td>4.43</td>\n",
              "      <td>6.5</td>\n",
              "      <td>196</td>\n",
              "      <td>31.2</td>\n",
              "      <td>32.7</td>\n",
              "      <td>95.3</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 11 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import optim\n",
        "from functools import partial"
      ],
      "metadata": {
        "id": "OUO7J0jFdt0V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def augment(df,target):\n",
        "  X_train, X_test, y_train, y_test = mlp.split_df(df, dep_var='SOURCE', test_size=0.3, split_mode='random')\n",
        "\n",
        "  x_scaler = StandardScaler()\n",
        "\n",
        "  X_train_scaled = x_scaler.fit_transform(X_train)\n",
        "\n",
        "  X_test_scaled = x_scaler.transform(X_test)\n",
        "\n",
        "  X_train_fraud = X_train_scaled[np.where(y_train==target)[0]]\n",
        "  X_test_fraud = X_test_scaled[np.where(y_test==target)[0]]\n",
        "\n",
        "  datasets = dta.create_datasets(X_train_fraud, y_train.values[np.where(y_train==target)], X_test_fraud, y_test.values[np.where(y_test==target)])\n",
        "  data = dta.DataBunch(*dta.create_loaders(datasets, bs=1024, device='cuda'))\n",
        "\n",
        "  D_in = X_train_fraud.shape[1]\n",
        "  VAE_arch = [50, 12, 12]\n",
        "  target_name = 'SOURCE'\n",
        "  target_class = target\n",
        "  df_cols = list(df.columns)\n",
        "\n",
        "  model = dta.Autoencoder(D_in, VAE_arch, latent_dim=5).to('cuda')\n",
        "  opt = optim.Adam(model.parameters(), lr=0.01)\n",
        "  loss_func = dta.customLoss()\n",
        "\n",
        "  learn = dta.Learner(model, opt, loss_func, data, target_name, target_class, df_cols)\n",
        "\n",
        "  run = dta.Runner(cb_funcs=[dta.LR_Find, dta.Recorder])\n",
        "\n",
        "  run.fit(100, learn)\n",
        "\n",
        "  sched = dta.combine_scheds([0.3, 0.7], [dta.sched_cos(0.01, 0.1), dta.sched_cos(0.1, 0.01)])\n",
        "\n",
        "\n",
        "  cbfs = [partial(dta.LossTracker, show_every=50), dta.Recorder, partial(dta.ParamScheduler, 'lr', sched)]\n",
        "  model = dta.Autoencoder(D_in, VAE_arch, latent_dim=20).to('cuda')\n",
        "  opt = optim.Adam(model.parameters(), lr=0.01)\n",
        "  learn = dta.Learner(model, opt, loss_func, data, target_name, target_class, df_cols)\n",
        "  run = dta.Runner(cb_funcs=cbfs)\n",
        "  run.fit(400, learn)\n",
        "\n",
        "  df_fake = run.predict_df(learn, no_samples=1000, scaler=x_scaler)\n",
        "  std_list = list(df[df['SOURCE']==0][df_cols].std()/10)\n",
        "  df_fake_with_noise = run.predict_with_noise_df(learn, no_samples=1000, mu=0, sigma=std_list, scaler=x_scaler)\n",
        "  # df_fake_with_noise.head()\n",
        "  return df_fake, df_fake_with_noise\n"
      ],
      "metadata": {
        "id": "xACNRGoCbp_Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fake_0, df_fake_with_noise_0 = augment(df, 0)"
      ],
      "metadata": {
        "id": "q17RPK79eb9I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e22930ae-2310-407b-8003-015092180b82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 50\n",
            "train loss is: 5.327569007873535\n",
            "validation loss is: 1.4877498149871826\n",
            "epoch: 100\n",
            "train loss is: 1.7094123363494873\n",
            "validation loss is: 1.065248966217041\n",
            "epoch: 150\n",
            "train loss is: 1.4253404140472412\n",
            "validation loss is: 1.0651836395263672\n",
            "epoch: 200\n",
            "train loss is: 1.3279093503952026\n",
            "validation loss is: 1.0677268505096436\n",
            "epoch: 250\n",
            "train loss is: 1.2789512872695923\n",
            "validation loss is: 1.0690504312515259\n",
            "epoch: 300\n",
            "train loss is: 1.2495061159133911\n",
            "validation loss is: 1.069838047027588\n",
            "epoch: 350\n",
            "train loss is: 1.2298369407653809\n",
            "validation loss is: 1.0703176259994507\n",
            "epoch: 400\n",
            "train loss is: 1.2157561779022217\n",
            "validation loss is: 1.0706586837768555\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fake_1, df_fake_with_noise_1 = augment(df, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JJATpy5dcpg",
        "outputId": "c82d750b-3b33-4cf5-e123-f17f4bb49950"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 50\n",
            "train loss is: 4.13468074798584\n",
            "validation loss is: 1.0873714685440063\n",
            "epoch: 100\n",
            "train loss is: 1.2441308498382568\n",
            "validation loss is: 0.9171944260597229\n",
            "epoch: 150\n",
            "train loss is: 1.0772535800933838\n",
            "validation loss is: 0.9047187566757202\n",
            "epoch: 200\n",
            "train loss is: 1.0205663442611694\n",
            "validation loss is: 0.9004605412483215\n",
            "epoch: 250\n",
            "train loss is: 0.9932506680488586\n",
            "validation loss is: 0.8983933329582214\n",
            "epoch: 300\n",
            "train loss is: 0.9760065078735352\n",
            "validation loss is: 0.8971193432807922\n",
            "epoch: 350\n",
            "train loss is: 0.9644944667816162\n",
            "validation loss is: 0.8962224721908569\n",
            "epoch: 400\n",
            "train loss is: 0.9561789035797119\n",
            "validation loss is: 0.895548403263092\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_N2jipzgRSk",
        "outputId": "0948c5f6-65df-4e2e-d01f-04bcd161cb54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2100, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df_merged = pd.concat([df,df_fake_with_noise_0, df_fake_with_noise_1])\n",
        "df_merged = pd.concat([df,df_fake_1, df_fake_0])"
      ],
      "metadata": {
        "id": "O-ZLxcjgd9QK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged.sample(frac=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "77e4aff0-b103-452e-8226-7257b7891faa",
        "id": "DFJ4ZDP_d9QL"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "2214    26.400000      8.000000     3.100000  10.000000    64.000000   \n",
              "995     46.200000     15.900000     5.370000   6.800000   189.000000   \n",
              "771     49.300000     16.100000     6.270000   5.700000   169.000000   \n",
              "2302    34.900000     11.000000     4.250000   5.600000   333.000000   \n",
              "2577    35.500000     12.100000     4.130000   3.900000   251.000000   \n",
              "...           ...           ...          ...        ...          ...   \n",
              "2130    36.300000     12.100000     4.200000   4.800000   152.000000   \n",
              "1525    44.500000     14.600000     5.800000   6.200000   267.000000   \n",
              "1272    42.800000     14.100000     5.350000   6.800000   302.000000   \n",
              "443     40.268879     13.434685     4.848054   7.933866   284.004974   \n",
              "1508    42.800000     14.400000     4.860000   9.500000   425.000000   \n",
              "\n",
              "            MCH       MCHC        MCV        AGE      SEX  SOURCE  \n",
              "2214  25.800000  30.300000  85.200000  47.000000  1.00000       0  \n",
              "995   29.600000  34.400000  86.000000  28.000000  1.00000       1  \n",
              "771   25.700000  32.700000  78.600000  25.000000  1.00000       1  \n",
              "2302  25.900000  31.500000  82.100000  49.000000  1.00000       1  \n",
              "2577  29.300000  34.100000  86.000000  54.000000  0.00000       1  \n",
              "...         ...        ...        ...        ...      ...     ...  \n",
              "2130  28.800000  33.300000  86.400000  46.000000  0.00000       0  \n",
              "1525  25.200000  32.800000  76.700000  36.000000  1.00000       1  \n",
              "1272  26.400000  32.900000  80.000000  32.000000  0.00000       1  \n",
              "443   27.832914  33.313221  83.429184  33.774952  0.50629       1  \n",
              "1508  29.600000  33.600000  88.100000  36.000000  0.00000       0  \n",
              "\n",
              "[5000 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53edb8e3-d5ba-4b53-8eaa-77768f4b6fa2\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2214</th>\n",
              "      <td>26.400000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>25.800000</td>\n",
              "      <td>30.300000</td>\n",
              "      <td>85.200000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>46.200000</td>\n",
              "      <td>15.900000</td>\n",
              "      <td>5.370000</td>\n",
              "      <td>6.800000</td>\n",
              "      <td>189.000000</td>\n",
              "      <td>29.600000</td>\n",
              "      <td>34.400000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771</th>\n",
              "      <td>49.300000</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>6.270000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>25.700000</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>78.600000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2302</th>\n",
              "      <td>34.900000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>25.900000</td>\n",
              "      <td>31.500000</td>\n",
              "      <td>82.100000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2577</th>\n",
              "      <td>35.500000</td>\n",
              "      <td>12.100000</td>\n",
              "      <td>4.130000</td>\n",
              "      <td>3.900000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>29.300000</td>\n",
              "      <td>34.100000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2130</th>\n",
              "      <td>36.300000</td>\n",
              "      <td>12.100000</td>\n",
              "      <td>4.200000</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>28.800000</td>\n",
              "      <td>33.300000</td>\n",
              "      <td>86.400000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1525</th>\n",
              "      <td>44.500000</td>\n",
              "      <td>14.600000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>25.200000</td>\n",
              "      <td>32.800000</td>\n",
              "      <td>76.700000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1272</th>\n",
              "      <td>42.800000</td>\n",
              "      <td>14.100000</td>\n",
              "      <td>5.350000</td>\n",
              "      <td>6.800000</td>\n",
              "      <td>302.000000</td>\n",
              "      <td>26.400000</td>\n",
              "      <td>32.900000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>40.268879</td>\n",
              "      <td>13.434685</td>\n",
              "      <td>4.848054</td>\n",
              "      <td>7.933866</td>\n",
              "      <td>284.004974</td>\n",
              "      <td>27.832914</td>\n",
              "      <td>33.313221</td>\n",
              "      <td>83.429184</td>\n",
              "      <td>33.774952</td>\n",
              "      <td>0.50629</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1508</th>\n",
              "      <td>42.800000</td>\n",
              "      <td>14.400000</td>\n",
              "      <td>4.860000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>425.000000</td>\n",
              "      <td>29.600000</td>\n",
              "      <td>33.600000</td>\n",
              "      <td>88.100000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53edb8e3-d5ba-4b53-8eaa-77768f4b6fa2')\"\n",
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              "        document.querySelector('#df-53edb8e3-d5ba-4b53-8eaa-77768f4b6fa2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        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",
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              "  .colab-df-spinner {\n",
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              "\n",
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              "    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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c84c9638-1571-4f3d-9a47-4feb8e11b531 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(df_merged[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df_merged['SOURCE'], test_size = .25)"
      ],
      "metadata": {
        "id": "KzIuEoNsd9QL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# without noise\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00e89dd0-f233-4c39-cd9d-e563bcd60818",
        "id": "ck75KjAgd9QM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.9328375976632042\n",
            "auprc 0.9456073825648899\n",
            "sens 0.8861522689257001\n",
            "spec 0.8285579937304075\n",
            "ppv 0.8745203035293105\n",
            "npv 0.8449394206406309\n",
            "acc 0.8615999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# with noise\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "deFJ2aWRgiXm",
        "outputId": "491b2c7b-e8d7-4d7f-bd16-f5ece72cd509"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.9279695225765237\n",
            "auprc 0.9424886259349275\n",
            "sens 0.8911005843430437\n",
            "spec 0.8128388635870744\n",
            "ppv 0.8668980380869563\n",
            "npv 0.8500840520648836\n",
            "acc 0.8578666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CV_rfc.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59e12953-47ab-4ba3-cc94-167017222f75",
        "id": "YGaidA-9d9QM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 4,\n",
              " 'xgb__n_estimators': 50}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# best_model = CV_rfc.best_estimator_\n",
        "bst = XGBClassifier(n_estimators=50, max_depth=4, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "bd8eb907-6fe5-4872-f55f-8ce995e985d7",
        "id": "Q8EhndHXd9QN"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y_pred = CV_rfc.predict_proba(X_test)[:, 1]\n",
        "#without noise\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a306ff89-e14b-45bf-cfef-07f1a8db8f9c",
        "id": "hdsh8WOnd9QN"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "auroc:  0.8451796356921011\n",
