Supervised_learning_lithofacies_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Supervised_learning_Capstone.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nPGM4mMk6ocj"
      },
      "source": [
        "**Introduction**\n",
        "\n",
        "In this project, I will use machine learning algorithm to predict facies type (classes of rocks) from well log data. The labeled well log data are from real wells of Council Grove gas reservoir in Southwest Kansas. The Panoma Council Grove Field is predominantly a carbonate gas reservoir encompassing 2700 square miles in Southwestern Kansas. There are totally seven features for each well log: gamma ray (GR), resistivity logging (ILD_log10), photoelectric effect (PE), neutron-density porosity difference, average neutron-density porosity (DeltaPHI and PHIND), nonmarine-marine indicator (NM_M), and relative position (RELPOS). Those seven features will be used as predictor variables.\n",
        "\n",
        "The target, facies, are obtained by examining the cores from the wells taken vertically at half-foot intervals. Below lists the nine facies:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YW5UUTsIy-mS"
      },
      "source": [
        "\n",
        "![facies1.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6RhHhMn6q0F"
      },
      "source": [
        "As claimed by the data introduction, the facies aren't discrete, and gradually blend into one another. Some have neighboring facies that are rather close. The adjacent facies of each face are also shown in above list. Mislabeling within these neighboring facies can be expected to occur. So the challenge is to predict the facies accurately.\n",
        "\n",
        "So the object of this project will be:\n",
        "\n",
        "Find the most optimal machine learning algorithm to predict the facies from well log.\n",
        "\n",
        "And the experiment design is:\n",
        "  1. load and explore the data\n",
        "  2. pick one well log as blind test well and the other wells for model training\n",
        "  3. for each algorithm, scan the parameters to get the most optimized parameters and train the model\n",
        "  4. evaluate the model performance\n",
        "  5. evaluate the prediction performance by predicting the facies of the blind test well\n",
        "  6. plot well predicted facies for comparison\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQKprZnLxTsc"
      },
      "source": [
        "**1. Data loading and exploration**\n",
        "\n",
        "The well log data is download from Github: https://github.com/seg/tutorials-2016/tree/master/1610_Facies_classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOxu_aomxlzb"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pylab as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjVfeBmOxw-V"
      },
      "source": [
        "url = 'https://drive.google.com/file/d/19UU1UL6J-VfnV29AQoVlpWaqoVOdywPO/view?usp=sharing'\n",
        "path = 'https://drive.google.com/uc?export=download&id='+url.split('/')[-2]\n",
        "data = pd.read_csv(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yH6mei5X1trb",
        "outputId": "a624f5c3-2fff-4fc2-9249-6b3f2d98f469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Facies</th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.0</td>\n",
              "      <td>77.45</td>\n",
              "      <td>0.664</td>\n",
              "      <td>9.9</td>\n",
              "      <td>11.915</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.5</td>\n",
              "      <td>78.26</td>\n",
              "      <td>0.661</td>\n",
              "      <td>14.2</td>\n",
              "      <td>12.565</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.0</td>\n",
              "      <td>79.05</td>\n",
              "      <td>0.658</td>\n",
              "      <td>14.8</td>\n",
              "      <td>13.050</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0.957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.5</td>\n",
              "      <td>86.10</td>\n",
              "      <td>0.655</td>\n",
              "      <td>13.9</td>\n",
              "      <td>13.115</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2795.0</td>\n",
              "      <td>74.58</td>\n",
              "      <td>0.647</td>\n",
              "      <td>13.5</td>\n",
              "      <td>13.300</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.915</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Facies Formation  Well Name   Depth  ...   PHIND   PE  NM_M  RELPOS\n",
              "0       3     A1 SH  SHRIMPLIN  2793.0  ...  11.915  4.6     1   1.000\n",
              "1       3     A1 SH  SHRIMPLIN  2793.5  ...  12.565  4.1     1   0.979\n",
              "2       3     A1 SH  SHRIMPLIN  2794.0  ...  13.050  3.6     1   0.957\n",
              "3       3     A1 SH  SHRIMPLIN  2794.5  ...  13.115  3.5     1   0.936\n",
              "4       3     A1 SH  SHRIMPLIN  2795.0  ...  13.300  3.4     1   0.915\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZdIPXwVA4Xe",
        "outputId": "fa65eaf3-06eb-4d1e-b5bf-7fc8c3da9ff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "### well name list, there are totla 10 wells\n",
        "data['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SHRIMPLIN', 'ALEXANDER D', 'SHANKLE', 'LUKE G U', 'KIMZEY A',\n",
              "       'CROSS H CATTLE', 'NOLAN', 'Recruit F9', 'NEWBY',\n",
              "       'CHURCHMAN BIBLE'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MYLFNoEzKNd",
        "outputId": "966eb69f-288f-4167-ba21-440998120fa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Facies</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4149.000000</td>\n",
              "      <td>4149.000000</td>\n",
              "      <td>4149.000000</td>\n",
              "      <td>4149.000000</td>\n",
              "      <td>4149.000000</td>\n",
              "      <td>4149.000000</td>\n",
              "      <td>3232.000000</td>\n",
              "      <td>4149.000000</td>\n",
              "      <td>4149.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.503254</td>\n",
              "      <td>2906.867438</td>\n",
              "      <td>64.933985</td>\n",
              "      <td>0.659566</td>\n",
              "      <td>4.402484</td>\n",
              "      <td>13.201066</td>\n",
              "      <td>3.725014</td>\n",
              "      <td>1.518438</td>\n",
              "      <td>0.521852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.474324</td>\n",
              "      <td>133.300164</td>\n",
              "      <td>30.302530</td>\n",
              "      <td>0.252703</td>\n",
              "      <td>5.274947</td>\n",
              "      <td>7.132846</td>\n",
              "      <td>0.896152</td>\n",
              "      <td>0.499720</td>\n",
              "      <td>0.286644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2573.500000</td>\n",
              "      <td>10.149000</td>\n",
              "      <td>-0.025949</td>\n",
              "      <td>-21.832000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2821.500000</td>\n",
              "      <td>44.730000</td>\n",
              "      <td>0.498000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.277000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>2932.500000</td>\n",
              "      <td>64.990000</td>\n",
              "      <td>0.639000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>3.551500</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.528000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>3007.000000</td>\n",
              "      <td>79.438000</td>\n",
              "      <td>0.822000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>16.050000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.769000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>3138.000000</td>\n",
              "      <td>361.150000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>19.312000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>8.094000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Facies        Depth  ...         NM_M       RELPOS\n",
              "count  4149.000000  4149.000000  ...  4149.000000  4149.000000\n",
              "mean      4.503254  2906.867438  ...     1.518438     0.521852\n",
              "std       2.474324   133.300164  ...     0.499720     0.286644\n",
              "min       1.000000  2573.500000  ...     1.000000     0.000000\n",
              "25%       2.000000  2821.500000  ...     1.000000     0.277000\n",
              "50%       4.000000  2932.500000  ...     2.000000     0.528000\n",
              "75%       6.000000  3007.000000  ...     2.000000     0.769000\n",
              "max       9.000000  3138.000000  ...     2.000000     1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ifv-yvzzBGzt"
      },
      "source": [