            "auprc:  0.9059210498335152\n",
            "sens:  0.925207756232687\n",
            "spec:  0.7651515151515151\n",
            "ppv:  0.8434343434343434\n",
            "npv:  0.8820960698689956\n",
            "acc:  0.8576\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#with noise\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ],
      "metadata": {
        "id": "7SjJhQe2fBS1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bf96897-2660-4a90-e5e3-eed06b548dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "auroc:  0.8602305776996632\n",
            "auprc:  0.91320308385685\n",
            "sens:  0.9245810055865922\n",
            "spec:  0.795880149812734\n",
            "ppv:  0.8586251621271076\n",
            "npv:  0.8872651356993737\n",
            "acc:  0.8696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged['SEX'] = df_merged['SEX'].apply(lambda x: round(x))"
      ],
      "metadata": {
        "id": "8IiePXzcfJDV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uLaiw4V3nVwM",
        "outputId": "1aa2bb89-9684-4a37-e23a-451b0887cbb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "0      35.100000     11.800000     4.650000   6.300000   310.000000   \n",
              "1      43.500000     14.800000     5.390000  12.700000   334.000000   \n",
              "2      33.500000     11.300000     4.740000  13.200000   305.000000   \n",
              "3      39.100000     13.700000     4.980000  10.500000   366.000000   \n",
              "4      30.900000      9.900000     4.230000  22.100000   333.000000   \n",
              "..           ...           ...          ...        ...          ...   \n",
              "995    37.431370     12.463284     4.531254   8.447145   210.732391   \n",
              "996    37.429138     12.462743     4.530303   8.435891   210.655319   \n",
              "997    37.432430     12.463507     4.532059   8.455031   210.763550   \n",
              "998    37.434586     12.463912     4.532618   8.463583   210.896347   \n",
              "999    37.436829     12.464399     4.533598   8.475572   211.048660   \n",
              "\n",
              "           MCH       MCHC        MCV        AGE  SEX  SOURCE  \n",
              "0    25.400000  33.600000  75.500000   1.000000    0       1  \n",
              "1    27.500000  34.000000  80.700000   1.000000    0       1  \n",
              "2    23.800000  33.700000  70.700000   1.000000    0       1  \n",
              "3    27.500000  35.000000  78.500000   1.000000    0       1  \n",
              "4    23.400000  32.000000  73.000000   1.000000    1       1  \n",
              "..         ...        ...        ...        ...  ...     ...  \n",
              "995  27.639742  33.267876  82.991020  36.473259    1       0  \n",
              "996  27.638136  33.267197  82.966179  36.365192    1       0  \n",
              "997  27.657703  33.266472  83.076622  36.612076    1       0  \n",
              "998  27.646315  33.267937  83.034615  36.582214    1       0  \n",
              "999  27.656555  33.267422  83.075714  36.700893    1       0  \n",
              "\n",
              "[5000 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-95070b1d-b084-4d74-9719-cb6e06ed20af\" 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>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.100000</td>\n",
              "      <td>11.800000</td>\n",
              "      <td>4.650000</td>\n",
              "      <td>6.300000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>25.400000</td>\n",
              "      <td>33.600000</td>\n",
              "      <td>75.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.500000</td>\n",
              "      <td>14.800000</td>\n",
              "      <td>5.390000</td>\n",
              "      <td>12.700000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>80.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.500000</td>\n",
              "      <td>11.300000</td>\n",
              "      <td>4.740000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>305.000000</td>\n",
              "      <td>23.800000</td>\n",
              "      <td>33.700000</td>\n",
              "      <td>70.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.100000</td>\n",
              "      <td>13.700000</td>\n",
              "      <td>4.980000</td>\n",
              "      <td>10.500000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>78.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.900000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>4.230000</td>\n",
              "      <td>22.100000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>23.400000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>37.431370</td>\n",
              "      <td>12.463284</td>\n",
              "      <td>4.531254</td>\n",
              "      <td>8.447145</td>\n",
              "      <td>210.732391</td>\n",
              "      <td>27.639742</td>\n",
              "      <td>33.267876</td>\n",
              "      <td>82.991020</td>\n",
              "      <td>36.473259</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>37.429138</td>\n",
              "      <td>12.462743</td>\n",
              "      <td>4.530303</td>\n",
              "      <td>8.435891</td>\n",
              "      <td>210.655319</td>\n",
              "      <td>27.638136</td>\n",
              "      <td>33.267197</td>\n",
              "      <td>82.966179</td>\n",
              "      <td>36.365192</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>37.432430</td>\n",
              "      <td>12.463507</td>\n",
              "      <td>4.532059</td>\n",
              "      <td>8.455031</td>\n",
              "      <td>210.763550</td>\n",
              "      <td>27.657703</td>\n",
              "      <td>33.266472</td>\n",
              "      <td>83.076622</td>\n",
              "      <td>36.612076</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>37.434586</td>\n",
              "      <td>12.463912</td>\n",
              "      <td>4.532618</td>\n",
              "      <td>8.463583</td>\n",
              "      <td>210.896347</td>\n",
              "      <td>27.646315</td>\n",
              "      <td>33.267937</td>\n",
              "      <td>83.034615</td>\n",
              "      <td>36.582214</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>37.436829</td>\n",
              "      <td>12.464399</td>\n",
              "      <td>4.533598</td>\n",
              "      <td>8.475572</td>\n",
              "      <td>211.048660</td>\n",
              "      <td>27.656555</td>\n",
              "      <td>33.267422</td>\n",
              "      <td>83.075714</td>\n",
              "      <td>36.700893</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95070b1d-b084-4d74-9719-cb6e06ed20af')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-95070b1d-b084-4d74-9719-cb6e06ed20af 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-95070b1d-b084-4d74-9719-cb6e06ed20af');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-36a83038-ecf2-49b4-b84f-c821b8137bf5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-36a83038-ecf2-49b4-b84f-c821b8137bf5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-36a83038-ecf2-49b4-b84f-c821b8137bf5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from aif360.datasets import StandardDataset\n",
        "from aif360.metrics import BinaryLabelDatasetMetric\n",
        "from aif360.algorithms.preprocessing import DisparateImpactRemover\n",
        "\n",
        "# Import your dataset\n",
        "dataset = df_merged\n",
        "\n",
        "# Encode the 'SEX' column ('M' as 1, 'F' as 0)\n",
        "dataset.loc[dataset.SEX == 'M', 'SEX'] = 1\n",
        "dataset.loc[dataset.SEX == 'F', 'SEX'] = 0\n",
        "\n",
        "# Encode the 'SOURCE' column ('In' as 1, 'Out' as 0)\n",
        "dataset.loc[dataset.SOURCE == 'in', 'SOURCE'] = 1\n",
        "dataset.loc[dataset.SOURCE == 'out', 'SOURCE'] = 0\n",
        "\n",
        "# Create the 'PROTECTED_CLASS' column based on 'SEX'\n",
        "dataset['PROTECTED_CLASS'] = dataset['SEX'].astype(int)\n",
        "\n",
        "# Create a StandardDataset for AIF360\n",
        "privileged_groups = [{'PROTECTED_CLASS': 1}]  # 'M' is considered privileged\n",
        "unprivileged_groups = [{'PROTECTED_CLASS': 0}]  # 'F' is considered unprivileged\n",
        "dataset_aif360 = StandardDataset(\n",
        "    df=dataset,\n",
        "    label_name='SOURCE',\n",
        "    favorable_classes=[1],  # 'In' is considered favorable\n",
        "    protected_attribute_names=['PROTECTED_CLASS'],\n",
        "    privileged_classes=[[1]],\n",
        ")\n",
        "\n",
        "# Calculate and display the initial disparate impact\n",
        "privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_aif360,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "initial_disparate_impact = privileged_metric.disparate_impact()\n",
        "print(\"Initial Disparate Impact:\", initial_disparate_impact)\n",
        "\n",
        "# If bias is detected, you can use the Disparate Impact Remover mitigation technique\n",
        "di_remover = DisparateImpactRemover(repair_level=0.1)\n",
        "dataset_transf = di_remover.fit_transform(dataset_aif360)\n",
        "\n",
        "# Calculate and display the new disparate impact after mitigation\n",
        "new_privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_transf,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "new_disparate_impact = new_privileged_metric.disparate_impact()\n",
        "print(\"New Disparate Impact after Mitigation:\", new_disparate_impact)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_5rEO6GnrnU",
        "outputId": "243b3286-aaed-4c2a-df02-d23f5b60a8cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Disparate Impact: 1.1534408719315596\n",
            "New Disparate Impact after Mitigation: 1.1534408719315596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shap"
      ],
      "metadata": {
        "id": "NGKG5dWAih9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.initjs()\n",
        "explainer = shap.TreeExplainer(bst)\n",
        "shap_values = explainer.shap_values(x_train.to_numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 44
        },
        "outputId": "7e2f9fc1-8d44-4c4e-ca47-a140af8494d6",
        "id": "xMqE81Nnih9D"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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e=null!==i?i.baseLanes|n:n,t.lanes=t.childLanes=1073741824,t.memoizedState={baseLanes:e,cachePool:null,transitions:null},t.updateQueue=null,Ca(Al,Ol),Ol|=e,null;t.memoizedState={baseLanes:0,cachePool:null,transitions:null},r=null!==i?i.baseLanes:n,Ca(Al,Ol),Ol|=r}else null!==i?(r=i.baseLanes|n,t.memoizedState=null):r=n,Ca(Al,Ol),Ol|=r;return wu(e,t,a,n),t.child}function Cu(e,t){var n=t.ref;(null===e&&null!==n||null!==e&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function Tu(e,t,n,r,a){var i=La(n)?Pa:Ma.current;return i=za(t,i),Si(t,a),n=So(e,t,n,r,i,a),r=Eo(),null===e||_u?(ai&&r&&ei(t),t.flags|=1,wu(e,t,n,a),t.child):(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~a,Hu(e,t,a))}function Mu(e,t,n,r,a){if(La(n)){var i=!0;Da(t)}else i=!1;if(Si(t,a),null===t.stateNode)Vu(e,t),Wi(t,n,r),Hi(t,n,r,a),r=!0;else if(null===e){var o=t.stateNode,u=t.memoizedProps;o.props=u;var l=o.context,s=n.contextType;s=\"object\"==typeof s&&null!==s?Ei(s):za(t,s=La(n)?Pa:Ma.current);var c=n.getDerivedStateFromProps,f=\"function\"==typeof c||\"function\"==typeof o.getSnapshotBeforeUpdate;f||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==r||l!==s)&&Vi(t,o,r,s),Pi=!1;var p=t.memoizedState;o.state=p,Ri(t,r,o,a),l=t.memoizedState,u!==r||p!==l||Na.current||Pi?(\"function\"==typeof c&&(Ii(t,n,c,r),l=t.memoizedState),(u=Pi||Bi(t,n,u,r,p,l,s))?(f||\"function\"!=typeof o.UNSAFE_componentWillMount&&\"function\"!=typeof o.componentWillMount||(\"function\"==typeof o.componentWillMount&&o.componentWillMount(),\"function\"==typeof o.UNSAFE_componentWillMount&&o.UNSAFE_componentWillMount()),\"function\"==typeof o.componentDidMount&&(t.flags|=4194308)):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=l),o.props=r,o.state=l,o.context=s,r=u):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),r=!1)}else{o=t.stateNode,Li(e,t),u=t.memoizedProps,s=t.type===t.elementType?u:gi(t.type,u),o.props=s,f=t.pendingProps,p=o.context,l=\"object\"==typeof(l=n.contextType)&&null!==l?Ei(l):za(t,l=La(n)?Pa:Ma.current);var d=n.getDerivedStateFromProps;(c=\"function\"==typeof d||\"function\"==typeof o.getSnapshotBeforeUpdate)||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==f||p!==l)&&Vi(t,o,r,l),Pi=!1,p=t.memoizedState,o.state=p,Ri(t,r,o,a);var h=t.memoizedState;u!==f||p!==h||Na.current||Pi?(\"function\"==typeof d&&(Ii(t,n,d,r),h=t.memoizedState),(s=Pi||Bi(t,n,s,r,p,h,l)||!1)?(c||\"function\"!=typeof o.UNSAFE_componentWillUpdate&&\"function\"!=typeof o.componentWillUpdate||(\"function\"==typeof o.componentWillUpdate&&o.componentWillUpdate(r,h,l),\"function\"==typeof o.UNSAFE_componentWillUpdate&&o.UNSAFE_componentWillUpdate(r,h,l)),\"function\"==typeof o.componentDidUpdate&&(t.flags|=4),\"function\"==typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=l,r=s):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),r=!1)}return Nu(e,t,n,r,i,a)}function Nu(e,t,n,r,a,i){Cu(e,t);var o=0!=(128&t.flags);if(!r&&!o)return a&&Ra(t,n,!1),Hu(e,t,i);r=t.stateNode,bu.current=t;var u=o&&\"function\"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=Ki(t,e.child,null,i),t.child=Ki(t,null,u,i)):wu(e,t,u,i),t.memoizedState=r.state,a&&Ra(t,n,!0),t.child}function Pu(e){var t=e.stateNode;t.pendingContext?Aa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Aa(0,t.context,!1),ro(e,t.containerInfo)}function zu(e,t,n,r,a){return di(),hi(a),t.flags|=256,wu(e,t,n,r),t.child}var Lu,Ou,Au,Fu,Du={dehydrated:null,treeContext:null,retryLane:0};function Ru(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,a=t.pendingProps,o=uo.current,u=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),Ca(uo,1&o),null===e)return si(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:\"$!\"===e.data?t.lanes=8:t.lanes=1073741824,null):(l=a.children,e=a.fallback,u?(a=t.mode,u=t.child,l={mode:\"hidden\",children:l},0==(1&a)&&null!==u?(u.childLanes=0,u.pendingProps=l):u=Rs(l,a,0,null),e=Ds(e,a,n,null),u.return=t,e.return=t,u.sibling=e,t.child=u,t.child.memoizedState=Ru(n),t.memoizedState=Du,e):Uu(t,l));if(null!==(o=e.memoizedState)&&null!==(r=o.dehydrated))return function(e,t,n,r,a,o,u){if(n)return 256&t.flags?(t.flags&=-257,Iu(e,t,u,r=fu(Error(i(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(o=r.fallback,a=t.mode,r=Rs({mode:\"visible\",children:r.children},a,0,null),(o=Ds(o,a,u,null)).flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,0!=(1&t.mode)&&Ki(t,e.child,null,u),t.child.memoizedState=Ru(u),t.memoizedState=Du,o);if(0==(1&t.mode))return Iu(e,t,u,null);if(\"$!\"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,Iu(e,t,u,r=fu(o=Error(i(419)),r,void 0))}if(l=0!=(u&e.childLanes),_u||l){if(null!==(r=Pl)){switch(u&-u){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 536870912:a=268435456;break;default:a=0}0!==(a=0!=(a&(r.suspendedLanes|u))?0:a)&&a!==o.retryLane&&(o.retryLane=a,Ni(e,a),rs(r,e,a,-1))}return gs(),Iu(e,t,u,r=fu(Error(i(421))))}return\"$?\"===a.data?(t.flags|=128,t.child=e.child,t=Ms.bind(null,e),a._reactRetry=t,null):(e=o.treeContext,ri=sa(a.nextSibling),ni=t,ai=!0,ii=null,null!==e&&(Qa[Ya++]=Ka,Qa[Ya++]=Za,Qa[Ya++]=Ga,Ka=e.id,Za=e.overflow,Ga=t),(t=Uu(t,r.children)).flags|=4096,t)}(e,t,l,a,r,o,n);if(u){u=a.fallback,l=t.mode,r=(o=e.child).sibling;var s={mode:\"hidden\",children:a.children};return 0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Wu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(wu(e,t,r.children,n),0!=(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&$u(e,n,t);else if(19===e.tag)$u(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Ca(uo,r),0==(1&t.mode))t.memoizedState=null;else switch(a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===lo(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Bu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===lo(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Bu(t,!0,n,null,i);break;case\"together\":Bu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Vu(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Hu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Rl|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=As(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=As(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function qu(e,t){if(!ai)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Qu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=14680064&a.subtreeFlags,r|=14680064&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yu(e,t,n){var r=t.pendingProps;switch(ti(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Qu(t),null;case 1:case 17:return La(t.type)&&Oa(),Qu(t),null;case 3:return r=t.stateNode,ao(),Ea(Na),Ea(Ma),co(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),null!==e&&null!==e.child||(fi(t)?t.flags|=4:null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==ii&&(us(ii),ii=null))),Ou(e,t),Qu(t),null;case 5:oo(t);var a=no(to.current);if(n=t.type,null!==e&&null!=t.stateNode)Au(e,t,n,r,a),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(i(166));return Qu(t),null}if(e=no(Ji.current),fi(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[pa]=t,r[da]=o,e=0!=(1&t.mode),n){case\"dialog\":Ur(\"cancel\",r),Ur(\"close\",r);break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",r);break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],r);break;case\"source\":Ur(\"error\",r);break;case\"img\":case\"image\":case\"link\":Ur(\"error\",r),Ur(\"load\",r);break;case\"details\":Ur(\"toggle\",r);break;case\"input\":K(r,o),Ur(\"invalid\",r);break;case\"select\":r._wrapperState={wasMultiple:!!o.multiple},Ur(\"invalid\",r);break;case\"textarea\":ae(r,o),Ur(\"invalid\",r)}for(var l in me(n,o),a=null,o)if(o.hasOwnProperty(l)){var s=o[l];\"children\"===l?\"string\"==typeof s?r.textContent!==s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",s]):\"number\"==typeof s&&r.textContent!==\"\"+s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",\"\"+s]):u.hasOwnProperty(l)&&null!=s&&\"onScroll\"===l&&Ur(\"scroll\",r)}switch(n){case\"input\":q(r),J(r,o,!0);break;case\"textarea\":q(r),oe(r);break;case\"select\":case\"option\":break;default:\"function\"==typeof o.onClick&&(r.onclick=Jr)}r=a,t.updateQueue=r,null!==r&&(t.flags|=4)}else{l=9===a.nodeType?a:a.ownerDocument,\"http://www.w3.org/1999/xhtml\"===e&&(e=ue(n)),\"http://www.w3.org/1999/xhtml\"===e?\"script\"===n?((e=l.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild)):\"string\"==typeof r.is?e=l.createElement(n,{is:r.is}):(e=l.createElement(n),\"select\"===n&&(l=e,r.multiple?l.multiple=!0:r.size&&(l.size=r.size))):e=l.createElementNS(e,n),e[pa]=t,e[da]=r,Lu(e,t,!1,!1),t.stateNode=e;e:{switch(l=be(n,r),n){case\"dialog\":Ur(\"cancel\",e),Ur(\"close\",e),a=r;break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",e),a=r;break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],e);a=r;break;case\"source\":Ur(\"error\",e),a=r;break;case\"img\":case\"image\":case\"link\":Ur(\"error\",e),Ur(\"load\",e),a=r;break;case\"details\":Ur(\"toggle\",e),a=r;break;case\"input\":K(e,r),a=G(e,r),Ur(\"invalid\",e);break;case\"option\":default:a=r;break;case\"select\":e._wrapperState={wasMultiple:!!r.multiple},a=R({},r,{value:void 0}),Ur(\"invalid\",e);break;case\"textarea\":ae(e,r),a=re(e,r),Ur(\"invalid\",e)}for(o in me(n,a),s=a)if(s.hasOwnProperty(o)){var c=s[o];\"style\"===o?ge(e,c):\"dangerouslySetInnerHTML\"===o?null!=(c=c?c.__html:void 0)&&fe(e,c):\"children\"===o?\"string\"==typeof c?(\"textarea\"!==n||\"\"!==c)&&pe(e,c):\"number\"==typeof c&&pe(e,\"\"+c):\"suppressContentEditableWarning\"!==o&&\"suppressHydrationWarning\"!==o&&\"autoFocus\"!==o&&(u.hasOwnProperty(o)?null!=c&&\"onScroll\"===o&&Ur(\"scroll\",e):null!=c&&b(e,o,c,l))}switch(n){case\"input\":q(e),J(e,r,!1);break;case\"textarea\":q(e),oe(e);break;case\"option\":null!=r.value&&e.setAttribute(\"value\",\"\"+V(r.value));break;case\"select\":e.multiple=!!r.multiple,null!=(o=r.value)?ne(e,!!r.multiple,o,!1):null!=r.defaultValue&&ne(e,!!r.multiple,r.defaultValue,!0);break;default:\"function\"==typeof a.onClick&&(e.onclick=Jr)}switch(n){case\"button\":case\"input\":case\"select\":case\"textarea\":r=!!r.autoFocus;break e;case\"img\":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return Qu(t),null;case 6:if(e&&null!=t.stateNode)Fu(e,t,e.memoizedProps,r);else{if(\"string\"!=typeof r&&null===t.stateNode)throw 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0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return 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i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var 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              "</script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, x_train.to_numpy(),feature_names = x_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "Pfpj7KgTih9D",
        "outputId": "6d7626a8-d6fe-4044-9a44-7a91f8dc977b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, plot_type = 'bar', feature_names = x_train.columns,show=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "1lg4orCpih9E",
        "outputId": "67a944cc-5dba-4dde-f754-d2900b2f4bdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots._waterfall.waterfall_legacy(explainer.expected_value, shap_values[5], feature_names = x_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "qAf-mWaQih9E",
        "outputId": "db8e6418-2171-4033-dbf5-937206d62d83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": 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pqXTs2JEhQ4bg6+vL4cOHWbFiBQMHDmTBggV89tlntG/fnsGDB7Nr1y67tg8ePMjEiROpXr06y5YtY/78+XZtfPfdd/z111/cf//9NG7cONPvy2233UaPHj0cys1mTacTERGxE3+dv/heszx1jni4g4cbJCRdX9tijFQLfLoGXh9K6dKljY7G5eRb4jRu3Di71ykpKSxbtow2bdo4nAOuO3F6+OGHqV69esbrefPm8cgjj/D666/bJU4Wi4VevXpx+PBhXnrpJSZNmmR3n+3bt9O5c2cef/xxmjRpQtu2be3ON2nShIiICD788EOHxOmDDz7Az8+PevXqsWXLFqdx9unTh65du9qVtW/fnr/++ou1a9dy++23A7BkyRJeffVVgoKC+PPPP+2ebfr06TzwwAMsWLCA/v3789tvv2WcW79+PY8++iilSpVi+fLltG7d2q4ti8XC9OnT8fX1pVWrVjz55JNMmzaNcePG8c4772TUGzJkCImJiXz77beULVvW4XsWHh7OX3/9Rc+ePenbt6/TZwUICQlx+v0WERERg93eFBI+B3c3OHLGNl/mv94LKUZS02D+Cmo82dPoSFxOiekG6NevH+CYkC1YsIA9e/bQoUMHh6QJoHnz5rz11lskJyfz7LPPOpz38PDgzjvv5LfffiM+/sonOwkJCfz22290794dd/fc5Z+VK1cGwNvbO6PsxRdfBGwJ1NVJU7r58+dTv359VqxYwZ9//plR/swzz5CcnMyMGTMckiaw9fSMGzeO/v37A/D666/TpEkTZs2axd9//w3AG2+8wYYNG3jggQcckjwREREpAXYchZe+hHvehAdmwbGzMONBeH2o0ZFJblmsMOMn9u7M4SIikm8MT5wuX77MiRMnnB5WqzXH99mxYwcAfn5+duVfffUVYBsyl5n//e9/lC9fng0bNpCQkOBwfuTIkSQkJDBv3ryMsrlz53Lp0iVGjcpi7DK24Xnpz7Nt2zamTJnCjz/+SKNGjTISnR07dnDo0CEaNGjg0OOVzmw2M2TIEAAWLVoEQHx8PBs2bKBChQoMHZqzH3xms5nFixfj5ubG0KFD2b17N5MnTyY4OJhZs2bl6B5ZSUhIcPq9jI6Ovu57i4iIFFnublDBz/7wcAcvD8fya+ZFF4reU2Had7Z5UQt+h44vwi9bYWwoVKtQ+PHI9Yk8T5nV+4yOwuUYvqre8uXLqVGjRqbnfX19nZanb/p1+fJl/v33X8aPHw9c6XlKd+CAbdJjx44dM23DbDZTr149NmzYwNatW2nXrp3d+Y4dOxIcHMyiRYsYM2YMAJ9++il16tRxmL91rYEDBzqUtWnThp9++ilj3s+///4LkOXcISAjrj179gCwbds2UlNTqVu3bpbXXatZs2Y8//zzTJo0iTZt2pCcnMynn36Kl5dXru7jzJw5c5gzx3FZ01atWrFhw4brvr+IiEiR1K4BrHnFefmgW+3LMlsyvLC9swy6t4BOjW0LRUjx4Wamyp+H4WmjA3Ethvc4tWnThiVLljg9ypQpk+l1N998MzVq1KBevXoMHjyYM2fOMGbMGF55xf6HVnoPUkBAQJZxpE+wO3funNPzgwYNYtu2bezbt499+/axbds2Bg0alO3zvfDCCxnP8/777zNgwAD+/fdfunTpQmJiIgAXLlwAwN/fP8t7lS9fHiBjyGBMTIxd7LkxceJEWrZsSVxcHI8++qhDsphXoaGhTr+Xb775Zq7uExMTQ1LSlYmr8fHxxMXFZbxOTk52+F5FRkZm+ToqKsquF1NtqA21oTaKahtSDG0/Al1esj+2H4FftzqWR10wKMhrHP9vNEh5LTJQ7KRZSOrZQj+vCpnhPU5VqlTJNAEZPXp0ptd98MEHlC1blosXL/Ltt9+yYsUKp8sypvdYnT17lqpVq2Z6v/RkpEIF593Vo0aN4o033mDmzJlYrVbc3d0ZOXJkpvdL16FDB7t5QyNGjOCJJ55g5syZvP7667z88ssZq+TFxsZmea9rE6X0ROrSpUvZxuHMjTfeyJYtW7LsjcutunXr5iihzE76s6W7Njn09PR0+F5VqVIly9eBgYFqQ22oDbVRLNqQYujCJVi1w77sfDxEnncsLyqCbXOuOZv17x9SBFUuw+EWlQnRz6tCZXiPU16FhoYyaNAgHn30UX799VdCQ0OZPXs2n3zyiV299GFsa9euzfJ+EREReHh40KJFC6fnAwMDad++Pd988w3ffPMN7du3d/jLmFN33303AGvWrAFsvWcAu3dnPclv/fr1ADRq1AiwrWDn7u5OREREnuIQERGRYqJGRbihWt6uLVcart0WxN3NtvluUgqs3nX98UnhMZtgdE/bsvJSqIpt4nStOXPm4Ovry/PPP2+3p1L6Brtz587N9NpFixYRExNDq1atMp1TBfDQQw9x+vRpzpw5w0MPPZTnWJOTk4ErPUXNmjWjVq1a7Nu3j3/++cfpNRaLhc8++wwgY5GI0qVLc8stt3Du3DkWL16c53hERETEIBP62o57/lsZd2jHK2VX+3Q07HvPvszf90rdzk1tZaN62F6PvPNKvbtuhv3vwdR74ZE7bAnTlregfUPbSnunLxTY40kBcDPDw10zPkiXwlNiEqfAwEAGDx7MqVOnmDFjRkb5Qw89RMOGDVmzZg1TpkxxuG7Xrl2MGTMGDw8P3njjjSzbGDhwII899hgjRoxgwIABeY71iy++AGwJU7rJkycDMHjwYE6ePOlwzfDhwwkPD6dr1652C1K88cYbeHp68sQTT2QsMnGt6dOnZ6wuKCIiIkXIq4NtR/oCEg92uVKWnXKlrtS9s6WtbFxv2+txva/U23kU9pyAezvCzAfh+XtsQwv7TYPXv8n/Z5KC426GIR0goAxHjx41OhqXY/gcp/z08ssv89lnnzF9+nSeeOIJPDw8MJvNLFu2jC5duvDCCy/wzTffcMcdd1CqVCl27NjBsmXLsFgszJo1K9sFEtzc3Pjggw9yFdM333zD9u3bAbh48SJ//fUXa9asoXz58nb7Sg0dOpS9e/fy+uuv07BhQ3r37k2DBg2IiYlh+fLl7Nu3jxYtWjgkQO3atePDDz9kxIgRtG3blttuu41WrVrh4+PDkSNHWLlyJUeOHOHTTz/NVdx5tW3bNt566y2n5x588EHKlStXKHGIiIgY7raJ2dcx9cn7vY6ezdn1Ww7ZliOX4i/VAk/0AvI+x13yrkQlTlWrVqV///58+umnTJ8+PWND2zp16rBz504mT57M999/z4wZM0hJSaFcuXLccccdvPzyy4SEhBRITB9++GHGn81mM+XLl+euu+7i7bffpmbNmnZ1X3vtNbp06cLUqVNZvnw5n3/+OV5eXgQHBzNlyhSefvppPDw8HNoYNmwY7du356WXXuKPP/5g3bp1pKWlUa5cOZo3b860adPo27evw3UFYfXq1axevdrpuc6dOytxEhEREckLNzO0rg8htYHMt+yRgmOy5maXWRERESk4EaegftYbq4uICwt7Gu5pA0BKSorTD9Sl4JSYOU4iIiIiIiVW1XLQu1XGy+xWY5b8p8RJRERERKQoM5tsc5vctQS5kZQ4iYiIiIgUZR5u8FAXuyJX3ojWKEqcRERERESKKnczDO0E5f3sit3c1PtU2JQ4iYiIiIgUVakWeKKnQ/GJEycMCMa1lajlyEVERERESgw3M7RvCE1qZl9XCpx6nEREREREiqI0C4wJdXqqQYMGhRyMKHESERERESmKalSAXjc6PXXy5MlCDkaUOImIiIiIFDUmEzwZCpksAhEXF1fIAYkSJxERERGRosbLHR7onOlpb2/vQgxGQItDiIiIFB01A2DQraTtPY6bWZ9tiri00JuhbKlMT9erV68QgxEAk9VqtRodhIiIiFyxbds2QkJCjA5DRIow/ZwofPo4S0REREREJBtKnERERIqYWrVqGR2CiBRxlStXNjoEl6PESUREpIi5dOmS0SGISBHn5eVldAguR4mTiIhIEXP27FmjQxCRIu7YsWNGh+BylDiJiIiIiIhkQ6vqiYiIFDFWqxWTyWR0GCJShCUkJODr62t0GC5F+ziJiEjeJKfAj5tBn7/lXq+bwMsj09N79+6lUaNGhRiQiBQ3UVFRBAcHGx2GS1HiJCIiefPKUng1zOgoiqdPR8PQTpmeTk5OLrxYRKRYio2NNToEl6M5TiIikjfnL2Fx138juWY2wds/ZNlT5+/vX4gBiUhx5OnpaXQILkf/44mIiBQmixW2H4F/wjOtEhgYWHjxiEix1LBhQ6NDcDlKnEREJM9MaAGDPHE3w4wfMz0dHp55UiUiArB9+3ajQ3A5SpxERCTvlDflTaoFwv6Gk+eMjkRERHJIiZOIiOSZdrS4TrN/dVocFBRUyIGISHETEBBgdAguR4mTiIiIEdIs8P7PcNlxBb2kpCQDAhKR4qRUqVJGh+BylDiJiEieaZPW63ThEnzxp0Px6dOnDQhGRIqTI0eOGB2Cy1HiJCIieaehetfHlP3S5CIiUjQocRIRkTzTr/vXyWqFXcfgz712xU2bNjUoIBEpLurWrWt0CC5HiZOIiIiR3M3wrv3S5BEREQYFIyLFRXR0tNEhuBx3owMQEZHiy1wU5jiV8YU3/wf/dwv4esHGCHjqE9h6KPtrH+oC93aEBtWgbCk4FQNrdsPLX8LRs1fq3XcbLHw88/sMeReWrMtb/KkW+G4DHDsLQbZVsi5fvpy3e4mIy7hw4YLRIbgcJU4iIpJnFqvV2KELJhP89AI0rwnTvofoWBjRHdZMhhufhgORWV/fIhgOn4Yf/oXz8VC7MjzcBXrdCM3HQuR5W711e+Dedx2vHxMKzWvBqh3X/xwf/AKvDwXAz8/v+u4nIiWeu7t+jS9suf7/LiwsDJPJxNixYzOtYzKZuOWWW5yeGzp0KCaTiSpVqmR6/dSpUzGZTJkegYGBGXV37dqVUZ5Zm0lJSfj7+2MymTJd837dunV069aNihUr4uHhQenSpWnWrBmvvfYaKSkpmcZ66NAhhg0bRp06dfD19cXNzQ1/f3+aN2/O2LFjOXnypF397t27YzKZOHHiRKb3TBcdHc1jjz1GcHAw3t7eeHl5Ub16dYYMGcLhw4cd6l/9XqQf7u7uVKxYkVtvvZWff/7Z4ZqAgACH/ULSY/T19XWIH658f95//32H92Lw4MEEBQXh7e2Nj48PVapU4bbbbmP27NnZPq+IiIPVk2HBqMzP920D7RrA/bNg8le25KPTRNtS3y8PyP7+I+fCsFkw/QdY8DtM/Bx6ToGAMvC/TlfqHT4Ni9fZH1//A/WqwO874fSF63vONIttT6cE2zLk1apVu777iUiJ16RJE6NDcDmFmqomJyezbNkyKleuTFRUFF9//TX33HNPpvX79etHq1atHMr9/f0dyjw8PNi0aRMHDx6kTp06duc++ugj4uLi8PDwcNrOxIkTmTJlCqVKlSI0NJSGDRsSExPDTz/9xIQJEwgLC+P333+nbNmydtctWrSI4cOHk5yczK233so999xD2bJlOXv2LP/++y/vvfceX3zxBadOncrBu2Nv06ZN9OjRg+joaNq1a8fgwYPx8PDgn3/+4csvv+SHH37giy++oGfPng7XtmjRgsGDBwO24R779+/nm2++oVevXvz000907949RzEkJiYyduxYvvzyy2zr7t69m7Zt25KYmEjXrl1p0aIFYBunv3HjRhYuXMhjjz2Wi3dARIoDEwYP1evbBqLOwzf/XCmLjoWv1sO9HcDTHZJTc3fPI2dsX8tms0dK6E3g72tLovJDbIJtuN9DXdm3bx8hISH5c18RKZG2b99O8+bNjQ7DpRRq4rRw4UIuXrzI3Llzefjhh5k7d26WiVPHjh0ZOXJkju7dunVr/vrrL9577z3effddh3Zr1qyJxWIhMTHR7tySJUt49dVXCQoK4s8//6R69eoZ56ZPn84DDzzAggUL6N+/P7/99lvGufXr1/PQQw/h5+fH77//TuvWrR1iOnz4MJMnT85R/FeLjY3lrrvuIiYmho8++ohhw4bZnV+xYgW9e/dm0KBBbN++ndq1a9udr127NuPGjbMr69ChA4888ggff/xxjhOnOnXq8M0337B7924aN26cZd1JkyYRGxvLnDlzeOSRRxzOHzqUg7kGIlL8GD3FqUVt2HLIcTnvjREw/A6oX9W2al12ypcGNzcIqggT+9vKVu3M+pohHWw9RFcnbdcjfWnyB7vkz/1EpESzahuDQleoQ9M//vhjKlWqRN++fbnzzjtZs2ZNvq0IEhAQQKtWrVi6dKld+cGDB9m8eTP9+/d3et2LL74I2BKoq5OmdPPnz6d+/fqsWLGCP/+8sknhM888Q3JyMjNnznSaNIEtgVmwYEGun2Xq1KlERkYycOBAh6QJoGvXrjz11FPExcUxYcKEHN2zVq1aAJn2ujnzyiuvkJaWxpNPPplt3fShg/369XN6Pjg4OMftikjxYfh/3FXKXZmHdLX0sqrlc3afk/PhzALYNA3a3gCPz4eV2zOvX640dG8ByzZBfD4t5GC1wr6TsGaX0/+PRESuVqFCBaNDcDl5TpwSEhI4ceKE08OZQ4cO8e+//3L33XdjNpsZMWIEycnJDvNkrnbx4kWn989sFZFhw4Zx6tQpfvrpp4yyGTNmYDabGT16tEP9HTt2cOjQIRo0aEDbtm2d3tNsNjNkyBDANjQPID4+ng0bNlCxYsWMIXH5admyZQA89dRTmdZ5+umncXNzY+XKlQ7nLl++nPFeHThwgLCwMEaNGoWnpyfDhw/PcRy33nort99+O6tWrWLt2rVZ1q1ZsyYAr732GhaLJcdtiIhkcHeDCn72h4c7eHk4lqev5ufjCUlOhuJdTr5yPifufBXufAXGLoBj0VDKK+v6fdvY4sqvYXrp3GxLk6elpeXvfUWkxHE2dUUKVp4Tpzlz5lCjRg2nhzMzZszAarVmDL3r0KEDtWrVYvHixZm2MWHCBKf3f/DBB53Wv//++ylTpgwffvhhRllYWBitW7d2+undv//+C5DtMLR27doBsGfPHgC2bdtGamqq043H4uPjHRK95OTkLO9/rSNHjuDt7Z0xT8gZf39/qlWrxtmzZzl/3v7T1uXLl2e8V/Xq1aNfv35ER0cTFhZGhw4dchXLu+++i5ubW5aLgYBtqJ6Pjw9vvfUWlSpVokuXLjzzzDNOEzsRKTlM+bkcebsGEP2J/dGuAQy61bE8qKLtmsRk8HIy6tzb88r5nFizC37ZCu8sg37TYFJ/GHln5vWHdIBzcfDzltw9Y3bSLLDsX85t2pO/9xWREsfZQmFSsPKcOIWGhrJkyRKnhzNLly6lUaNGNGvWLKOsf//+REREsH79eqfXPPTQQ07v/9xzzzmt7+npSc+ePVm1ahVxcXH88MMPREZGZppopfdcZZexly9vG+oRHx8PQExMDAClS5d2qPvaa685JHrZ9dZcKzExEV9f32zrlSplm7h87XDHNm3aZLxXCxcu5LnnnsPd3Z3Bgwfz+++/5yqWJk2a0KdPH7Zs2cJXX32Vab3mzZuzceNG+vTpA8CqVauYNm0aXbt2pWbNmrlOoGJiYkhKSsp4HR8fT1xcXMbr5ORkzp07Z3dNZGRklq+joqLshhWpDbWhNq6vjcuXLzvOLboe249Al5fsj+1H4NetjuVRF/57kPO24XrXSi87FZP7OA6dhq2HbcmRMzUqwq0NYel6SC2AniE3M6kebkXye6421IbaUBvXvnYleV4com7dugwaNMjpuWuHr3333XdERkbSu3dvtm7dmlHesWNHpk2bxsyZM50OlQsJCcm0jcyMGjWKJUuWMGfOHFavXk25cuUyhtpdK32VvNjY2CzveW2idG0idbX77ruPpk2bArBgwQJWrFiRq/gBfHx8HBaxcObSpUsAVKxY0a68SpUqDu/bvffeS0hICI899hj79+/PVTzTp09n2bJlPP/88/Tt2zfTek2aNOHrr78GbHPLfv75Zz777DM2bNhA37592bNnD1WrVs1Rm+nvcbprk1RPT0+Hsb3XLnF/7eurl7FXG2pDbVx/G3h7YyEf14e4cMlxP6Tz8bbkKLN9krYdsSUxJpN9EndLPbh0GcJzv6opYBvi55XJnNBBt4LZnP/D9ADczTCgHQ1va2PXm1dUvudqQ22ojaLTRnBwsMOH/0Y8hysplMUh0ofOffjhh7Rs2TLj6NmzJ1arleXLl9tlv9ejTZs21K9fn3nz5rFq1SpCQ0MzXRDh5ptvBmxLaWclvUesUaNGgC2hc3d358CBAw51b7jhBgYNGsSgQYMyFmTIrVq1apGYmGiXZF4rNjaWkydPEhAQQLlyTj5tvUbjxo2pXr064eHhud5pulq1agwbNoyDBw8ya9asHF1Tp04dRo0axT///EPnzp25ePFipr2RIiJ5FvY3BJaDPlct0lPBD/q1tS3ccPVS5MGVbUc6N7PzJcdvrgtNa8Kmg87bHHwrHD0Lf+7Nn2e4WqoFnuilITgikq3c/j4n16/AE6dz586xZs0aWrRowaxZsxyOBx98kLi4OD766KN8a3Pw4MGEh4eTlJTEE088kWm9Zs2aUatWLfbt28c//zhfTtZisfDZZ58BZPRclS5dmltuuYXo6OgCSQbS92Z65513Mq3z9ttvk5aWRufOnXN839RU2y8Q186JyompU6dStmxZpk6dmuWGwM6kJ6jHjx/PdbsiUrSZ83OOU16E/Q1/77dtkvtiP3isO6x5xZYUTfrCvu6ql21HutLecHwuzB8BY0LhkTvgvYdsm+5eTIBX7FdpBaBxEDSvZdtvKb+ZTXBTHbi5HgkJCfl/fxEpUdJHREnhKfB9nN5//32SkpJ4+OGHnW6AGhsby6JFi1i4cCEjRozIlzZHjhzJyZMnqVChAi1btsyy7uTJk7nvvvsYPHgwf/zxh8Nu7cOHDyc8PJyuXbvaLazw5ptvcttttzF69GiCg4OdLkme12V6x48fz6effsqSJUvo2rUrQ4cOtTu/atUq3nrrLfz8/JgyZUqO7vn3339z6tQpypUr57DvU074+/vz5JNP8tJLL2WsLni1sLAwunXrhp+fn115Wloav/76K4Dd/DYRKRksVmvh7mvhEIAFerwK0+6D0T1tQ+z+PQD3v5f9ML2EZJi/Cm5rYlslz8cTTp2Hz/+EV5faepWuNeRW29clfxTAs1jhqd7AlTmsIiKZMZsN/enrkgo8cVq8eDGenp6ZzjPy9/enVatWrF+/noiICOrVq5dxbu3atZnO9Rk7dmymf2EqVqzI3LlzcxTf0KFD2bt3L6+//joNGzakd+/eNGjQgJiYGJYvX86+ffto0aKFw8IIbdu2Zf78+QwfPpz27dtz66230qpVK8qUKcOZM2fYsmUL69evx8fHh4CAAId2X3jhBacLQNx5552Ehoby7bff0qtXL+677z7mz59Phw4dcHd3Z+PGjfz22294eXmxePFip/sjHT58mLfeeguwTfJLX5LcYrHkeN8nZ55//nnmzp3rdIjitGnTuO+++2jXrh0hISGULVuWyMhIfv75Zw4ePEizZs24//7789y2iLio2yZmX+fCJXj4A9uRldqP2r9OSYUxH+cunucX246CUKkM3GP7EC59iwcRkczoA+nCV6CJ0/r16wkPD6ddu3ZZrlzXp08f/vzzT2bMmGE3h2bp0qUOG9qmGz16tG1ycj547bXX6NKlC1OnTmX58uV8/vnneHl5ERwczJQpU3j66aedzpMaOnQobdu25dVXX2Xt2rVs2LCBpKQkSpUqRe3atRk9ejRPPfWUQy8WwCeffOI0Fnd3d0JDQ2ndujV79+7lxRdf5Ndff2XatGlYLBYCAgLo168fr776aqabym7dujVjfpTJZMLX15cbbriBp5566rr2nfLw8ODFF1902nM4adIkFi1axL///suGDRuIj4/Hy8uLGjVq8NRTTzF58mTc3Nzy3LaIFE0GD9QrOcwmeLyHbd8qbNtfhISEGBuTiBRpO3fuzFiQTAqHyWr4tu8iIlIsjZqHde5vmFK0Wet183CDk/MhoAxg2y9QiZOIZEU/JwqfBkeKiEie6bO3fOBuhsEdMpImIMdbN4iI68rJqsqSv5Q4iYiIGCnVAk/0tCsyGb1aoYgUedfutyQFT4mTiIjkmX7Bv05mE7S5AVrYz1k9efKkQQGJSHHhbLEuKVhKnEREJO80VO/6WKwwNtToKEREJAeUOImISJ4pbbpOgWXh7lscihs2bFj4sYhIsVKrVi2jQ3A5SpxERESMYDbBE73A3XGrhuPHjxsQkIgUJ/Hx8UaH4HKUOImISJ6ZNccp79zd4OGuTk/pFyIRyU50dLTRIbgcJU4iIpJnFs1xyht3MwztCBX8nJ729vYu5IBERCQ7SpxEREQKW6rFNkwvE3Xr1i3EYESkONLmt4VPiZOIiOSZSR1Ouedmhg6NoGnNTKvs2rWrEAMSkeJo9+7dRofgcpQ4iYhI3gRVxJRmMTqK4ifNAmO0BLmIXJ+UlBSjQ3A57kYHICIixdS43uyv5skN9eobHUnxc2NwlqcDAwMLKRARKa7KlCljdAguR4mTiIjkjdlMYuNqEFLP6EhKHCVOIpKdypUrGx2Cy9FQPRERyTNtwCgiYozw8HCjQ3A5SpxERCTPLl26ZHQIIiIihUKJk4iI5NnZs2eNDkFExCUFBQUZHYLLUeIkIiIiIlLMXL582egQXI4SJxERybPmzZsbHYKIiEs6c+aM0SG4HCVOIiKSZ3v37jU6BBERkUKh5chFRNK99T28+a3RURQtt9SHZc9nejo5ObkQgxERkXRNmzY1OgSXo8RJRCTd13/D2VijoyhaftwEO49C05pOT/v7+xdyQCIiArblyBs2bGh0GC5FQ/VERCRz7maY8WOmp7VRq4iIMZKSkowOweUocRIRkcylWmDRWjgX5/S0NmAUETGGn5+f0SG4HCVOIiKStdQ0mL/C6ChEROQq1apVMzoEl6PESUREsmaxwoyfbAnUNbQBo4iIMfbt22d0CC5HiZOIiGQv8jx8t8GhWGPsRUTEVShxEhGR7JlN8M4yh+LTp08bEIyIiFSvXt3oEFyOEicREcmexQrr98PWQ0ZHIiIiQGpqqtEhuBwlTiIikjPuZttcp6toA0YREWNERUUZHYLLUeIkIiI5k2qBxevgzIWMooiICOPiERERKURKnEREJOcsFpi3MuPl5cuXDQxGRMR1NWnSxOgQXI4SJxERI5XxhTmPwpkFEL8Efn8ZWgTn7NqHusCaVyDqY7j8JRyaDR+PgpoB9vW8PWH+CNj5LlxYBHGLYdt0GN0T3N1yF6/FCjN/ghTb2HptwCgiYoyDBw8aHYLLcTc6ABERl2UywU8vQPOaMO17iI6FEd1hzWS48Wk4EJn19S2C4fBp+OFfOB8PtSvDw12g143QfKxtCXEAH09oHATLN8ORs7Zeo7YN4J1hcEs9GPJu7uI+cxG+/gcGttcGjCIiBklMTDQ6BJdT6D1OYWFhmEymTA83tyuffl57zsPDg8DAQPr378/JkycB2L9/P76+vgQFBWX6Fyg0NBSTycTzzz+fZdtXH+kTnh955BFMJhMrVqzI8nnGjh1rV56T2J3ZsWMH//d//0eVKlXw9PTE19eXG264gWeeeYaEhIRMr4uKiuLRRx+lbt26+Pr64u7uTvny5Wnfvj2zZ8/GYrFw6623Yjab+f77753e4/vvv8dsNnPrrbfm+H0ymUzs2rWLXbt2ZVtv//79mcYvUiKtngwLRmV+vm8baNcA7p8Fk7+CD36BThMhzQIvD8j+/iPnwrBZMP0HWPA7TPwcek6BgDLwv05X6p2PhzbPwbOLYPYvMOc3uG8mvP8LDO4Alcvm7rnMJlubaANGERGjlCpVyugQXI5hPU633XYbPXr0cCg3m+1zuVq1ajFy5EgAzp07x++//87SpUv5559/2L9/PzfccAOTJk3iueeeY/jw4Xz66ad213/22Wf8+OOPdO/enQEDBlC+fHm78++88w6nTp1i2rRpduVVq1a97mfMLnYfHx+7+nPnzuXxxx/HZDLRs2dPmjdvzqVLl1i5ciXTpk0jLCyMVatWUbt2bbvrfvnlFwYOHEhsbCxt2rShT58+lClThhMnTvD7778zYsQITp06xeLFi2natCnDhw+nY8eOlC1bNuMesbGxDB8+HD8/Pz7//HO++OILuzbWrVvHsmXLCA0NpUOHDnbnqlevzokTJwBo0aIFgwcPdvp+VKlSJU/vo0iJ1bcNRJ2Hb/65UhYdC1+th3s7gKc7JOdyudkjZ2xfy+bgP9Sr656+kPM2LFb49wD8GwEeuQtPRETyR1BQkNEhuBzDEqeQkBDGjRuXbb1KlSo51GvXrh3r169nwYIFjBgxgmeffZbvv/+exYsXM3DgwIyE7OzZszzxxBNUqFCBRYsWUbFiRZo3b253r08++YRTp07lKJbcykns6f766y9GjRqFn58fq1atIiQkxO66yZMnM2nSJEJDQ9mxY0dGgnno0CEGDBhASkoK3377Lb1793aI47PPPiM6OpqgoCCmTZvG8OHDeeCBB/jmm28y6jzwwAOcPn2aOXPmUL16dYe4U1JSWLZsGW3atHH6XqUnTrVr1y6Q91KkRGpRG7YcAqvVvnxjBAy/A+pXhV3Hsr9P+dLg5gZBFWFif1vZqp2O9Tzcwd/HNnTvprowrrctecpuSKAz7maY8SPV3x2a+2tFROS67d271+H3RSlYxXJxiNtuuw2wHyLy+eefU6pUKR566CHi4uIA+N///sf58+f54IMPqFixoiGxXstZ7ADPPvssKSkpvP32207/EUycOJEOHTqwe/duFi5cmFE+YcIEYmNjefrpp50mTQD33nsvTz75JGAbeti1a1e+++47vvzyS8A23PCbb77hjjvu4JFHHrn+hxSRnKlS7so8pKull1Ut73jOmZPzbYtLbJoGbW+Ax+fDyu2O9frcAtGfwPF58O2zcOIchL5mGxqYW6kW+PIvrJExub9WRESkGDKsxykhISGjl+Jq3t7e2SY5Bw4cAKBChQoZZTVr1mTq1KmMGjWKBx98kDvvvJNffvmFe+65h/79+193vDExMU7jjYnJ3S8NzmKPj49nw4YNlC9fnvvvvz/Ta4cPH866detYunQpDzzwAAArV67E3d09V708ixYtolGjRowcOZIbb7yRESNGUK5cOT777LNcPYszly9fdvo+eXh4ULly5eu+v0iR5e5mWyHvah7u4OUBFa5ZeS4m3tbL5OMJSU6G4l1Otn318cxZ23e+Ct4e0LA63NsRSnk5r7d6F3R5yTY0r3NTaF4LSnnnrA1nrJD2/nL4sH7e7yEiInmSH9NKJHcM63GaM2cONWrUcDh69uxpVy81NZUTJ05w4sQJtm7dygsvvMDXX3+Nl5eXQ5IxcuRIOnfuTFhYGI8//jiBgYF8/PHH+RLvwIEDncY7fPjwTK/Jaezbtm0jNTWVevXqZRlDp06dgCsbTp47d47o6GiqVq2aqyWBK1euzMyZMzl37hwtW7YkOjqa9957j4CAgOwvzsby5cudvk8333xzru4TExNDUlJSxuv4+PiMnkSA5ORkzp07Z3dNZGRklq+joqKwXjUkSm2ojWvbsFw7ZC432jWw9eZcfbRrAINudSwP+u/DocRk8HLy+ZW355XzObFmF/yyFd5ZBv2mwaT+MPJOx3pnLsKqHfD13zBiLvy4GVZMyv3iEP+xWiyU/udQxuvi+D1XG2pDbagNtXF9bbgSk9V6Pb8p5F5YWBj9+vUjNDSUQYMGOZyvWrUqHTt2tAVnMjm9R3BwMLNmzeLOOx1/MYiKiqJBgwZcvHiRH3/80SERu1bTpk3ZtWsXmb0NjzzyCPPmzeOFF16gUaNGDuc3bdrE9OnTGTNmDNOnT88oz03sP/zwA71796ZLly6Zrt4Htr/MXl5eBAYGEhkZSUREBPXr16dhw4bs2bMny+d05q677spY8OGHH37Isu7UqVN5/vnnee211xg/frzD+V27dtG0aVPatGnD448/7nC+TJkyThcDESlS2jwH/4Tn7dqypeDGOvZlb99vW/xh2jUrWf65F5JSIHwWRETaVsK72gOd4aOR0PTJnM1xutZfr9mWOm/r+G/VTr0qEP4+DP8Q5v6W+3aA1DnDcX+kW56uFRGRvNu2bZvmOBUyw4bq1a1b12nidK369evz0ksvYbVaOXToEHPmzCE6OhovL+dDUQIDA6lRowYXL17MNmnKjQ4dOtC1a1eHcg+PzJeUymns6Sv9XZ3xOxMdHQ1cWX4y/bqslinPSps2bTIWfMgvVapUydH3VaTEuXDJ1ptztfPxtvlK15an23YEbm1oS3Ku/vDmlnpw6TKEn8pbLD6etiGC2db772fRtUMMc6qML4dbVyfrvnIREZGSochvgFu2bFm7X8QfeOABmjRpwsCBAzl48GCR3rU+p7GHhITg7u6eMQQvM2vXrgXIGNJXoUIFKlasyMmTJ4mLiyvS74WIOBH2N/RrC31a24bPgW0+VL+2sGyT/VLkwf/NETx02vbVzQx+PraE7Wo314WmNWHJH1fKKvjBOScfzDzUxfZ104Hcx+5mhse6c8mSkvtrRUTkujVs2NDoEFxOsVtVr2rVqowbN46zZ886HTJWlGUWe+nSpWnVqhUxMTEsWrQo0+vnzp0LQN++fTPKOnfuTGpqqt0wQREpJsL+hr/32zbJfbEfPNYd1rxiS0om2e+lxqqXbUe60t5wfC7MHwFjQuGRO+C9h2yb7l5MgFeWXql7b0fYOxOm3gsPd4Wxd8GvE+HxHvDDv7ZFI3LLaoUR3bUBo4iIQY4dy8NQbrkuxS5xAhg3bhyVKlVi4cKFGcPXiovMYp86dSru7u6MGTOGXbscf4mZMmUKa9asoXHjxgwbNiyj/LXXXsPPz4833niDn376yWmbixcv5t133833ZxGR62SxQI9X4cu/YHRPmPY/2wa4t0/KfpheQjLMX2Xbj2lSf3jvQeh1E3z+J9w4zn5vpj/3wo6jtoUqZj4ILw+w9UKN+Rj6vJH7uN3N8H+3QI2K1KxZM/fXi4jIdbt06VL2lSRfGTZUb9u2bbz11ltOzz344IOUK1cu02s9PT0ZNWoUEydO5MUXX2T27NkFFWa+yyz2Dh06MGPGDJ588kluuukmevXqRfPmzUlISGDFihVs3ryZWrVqsWzZsozNb8G22MRXX33FwIEDCQ0NpW3btrRr144yZcpw8uRJ1qxZw549e5g4cWKhPN/hw4cz/b726dOH4ODgQolDpEi4LQf/7i5cgoc/sB1Zqf2o/euUVFvikxObD8KAt3NWNydSLfBkKAB79uzR5GQREQP4+PgYHYLLMSxxWr16NatXr3Z6rnPnzlkmTgDPPPMMM2bM4NNPP2Xy5Mn5spR2Ycks9hEjRtCmTRteeukl/vjjD77//nvc3d0JCgpi3LhxTJo0idKlSzvcr3v37uzZs4eXX36ZVatW8d5775GcnIy/vz+NGzdm7ty5PPzww4XybFu3bmXr1q1Oz1WrVk2Jk0hxZzZBkyDbUusiImKYOnXqZF9J8lWhL0cuIlJkXc9y5K5k4eNw320AnDlzhkqVKhkckIiI69Fy5IWvWM5xEhERg5QrDQPaZbzMbM86ERGRkkaJk4iI5IybGUbdCd6eGUUnT540MCAREdcVGBhodAguR4mTiIjk3KPdjI5AREQAd/civx1riaPESUREsuduhn5toGp5u2JtwCgiYowTJ04YHYLLUeIkIiLZS7XAE70cio8fP25AMCIiIoVPfXwiIpI1swlCakPrGxxOxcfHGxCQiIg0aKBtIQqbepxERCRrFiuMDXV6ytvbu5CDERER0OI8RlDiJCIiWavoB/3aOj1Vt27dQg5GREQA4uLijA7B5ShxEhGRzJlN8HhP8PRwenrXrl2FHJCIiAB4eXkZHYLL0RwnEZGrWE1gMuszJQCsVlviNPwOoyMREZFr1K9f3+gQXI4SJxGRdFPv5fxbSylfrnz2dV1F85pQuWymp7UBo4iIMXbu3ElISIjRYbgUJU4iIuk6NeFY2VTK6z+iHFPiJCIirkLjUURErlKrVi2jQxAREclWpUqVjA7B5ShxEhG5yqVLl4wOQUREJFvaDqLwKXESEbnK2bNnjQ5BREQkW8eOHTM6BJejxElERERERCQbJqvVajU6CBGRosJqtWIymYwOQ0REJEsJCQn4+voaHYZLUY+TiMhV9u7da3QIIiIi2Tp9+rTRIbgcLUcuIoXnXBzMX2FsDO0bQruGmZ5OTk4uxGBERETy5uLFi0aH4HKUOIlI4XlxCdYPf8Xk5mZM+xYLNK4B29+BTIbj+fv7F3JQIiIiuefh4WF0CC5HiZOIFJ7kVDCbITXNuBh2HoO/9tl6npzQhq4iIlIcNG7c2OgQXI7mOImIa3E3w7vLMj0dHh5eiMGIiIjkzbZt24wOweUocRIR15JqgW83wPFooyMRERGRYkSJk4gUrqKw0rfJBB/84vRUUFBQIQcjIiKSexUrVjQ6BJejxElECldR2DkuzWJLnBKTHE4lJTmWiYiIFDWlS5c2OgSXo8RJRFxTbAIs+cOhWPtiiIhIcXDkyBGjQ3A5SpxEpHAVhaF6YBuu9/YPYC0KXWAiIiJS1ClxEpHCVVTyFKsV9p6Atbvtips2bWpQQCIiIjlXt25do0NwOUqcRMR1uTkuTR4REWFQMCIiIjl37tw5o0NwOUqcRMR1pVngh01w5ExG0eXLlw0MSEREJGfOnz9vdAgux93oAETEtZhMRWWS03/MJnj/Z5h2HwB+fn4F19aFS/DMp7Z9pBKSoFU9ePs+aFkn+2s3RsDC32FDBOw4CqlpYP3Gsd7C32HYrMzv89kTMKRj3p9BRESKBDc3N6NDcDkmq1Uzo0WkkDz0PtaFqzGlWYyOxJ6fN0R+DKW8uXz5Mt7e3vnfhsUCt06A7Ufh6d5Q0d+2JPrxaNg8DepVzfr6l76A176BZjUhLhHCTzlPnA5Fwfr9juXvLIPtR+DEPAgsly+PJCIi4koKfaheWFgYJpOJsWPHZlrHZDJxyy23OD03dOhQTCYTVapUyfT6qVOnYjKZMj0CAwMz6u7atSujPLM2k5KS8Pf3x2QyERAQkGm7EydOxGQy4ePjQ2xsbI7jufq4dmL6jh07+L//+z+qVKmCp6cnvr6+3HDDDTzzzDMkJCRkGktUVBSPPvoodevWxdfXF3d3d8qXL0/79u2ZPXs2FsuVX1wDAgLsYnB3d6dixYr07NmTvXv3Zvr+vv/++06vz+oICwvLNGYRw8Rfhs/WArBv37683aPTi3D/e5mfD/vbltAsHAWTBsDIO2HNZNs8q0lfZn//x7rDxUWwaRp0bZ55veBAuLej/XFPa4iIhNubKmkSESkhduzYYXQILqdYDdVLTk5m2bJlVK5cmaioKL7++mvuueeeTOv369ePVq1aOZT7+/s7lHl4eLBp0yYOHjxInTr2w2Y++ugj4uLi8PDwyDK+JUuWULlyZU6fPs3s2bN59tlnAejRo4fDte+88w6nTp1i2rRpduVVq1751Hnu3Lk8/vjjmEwmevbsSfPmzbl06RIrV65k2rRphIWFsWrVKmrXrm13j19++YWBAwcSGxtLmzZt6NOnD2XKlOHEiRP8/vvvjBgxglOnTvHKK69kXFO+fHnGjx8PQFxcHH/99Rc///wz69evZ9euXVSrVi3T554yZYpdorh7924WLlxIu3btuPvuu+3q3njjjVm+hyKGmb4MHrmj4O4f9jdULgt9Wl8pCygD/dvCZ+sgKQW8svgZU7ls3ttetsnWSzWkQ97vISIiRcrVH4JL4ShWidPChQu5ePEic+fO5eGHH2bu3LlZJk4dO3Zk5MiRObp369at+euvv3jvvfd49913HdqtWbMmFouFxMREp9evXbuWgwcP8sYbb/Dhhx+yaNGijMSpefPmNG9u/wnxJ598wqlTpxg3bpzT+/3111+MGjUKPz8/Vq1aRUhIiN35yZMnM2nSJEJDQ9mxYwdms63z8NChQwwYMICUlBS+/fZbevfu7XDvzz77jOjoaLuyUqVKOcQycOBAvvzyS6ZPn87bb7/tNE6ARx55xO51WFgYCxcupFGjRpk+n7iwIjbFCbAtkR5+ClbtoHpI9YJpY+thaBkM5ms6+lvVg7krbO03rVkwbS9eBz6e9kmbiIgUa+XLlzc6BJdTrFbV+/jjj6lUqRJ9+/blzjvvZM2aNQ4JQF4FBATQqlUrli5dald+8OBBNm/eTP/+/bO8ftasWXh5efHII48wYMAAdu/ezdatW/Mcz7PPPktKSgpvv/22Q9IEtmGBHTp0yOjdSTdhwgRiY2N5+umnnSZNAPfeey9PPvlktjF069YNgAMHDuTlEUScK6qzKt3M8O6PpKWlFcz9I89DFSfD5NLLTsUUTLsxcfDLVgi9Cfx8CqYNEREpdGXLljU6BJdjWOKUkJDAiRMnnB7OHDp0iH///Ze7774bs9nMiBEjSE5Ozphn48zFixed3v/ChQtO6w8bNoxTp07x008/ZZTNmDEDs9nM6NGjs3yWn3/+mdtuu42yZcsycuRI3NzcmDFjRs7ejGvEx8ezYcMGypcvz/33359pveHDhwPYJXsrV67E3d09X3p6wsPDAShXTnMixAWkWWD5Zs5t3J193ZRUiI61P1JSbcPtri1PH0qRmAxeTjr5vT2vnC8IYX9DcqqG6YmIlDCHDh0yOgSXY1jiNGfOHGrUqOH0cGbGjBlYrdaMoXcdOnSgVq1aLF68ONM2JkyY4PT+Dz74oNP6999/P2XKlOHDDz/MKAsLC6N169ZUr5758J358+dz6dKljPtWr16dW265hR9++IGUlJRs34trbdu2jdTUVOrVq5dlvU6dOgFXNuw8d+4c0dHRVK1aNddLKqelpWUklrt27eKtt97ivffew2w288ADD+T6GUQyVRSH6qUzm6kYtjn7en/tg4D77Y/1++GLPx3Lj/3XK+7jCUmpjve6nHzlfEFYvA7Kl4Y7WxbM/UVERFyEYYlTaGgoS5YscXo4s3TpUho1akSzZs0yyvr3709ERATr1693es1DDz3k9P7PPfec0/qenp707NmTVatWERcXxw8//EBkZGSmiVa6hQsXEhAQQJ8+fTLK7rvvPs6fP89nn32W3VvhICbGNmQnu+SnYsWKAFy6dMnuulKlSuW6zVOnTmUklk2bNuXpp5/Gz8+Pjz76iA4dCv+T6piYGJKSkjJex8fHExcXl/E6OTnZYcfsyMjILF9HRUVx9er7asOgNorqUD3AmmbBO6SuXZnT52heC1ZMghWTiPlyNNbfJtqWCb8jhNivx5L80/MZ5+NLu9veqyrlIPK843sV+d8GhlXLZ9rmtd+PazfpzfT7cews/LEX+rUlMvpsls9V7P9eqQ21oTbUhou1Ubt27SLxHK7EsMUh6taty6BBg5yeGzx4sN3r7777jsjISHr37m03b6hjx45MmzaNmTNn0rZtW4f7hISEZNpGZkaNGsWSJUuYM2cOq1evply5cgwZMiTT+rt372bbtm306tWL7du3Z5Q3a9YMb29vPvroI4YNG5arGNIn+139F9mZ9Pld6YlS+nVZLVOemYCAgIyhhSdPnuSjjz7i8OHD2a4kWFCunfBYunRpu9eenp5UqFDBruzaJeqvfX31MvRqw/g2iiJTGV9Od67P1etuZvocXWwLvmS8M+W+hirl8O/T3q5+xjsVUgv+2Iunu7v9e7UhAny9oP6VFTWz+35cu89Upt+P+d+A1QpDOhSJ77naUBtqQ22ojfxrIzY21mGklv4/L1jFYlW99KFzH374od0wunTLly8nKSkJLy+v626rTZs21K9fn3nz5nH06FEGDBiQZfKQPoRw2bJlLFu2zOH8P//8w7FjxwgKCspxDCEhIbi7u2cMwcvM2rW2fWfSh/RVqFCBihUrcvLkSeLi4nI1XM/b29suyXz00Udp2rQpjzzyCK1bt3ZYol2kxHEzw4juXLLkfnhtjvRtY5tv9M0/0Pe/D3qiY2HpetvCDVcvRX4wyva1TqDjfXJjyR8QVBHaN7y++4iISJFz7ty5TKe4SMEo8onTuXPnWLNmDS1atHA6ZG7r1q189NFHfPTRR4wYMSJf2hw8eDAvvfQSAE888USm9SwWC99++y21atVyuhjDyZMnmTp1KjNmzMhyOe9rlS5dmlatWrF+/XoWLVrE0KFDndabO3cuAH379s0o69y5c8YS4pMmTcpxm85iePPNN+nfvz9PPvmk06RQJC9MpiI6yclqhce6U+ry+YK5f9820Lo+DJsFe05ART/44BfbohQvD7Sv2/m/f7tH5lwpO3oGFtk+LGHTfytdvvrfwjA1A2BoJ/t77DoKO47Cc32gqL7nIiKSZ0X2/9MSrMgnTu+//z5JSUk8/PDDPPbYYw7nY2NjWbRoEQsXLsy3xGnkyJGcPHmSChUq0LJl5hOqv/jiC6Kjo7nvvvsy3S9q/vz5fPXVV7lKnACmTp1K586dGTNmDC1atKBJkyZ256dMmcKaNWto3Lix3VDA1157jeXLl/PGG29w00030bNnT4d7L168mLNnz2a7JHm/fv1o3LgxP//8Mzt27LCbXyaSV1arteitD+Fuht6toEZFaiY7bpCdL9zcYPkL8PQnMPMn2yp6N9eFhY/DDZlvMJ3h8Bl48XP7svTXHRs7Jk6L/7B9HXzrdYcuIiJFz7V7hErBK/KJ0+LFi/H09Mx0npG/v39G70xERITdSnRr167NdMPasWPHZmwae62KFStm9OZkZd68eQBZLhnepUsXPv/8c5YvX06PHj2yvWe6Dh06MGPGDJ588kluuukmevXqRfPmzUlISGDFihVs3ryZWrVqsWzZMrvnCA4O5quvvmLgwIGEhobStm1b2rVrR5kyZTh58iRr1qxhz549TJw4MUdxTJgwgcGDBzN+/Hi7ZdpFSpRUC4wJBWDPnj1O907L1ppXsq9TrjTMH2k7snJ1T1O6Tk3A+k3O45l6r+0QEZESadeuXQ4frEvBKtKJ0/r16wkPD6ddu3b4+2f+KXCfPn34888/mTFjBrNmzcooX7p0qcOGtulGjx6Np2fel/89deoUf/75J8HBwVn+pR0yZAiff/45s2fPzlXiBDBixAjatGnDSy+9xB9//MH333+Pu7s7QUFBjBs3jkmTJjlM6gPo3r07e/bs4eWXX2bVqlW89957JCcn4+/vT+PGjZk7dy4PP/xwjmIYNGgQL730Er/88gvbtm3L2y+UIkWZ2QRNgqBtA6MjERERybHUVCdbXEiBMlmvXqNQRKQgPfQ+1k9WY0q1GB2JvYWPw323AXDmzBkqVapkcEAiIiJZO3LkCLVq1TI6DJdi2D5OIuKiitpHNeVKw4B2GS812VZERIqD9P08pfAocRIR1+VmhlF3gveVYbsnT540MCAREZGcOXDggNEhuBwlTiJSuIpah86j3YyOQERERIqBIr04hIiUQEVlqJ672ba3UlX7XdQbNtRmsSIiUvRpflPhU4+TiLimVAs80cuh+Pjx4wYEIyIikjuXLl0yOgSXo8RJRFyP2QQtg6H1DQ6n4uPjDQhIREQkd86ePWt0CC5HiZOIFKoisWqdxQpjQ52e8vb2LuRgREREpDhQ4iQihapIbB1X0Q/6tXV6qm7duoUcjIiISO41b97c6BBcjhInEXEtZhOM6gGeHk5P79q1q5ADEhERyb29e/caHYLLUeIkIoXL6B4ns0lLkIuISLGXnJxsdAguR4mTiBSelsGYLAYnTgPbQ+WymZ4ODAwsvFhERETyyN/f3+gQXI7JWiQmHIiIq9i57m+aNm1qXAC+XuDlfJieiIhIcZGQkICvr6/RYbgUbYArIoUqzd8HypU2OgwREZFiLTw8nJCQEKPDcCkaqicihUo7nYuIiEhxpMRJRAqVdjoXERG5fkFBQUaH4HKUOIlIodJO5yIiItcvKSnJ6BBcjhInEREREZFi5vTp00aH4HKUOIlIodJO5yIiIlIcaTlykZIuLQ2SUwuvPXc38Mh8wc49e/bQqFGjwotHRESkBEpLS8PNzc3oMFyKliMXKem6vgyrdxVee7fUg3/eyPS0djoXERG5fhERETRo0MDoMFyKhuqJlHT/Hijc9jZEwKbM29RO5yIiItfv8uXLRofgcpQ4iUj+cjfDzJ8yPR0YGFiIwYiIiJRMfn5+RofgcpQ4iUj+SrXA53/C6QtOT4eHhxduPCIiIiVQtWrVjA7B5ShxEpH8Z7HAnN+MjkJERKTE2rdvn9EhuBwlTiKS/yxWeO8nSE5xOKWdzkVERKQ4UuIkIgUjOg7C/nYo1k7nIiIi16969epGh+BylDiJSMEwm+DtHxyKtdO5iIjI9UtLSzM6BJejxElECobFClsOwQYtBiEiIpLfIiMjjQ7B5ShxEpGC426Gd3+0K2ratKlBwYiIiIjknRInESk4qRZYuh5OxWQURUREGBiQiIhIydC4cWOjQ3A5SpxEpOB9+GvGH7XTuYiIyPU7fPiw0SG4HHejAxCREi7NArN+hgl9wcujYHc6v3AJnvkUvt0ACUnQqh68fR+0rJP9tRsjYOHvsCECdhyF1DSwfuO8rqmP8/Kp98JzmZwTERHJRwkJCUaH4HKUOIlIwTsfD1/8CffdVnA7nVss0PNV2H4Unu4NFf3hg1+g00TYPA3qVc36+uWbYf4qaFYTgitD+Kms63dtDv/rZF/WovZ1PYKIiEhOlSpVyugQXI6G6rmYqKgoPD09MZlMvPnmm5nWi4+P58UXX6R58+b4+/vj5uaGr68vdevWZejQoWzcuNGu/tSpUzGZTJkegYGBBf1oUpSlL01uteZ9p/NOL8L972V+PuxvWL8fFo6CSQNg5J2wZjK4mWHSl9nf/7HucHERbJpmS4qyU78q3NvR/miszX1FRKRw1KxZ0+gQXI56nFzMzJkzSU1NJSAggM8++4xnnnnGoc727dvp2bMnJ0+epEmTJvzvf/+jatWqxMXFsWPHDr7//nsWL17M/v37qVevnt21/fr1o1WrVg739Pf3L7BnkmLAYoWdR2H9PiioD8jC/obKZaFP6ytlAWWgf1v4bB0kpYCXR+bXVy6b+zYTk8BkAm/P3F8rIiJyHfbs2UNISIjRYbgUJU4u5vPPP6dp06b06NGDN954g+3bt9O8+ZVP1+Pi4ujRowenT59m9uzZPProow73iI+PZ/z48ZhMJodzHTt2ZOTIkQX6DFJM/bc0efXZwwrm/lsPQ8tgMF/Tkd6qHsxdYRt61zQfP51buNo2FNBqhYbV4YW+MLhD/t1fREREihQN1XMhq1at4siRIwwZMoSRI0diNpuZMWOGXZ3XXnuNU6dOMXToUKdJE0Dp0qV57733qFu3bmGELSVFqgW++QfrsbMFc//I81ClnGN5etlVS6Jft7Y3wJTB8N2zMHu4bTjgkHdh9i/514aIiEgWqlbNZu6u5Dv1OLmQ999/Hy8vLx555BHKli1L69at+f7770lLS8PNzQ2AZcuWATBmzJg8tXHx4kVOnDjhUF66dGnKli2b59ilhDCZsLy/HD5qmHW9lFS4mOBYlpQC0bH25eVL23qZEpPBy8mPtPRhdInJeY/7Wn9NtX/9wO1w49Pw/GK4/zbw8cq/tkRERJxwNvJHCpZ6nFxEfHw8v/76K506dcpIYP73v/8RExPDZ599llHvyJEj+Pj40KxZM7vrU1JSOHHihN0RFxfn0M6ECROoUaOGw/Hggw/mKt6YmBiSkpLs4r+6veTkZM6dO2d3TWRkZJavo6KisFqtLteGlaLFO/w0kM1z/LUPAu63P9bvt63Md235sWjbNT6eJJy3T6qioqKwJiZlnC+w74eHO4y6Ey5cInb1NsO/52pDbagNtaE2Sn4bJ0+eLBLP4UpM1qvfTSmxpk+fzlNPPcVXX31Fv379AEhKSiIgIICQkBDWrVsHgJubG2XLlnX4R/PXX3/Rvn17u7IxY8Ywffp0wLaq3vPPP89DDz3E7bff7tB+3bp1ufnmmwvi0SQ7foMhvuhsOpv63bO4974l60rn42HzQfuypxZCYDnbUuNXa9/Q1qtUbyTUqwLLX7A//9FKeOgD2PFOzuc4jZoH7/+c+T5OzizfDD2nwPfPwV2OC6SIiIjkp23btmlxiEKmoXou4pNPPsHPz486deqwdevWjPJWrVqxdu1aTpw4QfXq1fHx8SExMdHh+saNG7NkyRIANm3alJEwXSskJIRBgwYVzENI8VerEoduKEP97OqVKw1dmjuWVSnnWJ4upBb8sde2n9PVC0RsiABfL9vy4QXpkK0njYAyBduOiIgI0LBhNsPeJd8pcXIBO3bsYOfOnVitVm688UandWbOnMmbb75JrVq12L17Nzt27LAbrle2bNmMhMjDI4slnUUyYzLBmFASLhdQ71ffNrYlyb/5B/q2tZVFx8LS9RB6k/1S5AejbF/r5GF/sbMXHZOjuER490fbprs3BuctfhERkVw4fvy4FuoqZEqcXMDMmTOxWq1MnjyZ8uXLO5x/7bXX+PLLL3nzzTcJDQ1l9+7dvPPOOyxYsMCAaKXE8vGE+2+j1OmTBXP/vm2gdX0YNgv2nICKfrblwtMs8PJA+7qdJ9m+HplzpezoGVi01vbnTQdsX19davtaMwCGdrL9+f2f4buNtmQsKMC2mt/Hq2xzrRaNBk99sCAiIgUvPj7e6BBcjhKnEi4tLY3vvvuOoKAgXnzxRad1duzYwdy5c/n11195/vnn+fTTT1m0aBG33HKL0yXJNS1Ocs3NDA92Bn9fanoX0E7nbm62+U1PfwIzf7KtondzXVj4ONxQLfvrD5+BFz+3L0t/3bHxlcSpXQPbQhXzV8K5eCjlZdsr6uNRcHvTfH0kERGRzHh7exsdgstR4lTCLVmyhHPnznHPPfdkWud///sfc+fO5YMPPqBbt24sX76cnj178thjj/HBBx/QsWNHqlSpwsWLF9m/fz8rV67EbDZTq1Yth3utXbvW6RwpgLFjx2K+dnNScQ1pFni8B3AdO52veSX7OuVKw/yRtiMrV/c0pevUJGeLQXQNsR0iIiIG0jC9wqdV9Uq4Dh068Mcff/DHH384rIp3tapVq3LhwgVOnz6Nn58fcXFxvPHGG/zwww8cPnyYhIQEvLy8qFq1Kq1bt+aJJ56wWyUvfVW9rCQlJeHp6ZlvzyY5ZPSqem5m6Nocfrb1eGoVIBERkeun/08LnxInkZLO6MQJ4JcXoVsLAM6cOUOlSpWMjUdERKSYU+JU+DRuSkQKVt1AuCMk46V2OhcREbl+gYF5WBlWrosSJxEpOCZgTKhtKfL/nDxZQKvqiYiIuBBtD1P4lDiJSMEp5Q3/62R0FCIiIiXO8ePHjQ7B5ShxEpGC4WaGh7tCaR+7Yu10LiIiIsWREicRKRgWC4y606FYn5CJiIhcvxtuuMHoEFyOEicRyX9uZuh5EwQ7TlzVTuciIiLXLzIy0ugQXI4SJxHJf2kW26IQTmincxERkesXGxtrdAgux93oAESkhDGZoH4VuK2J09Pa6VxEROT6eXl5GR2Cy1GPk4jkL6sVxt5ltwT51Xbt2lXIAYmIiJQ8muNU+NTjJFLSebhhNZswuRXC5yQWK5Tygns7FnxbIiIiLmzHjh2EhIQYHYZLUeIkUtJ99xxRn6+kSmHtMN60JvhmPnxAO52LiIhIcaTESaSk69CY0/4pVCkin0opcRIREbl+lSpVMjoEl6M5TiIuoFatWkaHICIiIvnIx8cn+0qSr5Q4ibiAS5cuGR2CiIiI5KOjR48aHYLLUeIk4gLOnj1rdAgiIiIixZoSJxERERGRYqZevXpGh+ByTFar1Wp0ECJSsKxWK6ZM9lUSERGR4ufIkSOaw1zI1OMk4gL27t1rdAgiIiKSjy5cuGB0CC5Hy5GLuIDk5ORMz038M41T+bh2RK9gE3fX02cyIiIiBcnDw8PoEFyOhuqJuIBDhw4RHBzsUL7jrJXmn6ThZoL8GMhnsUJ5Hzg7Up/JiIiISMmij4VFXEBmm87O3GLB3QRpVkjNh8MCpKamFu7DiYiIuKDt27cbHYLLUeIk4gLCw8MdyqITrCzabSVVfc4iIiLFjgaNFT4lTiIuav7OgkmazGb9WBERESloFStWNDoEl6PfcERcQFBQkN3rVIuVGVssWAogcdKi5yIiIgXPz8/P6BBcjhInEReQlJRk9/rbCCtR+biS3tXSLJaCubGIiIhkOHz4sNEhuBwlTiIu4PTp03av39lkwayuIREREZEc05rBIi5my2krf0cW3P3d3NwK7uYiIiICQJ06dYwOweWox0nEBTRt2jTjz+lLkBcUi4bqiYiIFLjz588bHYLLUeIk4gIiIiIAOHPJyuK9BbsEuZZHFRERKXgxMTFGh+BylDiJuIDLly8DMHeHtUBW0ruapk6JiIgUPA2NL3ya4yRyHS5ctvLMOgvfRlhJSIFWVUy83clMy8rZpw8bI60s3GVhQ5SVHWch1QLWcY7/JI/HWvl4l5WfDlmIOA9uZmhSAV5oY6ZLzZx99uHn50dKmpWZBbQE+dXc3PVjRUREpKBdPQxfCod6nETyyGK10vObNJbstTKqhZk3O5o5k2Cl05dpRJzPPjtZfsjC/J1WTEBwmczrfX/AyhsbLdQta+LV9mZebG0mLgW6LrWwYGfO5hNVq1aNsHArZxNz+HDXITU1teAbERERcXE7d+40OgSXo8TJxWzfvp3Q0FCqVauGl5cXpUqVolq1anTr1o2lS5dm1AsICMBkMmV6vPXWWwDExsZSpUoV/P39OXr0qEN7f//9Nx4eHjRu3Ji0tLRCe87CELbfyvpTsLC7mUltzYxsYWbNADfcTDDpr+wTmsdCzFx83I1NQ93pWjPzHqrbgkwce8SNJb3cGNnCzBM3mlk/yI0G5WHi+pwlTvv27WO6liAXEREpMUra71XFgcbUuJAVK1bQq1cv3Nzc6NmzJ40bNyYhIYEDBw7w999/88UXX9CvX7+M+uXLl2f8+PFO73XHHXcA4O/vz4cffsj//d//MWTIEP7888+MOsnJyQwdOhQ3NzcWL15c4sbihoVbqewLfepfyUYCfE30v8HEZ3usJKVa8cpi+brKpXKWxTSu6FjPy91Ej9ompm+2Epdsxc8z63vtuujDptNZVsk3ZmVnIiIiBa58+fJGh+BylDi5kBdeeIHk5GTWrFlDx44dHc4fOnTI7nWpUqUYN25ctvft3bs3/fv358svv2T69OmMHTsWgMcff5yDBw8yYcIEQkJC8uUZipKtZ6y0rGzCbLJPFFpVMTF3h5Xw89A0oODaj0oAX3fbkZ3vomvgbqJAV9NLZ9LyECIiIgWuXLlyRofgcjRUz4WcOHGC0qVLO02aAIKDg/N873nz5lGlShUmTpzIoUOHWLVqFR999BHNmjVj8uTJeb5vURZ5CaqUcixPLzsVX3BZyoHzVr6JsHJPfRNu2fTwRMZb+e6IR6EkTQBp2sdJRESkwB08eNDoEFyOepxcSLVq1Th16hSzZ8/msccey7Z+WloaJ06ccHquatWqmM1X8m4/Pz/mz59PaGgoAwcOJCoqCg8PD5YsWWJXr6iyWK0k53CosJcbmEwmElNtf76W93//qhILaI2EhBQr/Zal4eMOr3fI/r2ds92CtlYSERERuT5KnFzIpEmT6N27NyNGjGDy5MmEhIRw880306NHD1q3bu1Q/9SpU9SoUcPpvY4fP0716tXtynr06MHgwYP57LPPAHj55Zdp3Lhx/j9IAVh33MptX+Wsp2TvMDcaVAAfd0hykmxd/i9h8imAf11pFisDf7Sw5xz8fI+ZqqWzHxY3b4cVSyEOnytpc9lERESKotq1axsdgssp+l0Bkm969uzJmjVr6NatGwkJCfzyyy+88sortGnThsaNG7N9+3a7+gEBASxZssTpERDgfPJOernZbKZ///55jjUmJoakpKSM1/Hx8cTFxWW8Tk5O5ty5c3bXREZGZvk6KioK61VdL1e30aCCidmdUpjdKZkF3c0s6G5mXhcL77VPyHi9oLuZd1rHUqW07foqpeBIzGWHNtKH6FUtbcr353j4Nws/HrSwoJuJ24PMOXqvOla34mYqvC4n639D9a7n+wEF/z1XG2pDbagNtaE2inMbcXFxReI5XInJatUgHle1e/dufvrpJz755BP27NlDjRo12L9/Pz4+PgQEBODj48OxY8dyfL9//vmHDh06EBgYyMmTJwkJCWHz5s0F+ATG6vdDGn+csHLqMTe7BSIe+S2NxXusxIxyy3JVvauNWpnG+9usTjfATff0mjTe2mTl3dtsS5Ln1L+RVlotLrwlS8t6pHL+Ce9Ca09ERMQVbdu2rUQuvlWUqcfJhTVu3JhnnnmGnTt30rBhQ44fP85vv/2Wp3ulpKQwdOhQTCYT33//Pffddx9btmzh1Vdfzeeoi46+9U2cToBvwq989hCdYGXpfiuhdUx2SdPBC1YOXsj7ZxTTNlp4a5OV528x5SppAri5ionG/gnaw0lERKQEMZn0H3th0xwnwWw207x5c/bu3et0E9ucePLJJzlw4ADPPfccLVq04P3332flypW89tpr9O3blwYNGuRz1MbrW99E6yow7BfbnKOKPvDBNgtpVni5nX1y0/krW4/PkUeu/JM7etHKoj22ZGrTadvXV/+2DXOr6Q9DG9vu8W2EhWfWWahXDhpWMPHZHvu5WF1rmrLdE+rBevGM3ex7HU+bc+7u+rEiIiJS0Jo3b250CC5HQ/VcyJIlS+jbty+enp525XFxcTRu3Jjjx4/zxx9/0L59+1wN1Vu3bh2dO3emQYMGbN++PWMVvZUrV9KtW7cSPWTv/GUrT6+18N0BK4kpcHMgvNXJjZsC7ROZWnNtK0ZcnTitOWbJdEGKjtVhzUBb3Zf+SuPlvzP/Z7q6v5lOQVn3Ql1KTCZ4gZkzCTl6rOuioXoiIiIFb/fu3cVmEa6SQomTCwkKCiI2NpYOHTrQpEkTSpUqxbFjx1i2bBmRkZHccccd/Prrr4BtkQeLxcL48eOd3uvmm2+mY8eOJCUl0aBBAyIjI9m4cSPNmjWzq/fAAw+wYMECXnnlFV544YUCf0Zxbtu2bfx0uRkT/7JgKeB/8UqcRERECp7mOBU+JU4uZPHixXz99dds2bKFc+fOkZCQgI+PD8HBwQwYMIDnnnsuYynpgIAAoqOjM73XsGHD+Pjjj3nooYf46KOPeOGFF3jllVcc6iUmJnLDDTcQHR3Nli1bSuSQveJg27ZtVKvfnGofppFSwPvTlvdM49xor4JtRERExMUdOXKEWrVqGR2GS1HiJOICzpw5Q6VKlRj2cxqf7bGSWoD/6it4W4ke5VFwDYiIiAiXLl2iVKlSRofhUrSqnogLSF95Z3RLc4EmTQBpaYW39LmIiIirioiIMDoEl6PEScQFnDx5EoAWlU20rYqWJhcRERHJJSVOIi5mzI3mAl0gIn2enIiIiBScmjVrGh2Cy1HiJOICGjZsmPHnu+uZqFKAQ6KtlgJefUJERERITEw0OgSXo8RJxAUcP34848/uZhNPtDQX2HA9i9abERERKXBnzpwxOgSXo8RJxAXEx8fbvX6omQkP/esXERERyTH96iTiAry97TekreBjYmgjE+4F0Ovk7u6e/zcVERERO82aNTM6BJejxEnEBdStW9ehrKCWJk9LTc3/m4qIiIid/fv3Gx2Cy1HiJOICdu3a5VDWNMBEh+rgls+9TprhJCIiUvCSkpKMDsHlKHEScWFjbzKTls+ZTvpmuyIiIlJw/P39jQ7B5WgygogLCAwMdFreK9hE4wpwIi7/2goNVp+TiIhIQatSpYrRIbgck9WqtYNFJP9s27aNkJAQo8MQEREp0fT/beHTUD0REREREZFsKHESkXxVq1Yto0MQEREp8WrUqGF0CC5HiZOI5KtLly4ZHYKIiEiJl5KSYnQILkeJk4jkq7NnzxodgoiISIkXFRVldAguR4mTiIiIiIhINrSqnojkK6vVqr2cREREClhqairu7tpZqDDp3RaRfLV3714aNWrk9FxskpUjsfnTTpOKYFaCJiIiLurAgQM0aNDA6DBcihInEclXycnJmZ57ab2FdzbnTyf3K+3MvNBGiZOIiLimy5cvGx2Cy9EcJxHJV/7+/k7L45OtzNuRP0mTmwkOXdQoYxERcV2lS5c2OgSXo8RJRPJVYGCg0/JFe6xcyqeVUzVCT0REXJ32cSp8SpxEJF+Fh4c7lFmtVqZvshgQjYiISMm0d+9eo0NwOZrjJCIFbsVRKwcuGB2FiIiISN6px0lE8lVQUJBD2bubLbhpeJ2IiEi+qVatmtEhuBz1OIlIvkpKSrJ7HXHeys+HDQpGRESkhNJWrIVPPU4ikq9Onz5t93rWVvU2iYiI5LdTp04ZHYLLUeIkIgUmNsnK/B1W0vShmIiIiBRzSpxEJF81bdo048+f7LaSmGpgMCIiIiVUo0aNjA7B5ShxEpF8FRERAYDFauWdzVqCXEREpCAcPXrU6BBcjhaHEJF8dfnyZQB+PWzl8EWDgxERESmhLl26ZHQILkeJk0gxceGylWfWWfg2wkpCCrSqYuLtTmZaVs5+5YWNkVYW7rKwIcrKjrOQagHrOOf//Gdvs/D7MSsbIq0cj4P7GptYeKdbjuP08/MDYPp/S5BrfpOIiEj+8/X1NToEl6OheiLFgMVqpec3aSzZa2VUCzNvdjRzJsFKpy/TiDiffWay/JCF+TutmIDgMlnXfWOjLXFqXMGEex5+QlSrVo1956ysPKqkSUREpKDUrl3b6BBcTolLnMLCwjCZTIwdOzbbOpkdbm5XPl3ftWsXJpOJ7t27Z3q/gIAAAgICnJ5bsWIF3bt3p1KlSnh6euLl5UVQUBD9+/dn48aNDvWjo6N57LHHCA4OxtvbGy8vL6pXr86QIUM4fDjzzXCSk5OZMmUKLVq0wN/fH3d3d0qXLk2zZs0YP348sbGxjB8/HpPJxKhRo5ze49SpU5QtW5ZKlSpRp06dLN+jq4+pU6dmvA9Z1XvrrbcyjV+yFrbfyvpTsLC7mUltzYxsYWbNADfcTDDpr+znET0WYubi425sGupO15pZ91CtHeBG9Eg3fu7rhlfOO5oy7Nu3j/e0BLmIiEiB2r17t9EhuByXHqp322230aNHD4dyszl/8snhw4czb948SpcuTffu3WncuDEWi4Xdu3fz22+/8fXXX3PmzBkqVKgAwKZNm+jRowfR0dG0a9eOwYMH4+HhwT///MOXX37JDz/8wBdffEHPnj3t2jl27Bhdu3YlPDyc+vXrM3ToUKpWrcr58+dZv349b775JuvXr2f16tUsX76cOXPm0L9/fzp06GB3n3vvvZfY2Fi++uorkpOT7fYHOHPmDNOmTaNRo0YMGzbM7rquXbtm/Ll8+fKMHz/e6ftxxx13XNf76crCwq1U9oU+9a9kIwG+JvrfYOKzPVaSUq14uWeeqVQulfMspmaZ68t44lLNfLxLS5CLiIhIyeLSiVNISAjjxo0rkHtPmTKFuXPn0qxZM1auXOnQIxUXF8fIkSMzdn2OjY3lrrvuIiYmho8++sghOVmxYgW9e/dm0KBBbN++PaN71mKx0KNHD8LDw3nuuecyen+utmXLFj7++GPMZjNLlizh5ptv5r777mPfvn14eXkBMHv2bFavXk3//v3p27evwz127drFtGnTqFGjRpbvWalSpQrsPXVlW89YaVnZhNlkn9S0qmJi7g4r4eehqfNOz0K39lJtkrQEuYiISIGqUqWK0SG4nBI3VK8oSExMZNq0aXh5ebFs2TKnw/j8/Pz49NNPqVixIgBTp04lMjKSgQMHOiRNYOvVeeqpp4iLi2PChAkZ5QsWLGD37t106tTJadIE0LJlS2bNmgVA48aNmTBhAkeOHGHEiBEAnDhxgueee46qVasyf/78635+yX+Rl6BKKcfy9LJT8UWjeyfNYuXD3Z4UjWhERERKrqunlkjhcOkep4SEBE6cOOFQ7u3tnZHQ5MWPP/7IxYsX6dq1K0FBQTm6ZtmyZQA89dRTmdZ5+umnmTp1KitXrswo++qrrwB47LHHchzfhAkT+OGHH1i4cCH9+/fntddeIz4+ni+//DJjRbS8SktLc/qeAlStWjXfhkEWZxarleS0nNX1cgOTyURiKk7nG3n/9y+4qGwyu/ywlZMJ+kEuIiJS0E6cOHFdv69K7rn0b7Fz5syhRo0aDse1c4hya8uWLQA0a9Ysx9ccOXIEb29vWrRokWkdf39/qlWrxtmzZzl//jwABw4cAKB9+/a5ivHzzz/H19eXfv36sW7dOu67774sF8DIqVOnTjl9T2vUqGE3Zyo7MTExJCUlZbyOj48nLi4u43VycjLnzp2zuyYyMjLL11FRURlDI41s45fwBHzeTcvRsT/G1oaPOySlObZx+b+Eyd2akqvnuFZ+vVcxiVk2k++Ky/dcbagNtaE21IbaKKltuBKT9ep3swQICwujX79+jBkzhunTp2dZJzQ0lEGDBjmcr1q1Kh07dgRsc3uaNm1Kt27d+OWXX5zeL30o3tmzZwEYOXIkH3zwAZMnT+bFF1/MUdxubm6ULVs2219wGzVqxN69ewkPD6devXpUqVKFqKgokpKS8PT0zFFb6d555x3Gjh1LjRo1iIiIyJjv5ExO3weTycSMGTOcnu/Tp0+WbbiKqEtWfjmcs392/1fPRBkvE/Xmp1KvnInl99j35ny008JDv1rYcZ8bTQNytqjDqJVpvL/Nmuk+TlcrPSOVvvVzvo/T5VQrVWencj6pYJfUczfD0EYmPu6u3i0REXFNly9fxtvb2+gwXIpLD9WrW7eu08QpL0xXTdr39/cHbAs+5JSPjw+Jidl/XJ++S3R612z65mcxMTEEBgbmuD24shpeo0aN8i2h8fb2zrf3tKQKLGXi/ia5SyxCKpn444QVi9Vqt0DEhkgrvu5Qv1x+R5k33u4mBta8wNyIclpVT0REpACdPHmSOnXqGB2GS3HpoXo5UaqUbfZ9VknNtb09LVu2BGD79u05bqdWrVokJiaydevWTOvExsZy8uRJAgICKFfO9pty3bp1AVi3bl2O25Lip299E6cT4JvwK9lIdIKVpfuthNYx2S1FfvCClYMXjMta7q502rC2RUREXMXVQ+ykcChxykb16tXx8vLKdPPZY8eOERcXR9WqVTPKevXqRZkyZfjjjz84efJkjtpJn1f1zjvvZFrn7bffJi0tjc6dO2eUpS8d/uGHH+aoHSme+tY30boKDPvFwuT1Fj7YaqHTl2mkWeHldvb/jDt/lUbnr+xXnzh60cqrf1t49W8Lm07bkqr014t222+gu+ygJeNcigV2nL1y7Y6z2SdktSt40be+iSy2lRIREZHrpGF6hU+JUzY8PDxo27Ytx48fZ/HixQ7n0+cw9erVK6PMx8eHp59+msuXL9OzZ0+n85bi4+O5//77iY6OBmD8+PEEBgayZMkSFi1a5FB/1apVvPXWW/j5+TFlypSM8gcffJDGjRuzevXqTOdTbd26lccffzx3Dy5FipvZNr9pQAMTM7daeHqthYo+8Ht/N24on32GcviilRf/svDiXxY2/DenM/31RzvtE6evw6/UTU6DrWeu1N1yOvvEqWbNmjxxo5lUDdUTEREpMPXq1TM6BJdTYheHuPHGG2nVqpXD+YCAAJo2bUq/fv247bbb6NGjh9P7PPjggxnD4fbu3UubNm2Ii4uje/futGjRgoSEBNatW8fmzZtp0qQJmzZtcpgnNHz4cObNm4efnx/du3encePGWCwW9uzZw8qVK7l48SJnzpyhQoUKAPzzzz/06tWLmJgYbr31Vjp06IC7uzsbN27kt99+w8vLi88//5zQ0FC7do4dO0bXrl0JDw+nfv363HHHHVSpUoXz58/z999/8/fff9OhQwdWr15td11OFnzITd2AgAAsFgvjx493ev7mm2/OWHRDSq5t27bRvHlzblyUxvazYCmAnzBaHEJERFzdtm3bCAkJMToMl1JiF4fYvHkzmzdvdiivWrVqxqpvq1evdkgm0nXu3DkjcWrYsCGbNm3i2Wef5c8//+S3337DbDZTtWpVHn/8cV5//XWniyvMmTOHvn378tZbb7F69Wq++eYbzGYzlStX5o477uCZZ57JSJoAWrduzd69e3nxxRf59ddfmTZtGhaLhYCAAPr168err75KcHCwQztBQUHs2LGDadOmERYWxsKFC0lISMDHx4c6derw/PPP88wzz+TpfcytmJgYnn76aafnhg0bpsTJRZhMJsbeZGbockv2lUVERESKgRLX4yQixjpz5gyVKlUiKdVKtQ/TOHc5/9tQj5OIiLi6yMhIqlSpYnQYLkVznEQkX6Uvze/lbmJUCxNuWiRCREQk32lvzMKnxElE8tXVK0k+GmJGeZOIiEj+O3bsmNEhuBwlTiJSYAJLmRjQQEuTi4iISPGnxElE8lXDhg3tXj/RUkuTi4iI5Lf69esbHYLLUeIkIvnq+PHjdq9vrmLipspgVq+TiIhIvomKijI6BJejxElE8lV8fLxD2VM3mwtkPycRERFXFRsba3QILkeJk4jkK29vb4eye+qZqORrQDAiIiIllKenp9EhuBwlTiKSr+rWretQ5uFm4vEWZg3XExERySfXzimWgqfESUTy1a5du5yWP9LMlH+Jk4b9iYiIi9u+fbvRIbgcJU4iUigqlTIxuGH+ZE6pViijff9ERESkELkbHYCIlCyBgYGZnnulnZlutfKnu+juuhr3JyIirisgIMDoEFyOyWq1atCLiIiIiEgxcuHCBcqWLWt0GC5FQ/VERERERIqZI0eOGB2Cy1HiJCIiIiIikg0N1RMRERERKWbi4+MpXbq00WG4FPU4iYiIiIgUM9HR0UaH4HKUOImIiIiIFDMXLlwwOgSXo8RJRERERKSYcXfXrkKFTXOcRKTgWCzwxEew7XDurx3SER7tlv8xiYiIiOSBEicRKTjn4qDifXm7NsAfTswDT4/8jUlERKQE2L59O82bNzc6DJeioXoiUjSdjYWv/zE6ChERkSJJfR+FT4mTiBRNZhNM/8HoKERERIqkChUqGB2Cy1HiJCJFk8UKmw7CxgijIxERESly/P39jQ7B5ShxEpGiy90MM340OgoREZEi5/DhPCy8JNdFiZOIFF2pFvjqL4iMMToSERERcXFKnESkaLMAc34zOgoREZEiJTg42OgQXI4SJxEp2iwWeG85JKUYHYmIiEiRceHCBaNDcDlKnESk6IuJtw3ZExEREQBiYjSMvbApcRKRos9sgrd/AO1ZISIiAoDZrF/jC5vecREp+ixW2H4E/t5vdCQiIiJFQrNmzYwOweUocRKR4qEwlya/cAkemQ0B90OpQXDbRNhyMOfX7z0B3SdD6cFQ/n8wdAacvehY70Ak9H0Tyg0F34HQ/nlYvTPfHkNEREqunTv1/0VhU+IkIsVDqgXC/oYT0QXbjsUCPV+FJX/AqDvhzf/BmYvQaSJEnMr++hPR0OEFOBAFrw2BcXfBT5uh68uQfNUCF8ejoc14+HMfPN0bpt4L8ZfhjsmwbnfBPZ+IiJQIaWlpRofgcpQ4lUBhYWGYTCZMJhP33HOP0zpHjx7F3d0dk8lE06ZNHc6vWLGC7t27U6lSJTw9PfHy8iIoKIj+/fuzceNGh7bGjh2baTwmk4lbbrnl+h9MxGSCD69zafJOL8L972V+PuxvWL8fFo6CSQNg5J2wZjK4mWHSl9nf/7Wv4dJl+P1lGN0Tnu8LXz1lG2q4cPWVeq9/Y+vZWvuKrc4TvWD9VKhSDsYsuL5nFBGREq9cuXJGh+BylDiVYB4eHvz6668kJCQ4nJs5cyZWq9XpxMLhw4fTrVs31q9fT6dOnZgwYQLPPfccrVq14rfffqNNmzacO3euMB5BxF6aBd7/GS4nF1wbYX9D5bLQp/WVsoAy0L8tfL8x+2XRv/4Het0EQQFXyro0h/pV4av1V8r+2AstasMN1a6U+XrBXTfDlkM5690SERGXVaFCBaNDcDlKnEqw9u3bc+nSJebPn+9wbunSpbRq1QoPDw+78ilTpjB37lyaNm3KwYMH+eqrr5g0aRIvv/wyYWFhHD9+nCFDhmDV6mZilAuX4PM/Cu7+Ww9Dy2C49kOFVvUgIQnCs0hoTp6zDeu7qY7juVb1YOuhK6+TUsDH07Ger5ft6+ZDjudERET+c+DAAaNDcDlKnEqwkJAQatasyaeffmpX/ssvv3D8+HGGDRtmV56YmMi0adPw8vJi2bJlBAQEcC0/Pz8+/fRTKlasWKCxi2TKZILpywpuafLI87bhctdKLzuVxb4Zkeft6157fUz8lR6rG6rCjqMQl2hf78+9tq8n1asrIiJSlChxKuEGDBjAli1biIiIyCibPXs2/v7+3H///XZ1f/zxRy5evEiHDh0ICgrKVTsJCQmcOHHC6SGSr6xW2HUM/tiTfd2UVIiOtT9SUm3Jy7XlFovtmsRk8HJ3vJe355XzmUk/5+XheM7bw77OY91tvWcD3rb1RIWfgic/gk0Hs29HRERcXq1atYwOweUocSrhRo8ejZubGzNmzAAgLi6OFStW0LNnTzw97YcJbdmyBcjbvgBz5syhRo0aTg+RfGc2wdL12df7a59tSfGrj/X74Ys/HcuP/bdan48nJKU63it9XpWz4XXp0s85mwd1OcW+zp0t4b2HYN0eaDkObhgFP22BKYNt50t7Z/98IiLisuLj440OweUocSrhqlWrRps2bfj6668BmDt3LomJiTz++OMOdWNjYwEoU6ZMrtsJDQ1lyZIlTo+8iImJISkpKeN1fHw8cXFxGa+Tk5MdFqiIjIzM8nVUVJTd3Cy1Ubht5CuLFfq0zvY5Tgf6Yv1tIqyYBCsmEfv1WCxNg+COEFgxicQfniHh+2ds5wPLkpycTFol/ytD7q6+539lZz3sl3+1e6/+G6KXevxsxvmM9yryPJQvTbLJeuW9GtUDTn9M9A9PwaZpsG8mlPG1natf1XkbFJ/vudpQG2pDbaiNgmsjOjq6SDyHKzFZNcu/xAkLC6Nfv36MGTOG6dOns3DhQoYNG8Z3333HxIkTSUxMJDw8HABvb2/q1avHzp07GT9+PK+//jrjxo1j2rRpeWrLGZPJRKtWrdiwYUO+PaMUE+fioOJ9+XtPk8k2P2jPTNufc6vTi1CrEix0/PAAgH7TbCvenZpvv0DEI7Nh8TqI+dT5ULx0le6HTk3gq3H25TeMguoVYNXLWcfX/y34cRNEfgRlSuXokURExPVs27aNkJAQo8NwKepxcgFDhgyhXLlyvPTSS+zcuZPBgwc7rdeyZUsAtm/fXpjhieSO1QpP3ZW3pCkn+raB0xfgm3+ulEXH2oYGht5knzQdjLIdV7unjS3xOX7VRr2rdtjmMPVrm3Xb6/fZ2n2ws5ImERHJkpKmwudkBrSUNB4eHtx111188skneHp6MmrUKKf1evXqRZkyZfjjjz84efIk1apVc1pPxFD+vjC4Q8Hdv28baF0fhs2CPSegoh988IttD6mXB9rX7TzJ9vXInCtlz99jS7JumwhP9IT4yzDte2haE4bdfqXe0TPQ/23bvk2BZWH3cfjwV2hWE167t+CeT0RESoTdu3fTuHFjo8NwKepxchHPPvssDz/8MK+88kqmS4n7+Pjw9NNPc/nyZXr27Ol0k9v4+Hjuv/9+oqOjndxBpIC5meGxblf2OiqQNtxg+QswoB3M/Ame/hQq+sPvL9tvVpuZGhVh7StQJxCe+wze/A56tLTNo7q6t8rf1zYnatZyGDEXvtsIo3vC2lfBz6fAHk9EREqGlJRsNmSXfKceJxfRsGFD5s6dm229CRMmcOzYMebNm0dwcDDdu3encePGWCwW9uzZw8qVK7l48SJvv/12IUQtcg2rFUZ0v757rHkl+zrlSsP8kbYjK1f3NF2tcRD8OjH7Nr57LvtYREREnMjLYl5yfZQ4iYM5c+bQt29f3nrrLVavXs0333yD2WymcuXK3HHHHTzzzDNUqFDB6DDF1bib4a5WEOS4MbOIiIirqVy5stEhuBytqiciBSe/V9Vb9yrc2ij/7iciIlJMaVW9wqceJxEp+swmaFQD2jc0OhIRERFxUVocQkSKPksBL0EuIiJSzAQFBRkdgstR4iQiRV/ZUjCwvdFRiIiIFBmXL182OgSXo8RJRIo2NzOMvBO8PY2OREREpMg4c+aM0SG4HCVOIlL0PdbN6AhERETExWlxCBEputzNcE8bqKbl70VERK7WtGlTo0NwOepxEpGiK9UCT/QyOgoREZEiJzw83OgQXI56nESkaDKboFktaF3f6EhERESKnKSkJKNDcDnqcRKRoklLkIuIiGTKz8/P6BBcjhInESmaKvhBv7ZGRyEiIlIkVatWzegQXI6G6olIwSnrCw91gS2HcnedCfjfbeDlUSBhiYiIFHf79u0jJCTE6DBcihInESk4bm4wb4TRUYiIiIhcNw3VExEREREpZqpXr250CC5HiZOIiIiISDGTmppqdAguR4mTiIiIiEgxExUVZXQILkeJk4iIiIiISDZMVqvVanQQIiIiIiKSc6mpqbi7a523wqQeJxERERGRYubgwYNGh+BylKaKiKF+PGghKc2+rLQH3FHLhMlkMiYoERGRIi4xMdHoEFyOEicRMczmKCuh31qcnvv6LjN96itxEhERcaZUqVJGh+ByNFRPRAwzY4sF90xyo/NJhRuLiIhIcRIUFGR0CC5HiZOIGOL0JSuf77OSquVpREREcm3v3r1Gh+BylDiJiCHm7rBiUdIkIiIixYQSJxEpdMlpVt7bYlHiJCIikkdVq1Y1OgSXo8RJRApdWLiVs1oMSERERIoRJU4iUuimb7Jg1oJ5IiIieXbq1CmjQ3A5Wo5cRArVhkgrm08bHYWIiIhI7qjHSUQK1YzNmS9BLiIiIjnTsGFDo0NwOUqcRKTQRMZbWbpfS5CLiIhcr2PHjhkdgstR4iQihebD7RYsRgchIiJSAly6dMnoEFyO5jiJFGMXLlt5Zp2FbyOsJKRAqyom3u5kpmXlnI2F23vOypjVFv48acXTDXoGm5jeyUyAr+P1By9YefFPCyuPWolLgeqlof8NJqbc6pajtpJSrczaqr2bRERE8oOPj4/RIbgcJU4ixZTFaqXnN2lsPwtP32ymog98sM1Cpy/T2DzUjXrlsk6eTsRZ6fBFGmW84LVbzcQnw1ubLOw8m8bGe93wdLty/bYzVjp9mUa10vDUTWYq+MCxWCvH43Ie71f7rcRczuvTioiIyNXq1KljdAguR0P1SqiwsDBMJhMmk4l77rnHaZ2jR4/i7u6OyWSiadOmDudXrFhB9+7dqVSpEp6ennh5eREUFET//v3ZuHEjAB06dMBkMrF69epMY7FYLFSqVAlfX1/i4nLxm7ZkKWy/lfWnYGF3M5PamhnZwsyaAW64mWDSX9kPiHvtHwuXUuD3/m6Mbmnm+dZmvgo1s/0sLNx1pVvIYrUydHkaDcrDpnvdePYWMw81MzO5vRsL7sxZb5PVauVtLUEuIiKSb3bt2mV0CC5HiVMJ5+Hhwa+//kpCQoLDuZkzZ2K1WjGbHf8aDB8+nG7durF+/Xo6derEhAkTeO6552jVqhW//fYbbdq04dy5czz88MMAzJ49O9MYwsLCOHv2LF27dsXPzy//Hs7FhYVbqewLfepfyUYCfE30v8HE9wesJGWzAsPXEVZ6BZsI8r9yfZeaZuqXs/UOpfvtiJVd0TCpjRkfDxMJKVbScjne7u9TsP0sGqYnIiIixZYSpxKuffv2XLp0ifnz5zucW7p0Ka1atcLDw8OufMqUKcydO5emTZty8OBBvvrqKyZNmsTLL79MWFgYx48fZ8iQIVitVgYPHkyFChX4+eefSUxMdBrDvHnzABgxYkT+P6AL23rGSsvKJswm+26cVlVMJKRC+PnMrz0ZZ+VMAtwU6NgF1KqKia1nrmQ4K4/a/uzlDjctSqXUjDR8Z6QxcFkaMYk5y4Te1RLkIiIi+SowMNDoEFyOEqcSLiQkhJo1a/Lpp5/alf/yyy8cP36cYcOG2ZUnJiYybdo0vLy8WLZsGQEBAQ739PPz49NPP6VixYq4ublx9913Ex8fz8cff+xQNzo6mnXr1lGjRg26deuWvw/n4iIvQZVSjuXpZafiM09qIi/Z1732+pjLZPRYRfyXgPVfZqFBeRNhd5l59mYTX0dYCf02Das16+TpRJyVryO0BLmIiEh+cnfXUgWFTYmTCxgwYABbtmwhIiIio2z27Nn4+/tz//3329X98ccfuXjxIh06dCAoKChH9x89ejQmk4lPPvnE4dz7779PcnIyAwcOvK5nKOksViuXU3N2pCcqiang5WSKkfd/P0cTUzNvL/2c0+vd7OvEp9i+3lzZxGc93binvm1+0yvtzKw/BauOZZ0RfbjdgjqbRERE8teJEyeMDsHlKHFyAaNHj8bNzY0ZM2YAEBcXx4oVK+jZsyeenp52dbds2QJAs2bNcnz/Zs2a0bRpUzZv3szBgwftzi1ZsgR3d3dGjx59nU9Rsq07bsXn3bQcHftjbNf4uENSmuO9LqdeOZ+Z9HNOr0+zr5P+dVBD+/Rn8H+v15/MvJ3LqVbe32olTb1NIiIiUswpcXIB1apVo02bNnz99dcAzJ07l8TERB5//HGHurGxsQCUKVMmV23cd999WCwWZs6cmVG2ceNGwsPDadOmDdWrV8/V/WJiYkhKSsp4HR8fb7ciX3JyMufOnbO7JjIyMsvXUVFRdsPKilIbDSqYWNDdzILuZt5pHZvxZ2evzZfOYLVaqVLKNuTu2jYOn7P9uWppU6bPYb50xhb/JcfniLwE5TwteP7X81S1tO1reY8Uu+fwSYsH4HySNdP3yt18pQcsty5fTirR33O1oTbUhtpQG2rjetpo0KBBkXgOV2KyZjdBQYqlsLAw+vXrx5gxY5g+fToLFy5k2LBhfPfdd0ycOJHExETCw8MB8Pb2pl69euzcuZPx48fz+uuvM27cOKZNm5bj9uLj46lcuTJly5bl5ElbF8S9997L4sWLWbhwIffdd9//t3fvYVFVe9zAv3uG4a4gDpiogKhohxAkRawUs1LK1FNeSK28lNZblmmZnTimZlpHM83bkbS81dubetKOlxOlosdOeUkD9KCAgspF5aKCiFxmZr1/cEDHGRguw+wZ5vt5nnkeWXvvtX6b5bDnN2uvtZvlPO3Z6H9qcThbIPf/KPUWiJj6kxbfpAhcm6aEUx0rMvis1mBgJwlbh+vfr9f9Sw06tpKwf0xVeVySDq/+rMOXQxSYHHLnu5aMGwJd1mux8JGqpcxr88lRHWIP62B6gXR964co8FIIv9shIiIy5vz583yWk4XxU4mdGD9+PNq0aYN58+bh1KlTGDdunNH9wsPDAQBJSUkNqt/d3R1DhgxBbm4udu3ahcrKSuzevRteXl54/vnnmxw/GRoVJOFqKfB92p3vPgpKBbalCgzrIuklTedvCJy/of8dycggCbszBLKK75Tvv6hD2nVg9F1LnI/oKsFJCWw4rYPuru9Z1p+qSoWe8K97BtPLIRIc+JeGiIjIrPhsTMvjxxk7oVKpMHz4cCQmJkKlUmHatGlG93v66afh4eGBw4cP14wc1dfrr78OAIiLi8OWLVtQVFSEESNGQKms30NSqWFGBUmIbA9M+lGHD3/VYc0fOgz8TgutAOY/rP/WfmyrFo9t1Z/Q9H5fBVxVwKNbtVh5UoePj+owepcOIWpg0gN3kqH73CTERirwSw4Qvb2qnVd+0uKTowJje0jo077uxEntKuH5P0lcjpyIiMiMnJyc5A7B7jBxsiOzZ8/GlClTsGDBAqjVaqP7uLi4YNasWSgrK8PQoUMN7nMFqm7LmzhxIgoKCvTKH3vsMQQEBGD//v1Ys2YNJEnC9OnTm+VcCFAqJOwdqURMDwkr/tBh1iEd1C7AgTFKdPcynaV0ai3hUIwSXTwlvPdvHRYf0+GpzhJ+Hm14i99fIyWsHKTAxWKBtxJ0+FemQGykhE1P1u9PyPQHFVyOnIiIyIyCgoLkDsHucI5TC3XvHKe63D3Hqdorr7yCdevWoVWrVoiOjkZwcDB0Oh1SUlKwb98+FBUVIS8vD23bttWr6/3338fHH38MAAgJCUFycrL5T45s0oBvNfg1F/VeYY9znIiIiGqXmJiIsLAwucOwK/xUQkbFxcUhPj4ekZGRSEhIwIIFC/Dxxx/jyJEjGDx4MI4fP26QNAFVS5+rVCoA4Nwm0jOjt4LLkhMREZHN4ogTEVmEVifg/4UWOSX1258jTkRERLXLzc2Fr6+v3GHYFX4qISKLUCokvPWggn90iIiIzMDZ2VnuEOwOP8MQkcW8FCJBxUUWiYiImuzSpUtyh2B3mDgRkcW0cZYwIZhLkxMREZHtYeJERBb1ZjiXJiciImoqLkdueUyciMiigtUSHu0EKDnqRERE1GhXr16VOwS7w8SJiCyOS5MTERE1TVFRkdwh2B0mTkRkcU91luDXSu4oiIiIbFf1czPJcpg4EZHFVS9Nzrv1iIiIGic4OFjuEOwOEyciksXkEAlODnJHQUREZJsSExPlDsHuMHEiIll4OEmY/IDERSKIiIjIJjBxIiLZvNGr9kUiOrpbNhYiIiJbolar5Q7B7khCCK5tRUSyScwTqNDql7k6AA94cyiKiIioNjdu3ICnp6fcYdgVJk5ERERERDYmMTERYWFhcodhV3irHhERERERkQkccSIiIiIisjElJSVwd+eEYEviiBMRERERkY0pLCyUOwS7w8SJiIiIiMjGXL9+Xe4Q7A4TJyIiIiIiG6NUKuUOwe5wjhMREREREZEJHHEiIpvQ/1sNfs3h9zxEREQAkJycLHcIdoeJExFZvaxigV9ygL8c1premYiIyA7odDq5Q7A7TJyIyOr9dLFqpCnUW5I5EiIiIuvg5eUldwh2h4kTEVm9+MyqxMnLWeZAiIiIrISnp6fcIdgdJk5EZNW0OoH4C5zbREREdLeMjAy5Q7A7TJyIyKqduAoUV8gdBREREdk7B7kDICKqS/wFAaUESJzeREREVKNz585yh2B3OOJERFbtXxk6aAXAvImIiOiO4uJiuUOwO0yciMhqFZULHL0idxRERETWp7CwUO4Q7A4TJyKyWgmXBHRcF4KIiMiAxHvYLY6JExFZrR8vCDjwrxQREZGB0NBQuUOwO/xIQkRWSQiBPRkCmrsejF5Scku+gIiIiKzI6dOn5Q7B7nBVPSI7c6NM4N1/67AjXaC0EohoL2HpQAXC29VvyP9MocCMBB1+yRFwVAJDAyV8NlABb9c7x58tFPjqtA4/XRA4fwNwdwTCfSTMf1iB3vfVr53zN4Dsm/plJSUlAFrX70SJiIhaMI1GI3cIdocjTkR2RCcEhn6vxf89IzCtlwKLoxTIKxUY+J0W6ddNTybKvikw4P9pce6GwKL+CrzTW4E9GQJPbNOiQnvn+PWndFiXLND7vqqkbOaDCqReF4j8Rot9F3V1tHBH/AVhsJKes7NzQ06XiIioxfL09JQ7BLvDxMnKbd++HZIkQZIkjBw50ug+Fy9ehIODAyRJQkhIiMH2n3/+GdHR0fDx8YGjoyOcnJzg5+eHMWPG4NixYwZtzZw5s9Z4JElC3759jW6rbzskn+2pAr/mAhujFZj7kAKv91LgYIwSSgmY+x/TCc2iIzrcqgQOjFHizXAF3o9UYOswBZLygY2n7yROY3sokPWKEuuHKDE1VIFZEQocHa+ElzMw79f6JU4/ZuoMnt3k6uraoPMlIiJqqdRqtdwh2B3eqmcjVCoV4uPjUVpaavDhccWKFRBCQKEwzINfeeUVrFu3Du7u7oiOjkZwcDB0Oh3++9//4qeffsI//vEP5OXloW3btk2Kz1LtUNNsTxNo5wo8G3QnI/F2lTCmu4SvUwTKNQJODrXfSvePdIGnAyX4tb6zz+P+CgS10WFrqsDU/81TfdDI7XhtXST07yjhYJbpka0KrcD+SzBYUe/atWsAfE0eT0RE1NKdO3cOYWFhcodhV5g42YhHHnkECQkJWL9+Pd588029bdu2bUNERAT++OMPvfKFCxfiiy++QM+ePbFv3z54e3vrbb958yZef/11CNG09Z4t1Q413R95AuHtJCjuGcqJaC/hi2SBtOtAiLfxY3NuCuSVwugcpYj2EvZmmO7fK7cE1C6m4zySC9zmrdtERERkRXirno0ICwuDv78/Nm/erFf+448/IisrC5MmTdIrv337NpYsWQInJyfs2rXLIJkBgFatWmHz5s1NGuq1VDtkHpdvAe3dDMury3JLak9+Lt/S3/fe46+VAeWa2o8/nC3wWy4Q09304hDxF3QwNvDF+7mJiIiqBAQEyB2C3WHiZENiYmJw8uRJpKen15T9/e9/R+vWrTFx4kS9fXfv3o2ioiIMGDAAfn5+DWqntLQU2dnZRl/3ako71DQ6IVCmqd+rerTvtgZwUhrW5fy/see6Rnmqtxk9Xln38Xm3BMbt0aKzB/BuhOk/O3szBIzlYBWVFSaPJSIisge3bvERHZbGxMmGvPnmm1Aqlfj8888BVN0C9/PPP2Po0KFwdHTU2/fkyZMAgJ49eza4nbi4OHTq1Mno615Naacu165dQ3l5ec3PJSUluHnzztrUFRUVKCws1Dvm8uXLdf585coVvdsFbb2Nf2cJuCzX1ut1NOMGAMDFASjXGtaZm3e9Zntt56HUVSUt5VrD87hVodM7/u64b1UIPL1Di+IyHX74sxLujlKdvyshBM5egxECN4v11ye3pv5gG2yDbbANtsE2LNlGfn6+VZyHPZEEJ55Yte3bt2P06NGYMWMGPvvsMwwYMADp6em4fPkyli5dinfeeQe//vor+vXrB2dnZ3Tr1g2nTp3C66+/jjVr1uDDDz/EnDlzGtTWsGHDMHbsWKP7jBs3DhERETh69CgANKodMo8rtwR+zKzf2/eZbhI8nCR0W69BtzYS9o7UHzb68pQOL8frkDxBiRBv47fS5dwU6Binxd8GKAxGjV7Yq8XeDIHCafrTJiu0AsN26HAoSyB+lBJRner3DKcntmqw/xJw99mpFMBk/zysHcnFIYiIiBITE7k4hIVxcQgbM3nyZEyaNAk//PADNm/ejG7duqFfv34G+7VuXfWQ0OLi4ga30bVr1zoTJ3O1Q01zn5uEiQ/ULxGpFuYj4XC2gE4IvQUijl4WcHUAgtrUfmyHVhK8XYDfrxgma8cuC4T56MeiEwIv7tVh/0WBrcMU9U6aACC6swIHsnS492uddve1q3cdRERELVloaKjcIdgd3qpnY8aPH482bdpg3rx5OHXqlEEiUy08PBwAkJSU1KzxWKodMo9RQRKulgLfp93JSApKBbalCgzrIuktRX7+hsD5G/qZy8ggCbszBLKK75Tvv6hD2nVgdJB+YvTGfh2+SxVY87gCzwY17E/NkADJYClyACjIL2hQPURERC3VmTNn5A7B7nDEycaoVCoMHz4cmzZtgqOjI6ZNm2Z0v6effhoeHh44fPgwcnJy0KFDh2aJx1LtkHmMCpIQ2R6Y9KMOKYWA2gVYk6iDVgDzH9ZPbh7bqgUAXJh658/E+30V2JamxaNbtZgerkBJJbDkuA4hamDSXaNfy0/osCZRoJ8v4KoCvk7Rf+jtM10luDnWPgIVrAZ8XIC82/rlWq22sadORETUolRUcMEkS+OIkw2aPXs2pkyZggULFtS6xLeLiwtmzZqFsrIyDB061GCiH1A1IXDixIkoKGj8t/iWaofMQ6momt8U00PCij90mHVIB7ULcGCMEt29TN9K16m1hEMxSnTxlPDev3VYfEyHpzpL+Hm0Um+0KjGvarjot1zghb06g1f+7dpaqCJJEp4KlAyWJHdycmrwORMREbVE1dMlyHI44mSD7r//fnzxxRcm94uNjcWlS5ewbt06BAYGIjo6GsHBwdDpdEhJScG+fftQVFSEpUuXNikeS7VD5tHGWcL6IUqsH1L3fnePNN0tWC0hfpSRNcnvsvFJJTY+2dgIqwzpLGHjf/Xv13N3d29apURERC3EfffdJ3cIdoeJUwsXFxeHUaNG4dNPP0VCQgK+//57KBQKtGvXDoMHD8a7776Ltm3b2kw7ZD8e95MgQX9lvaoRTa6qR0RElJaWxlX1LIzLkROR1eq1SYPE/Kp/czlyIiKiO7gcueVxjhMRWa175zl5eHjIFwwREZEV8fPzkzsEu8PEiYis1pAABTR3jYlrtBr5giEiIrIi5eXlcodgd5g4EZHVivQFXO6aiXmr5JZ8wRAREVmRq1evyh2C3WHiRERWy1Ep4TE/QGF6pXQiIiKiZsXEiYisWnRnBYQAtALwaecjdzhERERWISQkRO4Q7A4TJyKyakMCJAgAOgFcK7wmdzhERERWIT09Xe4Q7A4TJyKyal08gY6tqv6t0XBxCCIiIgAoKyuTOwS7w8SJiKyaJEkYGlg1ycnRyVHmaIiIiKxDq1at5A7B7jBxIiKrFx1QlTgVitYyR0JERGQdOnToIHcIdoeJExFZvUf9qhKn7MISmSMhIiKyDmfPnpU7BLvjYHoXIiJ5eThJmNtPwkMOuQC85A6HiIiI7JAkhBByB0FEVB8FBQVQq9Vyh0FERCQ7XhMtj7fqEZHN0Gq1codARERkFXhNtDwmTkRkMy5fvix3CERERFaB10TLY+JERERERERkAuc4EZHNqKyshEqlkjsMIiIi2fGaaHkccSIim5GZmSl3CERERFaB10TLY+JERDajtLRU7hCIiIisAq+JlsfEiYhshpubm9whEBERWQVeEy2Pc5yIyGZUVFTA0dFR7jCIiIhkx2ui5XHEiYhsRkpKitwhEBERWQVeEy2PiRMREREREZEJTJyIyGb4+vrKHQIREZFV4DXR8pg4EZHNkCRJ7hCIiIisAq+JlsfEiYhsRk5OjtwhEBERWQVeEy2PiRMREREREZEJXI6ciGxGeXk5nJyc5A6DiIhIdrwmWh5HnIjIZmRlZckdAhERkVXgNdHymDgRkc0oKSmROwQiIiKrwGui5TFxIiKb4ezsLHcIREREVoHXRMvjHCcishkajQYODg5yh0FERCQ7XhMtjyNORGQzTp8+LXcIREREVoHXRMtjmkpWR6vVIi0tTe4wyAplZGRwBSEiIiJYzzUxKCgISqVS7jAsgokTWZ20tDT86U9/kjsMIiIiIjIhJSUF999/v9xhWATnOJHVsfcRp5KSEkRERODYsWNwd3eXOxyrceXKFQwaNAgHDhzAfffdJ3c49cb+bDnYly0L+7NlYX/Kx55GnJg4EVmZ4uJieHh4oKioCK1bt5Y7HKuRnZ2NTp06ISsrCx07dpQ7nHpjf7Yc7MuWhf3ZsrA/yRK4OAQREREREZEJTJyIiIiIiIhMYOJEZGWcnJwwd+5cq1gpx5q0bt0aUVFRNncLBvuz5WBftizsz5aF/UmWwDlOREREREREJnDEiYiIiIiIyAQmTkRERERERCYwcSIiIiIiIjKBiRMREREREZEJTJyILECn02HZsmXo0aMHnJ2d0alTJ7z99tu4deuWRY4n82lqX0iSZPTFJ93L4+OPP8bo0aMRGBgISZIQEBDQqHo2b96MXr16wcXFBe3atcPLL7+M/Px88wZLJpmjPwMCAmp9nxYUFJg/aDIqLS0NH3zwASIjI+Ht7Y1WrVohLCwMCxcubNC1b+/evXjooYfg5uYGLy8vjB49GpmZmc0YObVkXFWPyAKmT5+OFStW4JlnnsGTTz6JM2fOYOXKlejfvz/27dsHhaLu7zCaejyZT1P7QpIk9O/fH1OnTtUrV6lUiImJac7QyQhJkuDl5YXw8HCcOHECrVu3xoULFxpUx7JlyzBz5kxERUVh3LhxyM7OxmeffQZ/f38cO3YMbm5uzRM8GTBHfwYEBMDFxQWxsbEG20aPHs3lri3kvffew+rVqzF8+HBERkZCpVIhISEBW7duRc+ePXHkyBG4uLjUWcf333+PUaNGITQ0FFOmTEFRURGWL18OpVKJ33//Hb6+vhY6G2oxBBE1q9OnTwtJksSzzz6rV75ixQoBQHzzzTfNejyZjzn6AoCYMGFCM0VIDXX+/PmafwcHBwt/f/8GHZ+fny9cXV1Fnz59hEajqSn/5z//KQCIhQsXmitUqoem9qcQQvj7+4uoqCjzBUWNcvz4cXHjxg2D8tjYWAFArFy5ss7jKyoqhK+vr/Dz8xM3b96sKf/jjz+EQqEQU6ZMMXvM1PLxa2qiZvbtt99CCIG33npLr3zKlClwdXXF119/3azHk/mYsy8qKipQUlJi5gipoQIDA5t0/M6dO1FaWoo33ngDSqWypnzYsGEIDAzk+9PCmtqfd9NoNCguLjZbfdQwvXv3hoeHh0F59cj86dOn6zz+0KFDyM3Nxcsvv6x3K3RYWBgGDhyI7777DpWVleYNmlo8Jk5Ezez48eNQKBSIiIjQK3d2dkZYWBiOHz/erMeT+ZirL7Zv3w5XV1e0atUKPj4+eOONN1BUVNQcIVMzq+7zfv36GWyLjIzE2bNnmSDboKNHj8LV1RUeHh7w9PTEhAkTkJubK3dYBCA7OxsA0K5duzr3M/XeLC4uRlpamvkDpBaNiRNRM8vNzYVarTZ6X3yHDh1QUFCAioqKZjuezMccfREREYF58+Zh+/bt2LRpEwYNGoRVq1ahf//+/IBtg6o/THfo0MFgW4cOHSCE4AduGxMcHIzY2Fh8++23+Pbbb/Hcc8/hm2++QUREBPtSZlqtFgsWLICDgwPGjRtX576m3psAkJOTY/4gqUVzkDsAopautLS01snEzs7ONfs4Ojo2y/FkPuboi6NHj+r9/OKLL6Jnz56IjY3F559/bnRCOlmv0tJSADD6/+Lu/xNkO/bs2aP383PPPYcBAwZg/PjxmDt3LtatWydTZPTWW2/ht99+w6JFi9C9e/c69+V7k5oDR5yImpmrqyvKy8uNbisrK6vZp7mOJ/Nprr6YNWsWHB0dDT6wkfWr7m9j/y/4/mw5xo0bh4CAAL5HZTRnzhysWrUKU6dOxV/+8heT+/O9Sc2BiRNRM/P19UVBQYHRP945OTlQq9V1jlA09Xgyn+bqC5VKVVM32Zbq5YyN3fKTk5MDSZK45HELERAQwPeoTObNm4ePPvoIkyZNwtq1a+t1jKn3JmD8Nj6iujBxImpmffr0gU6nw7Fjx/TKy8rKkJiYiN69ezfr8WQ+zdUXZWVlyM7ONjnZmaxPnz59AAC//fabwbYjR46ge/fufLhxC3Hu3Dm+R2Uwb948zJ8/HxMmTMD69eshSVK9jjP13mzdujWCgoLMGiu1fEyciJpZTEwMJEnC8uXL9crXrVuH0tJSjB8/vqbs/PnzOHv2bKOPp+bV1L4sLCw0Wu+cOXOg0WgwbNgws8dM5nPp0iWcPXtWbwnjESNGwMXFBatWrYJWq60p37VrFzIyMvj+tGLG+vPatWtG9129ejWys7P5HrWwDz/8EPPnz8cLL7yAr776qtYHjF++fBlnz57Vm7MUFRWF9u3bY/369XoL7yQlJeHgwYMYPXo0VCpVs58DtSySEELIHQRRS/fGG29g1apVeOaZZ/DUU0/hzJkzWLFiBR5++GEcOHCg5mIQEBCAixcv4t63ZX2Pp+bXlL6cMWMGjhw5gkcffRR+fn4oKSnB3r17kZCQgL59+yIhIQEuLi5ynZpd2rJlCy5evAgAWLlyJSoqKvD2228DAPz9/fHCCy/U7Dtw4EAcOnQImZmZCAgIqClfunQp3nnnHQwcOBBjx45FTk4Oli5dik6dOuH48eMccbKgpvbn8uXL8eWXXyI6OhoBAQHQaDQ4ePAgdu7ciS5duuC3336Dt7e3xc/LHq1evRrTpk2Dn58fFixYYHCda9euHZ544gkAwMSJE7Fp0yYkJCRg4MCBNfts27YNMTExCA0NxZQpU1BcXIxly5ZBkiScOHGCt+pRw8n37F0i+6HRaMSnn34qgoKChKOjo/D19RUzZszQe5q5EFVPrDf2tqzv8dT8mtKXO3fuFIMHDxa+vr7CyclJuLq6itDQULFw4UJx+/ZtS54G/U9UVJQAYPQVFRVldN/MzEyDejZs2CB69uwpnJychLe3t5g0aZK4evWqZU6CajS1P3/55RcxbNgw0alTJ+Hs7CycnJxEjx49xOzZs8X169ctei72bsKECbX25b39Wb1vQkKCQT27du0Sffv2FS4uLsLT01OMHDlSnDt3znInQi0KR5yIiIiIiIhM4P09REREREREJjBxIiIiIiIiMoGJExERERERkQlMnIiIiIiIiExg4kRERERERGQCEyciIiIiIiITmDgRERERERGZwMSJiIiIiIjIBAe5AyAi+zFx4kRs2rQJABAcHIzTp0/rbdfpdFi0aBE2bNiAS5cuwc/PD+fPn8fixYvx1VdfISUlBQpFw7/vWbt2LRYtWoT09HQ4OTnpbVu+fDlmzJhR83N+fj7UanUjzq5xtFotfv/9d6Snp6O8vBxeXl7o06cPOnbsaPLYa9eu4cSJEygoKEBpaSkcHBzQpk0bhIaGwt/fv1HtNKROIiIie8IRJyIyi/j4eEiSVOtr8+bNAAC1Wo0tW7bgk08+MahjzZo1+OCDD/Dss8/iq6++QlxcHIqLi/G3v/0Ns2fPNkia5s+fD4VCgTNnzhjUNXnyZCiVSuzZswcTJ05ERUUF4uLiDPaLjo7Gli1b8Mwzz5jpN9EwBw8eRHJyMrp27YqHHnoICoUC//rXv3DlyhWTx5aUlKCyshJBQUF46KGHEB4eDqCqL+79ndS3nYbUSUREZE8kIYSQOwgisn2LFy/G7NmzsWLFCrRp08Zg+5AhQzBr1iwcPHgQFy5cMFrHgw8+CLVajfj4+Jqy5cuXY+7cubh69SqcnZ319s/Ly4O/vz9efPFFvaRo5cqVePPNN/HRRx8hNjYWADB79mx89913yMzMhCRJBm3PmzcP8+fPt+iIU15eHnbu3Im+ffsiNDQUAKDRaLB9+3a4uLhgxIgRDa5Tp9Nhx44d0Gg0iImJMUs7xuokIiKyN7xVj4jMIjk5GR4eHpg2bZrRxMSUsrIyJCUlYf78+XrlGzZswPDhww2SJgDw8fHB+PHjsWXLFixcuBBqtRqHDh3CzJkzMXLkyJqkCQDGjBmDxYsXIyEhAYMGDWr4CTaDjIwMSJKE+++/v6bMwcEB3bt3x/Hjx1FSUgJ3d/cG1alQKODm5ob8/HyztWOsTiIiInvDW/WIyCySkpLQq1evRiVNL730ElxcXKDVavHXv/4VkiShX79+yMzMRHJyMh5//PFaj50xYwZu376NtWvXIisrC2PGjEGPHj2wceNGvf0efPBBeHl54YcffmhwfMbodDqUlZXV61XbwH5hYSE8PDzg6OioV+7j41OzvT4qKytRVlaG4uJiJCcnIysrCx06dGhSO6bqJCIisjcccSKiJquoqEBqaioeeeQRFBQUGGz38PCASqWq9fjx48dDpVIhLi4On3/+Oby8vODv749ff/0VAGrm2RgTHByMwYMHY/Xq1di5cycqKyuxc+dOoyMo4eHh+M9//tOIMzR05coV7N69u177jh07Fq1atTIoLy0thaurq0F5ddmtW7fqVf+RI0dq5h9JkoSAgAA8/PDDTWrHVJ1ERET2hokTETVZSkoKKisrsXbtWqxdu9Zge2pqKoKCgmo9ftCgQdi/fz/c3Nwwbdq0mkUg5syZAwDo3Llzne3PnDkT0dHRyMvLw969e9GlSxej+wUGBmLLli31Pa06tW3bFk899VS99nVxcTFartFooFQqDcqry7Rabb3qDwkJQefOnVFaWoqMjAwIIfSObUw7puokIiKyN0yciKjJkpOTAQAbN240ejtXt27d6lVHcHCw3sp5hYWFcHBwMDnPp3pkJDAwEEOGDKl1vzZt2uD27du1jsA0hJOTU72WDK+Lg4OD0WSkusxYsmOMp6cnPD09AQBBQUHYs2cP4uPj8ec//xmSJDWqHVN1EhER2RsmTkTUZElJSXBwcMDYsWMN5tE0pI66kp7a7Nu3D++88w66deuG9PR0/PTTTxg8eLDRfavnGpnjg79Wq0V5eXm99nV2djb6/ClXV1ejt8mVlpYCANzc3BoVW2BgIA4fPoyioiJ4enqapZ176yQiIrI3TJyIqMmSk5PRuXPnRidNN27cQFZWFkJCQvTK27ZtC41Gg5s3bxqdI5SRkYGYmBj06tUL+/btQ1BQEJYtW1Zr4nT9+nW4urrWeutcQ1y9erXJc5zatm2L3NxcVFRU6P3u8vLyarY3hkajAVA198xc7dxbJxERkb1h4kRETZacnIzIyMgmHQ8APXv21Cvv0aMHACAzM9NgW0lJCUaMGAGVSoUdO3bAw8MDr732GubPn48zZ87oLb1dLTMz02h5Y5hjjlNgYCCSk5Nx5syZmucrabVapKamwsfHp+YWRY1Gg5KSEjg7O+sty3779m2DunU6HdLT06FUKmuep1XfdhpSJxERkb1h4kRETXLlyhXk5eXVJDmNkZSUBMAwcerXrx8A4Pfff9fbJoTACy+8gNTUVCQkJNTMNXrttdfwySefYPny5XoPxK128uRJjB8/vtFx3s0cc5x8fHwQGBiIY8eO4fbt2/Dw8EBaWhpu3ryJqKiomv3y8vKwe/duhIeHo3fv3jXlhw8fRkVFBdq3bw83NzeUlpbi3LlzuHHjBiIjI2tWMqxvOw2pk4iIyN4wcSKiJqlOevLz8/H1118bbA8NDTW4Be9eycnJ6NChA7y8vPTKAwMD8cADD2Dfvn2YPHlyTfm8efOwc+dOxMXF6S2R7e3tjeeffx5btmzBokWL9G5BO3HiBK5du4YRI0Y06jyby8CBA+Hu7o709HRUVFTAy8sL0dHRaN++vcljAwMDkZqaipSUFJSVlcHR0RFqtRoREREICAhoVDsNqZOIiMieMHEioiapvs1uw4YN2LBhg8H2zZs31ytxune0qdrkyZPxwQcf1NxCtmPHDixYsACvvvoqpk6darD/jBkz8OWXX2Lt2rWIjY2tKd+2bRv8/PwwaNCghpxes3NwcEBkZGSdtzr6+voaPdeuXbuia9euZmunoXUSERHZE0nU9kh7IiIzmzhxIg4cOICTJ0/CwcGhXquzFRUVITAwEIsXL8ZLL73UqHbLy8sREBCA9957D9OnT9fbVlZWhpKSEixevBhLlixBfn4+1Gp1o9ohIiKilstwfVwiomaUlZUFb29vPPLII/Xa38PDA++++y6WLFkCnU7XqDY3bNgAlUqFV1991WDb2rVr4e3tjSVLljSqbiIiIrIPHHEiIotJSUlBbm4uAMDd3b1JK/GZS1ZWFlJTU2t+joqK4gIIREREZICJExERERERkQm8VY+IiIiIiMgEJk5EREREREQmMHEiIiIiIiIygYkTERERERGRCUyciIiIiIiITGDiREREREREZAITJyIiIiIiIhOYOBEREREREZnAxImIiIiIiMiE/w8k+FI7SD/oLgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8FftTaTSfSbo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run.recorder.plot(skip_last=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "6Tlegj7QfcNX",
        "outputId": "8c57926a-4654-4294-a196-082eba634f35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CcROdBRjfhEG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O7776PHhOm9I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5f052d0-80ac-45e8-d23b-c75883f0ae12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 50\n",
            "train loss is: 5.432321071624756\n",
            "validation loss is: 1.3760168552398682\n",
            "epoch: 100\n",
            "train loss is: 1.6921555995941162\n",
            "validation loss is: 1.0988624095916748\n",
            "epoch: 150\n",
            "train loss is: 1.4089432954788208\n",
            "validation loss is: 1.0627561807632446\n",
            "epoch: 200\n",
            "train loss is: 1.3109451532363892\n",
            "validation loss is: 1.0503305196762085\n",
            "epoch: 250\n",
            "train loss is: 1.2616947889328003\n",
            "validation loss is: 1.0440627336502075\n",
            "epoch: 300\n",
            "train loss is: 1.2320780754089355\n",
            "validation loss is: 1.0403008460998535\n",
            "epoch: 350\n",
            "train loss is: 1.212273120880127\n",
            "validation loss is: 1.0377881526947021\n",
            "epoch: 400\n",
            "train loss is: 1.198103666305542\n",
            "validation loss is: 1.035987377166748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "teHiDO6kfqwQ",
        "outputId": "0ec2d2a8-eab5-44eb-a2d9-51f72625d84b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE        MCH  \\\n",
              "0    37.877669     11.763848     5.916638  14.411118   302.718365  25.730506   \n",
              "1    33.805319      9.181673     4.491710   9.302956   315.382014  28.281788   \n",
              "2    33.871286     12.368609     3.429194  -2.278183   228.413665  27.346201   \n",
              "3    39.712136     10.590954     5.082454   7.142779   -42.210355  29.161190   \n",
              "4    40.385541     11.964152     3.979029  11.933760   295.236999  27.105958   \n",
              "\n",
              "        MCHC        MCV        AGE       SEX  SOURCE  \n",
              "0  32.265610  77.484132  22.047350  0.594685       0  \n",
              "1  32.814583  67.588428  71.608113  0.321550       0  \n",
              "2  31.557218  85.975108  24.699305  0.317544       0  \n",
              "3  34.550140  74.670432  68.158186  0.698747       0  \n",
              "4  34.804375  91.331201  36.131801  0.563694       0  "
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              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37.877669</td>\n",
              "      <td>11.763848</td>\n",
              "      <td>5.916638</td>\n",
              "      <td>14.411118</td>\n",
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              "      <td>9.181673</td>\n",
              "      <td>4.491710</td>\n",
              "      <td>9.302956</td>\n",
              "      <td>315.382014</td>\n",
              "      <td>28.281788</td>\n",
              "      <td>32.814583</td>\n",
              "      <td>67.588428</td>\n",
              "      <td>71.608113</td>\n",
              "      <td>0.321550</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.871286</td>\n",
              "      <td>12.368609</td>\n",
              "      <td>3.429194</td>\n",
              "      <td>-2.278183</td>\n",
              "      <td>228.413665</td>\n",
              "      <td>27.346201</td>\n",
              "      <td>31.557218</td>\n",
              "      <td>85.975108</td>\n",
              "      <td>24.699305</td>\n",
              "      <td>0.317544</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.712136</td>\n",
              "      <td>10.590954</td>\n",
              "      <td>5.082454</td>\n",
              "      <td>7.142779</td>\n",
              "      <td>-42.210355</td>\n",
              "      <td>29.161190</td>\n",
              "      <td>34.550140</td>\n",
              "      <td>74.670432</td>\n",
              "      <td>68.158186</td>\n",
              "      <td>0.698747</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40.385541</td>\n",
              "      <td>11.964152</td>\n",
              "      <td>3.979029</td>\n",
              "      <td>11.933760</td>\n",
              "      <td>295.236999</td>\n",
              "      <td>27.105958</td>\n",
              "      <td>34.804375</td>\n",
              "      <td>91.331201</td>\n",
              "      <td>36.131801</td>\n",
              "      <td>0.563694</td>\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2fc530d-d009-4bbf-a343-5d9347ebf72d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fake_with_noise.to_csv('df_fake_with_noise_0.csv',index = False)"
      ],
      "metadata": {
        "id": "DQ9B8-nVglBE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df"
      ],
      "metadata": {
        "id": "70wcn9P1hTgc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fake_with_noise.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "8i8T_I_cgztu",
        "outputId": "527111e9-269e-4853-864d-fa171cee426b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1000.000000   1000.000000  1000.000000  1000.000000  1000.000000   \n",
              "mean     37.739141     12.627855     4.542726     8.368565   209.413072   \n",
              "std       5.803285      1.992395     0.781335     5.608040   125.745988   \n",
              "min      15.375707      6.311138     1.816757    -8.613687  -213.212753   \n",
              "25%      34.037503     11.280939     4.029746     4.634449   122.533910   \n",
              "50%      37.772484     12.654308     4.555357     8.549228   213.356437   \n",
              "75%      41.536483     13.951375     5.032594    12.234730   295.327438   \n",
              "max      57.457483     18.406232     7.395798    23.747377   650.846122   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  SOURCE  \n",
              "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000  1000.0  \n",
              "mean     27.827803    33.227669    83.232804    36.428730     0.546727     0.0  \n",
              "std       2.557686     1.280696     6.423955    15.962592     0.489737     0.0  \n",
              "min      18.843457    28.826031    64.326237   -13.679154    -1.108705     0.0  \n",
              "25%      26.141947    32.337348    78.912573    25.586264     0.213215     0.0  \n",
              "50%      27.867241    33.264005    83.169017    36.335538     0.558102     0.0  \n",
              "75%      29.464847    34.060367    87.612415    46.284851     0.880178     0.0  \n",
              "max      36.446349    36.848727   103.136218    84.524750     1.953732     0.0  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37.739141</td>\n",
              "      <td>12.627855</td>\n",
              "      <td>4.542726</td>\n",
              "      <td>8.368565</td>\n",
              "      <td>209.413072</td>\n",
              "      <td>27.827803</td>\n",
              "      <td>33.227669</td>\n",
              "      <td>83.232804</td>\n",
              "      <td>36.428730</td>\n",
              "      <td>0.546727</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.803285</td>\n",
              "      <td>1.992395</td>\n",
              "      <td>0.781335</td>\n",
              "      <td>5.608040</td>\n",
              "      <td>125.745988</td>\n",
              "      <td>2.557686</td>\n",
              "      <td>1.280696</td>\n",
              "      <td>6.423955</td>\n",
              "      <td>15.962592</td>\n",
              "      <td>0.489737</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.375707</td>\n",
              "      <td>6.311138</td>\n",
              "      <td>1.816757</td>\n",
              "      <td>-8.613687</td>\n",
              "      <td>-213.212753</td>\n",
              "      <td>18.843457</td>\n",
              "      <td>28.826031</td>\n",
              "      <td>64.326237</td>\n",
              "      <td>-13.679154</td>\n",
              "      <td>-1.108705</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34.037503</td>\n",
              "      <td>11.280939</td>\n",
              "      <td>4.029746</td>\n",
              "      <td>4.634449</td>\n",
              "      <td>122.533910</td>\n",
              "      <td>26.141947</td>\n",
              "      <td>32.337348</td>\n",
              "      <td>78.912573</td>\n",
              "      <td>25.586264</td>\n",
              "      <td>0.213215</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.772484</td>\n",
              "      <td>12.654308</td>\n",
              "      <td>4.555357</td>\n",
              "      <td>8.549228</td>\n",
              "      <td>213.356437</td>\n",
              "      <td>27.867241</td>\n",
              "      <td>33.264005</td>\n",
              "      <td>83.169017</td>\n",
              "      <td>36.335538</td>\n",
              "      <td>0.558102</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.536483</td>\n",
              "      <td>13.951375</td>\n",
              "      <td>5.032594</td>\n",
              "      <td>12.234730</td>\n",
              "      <td>295.327438</td>\n",
              "      <td>29.464847</td>\n",
              "      <td>34.060367</td>\n",
              "      <td>87.612415</td>\n",
              "      <td>46.284851</td>\n",
              "      <td>0.880178</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>57.457483</td>\n",
              "      <td>18.406232</td>\n",
              "      <td>7.395798</td>\n",
              "      <td>23.747377</td>\n",
              "      <td>650.846122</td>\n",
              "      <td>36.446349</td>\n",
              "      <td>36.848727</td>\n",
              "      <td>103.136218</td>\n",
              "      <td>84.524750</td>\n",
              "      <td>1.953732</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f4311c0-a970-45e6-93d4-edec5b0407eb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9f4311c0-a970-45e6-93d4-edec5b0407eb 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-9f4311c0-a970-45e6-93d4-edec5b0407eb');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8a15fab9-bb75-4126-8838-056782752f0d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a15fab9-bb75-4126-8838-056782752f0d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a15fab9-bb75-4126-8838-056782752f0d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['SOURCE']==0].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "0JYuxr_ziX1y",
        "outputId": "f5d7ff87-e842-4b0d-c92c-a4c9341ce211"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1126.000000   1126.000000  1126.000000  1126.000000  1126.000000   \n",
              "mean     37.541563     12.514121     4.548943     8.419716   210.713144   \n",
              "std       5.854355      2.067833     0.772000     5.499211   123.979792   \n",
              "min      16.700000      4.600000     2.150000     1.100000    10.000000   \n",
              "25%      33.300000     11.100000     4.030000     4.700000   111.000000   \n",
              "50%      38.000000     12.600000     4.570000     7.100000   205.500000   \n",