        "Looking at the count values, most values have 4149 valid values except for PE, which has 3232. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFch4g_E7QDu",
        "outputId": "15299053-56e2-4f6e-fcab-1c6c0072cb90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Facies</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Facies</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.306688</td>\n",
              "      <td>-0.386677</td>\n",
              "      <td>0.383166</td>\n",
              "      <td>-0.235005</td>\n",
              "      <td>-0.356453</td>\n",
              "      <td>0.704231</td>\n",
              "      <td>0.852721</td>\n",
              "      <td>0.076761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Depth</th>\n",
              "      <td>0.306688</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.088636</td>\n",
              "      <td>0.202272</td>\n",
              "      <td>0.067694</td>\n",
              "      <td>-0.095834</td>\n",
              "      <td>0.277577</td>\n",
              "      <td>0.282318</td>\n",
              "      <td>0.001537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GR</th>\n",
              "      <td>-0.386677</td>\n",
              "      <td>-0.088636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.205855</td>\n",
              "      <td>0.178863</td>\n",
              "      <td>0.270729</td>\n",
              "      <td>-0.289023</td>\n",
              "      <td>-0.316284</td>\n",
              "      <td>-0.182051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ILD_log10</th>\n",
              "      <td>0.383166</td>\n",
              "      <td>0.202272</td>\n",
              "      <td>-0.205855</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.104827</td>\n",
              "      <td>-0.544416</td>\n",
              "      <td>0.384589</td>\n",
              "      <td>0.493570</td>\n",
              "      <td>0.088227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DeltaPHI</th>\n",
              "      <td>-0.235005</td>\n",
              "      <td>0.067694</td>\n",
              "      <td>0.178863</td>\n",
              "      <td>-0.104827</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.189355</td>\n",
              "      <td>0.011489</td>\n",
              "      <td>-0.177500</td>\n",
              "      <td>0.022470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHIND</th>\n",
              "      <td>-0.356453</td>\n",
              "      <td>-0.095834</td>\n",
              "      <td>0.270729</td>\n",
              "      <td>-0.544416</td>\n",
              "      <td>-0.189355</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.573418</td>\n",
              "      <td>-0.481024</td>\n",
              "      <td>-0.033328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PE</th>\n",
              "      <td>0.704231</td>\n",
              "      <td>0.277577</td>\n",
              "      <td>-0.289023</td>\n",
              "      <td>0.384589</td>\n",
              "      <td>0.011489</td>\n",
              "      <td>-0.573418</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.657089</td>\n",
              "      <td>0.018905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NM_M</th>\n",
              "      <td>0.852721</td>\n",
              "      <td>0.282318</td>\n",
              "      <td>-0.316284</td>\n",
              "      <td>0.493570</td>\n",
              "      <td>-0.177500</td>\n",
              "      <td>-0.481024</td>\n",
              "      <td>0.657089</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RELPOS</th>\n",
              "      <td>0.076761</td>\n",
              "      <td>0.001537</td>\n",
              "      <td>-0.182051</td>\n",
              "      <td>0.088227</td>\n",
              "      <td>0.022470</td>\n",
              "      <td>-0.033328</td>\n",
              "      <td>0.018905</td>\n",
              "      <td>0.034015</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Facies     Depth        GR  ...        PE      NM_M    RELPOS\n",
              "Facies     1.000000  0.306688 -0.386677  ...  0.704231  0.852721  0.076761\n",
              "Depth      0.306688  1.000000 -0.088636  ...  0.277577  0.282318  0.001537\n",
              "GR        -0.386677 -0.088636  1.000000  ... -0.289023 -0.316284 -0.182051\n",
              "ILD_log10  0.383166  0.202272 -0.205855  ...  0.384589  0.493570  0.088227\n",
              "DeltaPHI  -0.235005  0.067694  0.178863  ...  0.011489 -0.177500  0.022470\n",
              "PHIND     -0.356453 -0.095834  0.270729  ... -0.573418 -0.481024 -0.033328\n",
              "PE         0.704231  0.277577 -0.289023  ...  1.000000  0.657089  0.018905\n",
              "NM_M       0.852721  0.282318 -0.316284  ...  0.657089  1.000000  0.034015\n",
              "RELPOS     0.076761  0.001537 -0.182051  ...  0.018905  0.034015  1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eEJqtEAN_fnX"
      },
      "source": [
        "All the features are weakly correlated to each other. While PE is highly correlated to Facies, so I want to drop the rows without PE values rather than to interpolate them. After dropping, there are still large enough variable numbers for model training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z27PpqJWAGsx",
        "outputId": "33ff2402-221b-4436-b0e4-c492679601f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "data_dropna = data.dropna(axis=0)\n",
        "data_dropna.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3232 entries, 0 to 4148\n",
            "Data columns (total 11 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Facies     3232 non-null   int64  \n",
            " 1   Formation  3232 non-null   object \n",
            " 2   Well Name  3232 non-null   object \n",
            " 3   Depth      3232 non-null   float64\n",
            " 4   GR         3232 non-null   float64\n",
            " 5   ILD_log10  3232 non-null   float64\n",
            " 6   DeltaPHI   3232 non-null   float64\n",
            " 7   PHIND      3232 non-null   float64\n",
            " 8   PE         3232 non-null   float64\n",
            " 9   NM_M       3232 non-null   int64  \n",
            " 10  RELPOS     3232 non-null   float64\n",
            "dtypes: float64(7), int64(2), object(2)\n",
            "memory usage: 303.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--18vCkPBidK",
        "outputId": "170c8b7b-b33a-44e0-d876-792ff21a5737",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "data_dropna['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SHRIMPLIN', 'SHANKLE', 'LUKE G U', 'CROSS H CATTLE', 'NOLAN',\n",
              "       'Recruit F9', 'NEWBY', 'CHURCHMAN BIBLE'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDhVhf-yCPHq"
      },
      "source": [
        "Two wells are exluded from data after dropping Nan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZxPxfdJ7V2Z",
        "outputId": "b7ed0083-f47c-410a-ac9d-6b57c4021765",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "col_list = ['Facies', 'GR',\t'ILD_log10',\t'DeltaPHI',\t'PHIND',\t'PE',\t'NM_M',\t'RELPOS']\n",
        "plt.figure(figsize=(15,10))\n",
        "i=0\n",
        "for col in col_list:\n",
        "  i+=1\n",
        "  plt.subplot(3,3,i)\n",
        "  plt.hist(data_dropna[col])\n",
        "  plt.title(col)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2zScCY_BJQ9",
        "outputId": "bd84be90-992e-4ef7-a8f4-88b1b9fe129d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "source": [
        "#plot the count of Facies\n",
        "data_dropna['Facies'].value_counts().sort_index().plot(kind='bar')\n",
        "print(data_dropna['Facies'].value_counts().sort_index())\n",
        "X_ind = np.arange(0,9,1)\n",
        "plt.xticks(X_ind,['SS', 'CSiS', 'FSiS', 'SiSH', 'MS', 'WS','D','PS','BS'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    259\n",
            "2    738\n",
            "3    615\n",
            "4    184\n",
            "5    217\n",
            "6    462\n",
            "7     98\n",
            "8    498\n",
            "9    161\n",
            "Name: Facies, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrB3AZ5yCX5R"
      },
      "source": [
        "**2. Prepare data for modeling and blind test well**\n",
        "\n",
        "The well 'SHANKLE' is taken as blind test well and other wells for modeling."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aq4Cb9VmDXI_"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "829Pd8l7CPSN",
        "outputId": "27e1c64a-48aa-48a9-89c6-255f08f68e48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "test_well = data_dropna[data['Well Name']=='SHANKLE']\n",
        "data_4mod = data_dropna[data['Well Name']!='SHANKLE']\n",
        "features = ['GR',\t'ILD_log10',\t'DeltaPHI',\t'PHIND',\t'PE',\t'NM_M',\t'RELPOS']\n",
        "y = data_4mod['Facies']\n",