              "75%      41.800000     14.000000     5.030000    10.200000   288.000000   \n",
              "max      54.000000     17.900000     7.610000    41.100000   830.000000   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  SOURCE  \n",
              "count  1126.000000  1126.000000  1126.000000  1126.000000  1126.000000  1126.0  \n",
              "mean     27.648046    33.297957    82.942274    36.547957     0.519538     0.0  \n",
              "std       2.621360     1.257908     6.400749    15.894902     0.499840     0.0  \n",
              "min      14.900000    26.400000    54.000000     1.000000     0.000000     0.0  \n",
              "25%      26.500000    32.700000    80.225000    25.000000     0.000000     0.0  \n",
              "50%      28.200000    33.400000    83.950000    38.000000     1.000000     0.0  \n",
              "75%      29.400000    34.100000    87.100000    50.000000     1.000000     0.0  \n",
              "max      33.200000    37.100000    98.100000    60.000000     1.000000     0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-521a6fcd-9bef-4935-b8e8-87d6c543e66b\" 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>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37.541563</td>\n",
              "      <td>12.514121</td>\n",
              "      <td>4.548943</td>\n",
              "      <td>8.419716</td>\n",
              "      <td>210.713144</td>\n",
              "      <td>27.648046</td>\n",
              "      <td>33.297957</td>\n",
              "      <td>82.942274</td>\n",
              "      <td>36.547957</td>\n",
              "      <td>0.519538</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.854355</td>\n",
              "      <td>2.067833</td>\n",
              "      <td>0.772000</td>\n",
              "      <td>5.499211</td>\n",
              "      <td>123.979792</td>\n",
              "      <td>2.621360</td>\n",
              "      <td>1.257908</td>\n",
              "      <td>6.400749</td>\n",
              "      <td>15.894902</td>\n",
              "      <td>0.499840</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.700000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>2.150000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>26.400000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.300000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>4.030000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>26.500000</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>80.225000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>4.570000</td>\n",
              "      <td>7.100000</td>\n",
              "      <td>205.500000</td>\n",
              "      <td>28.200000</td>\n",
              "      <td>33.400000</td>\n",
              "      <td>83.950000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.800000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>5.030000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>29.400000</td>\n",
              "      <td>34.100000</td>\n",
              "      <td>87.100000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>7.610000</td>\n",
              "      <td>41.100000</td>\n",
              "      <td>830.000000</td>\n",
              "      <td>33.200000</td>\n",
              "      <td>37.100000</td>\n",
              "      <td>98.100000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-521a6fcd-9bef-4935-b8e8-87d6c543e66b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-521a6fcd-9bef-4935-b8e8-87d6c543e66b 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-521a6fcd-9bef-4935-b8e8-87d6c543e66b');\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",
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              "    display: none;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "        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-ea19e33f-d4ef-477a-aff3-61f84951d876 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "std_list_generated = list(df_fake_with_noise[df_fake_with_noise['SOURCE']==1][df_fake_with_noise.columns].std()/10)"
      ],
      "metadata": {
        "id": "L7q5I28Gg3Ra"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "std_list_generated"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npFey4SHhA6W",
        "outputId": "d1fe2e7d-47bf-4cd0-b2af-289406f033ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05238817503324439,\n",
              " 0.01897255878618901,\n",
              " 0.006773281169435608,\n",
              " 0.039552729227325285,\n",
              " 0.9090497565260494,\n",
              " 0.026514515060323318,\n",
              " 0.011197007095399997,\n",
              " 0.06721115774799825,\n",
              " 0.14939557002236464,\n",
              " 0.005061337785706571,\n",
              " 0.0]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainloader.dataset.x.shape, testloader.dataset.x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuSHBM4FOqZ_",
        "outputId": "c9d0bdd8-7e3b-4152-ed39-934b109d9bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([2100, 11]), torch.Size([900, 11]))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged = pd.concat([df,df_fake_with_noise_0, df_fake_with_noise_1])"
      ],
      "metadata": {
        "id": "EYvxlV6fkwXM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fake_with_noise_0 = pd.read_csv('df_fake_with_noise_0.csv')\n",
        "df_fake_with_noise_1 = pd.read_csv('df_fake_with_noise_1.csv')"
      ],
      "metadata": {
        "id": "BejsOMTdk8F5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_merged[df_merged['SOURCE']==0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhOqy_7TlJaC",
        "outputId": "d35d5d73-9a5f-4e00-fc2c-813a8938da7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2126"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged.sample(frac=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9T12P4qzlLm2",
        "outputId": "327f53f1-21f7-424b-a1b1-9a17ea35077d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "1192    37.800000     13.000000     4.880000  14.200000   325.000000   \n",
              "419     34.955219     11.307184     6.209365  -1.650396    91.028635   \n",
              "33      42.331349     10.434886     5.130039  14.369216   -19.045737   \n",
              "1730    38.200000     12.600000     4.450000  14.400000   221.000000   \n",
              "356     44.431569     14.983157     4.515557  -3.289724   109.470001   \n",
              "...           ...           ...          ...        ...          ...   \n",
              "400     43.700000     15.300000     5.430000  12.800000   307.000000   \n",
              "173     43.200000     15.600000     6.220000   9.400000   100.000000   \n",
              "2118    45.800000     16.000000     5.600000   6.900000   250.000000   \n",
              "118     39.370633      8.864371     5.159390  12.719737   276.829980   \n",
              "1140    33.900000     11.200000     4.130000   6.900000   259.000000   \n",
              "\n",
              "            MCH       MCHC        MCV        AGE       SEX  SOURCE  \n",
              "1192  26.600000  34.400000  77.500000  31.000000  0.000000       1  \n",
              "419   25.607950  33.792173  83.169365  33.717160 -0.705896       0  \n",
              "33    26.575475  32.202920  73.230625  28.278144  0.336007       0  \n",
              "1730  28.300000  33.000000  85.800000  40.000000  0.000000       1  \n",
              "356   26.451709  32.866812  84.476028  43.453665  0.117214       0  \n",
              "...         ...        ...        ...        ...       ...     ...  \n",
              "400   28.200000  35.000000  80.500000  17.000000  1.000000       1  \n",
              "173   25.100000  36.100000  69.500000   7.000000  1.000000       0  \n",
              "2118  28.600000  34.900000  81.800000  46.000000  1.000000       0  \n",
              "118   26.927799  34.683008  77.494330  28.962231  0.579185       1  \n",
              "1140  27.100000  33.000000  82.100000  30.000000  0.000000       1  \n",
              "\n",
              "[5000 rows x 11 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
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              "    <tr>\n",
              "      <th>1192</th>\n",
              "      <td>37.800000</td>\n",
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              "      <td>4.880000</td>\n",
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              "      <td>325.000000</td>\n",
              "      <td>26.600000</td>\n",
              "      <td>34.400000</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>11.307184</td>\n",
              "      <td>6.209365</td>\n",
              "      <td>-1.650396</td>\n",
              "      <td>91.028635</td>\n",
              "      <td>25.607950</td>\n",
              "      <td>33.792173</td>\n",
              "      <td>83.169365</td>\n",
              "      <td>33.717160</td>\n",
              "      <td>-0.705896</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>42.331349</td>\n",
              "      <td>10.434886</td>\n",
              "      <td>5.130039</td>\n",
              "      <td>14.369216</td>\n",
              "      <td>-19.045737</td>\n",
              "      <td>26.575475</td>\n",
              "      <td>32.202920</td>\n",
              "      <td>73.230625</td>\n",
              "      <td>28.278144</td>\n",
              "      <td>0.336007</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1730</th>\n",
              "      <td>38.200000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>4.450000</td>\n",
              "      <td>14.400000</td>\n",
              "      <td>221.000000</td>\n",
              "      <td>28.300000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>85.800000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>44.431569</td>\n",
              "      <td>14.983157</td>\n",
              "      <td>4.515557</td>\n",
              "      <td>-3.289724</td>\n",
              "      <td>109.470001</td>\n",
              "      <td>26.451709</td>\n",
              "      <td>32.866812</td>\n",
              "      <td>84.476028</td>\n",
              "      <td>43.453665</td>\n",
              "      <td>0.117214</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>400</th>\n",
              "      <td>43.700000</td>\n",
              "      <td>15.300000</td>\n",
              "      <td>5.430000</td>\n",
              "      <td>12.800000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>28.200000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>80.500000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>43.200000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>6.220000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>25.100000</td>\n",
              "      <td>36.100000</td>\n",
              "      <td>69.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2118</th>\n",
              "      <td>45.800000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>28.600000</td>\n",
              "      <td>34.900000</td>\n",
              "      <td>81.800000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>39.370633</td>\n",
              "      <td>8.864371</td>\n",
              "      <td>5.159390</td>\n",
              "      <td>12.719737</td>\n",
              "      <td>276.829980</td>\n",
              "      <td>26.927799</td>\n",
              "      <td>34.683008</td>\n",
              "      <td>77.494330</td>\n",
              "      <td>28.962231</td>\n",
              "      <td>0.579185</td>\n",
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              "    <tr>\n",
              "      <th>1140</th>\n",
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              "      <td>6.900000</td>\n",
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              "      <td>27.100000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>82.100000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-99279f5c-1328-45b9-8ee9-d08e44074aae 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-99279f5c-1328-45b9-8ee9-d08e44074aae');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8a30af26-9070-4bb3-b8c2-e0fec022cafa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a30af26-9070-4bb3-b8c2-e0fec022cafa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a30af26-9070-4bb3-b8c2-e0fec022cafa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(df_merged[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df_merged['SOURCE'], test_size = .25)"
      ],
      "metadata": {
        "id": "HflCGu8_mKIo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b938a7eb-5650-4e61-ca77-28471e4749f1",
        "id": "b3bseJRhmKIq"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.8047290845784166\n",
            "auprc 0.8240488197347438\n",
            "sens 0.7796464060687753\n",
            "spec 0.7095682804947979\n",
            "ppv 0.7913192820405823\n",
            "npv 0.700943350027165\n",
            "acc 0.7504\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CV_rfc.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fabh6pEymNAz",
        "outputId": "04cb5674-f413-4c98-ca6e-fd2c93999f22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 3,\n",
              " 'xgb__n_estimators': 50}"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z0D8vIiGnA8I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# best_model = CV_rfc.best_estimator_\n",
        "bst = XGBClassifier(n_estimators=50, max_depth=7, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "23990f0b-172b-4f32-9543-969fc89de5b3",
        "id": "3IokdXrSr8BV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "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>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y_pred = CV_rfc.predict_proba(X_test)[:, 1]\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f7372f3-f030-48c3-c841-364bdb9973c6",
        "id": "-40jDn0hr8BX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Scores:\n",
            "auroc:  0.7118925179882156\n",
            "auprc:  0.8206053221407273\n",
            "sens:  0.8500727802037845\n",
            "spec:  0.5737122557726465\n",
            "ppv:  0.7087378640776699\n",
            "npv:  0.7582159624413145\n",
            "acc:  0.7256\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kiQ44p0FsApM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}