        "X = data_4mod[features]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQ88cN6vEBX1"
      },
      "source": [
        "### Data for modelling\n",
        "\n",
        "scaler = StandardScaler().fit(X)\n",
        "X_stnd = scaler.transform(X)\n",
        "\n",
        "# standarization of data for SVM\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_stnd, y, test_size=0.05, random_state=42)\n",
        "X1_train, X1_test, y1_train, y1_test = train_test_split(X, y, test_size=0.05, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iy6bQzN5GsXE"
      },
      "source": [
        "### Blind test well\n",
        "\n",
        "y_test_well = test_well['Facies']\n",
        "X_test_well = test_well[features]\n",
        "X_test_well_stnd = scaler.transform(X_test_well)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0bzOlfUMkUh"
      },
      "source": [
        "**3. Parameter optimization and classifier training**\n",
        "\n",
        "Modeling algorithms:\n",
        "1. SVM\n",
        "2. Gradient boosting\n",
        "3. Random forest\n",
        "4. KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJ7MH7zFEjwz"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn import ensemble\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IspYBjgvsDWU"
      },
      "source": [
        "3.1. SVM: Parameter optimiztion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oToM-d0oYe7D",
        "outputId": "472e5169-d9e9-4b39-9cc3-0a922363d10f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "c_list = [0.01, 1, 5, 10, 20, 50, 100, 1000, 5000, 10000]\n",
        "gamma_list = [0.0001, 0.001, 0.01, 0.1, 1, 10]\n",
        "\n",
        "i = 0\n",
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "for gamma_value in gamma_list:\n",
        "\n",
        "  i = i + 1\n",
        "  scores = list()\n",
        "  score_stds = list()\n",
        "  score_tests = list()\n",
        "\n",
        "  for c_value in c_list:\n",
        "\n",
        "    clf_cv = SVC(C=c_value, gamma=gamma_value)\n",
        "    cv_score = cross_val_score(clf_cv, X_train, y_train)\n",
        "    scores.append(np.mean(cv_score))\n",
        "    score_stds.append(np.std(cv_score))\n",
        "    clf_cv.fit(X_train, y_train)\n",
        "    score_test = clf_cv.score(X_test, y_test)\n",
        "    score_tests.append(score_test)\n",
        "\n",
        "  plt.subplot(2,3,i)\n",
        "  plt.semilogx(c_list, scores, 'b')\n",
        "  plt.semilogx(c_list, score_tests, 'r')\n",
        "  plt.semilogx(c_list, np.array(scores)+np.array(score_stds), 'b--')\n",
        "  plt.semilogx(c_list, np.array(scores)+-np.array(score_stds), 'b--')\n",
        "  plt.title('Gamma = {}'.format(gamma_value))\n",
        "  plt.xlabel('C values')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.ylim(0,1.1)\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KPkBHNOgsTMa"
      },
      "source": [
        "Gamma = 1 and C = 10 give the best accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPWIAX2oFWVU"
      },
      "source": [
        "SVM classifier training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Egho4G50E1Gj",
        "outputId": "3c5b9021-22bd-4f06-996b-35560c1f78bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "clf = SVC(C=10, gamma=1)\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,\n",
              "    probability=False, random_state=None, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWy41YJJI6sE"
      },
      "source": [
        "def plot_confusion_matrix(cm, classes,normalize,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    import itertools\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\", verticalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1x6EKLNIOr8",
        "outputId": "f89fa536-9c2f-4289-c8aa-8985e7c70bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        }
      },
      "source": [
        "pred_test = clf.predict(X_test)\n",
        "target_list = ['SS', 'CSiS', 'FSiS', 'SiSH', 'MS', 'WS','D','PS','BS']\n",
        "print(classification_report(y_test, pred_test, target_names=target_list))\n",
        "cm_test_SVM = confusion_matrix(y_test, pred_test)\n",
        "plot_confusion_matrix(cm_test_SVM, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          SS       1.00      0.69      0.81        16\n",
            "        CSiS       0.83      0.86      0.85        29\n",
            "        FSiS       0.65      0.93      0.76        14\n",
            "        SiSH       0.44      0.57      0.50         7\n",
            "          MS       0.83      0.56      0.67        18\n",
            "          WS       0.65      0.74      0.69        23\n",
            "           D       0.50      0.33      0.40         3\n",
            "          PS       0.83      0.83      0.83        23\n",
            "          BS       1.00      1.00      1.00         7\n",
            "\n",
            "    accuracy                           0.76       140\n",
            "   macro avg       0.75      0.72      0.72       140\n",
            "weighted avg       0.79      0.76      0.76       140\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1b_fwPL4LQNP"
      },
      "source": [
        "SVM blind well prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95GT-hpIHeiU",
        "outputId": "3d35cf09-42c1-4c27-bf23-ec3676c2683b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "pred_test_well = clf.predict(X_test_well_stnd)\n",
        "print(classification_report(y_test_well, pred_test_well, target_names=target_list))\n",
        "cm_SVM = confusion_matrix(y_test_well, pred_test_well)\n",
        "plot_confusion_matrix(cm_SVM, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in true_divide\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          SS       0.30      0.08      0.12        89\n",
            "        CSiS       0.36      0.72      0.48        89\n",
            "        FSiS       0.62      0.54      0.58       117\n",
            "        SiSH       0.25      0.29      0.27         7\n",
            "          MS       0.17      0.11      0.13        19\n",
            "          WS       0.66      0.54      0.59        71\n",
            "           D       0.71      0.29      0.42        17\n",
            "          PS       0.41      0.60      0.49        40\n",
            "          BS       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.46       449\n",
            "   macro avg       0.39      0.35      0.34       449\n",
            "weighted avg       0.47      0.46      0.43       449\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MOTGLLwaDwZAN84zRYDAYnBAzlTYYDIacmBGjwWAwuJI941RZwwRGg8FQoljKBiYwGgwGwwVEEJ+yHRjL9hPQIvDnb78wqGcnBvZoz5cfvZujfv3qldxxXXcuaxrE0oWu6mnxsYcYc+dABve+lCHXdCYuJmeOXPvpA6Rs+4aUrVNJP7IuR31a7B+kbJ9ulW1fk7z5EwAyzh0jZecPpGyfRsr26dhP7spxLMAfv/7MgO7t6du1LZ9OfDtH/dpVfzC4zxW0bRjAkvmzs/Zvj97M0Ouv5oarL2Fgr8tYNHdmrv7Lg6axt5+Dt/svCiLiVik1VNVjBagLTAf2AOuAhUAzYAKwBfgHSxc23GG/EAhw/H4WiAY2AxuBzgW117JVO12zN1FX7UrQsAaNdNbyjfrn9qPatEWUzli8StfsTcwqc1Zs0mkL/tB+A2/R1yZOcanr0LmrTpwyS9fsTdTf/onR36PjdM3eRK3YbrRWbDdaK7T9l4p/dfVveYdWaPOASsUg9W9xW1Z99mIL66a+NVtoxXaj1b/FUPVvOdTyEzVcsVXWCq3u1YrtRuuWmDO6JeaMbjqQqPUahuuilZt1w94Ebdaylc5ZtiarfkvMGV381xadueQvvW7QbfrOx1Oz9s9fsV4XrNigW2LO6LK1OzW4dh39M/pQVv35NNWk5HQNj4jQrTv26KmzKdq6dRtdvylaz6dpVnlvwiS9976Rej5NdcrX3+qgwUP0fJrq+k3R2rp1G01MStZtO/dqeESEJiWnuxzraf/l4Ry82T+wtihxwSewkVa7ZYpbxZ22gD7ADqwk10/lYTME2OqIKdMK7KOnAq5Y4X4WsNwha9gReBq4BQgF2qhqa2AgkAigqv1UNTEPrelDubWTG9Gb1lG/YQT1GjTCz9+f3gMG8dvPC11sQus1pGnLVoiP6yXYu2s79nQ7nbtZUgCVq1TN0m/ORM8dRSo460o3JeNU3kqC9pO78Am0BLF8KgbgUyHAukZ+VRBbJdR+3sX+n41radAogvoNw/Hz96fvDYNYtsRVFzusfkOaR7bCJ9uUpFFEUxpGNAGgdt0QagbV4mTCcReb8qBp7O3n4O3+i0pxjRhFxBeYBPQFIoHbRCQym01TrNjTVVWjgIcL8uvJqXQPIE1VP87coaqbsGQKDqtqhmNfjKqeBBCR/SISTC5a06oal6OFPDgWf5g6TqqAdUJCOeYkSJ8fB/ftplr1GjzxwB0MHdCN91/9N3a73cXG0pW+oApoqQaezdWfpp5GU0/jUzWnylrG2SOgGYh/DZf9Rw8fdlE1rFM3jKOH3eu/M/9sWEtaWir1G7kKdZUHTWNvPwdv918k5CJKwVwK7FbVvaqaijVDvSGbzX3ApMw4o6pHC3LqycDYCmv6nJ3vgOtEZKOIvC0i7XOxyaE17cF+umBPt7NhzV+MfWY8U2b/SuzB/cz/4ZvC+zu5G9+Axjl0pTXtLGkHf8GvwdUeeZZy7Eg8T4+9j/Fvf4SPT7l9lGzwQgT3RouOvxfBmbrxjnJ/NndhuM4mYxz7nGkGNBORlSKySkT6FNTHEv8bo6oxQHOsoW0GsFREemazyVVrOjd/InJ/5kU7eSIBgFp1Qzhy+MK/cEcOx1GrjnsSprVDQmkW2Yp6DRphs9m46pr+bI/e7NqmX1U0LelCf9OSEL8qufqzJ+7CN7Cpyz61p5K6dz62kM74VMmpF107JIR45/7Hx1I7xH0J1qQzpxl118089H8v0LZjTtnL8qBp7O3n4O3+i4qPj49bBWvm2MmpTC5EczagKXAVcBvwiYgE5Nu/QjTiLtFYwS0HqpqiqotU9QngFeDGXGxy1ZrOxW5y5kULrBkEQGSbDhzcv4fYQ/tJS03l5/kz6d6rr1udjmzTgaTTp7Key635cwXhTbLpSleujaacIiPltENXehc+1Rvl8JWRfBJNT0EqXwh+mmEnbd9CfANb4BvQJNc+tGrbkYP79hBz0Or/ojkz6dG7f6622UlLTWXsvbdz/c23cc2AHJcVKB+axt5+Dt7uv6gU46p0LFDfabueY58zMcBcVU1T1X3ATqxAmTceXJEW4G/gfqd9bYArgVDHtg/wFfC4Y3s/EIw1omzqdNx4YKK7q9Jr9ibqe599pw0aNdawBo30X489p2v2Juo9Dz6hb02epmv2JuqXs5Zp7bqhWrFSZa0REKjhTVtkHTtxyixt0jxSGzeL1P6DbtM/tx91WZWu2G60+kX0V6lQQ8W/utrqdtaK7Uarb51O6hfeL8vGt84l6lu7g+txDXop+KhUDMoq/s2GuKxKb4k5ox9O+UEbhjfWeg3D9cH/e163xJzRB8Y+qR98Pl23xJzRb+cv1zp1Q7WSo/+Nm7XQLTFn9NX3P1GbzabNI1tnlR8Wr3RZlT6fpjpr7gJt0rSphkdE6Ivjxuv5NNWnn/23fv/jHD2fpnryzHkdOOhmjWjcWDt2ukS37tiTdeyL48ZreESENm3WTGfPW5hjxbgk/JeHc/BW/xRxVdo3KFxr3jnNrVJQW1ijwb1AOOAPbAKiclm1nuL4HYw19Q7Kz69HdaVFJBR4D2vkmOwIfD8Bw4EKDrPVwChVTRaR/UAnoCE5tabvV1XX5dVsRLZur1/NXV7cp5FFt5ue9ZhvgLXzc75rVtw0rlO1YCODIR+KqittC47QgAGvuGWbMOW2AtsSkX5YccYX+FxVXxaRcVhBda7jDZm3sQKkHXhZVafn20e3eldIHCvJQ3Kp+iAP+0aOn8fJXWvaYDB4OZmLL8WFqi7Eegfaed/zTr8VeNRR3MJ8EmgwGEqcsv5JoAmMBoOhZBGTRMJgMBhyYAKjwWAwZMMERoPBYHCiuBdfPIEJjAaDoeQp23GxfAXGiv6+NA+p5jH/Xe8Z6jHfANe9vcKj/gG2vtHP420YDPkilPnv98tVYDQYDN6BmUobDAZDdsp2XDSB0WAwlDxmxGgwGAxOlLqeixuYwGgwGEocExgNBoMhG+ZbaYPBYMiGGTEaDAaDM16QRKJsv2VZBH5e8hMd2rSkbVQz3nnz9Rz1KSkpDL/jVtpGNaNHt8s5cGA/AMuW/kz3LpdwWae2dO9yCb8tX5ar/4Stq/jrP7fy50uD2b/kqzz7cXTjryx9sAunD25z2Z98Ip7lj/XkwNJpuR53bv9aYr68n0Of30vi6u9y1J+J/pkDH99G7NdjiP16DGf+cRVFz0g5x8FP7uT4so9y9V8exN69/Ry83X9hEUDEvVJaeDwwiojdoQiYWRqJSB0RmS8im0Rkq4gsdNiGisgPjt+VReQbEflHRLaIyB8i4lb6abvdzmMPP8jMOQtYs2ELP3w/ne3btrrYfPXl5wQEBrIpeiejHxzLC88+BUBQUDAzfpjDqrWb+PiTL7h/xF05/GuGnR3fv0W7f73NZc9O48i6X0g6nFNXOj35LIeWf0f1RlE56nbOmkBQ5GW59l8z7CQs+4g6N75Evbs+4uyOFaQmHMxhV6VZd8LumEjYHROp1vpal7qTf06lYlirPK/Pww+NZs68RWzYvJXvp3/Ltq2u1+fLzz8jMCCQ6O27eXDsIzz7zJMAbNu6le9nTGf9pmjmzv+JsQ+OyiEv62n/5eEcvN1/0bgolcBSoSRGjOdVtZ1T2Q+MA35W1baqGgk8BVbGb1W92XHcWOCIqrZW1VbAPUCaOw2uXbOaiMaNCQ+3xMYHDb6FBfPnutgsmD+H24beCcCNN93M8uXLUFXatmtPSGgoAC0joziffJ6UlBSXY08f2Eql4HpUCg7Dx+ZHnY69OP7P7zn6sXfBJzTsdQc+Nn+X/cc2/UaloFCqhITn2v+U+J34BYTiFxCC+PpRpXl3zu1Z5c6pW8cf2YX9XCKVGuamTFs+xN69/Ry83X9R8fERt0ppUVpT6RAs5S4AVHUzgGM0ucXJJtbJZoequkaoPDicTWw8NCyMuGxi44fj4rJsbDYb1avX4ERCgovNnFkzadeuAxUqVHDZn5x4jIqBdbK2KwTUIiXxmIvN6UM7SD55lOBWXV32p6ecY/8vXxPed0Se/bcnJeBbLThr27dqMOlJCTnszu1aSczU0RyZ9wrpZ6z2VTM4seIzana/J0//5UHs3dvPwdv9Fwk3p9GlOZUuicWXSiKy0fF7n6oOBCZhaUWPAX4BvnDowzjzObBERG4GlmKpfO3K7twhwH0/QP36DYqt09u2RvP8c08ze/5PF32sZmSw68cJRN7xXI66fQs/o0GPW7FVqFyk/lWO6EzV5lchNj9Ob17EscXvEHLzq5zZtIBKjTphcwqsBkNZQqBUR4PuUBKB8byqtnPeoaqLRSQCS7WrL7BBRFpls9nosLkG6AWsEZHLVXVbNrvJwGSADh07KUBINrHxuNhYQrOJjYeEhhITc4iwevUssfHTp6gZZOlSx8bEcPstg5j86ZdERDTOcUIVA2qRfPJI1nZK4jEqBNTK2rannOPs4b2snzAagNTTJ9j03ydpO/J1Tu3fytGNv7J7ziTSzyeBCD42f+pfeXPW8b5Vg7CfuSCIaE86jq1qkEsffCtVz/pdrdU1nPj9cwCSD28nOTaaM5sXkJGajGak4eNXkZrd7s6yvxgx9nqZ18dDYu+F8V8ezsHb/ReVMr4oXXqr0qp6QlWnqeowYA3QPRebJFX9UVVHAV8DbuXM6tjpEvbu3s3+/ZbY+MzvZ9Cv/3UuNv36X8+331irybN//IErr+yBiJCYmMjgm67jpf+8wmVduubmnmoNWnLuWAznj8eRkZ7GkXW/ENz6iqx6W6WqdH9tEV1f+pGuL/1I9UZRtB35OtUbtKTTIx9l7a9/1RAaXXOXS1AEqFC3GWknY0k7FY/a0zi7YwWVIzq72KQnncj6fW7v3/jXtKY+tfs+QYN7v6T+PV9Qs/sIqrbs6RIUoXyIvXv7OXi7/6JSnIsvItJHRHaIyG4ReSqX+uEicsxpAfjegnyWynuMInI1sEpVz4lINaAxcDCbTVdgq6qeFBF/IBJY7o5/m83Gm+9OYOB1fbHb7Qy7625aRkYxftwLdOjQkX4DrufO4SO4f8SdtI1qRmBgTb6Yar02M/njSezds5vXXx3P66+OB2D2vJ+oVbt2ln8fXxvNBz/Khg8fAbUTctkAqoZEsGfBJ1Rv0IJarbsV7fr4+BJ09b+I//HfoBlUi+qNf3BDTv45Ff86TanS+DJOb5zLuT1/Iz6++FSsSvC1j7jt32az8e77E7mu/7XY7XbuGj6CyKgoxr34PB06dmLAddczfMQ9jBg+jKgWTQgMrMnUbywZ3sioKAYNHkL7NpHYbDbemzAJX1/fEvVfHs7B2/0XiWJ8figivliP5npjrVusEZG5qro1m+kMVR3jtl9LctVziEiSqlbNtu8J4G4gHWvU+oWqvi0ijYD5qtpKRO4EHsfxSAJYADyp+XS4Q8dO+tvK4l09c2bgp397zDfA3r0nPeofTKJaQ9Gp5CfrVLVTYY+vHNpcm973oVu2m8f1yrctEbkceFFVr3VsPw2gqq862QwHOl1MYPT4iDF7UHTsexN4M5f9+4FWjt9fAXm/OW0wGLyWYnzGGAYcctqOATrnYjdIRLoDO4FHVPVQLjZZlNsvXwwGQ9nlIp4xBovIWqdyfyGamwc0UtU2wM/AlIIOMN9KGwyGkuXinjEeL2DaHgvUd9quh9P7zwCq6vwS8KfAGwU1akaMBoOhRLG+lS62Vek1QFMRCXcs0t4KuHzmJiIhTpvXA66JC3LBjBgNBkOJU1zPGFU13fGhyGLAF/hcVaNFZBywVlXnAg+JyPVYi70ngOEF+TWB0WAwlDjF+eWLqi4EFmbb97zT76eBpy/GpwmMBoOhZPGCfIzlKjAK4Gfz3GPT+Q9c7jHfJcXnq/d71P+ISxt51H/gpQ961P/J1R941L/hQj7Gsky5CowGg8EbMCqBBoPBkIMyHhdNYDQYDCWMmLRjBoPB4ELme4xlGRMYDQZDiWMCo8FgMGSjjMdFExgNBkPJU9ZHjOX2W2lv1+z1tP/oVb/x0q1X88KQq1gyNaf29NLpn/Kfob15+c4+vP/QUBLis7TLmP3ha4y/41rG33Et636ZXyr9t58+QMrWr0mJnkp6/Loc9Wkxv5OyfbpVtlGwhyYAACAASURBVE4lefPkrLrU3XNJ3jyZ1D3zcvVdUufg7f4LjReIYaGqHivAs0A0sBnYiJUn7VMg0lE/AvjHUb8FuMGx/0vg5my+kgpqr0OHjno+TTUpOV3DIyJ06449eupsirZu3UbXb4rW82maVd6bMEnvvW+knk9TnfL1tzpo8BA9n6a6flO0tm7dRhOTknXbzr0aHhGhScnpLsd6s/9JK/fpByt2a3BoA33pu9/0/eU7NKxJC33u6yU6aeW+rDJ2wjR9d+lWnbRyn97y+H+0w9X9ddLKffqvNz/TFp266oTfduk7v0RrgxZt9K0lm7OO83T/K7YfoxXajVLxr67+kcO0Qtt/qVQMUv8Wt2vF9mNyLbZ63dS3Zsusbb8mN6hfRH/1qd4wh215uMee9o/1DXKh40K1+i306gl/ulWK2lZhi8dGjI7MugOADo48aL2AQ6p6r6puFZF6jsB5haP+MkeALDLertnraf/7t22iVr2GBIc1wObnT8ee17H5959dbJp1vBz/ipUACI9qT+KxeAAO79tFk3aX4muzUaFSZcKatGDrqt9KtP967ghSoQY+FWogPr74BjYl49Re8sJ+chc+gU2ztn2r1QcfvzztS+IcvN1/UfERcauUFp6cSodg5VJLAVDV46oaJyLLRaQTUBs4AyQ56pNUdV9xNOztmr2e9p94LJ7A2hcyMQXUrpsV+HLjz3kziLzsSgDqNWnJ1r9XkJp8nqTEE+xc/xcnjx4u0f5r6lnEv1rWtvhXRdPO5tp3TT2NppzGp1q9PM8vN7z9HpdpXWnK/lTak4svS4DnRWQnlnb0DFV1HlpsAo4A+0RkKfCjqjo/9HlTRHIKM2fDRVe6QfHpShssVi+excHt//DwJEsoqWXn7hzYvpm3Rg6iWkBNwqM64ONTjEJJxYz95C58AxojUm4fp3sd4gVJJDz2p0VVk4COWEHrGDDDIUqTWW/H0pW+GUuH4V0RedHJxROq2i6z5NPOZFXtpKqdagVb2s4Xo6kLeFSztyz6D6hV12WUl3g0noBadcnO9jV/8NOUSTzwxif4+VfI2t/nrjE8M2UhD77/NaDUrh9eov0X/ypo6pmsbU1NQvyq5Og/OAJjYLNc6/LD2++xp/0XFR9xr5QWeQZGEflARCbkVdxxrqp2VV2uqi8AY4BB2epVVVerpeh1a/b6wuLtmr2e9t+wRRuOxuzneNwh0tNSWbd0Hq2v6OVic2hnNN++8SwPvP4J1QKDs/Zn2O0knbLUDGN3byN293ZaXuoqF+vp/kvlOmjKKTJSTqMZdusZYg3X4AyQkXwStacgVXIG/YLw9ntc1nWlfXzErVJa5DeVXlsUxyLSHMhQ1V2OXe2AAzhUAEUkFKirquuz1RcZb9fs9bR/X5uNIY+8xKRH7yTDnsHlAwYTGtGM+Z+8Q4MWrWnTrTezJr1KyvmzfPrcaABq1gnlgTc+xZ6ezrujhgBQsXJV7nr+XXxtthLtv4gPtnrdSdszB1TxDYrEp1IQaYf/xqdybXwdQdJ+cie+AU1zTNtSds5EU06CPY3kLV/g1+BqfKs3LFf3uCzrSgsglO2ptNu60iJSWVXPue1YpCPwARCAlVJ8N9a0+gcsvehjwBdAKJDs2H5AVfeIyJdY+tI/OPnLoU+dnY4dO+nKv4sUz8s9Jh9j/ph8jAVTVF3pgIYttfuz7ikjzxt5aZHaKiwFLr44Xrv5DKgKNBCRtsBIVR2V33Gqug7okkvVVU6/r87j2OG57Ms3KBoMBi/BfaGrUsOdxZf3gGuBBABV3QR092SnDAZD+aZcvK6jqoeyRXi7Z7pjMBjKOwKl+vK2O7gTGA+JSBdARcQPGIsbuqwGg8GQF2U9Ua07U+kHgNFAGBCHtXo82pOdMhgM5Rd3p9HuDipFpI+I7BCR3SLyVD52g0REHV/e5UuBI0ZVPQ4Mda+LBoPBUDDFNZUWEV9gEtAbiAHWiMhcVd2aza4a1mz3b7f650bDESIyT0SOichREZkjIhEXfwoGg8FgIW4WN7gU2K2qe1U1FZgO3JCL3X+A17FeDSwQd54xTsOKyAMd27cC32KlEDN4Gb0b1/ao/0MJbr/qWig2L3rdo/5LggYjv/Oo/5WvDPCo/+LgIl7XCRYR55eTJ6vqZKftMOCQ03YM2WKTiHQA6qvqAhF5wp1G3QmMlVV1qtP21+46NxgMhuxYq9Jumx8vygveYmUPeQcYfjHH5RkYRaSm4+cixwPN6YACtwALC9dNg8HwP48U63fQsUB9p+16jn2ZVMP6DHm5Y5RaF5grIterap6fyeU3YlyHFQgzz2CkU50CT7vddYPBYHCiGL98WQM0FZFwrIB4K3B7ZqWqngKysqCIyHLg8fyCIuQTGFU1Z7oSg8FgKCIXOZXOF1VNF5ExwGLAF/hcVaNFZByWLMLcwvh168sXEWkFRAIVnTrk3lfgBoPBkI3i/FZaVReS7fGeqj6fh+1V7vh0J4nEC1iJHyIdjfcF/gBMYDQYDIWibH/34t6XLzcDPYF4Vb0baAvU8GivDAZDuUUEfH3ErVJauBMYz6tqBpAuItWBo7iuApVJvF2z19P+VyxbwrVd29Hrstb894O3ctSv+esPbuzdhZZh1flp3iyXuntuu4GOzUK5/468E657u3/w/D1IjdnIiR8eJuH7hzi3aXaO+uRdyzk+7V5OzP4/Tsz+P87vWJpVl7Tma078+BgnZj5C0qovMiWGXSiJa1RYxJF6rKBSWrgTGNeKSADwCdZK9Xrgr6I27Phm8WunbZvj65r5ju06IjJfRDaJyFYRcfsVIbvdzsMPjWbOvEVs2LyV76d/y7atLl8I8eXnnxEYEEj09t08OPYRnn3mSQC2bd3K9zOms35TNHPn/8TYB0dht9vLnf+Xnn6UT6bNYuGKdcyf9T27d7jmBQkJq89r7/+XAQOH5Li+94x6mDcnfprv9fdm/5ltePIeaEYGZ/76nBrXPE3Nm94hee9K0k/G5OhHhfAu1LzxDWre+AaVmvcEIO3IDtKO7CDwxjcJHPg2acf2kBbv2reSuEZFoaynHSswMKrqKFVNVNWPsb5HvMsxpS4qZ4FWIlLJsd0b1/ePxgE/q2pbVY0E8vw4PDvertnraf+bN6ylYXgEDRqG4+/vT/8bb+aXxfNdbOo1aEiLyNb4+OT8I9KlWw+qVMk7b7C3+wfP34P047vxrV4H3+p1EF8bFSO6kHpwTb59ykIE7GmQkQ4ZaZBhx6eS69OtkrhGhUVwT1O6TOpKi0iH7AWoCdgcv4uDhUB/x+/bsD41zCQE6/MeAFR1s7tOvV2z19P+jxyOo27oBZ3luiFhHDnsqg1dFLzdP3j+HmScPYFvlaCsbZ8qQdjPnczRj9T9f3Ni1hOcWvYO9qTjAPjVboZfSBQJ00eS8O1I/MPaYgtw1c0uiWtUaIo5u44nyG9V+u186pQ8ZAkukulY2tPzgTbA50Cm5NwkLMnVMVi61F+oalx2B0ZX2lBe8a/fkQoRXRFfP85v/5kzv39IQN/nsZ+Ox54YS9AtHwGQ+NN4/OK34V+3ZSn32H28VtpAVXvkU4ojKGaOAhthjRazv4e0GIjAerbZAtggIrVy8WF0pS/Sf52QUOLjLjzPij8cS52QEIoLb/cPnr8HPlVqYj+bkLWdcTYB38qBrjYVqyG+fgBUbNaT9ON7AUg5sBpb7aaIX0XEryL+9dqRfnSny7ElcY0KiwC+Im6V0sKdxRdPMxd4C9dpNACqekJVp6nqMKxPf9zSmvF2zV5P+2/driP79+7h0IH9pKamsmD2D/S8pj/Fhbf7B8/fA1twY+yn4rGfOYra00ne+yf+DVxzJThPrVMPrsU3wAquPlWCSTu8Fc2woxnppMVvwzfbVLokrlFR8BH3SqmhqqVSgCTH/+sBDzl+X4UlmwrWVL2y43c1LDmFS/Lz2aFDRz2fpno+TXXW3AXapGlTDY+I0BfHjdfzaapPP/tv/f7HOXo+TfXkmfM6cNDNGtG4sXbsdIlu3bEn69gXx43X8IgIbdqsmc6etzBrv3PxVv8748/qzvizOvnrmdoooonWbxiujzz1gu6MP6ujH3lKP5ryne6MP6s/LFqhdUJCtVKlyhoQWFObNGuZdWzHzl00sGawVqhYUeuEhOpn387OqvN2/yVxD2qNmKG1RszQ6r2fUt/qIepTrY5W7nCL1hoxQyu3G6TVez2htUbM0EptblDfgHrqG9hA/epGauBN72itETM0ePi3WrF5T/WtEaq+AWFaKap/ls9aI2Z4/BphfWpX6L/7tRtH6SNztrlVitpWYYvbutLFTW460SJyFdYH3gMcqc3uxtKk9sF6xpjfc0+jK+0Gns6X6O3UD6rs8Ta8PR9js7pViqT1XLdpKx36zky3bN+5vkWZ1ZUWLGmDCFUdJyINgLqqurqAQ/Mle1B07FsOLHf8fhN4syhtGAyGskkZ18Jy6xnjh8DlWAskAGewVowNBoOhUHjz6zqZdFbVDiKyAUBVT4qIv4f7ZTAYyikC2Mr46zruBMY0hxKXAjhemcnwaK8MBkO5pozHRbcC4wRgFlBbRF7GyrbznEd7ZTAYyi1Syp/7uYM7utLfiMg6rNRjAtyoqtsKOMxgMBjypIzHRbdWpRsA54B5zvtU9aAnO2YwGMovZX1V2p2p9AIuiGJVBMKBHUCUB/v1P0lScrrH26hW0S01i0KTkJTqUf/Xv7PCo/6jX+/nUf8AHz54hUf9l8S7mEVBoFST0LqDO1Pp1s7bjsw6ozzWI4PBUL4p7c/93OCihw+qul5EOnuiMwaD4X8DKeOqL+48Y3zUadMH6ADkSP9lMBgM7lCc8qkAItIHeB9LPvVTVX0tW/0DwGjADiQB96vq1hyOnHDny5dqTqUC1jPHGy669waDweCguLLrON6xnoSlXhoJ3CYikdnMpqlqa1VtB7wBvFOQ33xHjI5Gq6nq4wV30WAwGNyjGBPVXgrsVtW9Dr/TsQZuWSNCVT3tZF8Fx8cq+ZFnYBQRm6qmi0jXQnfZYDAYsmHJp7ptHiwizimzJqvqZKftMOCQ03YMkGMNRERGA48C/rihPpDfiHE11vPEjSIyF/geS8AKAFX9sSDnBoPBkBsX8eXL8eJIO6aqk4BJInI71pd7d+Vn707crggkYEXZAcB1jv+Xabxd93npz4u5rH0Ul7Rtwftvv5Gr/3vvup1L2rbg2h5dOHhgv0t9zKGDNKwbwKT3c3+csuyXxVzRqRWXt2/JB+/mzO6WkpLCyLuHcnn7lvTreQWHnPxv3fIPA3p358rL2tGjSweSk5NzHP/Hrz8zoHt7+nZty6cTc6bRXLvqDwb3uYK2DQNYMv+CpnJczEEG97mCQdd04YarL2HG1M9y7T/AuX1rOfTF/Rz87F4SV+fMcXgm+mf2f3QbMVPHEDN1DKf/cb3WGSnnODD5To4v/ShX/56+xxtW/spDN3ZjzPVdmfX5xBz186b+l4dvuorHhvTipZFDOBbnKq96LukMI6/tyKevPVsq/S8smYsvxZTBOxZXnft6uKqNZmc6cGNBTvMLjLUdK9JbgH8c/492/H9LQY7zQkTeFZGHnbYXi8inTttvi8ijIjJBRLaIyD8iskZEwt1tozzoPj/12ENM/3EeK9dsZtYP09mx3dX/N199TkBAAGs2beeB0WMZ9/wzLvX/fvoJevbuk+f1eebxsXzzw1x++3sTs3+YwY7trl95fjv1C2oEBPDXhm3cP+ohxr9o/eVLT09nzP3Def2dify2aiMz5/+Mn59fDv/jn3uMj6b+yNxf17Bwzg/s2bndxSYkrD7j3/mYfje6ahrXql2Xb+YsZeaSP/l23q98NukdjsbnVLfTDDvHl31E3YEvUX/4RyRtX0FqQs6Psao26069YROpN2wi1Vtf61J34s+pVAxrlec18vQ9/uy1Z3l24te8O/NXVv40m0N7XHVbwlu04vVvFvH2d79wWc/+TH1/vEv99A/fpGWHy0ql/0WlGNOOrQGaiki4I+vXrVhyKU5tSVOnzf7AroKc5hcYfYGqjlLN6XdmKSwrgS4AIuIDBOP6FU0XrNXvUKCN4wXzgUCiuw14u+7z+rWraRTRmEbhlv8bB93CovnzXGwWLZjHLbcPA+C6Gwfx+/JlmZIRLJw3h4YNG9GiZfbFOYsN69bQKKIxDRtZ/m8YNITFC139/7RwHkNus/wPuOEmfv/tV1SV35b9TMtWrYlq3QaAmjWD8PX1dTn2n41radAogvoNw/Hz96fvDYNYtsRV0zisfkOaR7bCJ9uwwM/fH/8KFQBITU0hIyP3RE4p8TvxCwjFLyAE8fWjSovunN2zKlfbXI8/sgv7uUQqN2qfa72n7/HuLRuoW78Rdeo1xM/Pn67X3sDa5a4js1aXdKVCJUt2vVmbjpw4cuEfiD1bN3Mq4RhtL89dBsnT/S8ago+bpSBUNR0YAyzGkj/5TlWjRWSciGSK9IwRkWgR2Yj1nDHfaTTkHxgPq+o4VX0plzKuwB7nzZ9YiW/BCohbgDMiEigiFYCWWO8bHVbVDABVjVHVnKK7eeDtus+HD8cRFnZB3Cg0LIzDh11t4uPiCKvn6v9EQgJJSUl88O6bPP70v/O8PvGH4wgLu9CHkNAw4rP7PxxHqKMPNpuN6tWrc+JEAnt270IQbr2pP727d2bS+2/l8H/08GHqhlxQxatTN4yjF6FpfDguhoG9LqPXJS25Z9Qj1K6bU90uPSkBW7XgrG1b1WDsZxJy2J3dvZKYr0ZzZN4rpJ85BoBqBgm/fUZQ93vy7IOn7/GJo/EE1QnN2q5ZJ4SEY/F59mfp7G9p37UHABkZGXz1zjjufDTve+zp/hcFoXgT1arqQlVtpqqNVfVlx77nVXWu4/dYVY1S1XZqqZxGF+Qzv8UXj7yarqpxIpLuSE7RBfgLa2XpcuAU1rR9GvCHiHQDlgJfq+qGXDtpdKVdePOVcYwcM5aqVYsyqM8buz2d1atWsujXP6lUqTJDbuhDm3Yd6HZlsSjqAhASWo9Zv6ziaPxhHrrnNnr3v5HgWrUv2k/liM5UbX4VYvPj9OZFHP3pHUIHv8rpjQuoHN7JJbCWZVYsmMnerZt46VNLJ2Xxd1PocMXVLoHVqxCwlfFvAvMLjD092O6fWEGxC9bLlmGO36eAlaoaIyLNsRZ8rgaWishgVV2a3ZFj6X4yWGJYcHGawPXq1fOo7nNh/IeEhBIbe+FBe1xsLCEhrjZ1Q0OJjTlEaNgF/zWDgli3djXz5vzIuH8/zalTifj4+FChYgXuHTn6wrEhocTGXujD4bhYlxFepk1cbMwF/6dPU7NmECGh9bisSzeCgqygcnXvPvyzaYNLYKwdEuIyAj0SH0vtQmga164bQpMWLVn/959cM8D1ebmtahDpZ45nbacnHce3WpCLjW+l6lm/q7W6hoQVnwOQcng752OjOb1pARmpyWhGGuJfkaBud2fZe/oe16xdl4QjFz4gO3HkMEG16ua4BptXreDHzybw0qcz8fO3HjHs3LyObRv+ZvF3U0g+f5b0tDQqVqrCHWMvPGf2dP+LQuaIsSyT51RaVU94sN3M54ytsabSq7BGjF2wgiaqmqKqi1T1CeAV3FhJysTbdZ/bd7yEfXt2c2C/5X/2zBn06e/6IkCffgOYMW0qAPNmz+SKK3sgIsxfspz10btZH72bkaMe4uHHnnIJigDtOnRi357dHHT4nzPzO67t6+r/2r4D+O5by//8OT9yRferEBGu6tmbbVu3cO7cOdLT01m1cgXNmrd0ObZV244c3LeHmIP7SUtNZdGcmfTo7Z6mcXxcLMnnzwNwKvEkG1b/RaPGTXPYVajbjLTEWNJOxaP2NM5uX0GVCNfX19KTLvwRPrfnb/xrWtPD2v2eoOF9X9Lg3i8IunIE1Vr2dAmK4Pl73CSqHYcP7uNI7EHS0lJZuXgOna66xsVm3/YtTH75KZ589wtq1Lwwuh37ykQ+XrSGDxf+zbBH/k33ATe7BMWS6H9R8XEkqy2olBaezUGVN38CjwN7VdUOnBCRAKxnjvc5MvjEO6bdPkAbYLO7zm02G+++P5Hr+l+L3W7nruEjiIyKYtyLz9OhYycGXHc9w0fcw4jhw4hq0YTAwJpM/WY6AJFRUQwaPIT2bSKx2Wy8N2FSjsWFkvD/6lvvM+TG/mRk2Llt2HBatIzitfEv0q59R/r0v46hd45g1H3DuaRtCwIDA5n8xTduX3ybzcYrb77HbYMGYLfbufWO4TRvGckbL79E2/YduLbfddw27G4eHHk3l7dvSUBgTT7+3AqSAQGBjBw9lr5Xd0FE6Nm7D72u7ZfD/zP/eYuRQ2/EnpHBwFuG0aR5Sya+OZ6otu3pcU1//tm4jofvvZ3TpxJZ/vMiJr3zMnOWrWHv7h28Oe4ZRARVZfjIh2jWMmeGO/HxJbjHv4if+W9UM6jWqjf+wQ05sXIqFeo2pUrjyzi9YS5n9/6NiC8+FatSq88jF3WNPHmPfW027nlyPC+Pup2MjAx63HAL9Rs3Z/qHb9I4si2XXHUNU9/9D8nnzvL2/40EILhuGE+9/2WZ6H9RKesjxlLRlXZ8angSmKCqzzn2fQlcrqrNHR+Fv4y1Og3Wy+ajVDXnC3NOeLuudEnkY0y3e1aux+RjLJj5Wzybg2VAK88+e6zkJ0XSeg5v2UZf+Gp+wYbA3Zc2LJu60p7AMUqsnm3fcKffPwE/lXC3DAZDSSAX9eVLqVBaU2mDwfA/ivXliwmMBoPB4ELZDosmMBoMhlKgjA8YTWA0GAwljRRnPkaPYAKjwWAoUQT30nqVJiYwGgyGEscsvhjc5psNhwo2KiIjL3c7e1uhCKji71H/K5/v5VH///1rn0f9A+w7ke/ruEXG0+8xFhkpVmkDj2ACo8FgKFHMVNpgMBhywYwYDQaDIRtlOyyawGgwGEoYAXzNiNFgMBhcKeNx0QRGg8FQ0ghSxifTJjAaDIYSp6yPGMv6qnmh8XZd6W1//8bLQ3sy/rYe/PJ1Tt3jX2d8yqvDruH14X2Z9PBQTsRfkBKY+9FrvHZXH167qw/rl+ae987brw94Xhvb0/dg//rfmTKqH18+cC1rZn6So37zT9P5+qEb+ObhgXz39B0kHNoNwOkjsUwc0p5vHh7INw8PZOlHL+bqv0zrSheTSqCnKJOBUUTsIrLRIXm4SUQec2Tydgtv15XOsNv54d0XGPnmFzz11WLWL51H/H5XKdx6TaN47JM5PPnlItpe1Ze5H1l/8KP/WkbMrmie+Gw+j3z8I7/O+ITks2fK1fXJbMOT2tievgcZdjvL/zueG5//L8M+mMfO3xdmBb5MmncfwB0T5jD0vVl0GjiC3z9/I6suoG59hr43i6HvzaLnv17M9fqUWV1pNxUC3R1VikgfEdkhIrtF5Klc6h8Vka0isllElopIw4J8lsnACJx3SB1GAb2BvsAL7h7s7brSB7ZtIjisIcGhDbD5+dO+5wD++eNnF5umHS7Hv6KlOdwosj2nHNKbR/bvpnHbS/G12ahQqTKhES3Y9rdr1mtvvz7geW1sT9+DI7v+oUZIA2rUrY+vnz/NrujL3r+XudhUqHxB6TEt+fxFveNStnWli0/zxaEGMAkrRkQCt4lIdkH1DUAnVW0D/AC8QQGU1cCYhaoexZJHHSNuvhXq7brSp47HE1j7gqpeQK0QTh07kuf5rlrwHS07XwlAaOOWbP97BanJ50lKPMHuDatIPOqq6ezt1wc8r43t6XuQdOII1YIvqAJWDapL0omjOfxuWjiNL0deyx9T3ubKey8IXp06Esu0R27ih2fvJDY6p5xHWdeV9hH3ihtcCuxW1b2qmgpMB25wNlDVX1X1nGNzFVCPAvCKxRdV3ev4l6E24PKn839dV3rtktkc2vEPD074FoAWl3bj4PbNvDfqZqoG1KRRVHvEp8z/+1eiFLc2tifvQdt+t9O23+1s/20+a77/L9eMfZXKNWsx4pOlVKoewJHd0cx/9UHu+GCuywizrHMRq9LBIuIc+Sc7JJMzCQOckwzEAK5yka7cAywqqFGv/xujqpNVtZOqdqoVXAu4OE1dwKO60oXxXyO4LiedRhiJxw5To1adHOe+Y+0fLPlqEve+Ohmbf4Ws/dfcOZr/+3wBo96ZiqLUru+aOMLbrw9cnDZ2Vhu5aGNXrlw5SxvbGU/fg6o163DmeHzWdlJCPFVr1s7hP5Pm3fqx529LVt3m50+l6gEA1GkSRY269UmM2+9iXxL3oChcxDPG45l/vx1lcgGu82lT7gA6ATlX6rLhFYFRRCIAO5BzrpEL3q4r3aBFG47H7Cch7hDpaalsWDqfVl1ds8rE7Izmu7ee475XJ1Mt8ILmcIbdztlTJwGI27ONuD07aH5Jt3J1fcDz2tievgd1mrYi8fABTh2JwZ6Wys4/FhFxaQ8Xm5NOwW7f2t8ICLHWDM6dOkGGYzHkVPwhEg8foEYd19lhWdeVFjf/c4NYoL7Tdj3HPtf2RHoBzwLXq2pKQU7L/FRaRGoBHwMT1U2tV2/Xlfa12Rj08It8/PhdZGRk0LnfYELCm7Hws3dp0Lw1ra7oxdyPXiXl/Fm+eGEMAIG1Q7nvtU+wp6czYcwtAFSsUpU7nnsHX5utRPtfEprGntbG9vQ98PG1cdV9zzL7pftQewaRvQYS1KApf037gDpNooi49Go2L5zGwU1/4eNro2LVGlwz9hUAYqPXsurbD/DxtSE+Plz9wAtUrBZQ4vegsGQ+Yywm1gBNRSQcKyDeCtzu0p5Ie+C/QB/HmkXBfSwNXemCEBE78A/gB6QDU4F3VDVfUWRv15UuiVyAns7H6GkSz3pWt3rG5uJbZMgLT+djfK1/y4KNikBRdaVbtG6vn/64rGBDoFuzmgW2JSL9gPcAX+BzVX1ZRMYBa1V1roj8ArQGMp+NHFTV6/NwB5TRidRvBQAAF01JREFUEaOqFt8/TwaDocxRnK9uq+pCYGG2fc87/b7o7MZlMjAaDIbyi9GVNhgMhlwo22HRBEaDwVAalPHIaAKjwWAoccxU2mAwGLJRtsOiCYwGg6E0KOOR0QRGg8FQoggX9a10qWACYxnC21++BkhJK8a8fbkwbWOMR/3f0aF+wUZFpNm/ZnjU/0vXNPOo/yJzEbkWSwsTGA0GQ4lTxuOiCYwGg6GkEdxMrVpqmMBoMBhKnDIeF01gNBgMJYtgptIGg8GQkzIeGU1gNBgMJU5Zf13HKzJ4FwZv1032dv+/LPmJTm0jad+qOe++9Xqu/u8edhvtWzWnZ/fLOeDQfF63ZjVXdO7IFZ070rVzB+bNmZ2rf4Dtf//Ga8N68crtPVj6zcc56v+cM4037+7L2/cM4IMxQ1zkT5d+8xGv3N6D14b1YvvqFTmOBVj282K6dIiic9uWTHgnp7BcSkoK9w2/nc5tW9KnR1cOOs7h4IH9NKxdnau7duLqrp144uHRufpPi9vMmblPcGbOYyRHz8vVJvXA35yZ9yRn5j/FuT8+vLB/7++cmfs4Z+Y+Ture33M9tiTuQWEpTvlUj6CqpV6wZAs2AluA74HKjv3PAtHAZkd95/z8dOjQUc+nqSYlp2t4RIRu3bFHT51N0dat2+j6TdF6Pk2zynsTJum9943U82mqU77+VgcNHqLn01TXb4rW1q3baGJSsm7buVfDIyI0KTnd5VjjP2//iefSNeFMijYKj9CN0Tv1aOI5jWrdRlet26yJ59KzylvvfqB333O/Jp5L18+mfKMDBw3WxHPpGnf8tB4/nayJ59J1+55DGlyrVtZ24rl0fXv5Hn17+R59c+lODQptoM9M+1Vf/3mbhjRuoU98+VNW/dvL9+jLCzZm/b775f9v78zjq6quPf793YSGIcxECaBMKvMYEItWrVgFBEWJfVJb5Wm1th+rHT8+tc+xg4qIpdSCKLXiXFGLyAd4ilQGB0BFCCoyBGUwJSCSCAhJ1vvjnJvcXG9yTwZubpL9/XzOJ+fss/faa59z7sre5+y91izrNfw7NnXZFvvtY4sss2dvu3fJRrvl6WXWvtOJNuW1TTZ12RbLO3DE8g4csV1fHLKu3XrY2+s+ss/yC61v/wH2xjvvl57PO3DE7pk63a646hrLO3DEZs6Zaxdekm15B47Y6vWbrFefvuXyRm6tL59rrSb9w0Lpx1nLC6daq8v+bqE2J1j6uHus9eVzS7f08VMs1Lartcqeaa0vn2stJ87w/mb/zZSeYS2z/2atsmea0jNK87S+fO4xvwd4DmCr/XvvO3CIrd9REGiraV3V3ZKlxxiOI90fOAJcJ+nbwDhgqB8P9lzKRwOrkPoeN7m+y1+75h169OxJt+6e/InZ32fhgvnl8ix8ZT6TfujFfL7o4on8e9lSzIzmzZuT6ocBOPz14QqndXz60Trad+5K+3Dc53PGkbPy1XJ5mrZoWbp/5PDBUlk5K19lyDnjSP1WGu0zT6B95658+tG6cmXfXbOa7j3K2jBh4vdZ9EpU3OpXyuJWj58wkRXLXg//o49L8d4thFoeT6jlcSgllSZdT+PoZ2vL5Tmy+XXSTjkXpbUAINS0NQBFu9fTpGN/QmnpKK0FTTr25+juD8qVTcQ9qAm1GPPlmJAshjGS5cBJQCZehLCvAcws38x2BRFQ3+Mm13f5u3eVj/ncqXMXdu/aVWEeL+Zza/bt3QvAmnfe5rSsgZw+fDAP/Pmh0h9pJF/uyaNNRlnc59YZHWPGfV7x4lz++IPvsmDmvUy44baYZdvEKPv57p106lIWYKpTp858Ht2G3Tvp3KUsbnXLVq3Zt89rw6fbcxl1xnAmjBnFW6tWfEMvO/QFat6u9DjUvB126ItyeUoKPqf4wG4KF99F4aI7OLrLM352cF+5smreDju4r7xuCbgH1UUk/1A6qQyjpFRgDF68lyXACZI2SXpI0ll1q50jUQw7dQRvrf2ApcvfYtr993D4cPVjpJxx8Y+45anXGfeTm3h17l9rUcuKOb5jJu/mbOG1Fau5849T+OnVV1Bw4EDVBZWUUFKQR4vv3ULzM37GobcfxY58VfsKx6A270EsFHCrK5LFMDaT9D6wBvgUeNTMCoEs4FpgD/CspMnRBSVdK2mNpDV78vcA9T9ucn2Xn9mpfMznXTt3kNmpU4V5vJjPX9KufftyeXr17kOL9HQ+zNlANK0zjmf/nrK4z1/u+Txm3Ocwg88Zx4YV/xez7P4YZTtmdmbXjrJ12bt27aRjdBsyO7NzR1nc6oIDX9KuXXvS0tJK2zJoyFC6de/Bls2flCurZm3L9fJKDu5DzdqWyxNq3o4mXYaiUCqh9OMItexIcUHeN3qI0T1ISMw9qBFJbhmTxTCG3zEONrOfm9kRADMrNrNlZnY7cD0wMbqgmT0cDsad0SEDqP9xk+u7/KFZw9myeTO5fsznec8/x5gLxpfLM2bseJ5+wgtn+q8X53HmWd9FErm52ygqKgLg00+388nHH3Ni125Ec0IvP+7zbj/u89IF9Bs5qlyePTvKoi5++NbrdOjsyek3chTvLV1A0ZGv2bv7M/J35HJi70Hlyg7JGsbWrZvZ7rfhpXnPcf7YqLjVY8viVr/80jzOOMuLW52fv4diP+5z7ratbN2yma7dyjsISWnfg+KCzykp/A9WXMTR7W/RpMvQcnlST8iiKO9DAEoOF1BS8Dmh9AxSMwdwdPd67OuvsK+/4uju9aRmDkj4PagJISnQVlck7TxGSb2AEjML/6sdDGwPUra+x01uCPKnPPBnJl44luLiYn54xWT69O3HH+66nSFDhzF23Hh+NPkqfnL1lQzp34u2bdsy5/GnAHhr1UoenHofqalNCIVC3P/gDNp36EA0KampXHLj7Tz828lYSQmnjsmmY/dTWDRnGl16DaD/6eey8sW5bFq7ipSUVJq1bMWkm6cA0LH7KQw+eyz3TR5NKCWFS35xB6EYbfjTlAe57OILKC4uYdKPrqR3n37c+/s7GDQ0i9Fjx/ODK/6b66+dzIhBfWjTti2z/v6E14aVy7nvD3eS2sRrw30PzqBtu/I9OoVSaDbsCr5aOgWshCY9zySlTRcOr5tHSvvuNOkylNTMARTtXk/ByzeBQjQdchmhNO+DUtMBEyhcdJu/fzGhtPSE34OaUJsmT9Jo4M944VMfMbN7os6fiRdedSBwmZk9H1dmMsSVllRoZulRaVnAX4A2eLGlNwPXmll+RXLqe1zphsCxdjv26DuB/jdWm4bgdmz77EnHVH6b5qk1iivdf9BQe2HJNz9IxaJXxxaV1iUpBdgEfA/YAawGJpnZxog83YBWwG+A+UEMY1L0GKONop+2FhhZB+o4HI5jSC07qj0V2GxmWwEkPQNcBJQaRjPL9c+VBBWaLO8YHQ5HYyHgVJ2Arxg7U35+8w4/rUYkRY/R4XA0LqrQX+wgKfL92MNm9nCtKxSFM4wOhyPBVMlRbX6c95k7gcgXw138tBrhhtIOhyPh1OJQejVwsqTukr4FXAbMj1MmLs4wOhyOhBJ0bncQu2hmRXhznBcDHwLPmVmOpLskXQggabikHcClwCxJOfHkuqG0w+FIPLU4kdHMFgILo9Jui9hfjTfEDowzjA6HI+Eku6PaBmUYJWjaoFpU/2iamhI/Uw341Vk9jqn8RLD/iR/WtQp1jguG5XA4HJEIQs4wOhwORzTJbRmdYXQ4HAkl7Kg2mXGG0eFwJJwkt4vOMDocjsTjeowOh8MRxbEIsFWbOMPocDgSTnKbRWcYHQ5HgqnrCIBBqHPDKKkYLyqggGLgejNbJak5MBvPHbmA/cBoP0iWw+GoxyT7ypdkcCIRDoQ1CLgZ+JOffiOQZ2YDzKw/cDVwtCqCc3Nz6dOnD9dccw39+vXjvPPO49ChQ8yePZvhw4czaNAgJk6cyMGDBwGYPHkyN9xwAyNHjqRHjx48/3zlHtCd/IYtvyG0IRHXqFokeZRAzKxON6AwYv9S4CV/fzrw66rIysrKski2bdtmKSkp9t5775mZ2aWXXmpz5861/Pz80jy33nqrTZ8+3czMrrzySsvOzrbi4mLLycmxnj17WmU4+Q1bfkNow7GQD6yxGvzmBw0ZansKjgbaalpXdbc6H0pTFlO6KZAJnOOnzwGWSMoGXgP+YWURA0uRdC1e7GlOPPHEbwjv3r07gwcPBiArK4vc3Fw2bNjA7373O/bv309hYSHnn39+af4JEyYQCoXo27cveXl5cZV38hu2/IbQhkRco6pRt6FRg5BMQ+newGjgcUkys/eBHsAUoB2wWlKf6MIWGVc6I+MbwtPS0kr3U1JSKCoqYvLkycyYMYP169dz++23c/jw4Zj5LUAERSe/YctvCG1IxDWqCuGVL7XkqPaYkAyGsRQzexPoAGT4x4Vm9oKZ/Qx4AhhbG/UUFBSQmZnJ0aNHefLJJ2tDpJPfiOQnoo76Lr++kwxD6VIk9cYLmr1X0unARjP7wndZ3hdYVhv13H333YwYMYKMjAxGjBhBQUFBbYh18huJ/ETUUd/lxyPJR9LoWHSVq6RA2XQd8HrZt5jZK5KuwAuQLbye7SvATVaJwsOGDbM1a9ZUdNrhcNQCktZa5QGqKmXI0GG2bOU7gfK2aZ5So7qqS533GM0spmdTM3sceDzB6jgcjmONm+DtcDgc5XFuxxwOhyMGyb7yxRlGh8ORcJK9x5hU03UcDkfjoDZXBEoaLeljSZsl/U+M82mSnvXPvy2pWzyZzjA6HI7EU0uWUVIK8FdgDN6UvkmS+kZluxr4wsxOAqYB98aT6wyjw+FIKAJCUqAtAKcCm81sq5kdAZ4BLorKcxHwD3//eWCU4njKbVDvGNeuXZsvaXsVinQA8o+VPg1AfiLqcPLrn/yuNanw3XfXLm7WRB0CZm8qKXJy8sNm9nDEcWfgs4jjHcCIKBmlecysSNKXQHsqaXeDMoxm9s3F0pUgac2xnDxa3+Unog4nv2HLj4WZjU5kfdXBDaUdDkd9ZidwQsRxFz8tZh5JqUBrYG9lQp1hdDgc9ZnVwMmSuvs+FS4D5kflmQ9c6e9nA0srW1oMDWwoXQ0ejp+lUctPRB1OfsOWf0zx3xleDyzGc0Azx8xyJN2F5+R2PvAoMFfSZmAfnvGslDp3IuFwOBzJhhtKOxwORxTOMDocDkcUjcowSrpVUo6kDyS9L2mEpHGS3pO0TtJGST8JKKujpGckbZG0VtJCSadImi5pg6T1klZL6u7nXyipTUV6xJBf7J8Lb90kHS9pQYSuC/28nSQ97+83l/SkX/8GSSskpQe8Fo+EVw1IusqX8YEv5yI//TE/Dk+krEpD2koySU9EHKdK2iNpgX8cs10B78M0Sb+IOF4s6ZGI46mSflXRfakOEfcmx9f515Jq/FuKkLtB0j/lhRAO9LxUUf46Se9KGumnB3pmGhV1EYGrLjbg28CbQJp/3AFvououoIuflgb0CiBLvqzrItIGAf+LN7M+5Kd1AdoG0KNTjDoKY6TNAm6MOB4YI8/NwAMRx73CdQXVwdd7C9DaP04Huvv7jwHZ8XSNPg+8DzTzj8f4xwuCtqsS2dnAc/5+CFgLvBlx/k3/mlR6X6r4LEVGtjwOeBW4sxae0Ui5TwK/Cvq8VEP++cC/gz4zjW1rTD3GTCDfzL4GMLN8oADvy/xeP+1rM/s4gKzvAkfNbGY4wczWAV8Bu82sxE/bYWZfAEjKldQhlh5mtqsKbdgRUecHvuxukjZE5NkZkefjcF1Rcr6hg6Rlkobh/dgL8Awa5sXe2RZQx4pYCFzg708Cno7XroCswjMeAP2ADUCBpLaS0oA+QDEV3JeaYmb/wYtSeb1Uqz5jlgMnUbPnpTJaAeFrEOSZaVQ0JsO4BDhB0iZJD0k6y8z24c1x2i7paUmXBxwS9cfrmUTzHDDeH65MlTQkiB4V1NEsYhj9op/2V+BRSa/7w6tOMcrNAW6S9Kak30s6uRo6rAPygG2S/i5pfNT5KZHD/Ar0j+YZ4DJJTYGBwNsR54K0Kya+kSiSdCIwEq939TaesRyGFzbjKeLfl2pjZlvxpoocVxvy5E1CHoOne9DnJQjhZ+oj4BHgbj89yDPTqGg0htHMCoEsvP/ue4BnJU02sx8Do4B38GLMzKlBHTvwhiE3AyXAa5JGBdEjhrhwWNnBZnaxX3YxXkjZ2UBv4D1J5ZZBWoCws/F0MLNivFC22cAmYJqkOyJE/DZCt8EBr80HQDe83uLCqHNx2xWHVXhGMWwY34w4XhnkviQJ4Rjra4BPgUer8LwEoUahihsVdT2Wr6sN70f/clRaB6AgQNlRwBsB8v0G+Iu/nwt0CKKHn17pezs/zwJgIp7B2VBBnhnAr4NcC7wojMNinB8GrPf3H6Ma7xj9v7fhvbYYAJyN/46xonZV4V7+DJgOvIvXc2sHvAa8CFxY2X2p5rNTGHXcw2+XavhMBrnnMZ+XauqdBxxXnWemoW+NpscoqVfUEGEwkCfp7Ki0IN55lgJpkq6NkD9Q0lnhYaA/JB8YLa8CPQJ5BJJ0TsSXypZAT7yeRWSe0yW19ffDYWerpIO8r9xDq6NjHObgfaRYH5kYpF1xWAWMA/aZWbF5r0ja4A2nV0kaGu++VBe/ZzsTmGG+ValNavK8xJFbLlRxvGemsdGYlgSmA3+RN2WmCNgM3AjMkjQLOIT38WRyPEFmZpIuBh6UdBNwGK9HuAh4wH/pD97wfEYAPa4lGFnADElFeK9BHjGz1Srvkbgn8Df/Q0A47Oy8gDo8759vAtzvG5PDeEO46wLqWCHmDWmnB21XFUSvx+vtPxWVlm5m+f4Hpdlx7ktVCA95m+Bdv7nAAzWQVxk1eV6iCesN3syKK82sWFKQZ6ZR4ZYEOhwORxSNZijtcDgcQXGG0eFwOKJwhtHhcDiicIbR4XA4onCG0eFwOKJwhrGRoQo8uFRTVqmnHUV45qkg79nyvblUsY7wGvNA6VF5KvX6EyP/HZJ+U1UdHQ0PZxgbH+FlYf2BI0TNT/TX6VYZM/uxmW2sJMvZeEv0HI6kxxnGxs1y4CS/N7dc0nxgo6QUSVPk+S38QL6PSnnMkPSxpFeJcJqgMs88SBotz9/fOkmv+RPQrwN+6fdWvyMpQ9I8v47Vkk73y7aXtESe/8FH8CYiV4qkl+T5xMyJXI3kn5vmp78WXn8tqaekRX6Z5f4qEIejlMa08sURgco8uCzyk4YC/c1sm29cvjSz4f5qkZWSlgBD8Jwx9AWOBzYS5XTDNz6zgTN9We3MbJ+kmXhrde/38z0FTDOzFfI84yzGcxF2O7DCzO6SdAFwdYDmXOXX0QzPAcI8M9sLtMALiPRLSbf5sq/HCwB1nZl9Is/p60PAOdW4jI4GijOMjY/IZWHL8SKojQTesTKfi+cBA1Xmqbs1cDJwJvC0ed53dklaGkP+aXgONrYB+OuWY3Eu0FdlLgxbyfMafSZwiV/2FUlB/Cbe4C/RBC9+8Ml4Th1KgGf99CeAF/w6RgL/jKg7DYcjAmcYGx+HLMpVmG8gvopMAn5unjuwyHxja1GPEHCamR2OoUtg5DkBORf4tpkdlLQMaFpBdvPr3R99DRyOSNw7RkcsFgM/ldQEQF4smxbAG8B/+e8gM/E8mUfzFnCmymLdtPPTC4CWEfmWAD8PH0gKG6o3gB/4aWOAtnF0bQ184RvF3ng91jAhPDdd+DJXmNkBPAe8l/p1SNKgOHU4GhnOMDpi8Qje+8N35YVMmIU3ungR+MQ/9zieQ9hymNkePO8vL0haR9lQ9mXg4vDHF+AGYJj/cWcjZV/H78QzrDl4Q+p47scWAamSPgTuwTPMYb4CTvXbcA5wl59+OXC1r18OcFGAa+JoRDjvOg6HwxGF6zE6HA5HFM4wOhwORxTOMDocDkcUzjA6HA5HFM4wOhwORxTOMDocDkcUzjA6HA5HFP8PBIY+TYARVusAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fvAbwcFsID1"
      },
      "source": [
        "3.2 Gradient boosting (GB): Parameter optimiztion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WpBHOK4uxVG",
        "outputId": "9f8ef4a4-0298-47fb-ca57-9a2d20ecf911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "loss_list = ['deviance']\n",
        "max_depths = [2, 3, 4]\n",
        "\n",
        "i = 0\n",
        "plt.figure(figsize=(15,5))\n",
        "\n",
        "for los in loss_list:\n",
        "\n",
        "  i = i + 1\n",
        "  scores = list()\n",
        "  score_tests = list()\n",
        "\n",
        "  for depth in max_depths:\n",
        "\n",
        "    params = {'loss': los, ##  loss{‘deviance’, ‘exponential’}, default=’deviance’\n",
        "          'learning_rate': 0.1, ##  learning_ratefloat, default=0.1\n",
        "          'n_estimators': 500, ##  number of iterations, int, default=100\n",
        "          'max_depth': depth, ##  int, default=3\n",
        "          'subsample': 1, ## float, default=1.0\n",
        "          'min_samples_split': 2 ## int or float, default=2\n",
        "          }\n",
        "    clf_cv = ensemble.GradientBoostingClassifier(**params)\n",
        "    clf_cv.fit(X1_train, y1_train)\n",
        "    cv_score = clf_cv.score(X1_train, y1_train)\n",
        "    scores.append(np.mean(cv_score))\n",
        "    score_test = clf_cv.score(X1_test, y1_test)\n",
        "    score_tests.append(score_test)\n",
        "\n",
        "  plt.subplot(1,2,i)\n",
        "  plt.plot(max_depths, scores, 'b')\n",
        "  plt.plot(max_depths, score_tests, 'r')\n",
        "  plt.title('Loss = {}'.format(los))\n",
        "  plt.xlabel('Max depth')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.ylim(0,1.1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1p4_jnPFsBFp",
        "outputId": "f39ab61e-5d90-4d85-fdb1-7f089468db53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "learning_rates = [0.001, 0.01, 0.1, 0.2, 0.4]\n",
        "n_estimators_list = [100, 500, 1000]\n",
        "\n",
        "i = 0\n",
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "for est in n_estimators_list:\n",
        "\n",
        "  i = i + 1\n",
        "  scores = list()\n",
        "  score_tests = list()\n",
        "\n",
        "  for rate in learning_rates:\n",
        "\n",
        "    params = {'loss': 'deviance', ##  loss{‘deviance’, ‘exponential’}, default=’deviance’\n",
        "          'learning_rate': rate, ##  learning_ratefloat, default=0.1\n",
        "          'n_estimators': est, ##  number of iterations, int, default=100\n",
        "          'max_depth': 3, ##  int, default=3\n",
        "          'subsample': 1, ## float, default=1.0\n",
        "          'min_samples_split': 2 ## int or float, default=2\n",
        "          }\n",
        "    clf_cv = ensemble.GradientBoostingClassifier(**params)\n",
        "    clf_cv.fit(X1_train, y1_train)\n",
        "    cv_score = clf_cv.score(X1_train, y1_train)\n",
        "    scores.append(np.mean(cv_score))\n",
        "    score_test = clf_cv.score(X1_test, y1_test)\n",
        "    score_tests.append(score_test)\n",
        "\n",
        "  plt.subplot(1,3,i)\n",
        "  plt.semilogx(learning_rates, scores, 'b')\n",
        "  plt.semilogx(learning_rates, score_tests, 'r')\n",
        "  plt.title('N estimators = {}'.format(est))\n",
        "  plt.xlabel('learning rate')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.ylim(0,1.1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TW1j69Vebh7z2mtqxxIMSPJE08dZb8N13VhnzlltCRyMioSxYYO0QCheGYcPg8MNDRyQiUbd8OTz5JPTqBcWKwYQJcMUVoaOKLh3RFEkDv/wCjRvDDTfAzTeHjkZEQlm1CipVgi1b7IjmcceFjkhEosx76NrVkroPP7S5yLRpSu7iTTt4IhHnvR2HOPhgaNNGRzJF0tXmzXZMe/58a4mgEuQiEk8//2zzjzFj4LLLoEMHtV1KFO3giURcr14wahS8+iqceGLoaEQkhMxMq5Q7caK1RNDquYjEy/bt8MYbcN551oqpTRsbe5TcJY528EQibMUKO/N+6aW2iiYi6al+fSum0ry59bgUEYmHb76B6tXtzn+VKvDuu1pcDkE7eCIRVrcurFkDHTtaSwQRST+tW0OLFlC7to0JIiKxtmEDPP00XHwxLF1qBd0GDVJyF4p28EQiaswYO4r1/PNw7rmhoxGREAYNgieesLt3b72lO7giEnujRlnLgwULrNfu66+r9UFo2sETiaCNG22wPeMMaNgwdDQiEsLkydbz8pJLoGdP7eKLSGwtXw733AMVKkCePDB+PLRvr+QuGWgHTySCXnzRKuV99hnkzRs6GhFJtLlz4frr7XjUkCFw2GGhIxKRqPAeevSAp56CtWttIfn55zXfSCZK8EQiZto0aNkSHnoIypQJHY2IJNqyZdbrzjnrdXf00aEjEpGo+OUXK9o2apQVcOvYUddAkpGOaIpESEaGVa8qXNiq5YlIetm40XbuliyBoUPh3/8OHZGIRMH27Vas6Zxz4IsvrHjTpElK7pKVdvBEIqR1a5g6Ffr0gYIFQ0cjIomUkQF33mljwMCBdvdORORAffutLR5/+60tILVpAyedFDoq2R3t4IlExK+/2jn4a6+F224LHY2IJJL38L//2X27d96x/lMiIgdi40aoV89aHyxebL00P/pIyV0q0A6eSAR4DzVr2u/fe0+l0EXSTfPm9rNfrx489ljoaEQk1Y0ebdW4f/kFHn4Y3nhDJ4NSiXbwRCKgb18rptCsGZx8cuhoRCSReveG+vXhjjvgtddCRyMiqWzFCrjvPihfHnLntmrcHTsquUs1SvBEUtyqVdbI+KKLoHbt0NGISCKNHw/3328Vc7t0gYP0qi4i+8F765d51lnQq5e1PZg+XdW4U5WOaIqkuHr1YOVKK1msRsYi6eOHH+DGG61S5qBB1mhYRGRfLVhgrQ9GjrTiTB07wnnnhY5KDoTW+kRS2Lhx0KmTJXnFi4eORkQSZckS63WXNy8MH67jUyKy77Zvh1atrPXB559bgabPP1dyFwXawRNJUZs2wSOPwGmnQePGoaMRkUT580+rlrt6NUyYAKecEjoiEUk106ZZ8ZRvvoHrrrMiTaqOGR1K8ERS1Msvw7x5MGYMHHpo6GhEJBG2bYNbb4WZM2HYMChRInREIpJKNm6EF1+Eli3hqKOsSFvVqqq+HTVK8ERS0IwZVrL4/vuhXLnQ0YhIInhvZctHjYIPPoAKFUJHJCKpZMwYG0Pmz4cHH7T2KoUKhY5K4kF38ERSTEaGHc0sWBBatAgdjYgkStOm0LkzNGlikzMRkb2xcqUtCF9zjVXaHTvWFomU3EWXdvBEUsx778FXX1k546OOCh2NiCRC587wwgvwwAOW4ImI7In31ifzySftzu5zz0GjRrrWkQ6U4ImkkIULoUEDqFgR7rwzdDQikggjR0L16tZ4uH173ZURkT379VeoWRNGjLA+uWPGwPnnh45KEkVHNEVShPdQqxZkZkLbtprkiaSD776zoirnngsffgi5c4eOSESSWUYGvPWWtT6YMMF+P3mykrt0ox08kRTRvz8MHWqVr4oUCR2NiMTbb79ZO4SCBa3X3RFHhI5IRJLZ9Om22z9lClSubFc61EYlPWkHTyQFrF4Njz8OF14I//tf6GhEJN5Wr7ZG5hs32hGr448PHZGIJKtNm+x+3YUX2tHM3r1tQVjJXfrSDp5ICqhfH1assInewfqpFYm0LVvgppusz+XIkXbUSkTkn4wda60P5s2zIkwtWqg6pmgHTyTpjR8PHTtCnTpqaiwSdZmZNkkbPx66dIGyZUNHJCLJaNUqa5dSrpzd0f/0U+jUScmdGCV4Ikls82ZbmTv1VCuRLiLR1qCBHa967TVVyhWRv/Me+vSBs86Cbt3g2Wdh5ky46qrQkUky0WEvkST2yiswe7Yd0zrssNDRiEg8tWkDr79upc2feSZ0NCKSbH77zcaH4cOhZEkYNQqKFw8dlSQj7eCJJKkffrBV/Lvvtv5XIhJdQ4ZYAaXrr4d33lEbFBHZISMD3n4bzj4bPvsMWrWCL79Ucie7ph08kSSUmWmljo84wgZyEYmur76CO+6wCni9e6uQkojsMGOGzQe+/hoqVrQ+uGqVJHuiHTyRJNSunTUmffNNOPro0NGISLz8/LPt2h13nJU1z5cvdEQikgw2bbI7uRdeCL/8Aj172tFMJXeyN7ROKJJkFi+2S9PXXGPHM0UkmlassF53mZnwySfwr3+FjkhEksG4cfDII9b64L77oGVLOOqo0FFJKtEOnkiSqV0btm+3XTzdwxGJpo0bbedu4UL4+GM4/fTQEYlIaKtXw0MPWUXMzEwYPdrapSi5k32lBE8kiQwcCIMHw4svQtGioaMRkXjIyIC77rK7d716QalSoSMSkZC8h379rPVB165WRXfmTLj66tCRSarSEU2RJLF2re3eXXABPPVU6GhEJB68t5/vwYOtKt5NN4WOSERCWrgQatWyO7gXXggjRkCJEqGjklSnBE8kSTz7LPzxh5VLVxU9kWhq1Qpat4Y6dawtgoikp4wMeO89K6SSmWn37P73P73+S2zo20gkCUyYYHfunnrKmpeKSPT06wdPPw233QbNm4eORkRCmTnTWh989RVUqGCtD049NXRUEiW6gycS2KJFNuE77TRo2jR0NCISDxMnwj33wBVX2B2bg/TqK5J2Nm+Ghg3hP/+xFik9etiRTCV3EmvawRMJaNMmu4OzYQN8+inkzx86IhGJtR9/hCpVrHDS4MGQN2/oiEQk0caPt9YHc+bAvffakczChUNHJVGlNUSRQLy3wX7qVFvFO+ec0BGJSKwtXWq97g45xFbqCxUKHZGIJNoLL0DZsrBtG4wcabv4Su4knrSDJxLIm29aYte0qa3ui0i0rF8P111nDc3Hj4ciRUJHJCKJ1rWrtT66914rqpIvX+iIJB0owRMJYNQoqFcPbrkFnn8+dDQiEmvbt9vd2unTrTLuhReGjkhEEu2LL+ykTrly8P77kDt36IgkXSjBE0mwefPg9tvh3HOhSxcVWxCJGu+hZk07ktmhA1SuHDoiEUm0336zO/Ynn2wVdJXcSSJpaimSQOvWwQ03QK5cVmxBRVVEoqdZM1utb9jQSqGLSHpZv95e67dsgY8/1t1bSTzt4IkkSGamlUmfMwdGj1ZZZJEo6tYNGjWy+zZqeyKSfjIz7ed/5kwYNgyKFQsdkaQjJXgiCfLCC3YXp3VruPLK0NGISKyNHg0PPQRXXw0dO4JzoSMSkURr0gQGDbJCahUrho5G0pWOaIokQP/+8NJLNvl77LHQ0YhIrE2fbkWTzjrLft4POSR0RCKSaL17w8svw8MPwxNPhI5G0pkSPJE4mzED7rsPSpWCNm20qi8SNQsXWiGVI46A4cPhyCNDRyQiiTZlCjz4IFxxhV7rJTwd0RSJoxUrrMddwYIwYADkyRM6IhGJpTVrLLlbvx4mTYITTwwdkYgk2uLF9lp/7LH2Wq8dfAlNCZ5InGzbZn2wli6FiRPhuONCRyQisbR1K9x8M8yeDZ98AuedFzoiEUm0jRvhxhvhzz9h5Eg4+ujQEYkowROJm7p1Ydw4q6p30UWhoxGRWPLe7tSOGwfdu8NVV4WOSEQSzXs7lvnNN9b6SIs8kiyU4InEwQcfWLXMunWtNYKIREvDhtCjh/W8u/vu0NGISAjNmkHfvvDaa9b3TiRZqMiKSIx98QXUrAnly9ugLyLR0r49vPIKPPIIPPdc6GhEJISBA63n5T33wDPPhI5G5K+U4InE0KJFdifnlFOgTx84WHvkIpEydCjUqgXXXqtKeSLpato0S+wuvRQ6dNA4IMlH00+RGNm0CW66CTZsgLFjrXKmiETHlClw++1QooQWcETS1R9/2HHMQoWsoXnevKEjEvk7vTyJxID3dlxr6lT46CM4++zQEYlILM2fD9ddB8ccA8OGQf78oSMSkUTbvNkWcleutLYoxx4bOiKRf6YETyQGWrWyggsvvaSL1iJRs3IlVKoE27fDiBGW5IlIevEeHn0UJk+G/v1tJ18kWSnBEzlAI0faBetbb4Xnnw8djYjE0qZNtmjz66/w6adw5pmhIxKREFq0sLZHTZvCLbeEjkZk95TgiRyAuXPhjjvg3HOhc2ddtBaJkowMK6QweTL06welS4eOSERCGDoU6te3O7gNG4aORmTPlOCJ7Kd166BKFciVyxqc6k6OSLQ8/TQMGGBHsG+9NXQ0IhLC99/DnXfCf/4DnTppIVdSgxI8kf2QmWnNjefMgdGj4dRTQ0ckIrH01lv29sQT8NRToaMRkRBWrLAj2ocfbgXUDjssdEQie0cJnsh+aNIEPv4Y3n0XrrwydDQiEksDBkCdOnbPpmXL0NGISAhbt9oYsGQJTJgAJ5wQOiKRvacET2QfffghvPwyPPywNTwWkej4/HO46y4oVQq6d7cj2CKSXryHxx6zxK5nT7j44tARieybg0IHIJJKpk+H+++Hyy6z3TudxReJjtmz7TjWKafAkCFw6KGhIxKREFq3hvfft8rY1aqFjkZk3ynBE9lLK1ZYUZWCBe0IV548oSMSkVj54w/rdXfwwdbr7qijQkckIiGMGmX3bm+80VoiiKQiHdEU2QvbtkHVqjYJnDgRjj02dEQiEisbNsB119nP92efQdGioSMSkRBmz4bbbrPWR927w0HaBpEUpQRPZC/UqWMTv+7doWTJ0NGISKxs3269rb791qrkXXRR6IhEJIRVq+D66+GQQ+yItlofSSpTgieyB++/b/ftnn7aWiOISDR4D7Vrw7Bh0K6d7eKJSPrZts127hYsgHHj7B6uSCpTgieyG198YZUyy5eH114LHY2IxNJrr0H79vDcc/Doo6GjEZFQ6tSBTz+Fzp2hdOnQ0YgcOJ0uFtmFRYvg5pttJa9PH5VLF4mSnj2hQQNridCsWehoRCSUdu12nNK5//7Q0YjEhnbwRP7Bpk1w002wcSOMHWuVM0UkGsaOhQcegCuvhE6d1O5EJF2NGwePPw6VK+uUjkSLEjyRnXgPjzwC33xjRRfOPjt0RCISKzNn2uLNGWfAwIFWUEFE0s+8eXDrrTYW9O6tUzoSLUrwRHbSqhX06AEvv2wVtUQkGhYtspX6/Pmt112BAqEjEpEQ1q6FG26w3w8ZAkccETYekVhTgieSw8iR8Mwz1vOuQYPQ0YhIrKxbB9deaxO7iRPhpJNCRyQiIWRkwJ13wty5MHo0nHZa6IhEYk8JnkiWuXPhjjuswWnnzrqXIxIVW7fCLbfArFkwfDgULx46IhEJpX5928Fv3x7Klg0djUh8KMETwVb3q1SxM/gffQT58oWOSERiwXuoXh3GjIEuXeCaa0JHJCKhdO4MLVtaYZVHHgkdjUj8KMGTtJeZaaXS58yxSWCRIqEjEpFYadIEunWDpk3hvvtCRyMioUyaZP0ur77a7tqLRJkSPEl7jRvD0KHWB0fHNUSio2NHeOklePhhaNgwdDQiEsqCBdbXtkgR6NcPDtbsVyJOjc4lrfXrZ02OH34YatUKHY2IxMqIEVCzJlSsCO+9pzu1Iulq/XqrmLl1K3z8sfraSnqIa4LnnKvonJvtnJvnnHv2Hx4/2Tk3zjn3nXNuhnOucjzjEclp2jRrdly6NLRpowlgOtHYFG3ffGOVcM8/3xZxcucOHZHI3tP4FDuZmXD33VZgqV8/OPPM0BGJJEbcEjznXC6gDVAJOBu40zm3c8vohkA/730J4A7gvXjFI5LT8uVw441QqBAMGKBmx+lEY1O0LVhg7RAKF4Zhw+Dww0NHJLL3ND7FVqNGVjjtzQRFgjUAACAASURBVDehfPnQ0YgkTjx38C4G5nnv53vvtwJ9gCo7PccD2e0ljwSWxDEeEQC2bbPV/T/+gEGD4JhjQkckCaaxKaJWrYJKlewo1ogRcNxxoSMS2Wcan2KkVy945RWrllm7duhoRBIrntdMTwAW5vjzIuCSnZ7zAjDKOfc4kA+4Oo7xiADw1FMwfjz06AElS4aORgLQ2BRB69fbrvz8+VYN96yzQkcksl80PsXAV1/Bgw9CmTLQurWuYEj6CV1k5U6gi/f+RKAy0N0597eYnHOPOOemOuemLl++POFBSnR07Gj37erVs9YIIrugsSmFrFgB5crBF19A9+5wxRWhIxKJK41Pu7FokS32HH889O+vKxiSnuKZ4C0GTsrx5xOz3pfTQ0A/AO/9ZCAvUHjnT+S97+C9L+m9L3n00UfHKVyJus8/h8ces6p6r74aOhoJSGNThCxcaAnd9OkwcCDcdlvoiEQOiManA7BxI1SpYjv6H39sd3FF0lE8E7wpwOnOuVOdc4dgF4GH7PSc34ByAM65s7BBKn2WmSRhFi6EW26xHji9ekGuXKEjkoA0NkXETz9ZFdwlS2DUKCuFLpLiND7tJ+/h/vvhu++gd28455zQEYmEE7c7eN777c652sBIIBfQyXv/g3OuKTDVez8EqAt0dM49hV0avt977+MVk6SnTZvgpptsZW/cOPXASXcam6Lh66+hcmVrWDxhAhQvHjoikQOn8Wn/vfQSfPghNG8O110XOhqRsOJZZAXv/XBg+E7va5zj97OA0vGMQdKb91C9Onz7rZVKVuEFAY1NqW70aFu0OeYY27k77bTQEYnEjsanfffhh9CkCdx3H9StGzoakfBCF1kRiauWLaFnT3j5Zbj++tDRiMiB6tfP+tyddhpMmqTkTiTdffutJXalSkH79qqYKQJK8CTCPvkE6te3nnfPPRc6GhE5UO+9B3fcAZdeaq1O1OdOJL0tXWpFVQoXtr62efKEjkgkOSjBk0iaM8cmguedB507a0VPJJV5Dy++aFVwr7sORo6EAgVCRyUiIW3ebEe1V62CIUPsyLaImLjewRMJYd06W9HLnRsGD4Z8+UJHJCL7KzMTnngC3n3XjmG9/74VVhGR9JV9v/6rr6w9ygUXhI5IJLnoZVIiJTPTGpjPmwdjxlhbBBFJTVu3WlLXp48VTnjjDThI505E0t4bb0CPHna//qabQkcjknyU4EmkNG4MQ4dCmzZQpkzoaERkf23YYL0rR46E11+HZ54JHZGIJIMhQ+xe/R13QIMGoaMRSU5K8CQy+vWDZs3s2EbNmqGjEZH9tXKlVcqcMgU++AAefDB0RCKSDGbMgGrV4MILoVMn3a8X2RUleBIJ06bBAw9A6dJ2V0eDvkhqWrQIypeH+fNhwAC48cbQEYlIMli+HG64AY480vraHnpo6IhEkpcSPEl5y5dbUZVChWxCeMghoSMSkf0xe7Yld6tXW5uTsmVDRyQiyWDrVrj5ZvjjD5gwAY4/PnREIslNCZ6ktG3b4NZbYdkya3qsMskiqWnqVKhUyYqojB8PJUqEjkhEkoH3du1i0iQruHTRRaEjEkl+qkcmKe3JJ20174MP7Ey+iKSeMWPgyishf36bxCm5E5Fsb71l9+0aNYLbbw8djUhqUIInKatDB3jvPauuV61a6GhEZH/0728FVYoUgc8/h9NPDx2RiCSLESPg6afteOYLL4SORiR1KMGTlDRpEtSuDRUrwiuvhI5GRPZH+/Zw22125Er3akQkpx9/tFYI550H3bqpB6bIvtCPi6SchQutP1aRItC7N+TKFToiEdkX3luD4ho1oHJlGDUKChYMHZWIJIuVK+H66yFvXut7ly9f6IhEUouKrEhK2bjRyqZv3myFGAoUCB2RiOyLzEx46il45x245x67P5s7d+ioRCRZbNtmO/sLF8Jnn8HJJ4eOSCT1KMGTlOG9NTH/7jtb0StWLHREIrIvtm2zfpU9e1qS16KFjl2JyF898QSMHQtdu0KpUqGjEUlNSvAkZbRoAb162Z27664LHY2I7IsNG6BqVSua8Mor8Oyz4FzoqEQkmbz3HrRta8XT7r03dDQiqUsJnqSETz6B+vWtRPKzz4aORkT2xapVtijz1VdW/bZ69dARiUiy+fRT+N//bKxQ8TSRA6MET5LenDlWSat4cbuvo1V/kdSxeDFUqABz58KHH1q5cxGRnObOtR3+YsXsCLeKp4kcGCV4ktTWroUqVawIw+DBqqQlkkrmzIHy5a0i3ogRcNVVoSMSkWSzZo1VzDzoILtff8QRoSMSSX1K8CRpZWTAXXfBvHkwZgycckroiERkb33zjfWpdM4q4V14YeiIRCTZbN9uJ3R+/tmOaBYtGjoikWhQ/TJJWo0bw7BhVk69TJnQ0YjI3ho7FsqWtR33SZOU3InIP6tXD0aOtMIq//1v6GhEokMJniSlvn3tkvUjj1gzZBFJDQMGQKVKtuP++edwxhmhIxKRZPT++/DWW9YW4eGHQ0cjEi1K8CTpTJtmvbJKl4bWrVVURSRVdOxoDYpLloQJE+CEE0JHJCLJaMIEqFXL7ui2aBE6GpHoUYInSWX5ciuqctRRthNwyCGhIxKRPfF+x457hQowejQUKhQ6KhFJRr/8ArfcAqeeaqd1DlY1CJGY04+VJI1t2+DWW2HZMru3c8wxoSMSkT3JzIS6de2o1V13QefOVvVWRGRnf/4JN9xgxVU+/hgKFAgdkaQs72H6dKvElwgXXmirEilCCZ4kjSeftGMbPXuqKINIKti2DR58EHr0sHs0rVpZqXMRkZ1lV8b+8Uf45BPdz5X9sHatlVUfPty+iZYsSdzXzpcPunVLmWauSvAkKXToAO+9B888A9WqhY5GRPZk40a7bzdsGLz8MjRooPuyIrJrzz9vu3bvvgtXXx06GkkJ3sP331tCN2KEVe7avh2OPNIucFauDP/5T/xXFjdvhscft7PFL7wAjRol/WqmEjwJbtIkqF3bKu+98kroaERkT1avhuuug8mToV07ePTR0BGJSDLr3h1ef92qYteqFToaSWp//mlNEbOTukWL7P3Fi1tfjUqVoFSpxF/eHDcOata0BG/mTOjSBfLnT2wM+0AJngT122+2233qqdCrF+TKFToiEdmdJUuskMqcOdCvn92bFdknmZmweLF1t875tnQpXHON1cw/7rjQUUqMfPml/ZeWLWt9bbXTL3/hvZ3bzU7oJk608/+HH267dC+8ABUrhi/LnDcvdOoE558PTz8Nc+fCRx9BkSJh49oFJXgSzMaNcNNNsGWL/YzosrVIcps7115vV6yw1+Jy5UJHJElr82Yrl7hzEvfzz/b+rVt3PPfgg22SVKAANG4MTZvCjTfaavmVVyojSGELF9p/5YknQv/+KsAkWdavh7FjLaEbPtxW+wHOPReeesp26UqXTr5vGOcsvnPOgdtvh4susm/sMmVCR/Y3SvAkCO9tRe+77+xMfrFioSMSkd357jtbRM3MtJMqJUuGjkiCW7Xq78nb/Pn26+LFNtBny58fTjvNJkY33GC/z3476aQdx63mzoX27a0ca//+VomjRg247z713kgxGzZY26NNm2wuf9RRoSOSYLyH2bN3JHQTJtgiT/78diHz+ectqTvppNCR7p3y5eHrr20su/pqa9pco0boqP5CCZ4E0bw59O4Nr74K114bOhoR2Z3PPrPXsYIFYdQoOPPM0BFJwmzeDFOnwk8//TWB+/lnWLPmr8899lhL2K66yn4tWnRHEnf00Xu3E3f66db5+qWXLMFr2xbq1LEqPrffbrt6F1+sXb0kl5lpOfn06TB0KJx9duiIJOE2brTVwOyk7pdf7P1nn20FSypXhssvT92Gx6efbueP77rLxqUZM+Dtt5Nm11EJniTc8OHw7LP2Wl2/fuhoRGR3Bg+GO+6wOfrIkXbUSiJs40arnjN+vL199ZWdowfbZTvlFPtmuPjiv+7CFS1qZcRj5dBD4Z577G36dEv0evSArl2hRAmbUFWrFtuvKTHz4oswYAC0bGkbM5Im5s7dkdB99pmNHYcdZuf5swukJOmdtf1y5JF2x+j5562K0KxZ8OGHtqAVmPM5j1CkgJIlS/qpU6eGDkP20+zZcMklNheYNMl+7kWcc99471P60F8Ux6YPPoBHHrG5/NChOmIVSevXW+nx7IRuyhQrcHDQQZZIlSkD//0vnHcenHxy4ivX5bRunTVKbdvWqtgdcYQlgDVr2tHPONH4tG/69rVFoQcesDFEm60RtmmTjRvZBVKym46feaYlc5UrwxVXWIGSqOvVCx56CI45BoYMsWIscba7sUkJniTM2rWW3K1aZSd+Tj45dESSLDSBSi7ewxtv2E57hQq2Eq+NkohYu9ZW17ITum++sQ7UuXLZxcoyZeytdGlbnU5G3tsuY9u2Vsp161abRNaoYX2q8uSJ6ZfT+LT3pk61/4oLL7RK9zH+r5BkMH/+jl26ceMsycub145mV65siV3RoqGjDGPKFKsqtHZtQpqi725s0hFNSYiMDDum/PPPNugruRNJTpmZ8MwzdrTqzjut1U+qXpEQbEVt4sQdCd20afafnDu3bc3Wr28J3WWXJXVPp79wzuK97DJ48037Jm3Xzl5knnwSHnzQtp7TdZIZyJIlVlTlmGNg4EAld5GxZYsVRcnepZs9295/2mlWLa9yZRtDDj00bJzJ4KKLbJXj5pttsalJE6sMHKApuhI8SYhGjWDYMFtw/e9/Q0cjIv9k2zaoXt2uOdWubffFA7wuyYFYvtwmY9kJ3cyZtuOVJw9ceik0bGiTsUsvjcYZ+cKFrSdVnTowZowlei1a2BZ0hQp2fPPaa9VkNc42bdqxcfHFF/Cvf4WOSA7IggWWzI0YYavyGzfaGFK2rHWqr1TJiozI3x133I6m6C++aGNw164JX0BTgidx17evVct89NGkqyIrIlk2brTCR0OH2mtSo0a6O5MSfv/9rwndDz/Y+w891Ha4XnzRErqLL472PZiDDrLS5eXLw6JF8P770LGjbSmddJKtXKiBelx4b1ePpk6FQYMScvVIYm3rVtvpzz56+eOP9v5TT7XLlJUqWU/KKCwKJcLOTdFLl054U3TdwZO4+u47+77OPo+vo17yT3THJaw1a+D6663WRps2tvAoSWrx4h3J3PjxO45L5ctng232HbqLLtKAu22bNVpt1w5Gj7YCMfvRQF3j0+698ooVEXzlFXjuubh8CYmHhQt37NKNGWMFlw45xMaP7AIpZ5yhlb4DNWqUrZ4efHDMm6LrDp4EsWyZvZYWLmzf0+k+1xBJRkuX2km2n36CPn3gtttCRyR/8euvf03ofv7Z3n/EEdZD6sEHbcLwn/8kTf+lpJE7t92FufnmvzdQ/+QT+8aXAzJokCV31apZUSZJYtu22Spe9i7d99/b+08+Ge6+25K6q65Knbu4qWLnpujvvJOQVVQleBIXW7fCrbdakvf553bpWkSSy7x59tqzbJndkb3mmtARpTnvrUJdzoTu11/tsYIFrTxhrVqW0F1wge6V7YvsBuovv2zNHa++OnREKW/6dOtScfHFdiJWGz1JaMmSHQndmDHWaiR3bhtLmje3XbqzztJ/XrzlbIpeq9aOpuhx3PlQgidx8eSTdpy7Vy9bWBaR5DJtGlSsCNu3w9ixNkmTBPMe5sz5a0K3eLE9VriwVaSqU8cSuvPOU8WbWMib15q0yQH54w/bkChQwPJlFVBMEtu3WwuR7KRu+nR7/wkn2DHBypWt6fjhh4eNMx39U1P0/v3j1hRdCZ7EXPv2Vi2zfn0rsy4iyWX8eJucHXkkfPYZFCsWOqI04b29qGcncxMmWJEUsGMO2ffnypSxVXUldJKEtmyxU6/Ll9tCrurWBPb773bkePhwu2u6Zo3t7l9+Obz2miV1556rXbpkkCuX/Z+cf75VJrroIkv6iheP+ZdSgicxNXGilVevVAmaNQsdjYjs7KOPbCG3aFEYOdIKDEqcZGbaPZecO3QrVthjJ5xg912yEzoVM5AU4L1Vw/7iC+sxf+GFoSNKQxkZ8NVXO3bpvv3W3n/ccZZ5V65sR5CPPDJsnLJr1arZmH/jjVbtuFs365sXQ0rwJGZ++82+P4sWtaOZuh4iklw6d7ZK8SVL2rzgqKNCRxQxGRl2JCo7mZs40RqNA5xyyo6GwGXK2ECphE5STKtW1le+SROoWjV0NGlk2TJbkRs+3KoyrlplO/yXXWar6ZUr2y6QxpTUUbIkTJliSfmtt8a8KboSPImJjRvhppvs6MaQIXYuX5LQ1q1WSStfvtCRSII1bw7PPGNFVQYMUKG0mNi+3VbPsxO6SZOs0zNYAlelyo6ELoH9j0TiYfhwqFfP5qKNG4eOJuIyMqyx4PDhtlM3daptnx5zjPW0qVzZqmIVLBg6UjkQxx1n9yRq1LCepTNm2G5eDF6gleDJAfPedgW++86aJJ95ZuiI5G9++QU6dIAPPoA//7QqOPXrKxNPA95bYteihR3N7NZNLUv229atNtHKTug+/9x6R4Edt7ntth0J3Yknho1VJIZmzbLaNBdcYDt4uh4aBytW2C7diBF2p27lStuRu/RSaNrU7r6UKKF//KjJk8eaohcvDnXrWsuKwYMP+NMqwZMD9sYb0Lv3jru8kiQyMmz1r21be7Fwzlb+DjvM/rM6dIBGjawfS548oaOVONi+HapXtwlZrVrWfkdHp/fBli121yU7oZs82Y4rAJx9ttWIL1PGql2q0oRE1IoVO146PvpIB0BiJjPTTgAMH25vX39tK3KFC+9oNF6+vM7SpwPnbOH9nHNi9lqiBE8OyPDh8NxztrL3zDOhoxHAKmq9/74lcAsX2mDRqJFts2ZX1Hj6advBe+opm/U3a2bbO1oZjIxNm+zncsgQeOEFO1Kl6xl7sGmTJXHZCd2XX1qSBzuqnmUndHEqbS2STLJ72i5ebD8SKsp0gFatsjt02bt0y5bZwHzRRXYHq1Ilu5ul1+L0FMNmtErwZL/Nnm1tEC64wE7+afIYkPd2jrttWxg0yLZuypWDN9+0evi5c//1+f/5j5VTHjXKMvNq1aBlS9uOveqqIH8FiZ21a+2/feJEePddeOyx0BElqQ0brBxgdkL39dc2oz3oIBvYspuKX3EFFCoUOlqRhPIeHn/cfjS6d4dLLgkdUYrassUqXHXvbotGmZk2nlSoYLt0FSpowUhiTgme7Jc1a2wCmSePHRU+7LDQEaWp1auha1do184y7oIF4X//g0cftTtBe1K+vJVT7tnTmm+WK2criK+/bo2VJeX8/rs1MJ81y6rZqqdzDuvW2b257IRu6lRbDMmVy+q9P/GEJXSXX64S45L22rSxgyDPPmvXgmQfbdpkp2lef922QIsXt9fZSpXg4ot1Xl7iSgme7LOMDLjrLpg/H8aOhZNPDh1RmvHeSuu2awd9+tiLyKWXWqJXtSoceui+fb6DDrK7RFWr2nZPs2b2QnTffXaxW2dyUsb8+ZazL11qBY/Klw8dUWBr1tg2ZnZC9+23tnqeO7cdiapXzxK6yy6Dww8PHa1I0hg92q4EXX+9etrus40boX17OxHz++92pLtbN7jySh11koRRgif7rGFDu3vXrp2dXJIE2bDBqtm0bWsT1Xz5LDGrWdOOkx2ovHntbt6DD8Irr0Dr1pZAquJmSpgxw076bN1qCy9peZxq1aq/NhWfPt0WRA45xP5BGjSAsmWhVCkdOxDZhTlzrCDs2Wfb4Q5dB9tL69fb63OLFna37qqr7DW0TJnQkUkaUoIn++Sjj6wAY40adgpQEuCHHyyb7tbNjpide66dnbn7bjjiiNh/vUKF7AWqdm0rzqKKm0lv4kRbaT/8cPj0U5uYpRXv7Xv0ySdh82ZbrChVyooWlCljyd2+7myLpKHVq20sOfhgK9Ckje29sG6dvSa3bGmtDcqXt9fLyy8PHZmkMa3LyF7LyLCz+OecA2+/HTqaiNuyxXbrypSxhK5DB3vVnTjRtmpq1YpPcpdTkSJ2Kfzbb+1+0lNPQbFiFldmZny/tuy1jz+2+cSxx9r1srRL7tautYuGNWrYUaiJE+1o5tixluCVLavkTmQv3X+/tU0dONBeAmQ31qyBl16yf6gGDWwhafJk62Wn5E4CU4Ine61fP/jpJyu5rkbJcfLLL9Z34qSTrLLlokV2QXvRIujRw140En2Gv0QJq7Y5cqQVnqhWzS6Ijx2b2Djkb7p2hZtusjWAiRPT8D7slClWEXbAAHj1VSs9fvnl2mUW2U8NG1rfTF2/2I3Vq23xqEgR6z9zxRU2Fg0bZvfhRZKAEjzZKxkZVm/j3HPh5ptDRxMx3ls/nMqV4bTT7GL2ZZfZ++bOtTYGyVBCuXx5283r1g2WL7eKm5Urw8yZoSNLSy1a2Gp72bKWayfDt0jCeA+tWkHp0lYFc8IEO16gy0IiB+Sii2wNT/7BypWWAZ9yik2IypWz18SPPrLedSJJRK+Gsleyd++aNNEcKmaye+Ocf76VTZ42zc7t//qr9Z6oUCH5/rGzK27Ong3Nm9txlOLF4YEHrKm6xJ33VvOmXj1rQDxsWJrdk1mxwo4r160L114L331nCyIiIvGwfLktIBUpYgXIKla0Ak4DBtgJF5EklGSzR0lG2r2LsVWr7EWiSBGrWOmcnbVbsABefBFOPDF0hHuWXXHz559tot2rl/Xde/ZZu5cgcbF9Ozz8sG3y1qhhBdrS6jTihAlWMXb0aKvyOnCgGpCLSHz8/ru9zhUpYoPu9dfbiZV+/WxhViSJKcGTPdLuXYzMnw+PP273655/3l4gRo2ylcB7703Ni42FCtlO3pw51kfvjTfsmOmbb9oOpcTM5s32T9ypk230vvdeGvXJzV5luvJKK5jy5ZdW5VU9pUQk1pYssYq8p55qr2W33AKzZtlC5jnnhI5OZK9oui67pd27GPjyS5uZn366NT+tWtWSupEj4ZprojFJPeUUu5v3zTdWcbNOHSvEomqbMbF2rZ0KGjzYKtg2bRqNb5u9snSp/Zw0aQJ33ml3XnQsSkRibeFCWzgqWhTefdfGm9mz7bWtWLHQ0YnsE/XBk9368EPbvevXT7t3+yQjw5oItWxptesLFLBiKY8/DscfHzq6+MmuuDl6tFX+1DfNAfvjD0vuvv/emg6nVQGETz6x3e0NG2zr8v770yizFZGEWLDA+r126mSXnB94wK4bFC0aOjKR/aYET3Ype/funHPshILshY0b7T5dq1Ywb56d3X/7bbtrlz9/6OgS55prQkcQCb/8YsVLFy+29YJKlUJHlCDbtlm1ujfesOMD/frBWWeFjkpEomT+fLsP37WrLRw99JAldqecEjoykQOmBE926cMP4ccftXu3V/74A9q0sYtRK1fa8cR+/axJ2cH6MZN9N3OmFVLdvBk+/RRKlQodUYIsWGBHo778Eh591O7AqFG5iMTK3LmW2HXvbq/PNWrYCZuTTgodmUjMaOYp/0i7d3vpxx9tt657d9i6FW64wapulS6to2Sy3yZNsoJt+fJZA/O0udc/cKCtomdm2gJJ1aqhIxKRqPjpJ2jWzIqlHHKIXZmoVy/a1yYkbSnBk3+k3bvd8B4++8zu1w0bZi0DHngAnnrKWgWIHIBhw6y/3ckn23XGtDgttHmzLYy0aWOdlvv00f0XEYmNH36Al1+Gvn3tNECdOtbe59hjQ0cmEjdK8ORvtHu3C9u2WebbsqVV8jv6aOtbV7Om/V7kAHXvbmsFF1wAI0akybfV7Nlw++1WWbZuXTs6lYotQ0QkucyYAS+9BP372x34+vUtuUuLgVXSnRI8+Rvt3u1k3Tp4/3146y0ro3zmmdChA9x9t+4GScy8+abNPa66ytohHH546IgSoHt3WyDJmxeGDoVrrw0dkYikum+/tcRu8GA44ggr2PTkk3DUUaEjE0kYJXjyF9q9y2HhQnjnHUvm1q2DMmWsiErlysp8JWa8t773r75qP3M9e0KePKGjirP1663fVNeu8N//2l/6xBNDRyUiqWzKFJvADB0KRx5pvTOfeAIKFgwdmUjCKcGTv+jf33bv+vZN4xzmu+/sGGbfvjb7rlrVjo6VLBk6MomYjAzbwOrYER55xNYPcuUKHVWczZgBt90Gc+ZA48bQqJEqzYrI/ps82XbsRoywZO6ll6yAypFHho5MJBi9qsr/y969O/tsK/KQVry3psotWsDYsXZe//HHbfUvLapcSKJt3gx33WWFI59/3uYkkS686j20a2fFiAoVst4PV14ZOioRSVWTJtmkZfRoKFzYjkE89lianG8X2T0lePL/+veHWbPSbPduyxY7Htaypf3lTzgBXn/dtlMKFAgdnUTUunVw440wbpxd7XziidARxdmaNfDwwzBggDX369YN/vWv0FGJSKrxHsaPt8Ru3DgbR5o3t152+fOHjk4kaSjBEyANd+9WrrTdhNatrUl58eI26bz9dlXwk7havhwqVrSTit27W62eSPvqK7jjDli0CN54w447p80KkojEhPe269+0qTUHPfZYq0z1yCNw2GGhoxNJOkrwBEij3buff7YXhc6dYeNGm2nXrQvlykX8fJwki4MPhty54aOPrF5PZGVmQqtW8NxztjM+cSJcemnoqEQklXgPI0daYjd5so0lrVvDQw+pirXIbijBk/TYvZs82e7XDRpkM+y777aa9OeeGzoySTMFC8IXX0R8IWX5crjvPit6cPPN1mZElexEZG95D8OG2eRkyhQ4+WRo29YahUa+zLDIgVOCJ/+/e9enT8QmnRkZtk3SooUleAUL2m5C7dpw3HGho5M0Fqmfs5199plVj1m5Etq0sTKh2h0Xkb3hPQwZYondt99CkSJWZvjee3V9QmQfRHmaIXshMzOCu3cbNtjE8swzrbHY779bP7vffoNmzZTcb8HKlgAAIABJREFUicRDRga88IIdd86fH778EmrVUnInInuWmWlFmEqUsApUa9dCp07WTuXhh5Xciewj7eCluZy7dynff+v33+Hdd+0Yx6pVcMkl8NprcNNNEfjLiSSxxYtt1278eLjnHmvop4p2IrInGRk2EXnpJfjhBzjjDCt4dued6o8pcgD005PGMjPhxRcjsHv3ww9WzKFHD9i2zVb/6taFyy7T7oFIvI0YYcenNm6ELl3s7p2IyO5s325V3V5+GX76Cc46C3r1gttu04KsSAwowUtjKb175731wGnRwiaYhx5qVbWeegpOPz10dCLRt3WrdWhv0QLOP98ma8WKhY5KRJLZ9u3We7ZZM5g71wqd9etn1ykifTlZJLGU4KWplN2927bNXgxatIBp06zJadOmVsihcOHQ0Ymkh19+sd52X39tP3stW6pkuYjs2rZt1vizWTOYPx8uuAAGDoQqVZTYicSBErw0lb1717t3iuzerV1rlbTeftsaJhcrZn+++27Imzd0dCLpo39/K3oA8OGHKbZCJCIJtWULdO0Kr7wCv/4KJUvCW2/BddfpCoVIHCnBS0PZlTPPOguqVg0dzR789psldR07wp9/Qtmy0K4dVKqkVT+RRNq0yXpHtmtnBYx694ZTTw0dlYgko82brQrma6/BwoU2ZrRtCxUrKrETSQAleGlowACrS5LUu3fffmvHMPv1sz/fdpsVTrnwwrBxiaSjn36C22+HGTOgXj07ZpU7d+ioRCTZbNpkC7Kvvw5Lllixs/ffh2uuUWInkkBK8NJM9t27pNy9y8y0giktW1oBlcMPhyeesLeTTw4dnUh66trV+tkddhgMH2675yIiOW3YAO3bwxtvwB9/QJkydufuyiuV2IkEoAQvzSTl7t3mzdbioFUr+PFHOPFEaN4cqleHI48MHZ1Ielq/3hK77t3taHTPnnD88aGjEpFksn699b1s0QKWL4dy5ayibpkyoSMTSWtK8NJI0u3erVxpZ/Jbt4Zly6yqVo8edhxTx79Ewpk2zY5kzpsHL7wADRsm0YqQiAS3bh28+64tzK5cCRUqQKNGULp06MhEBCV4aSVpdu/mzYM334TOne28fqVK8PTTOsohEpr3thpfty4cdRSMHauVeBHZYc0aeOcdq4S5ejVce60ldpdcEjoyEclBCV6ayN69K1Ys4O7dF1/YMY7Bg22H7u67rSrfOecECkhE/t/q1db+YOBAqFwZunSBo48OHZWIJINVq6yi9dtvW9uiG26Axo1V+EwkScW1zrxzrqJzbrZzbp5z7tldPOc259ws59wPzrle8YwnnWXv3jVunODdu4wM65tVqpQd3fjsM2jQwPrhfPCBkjsJQmPTTiZPhhIlYMgQW4T5+GMldyKBJNX4tGIFPP88FCli/ZXKlbMq1x99pOROJInFbQfPOZcLaANcAywCpjjnhnjvZ+V4zunAc0Bp7/1q59y/4hVPOsvue1esmF1vS4j16+0I5ltvwfz5ULSonde//37Ily9BQYj8ncamHDIzLaFr0ABOOgkmTdJRK5GAkmZ8WrbMKlq3aQMbN9rRn4YN4bzzYv6lRCT24nlE82Jgnvd+PoBzrg9QBZiV4znVgTbe+9UA3vtlcYwnbQ0cCN9/D716JWD3bulSS+TatrUjX6VKWUXMKlVUpEGShcYmsAncvffCyJFw663Wu6pAgdBRiaS7sOPT77/ba3bbtrBlC9xxh+3gnX12zL6EiMRfPI9ongAszPHnRVnvy+kM4Azn3OfOuS+dcxX/6RM55x5xzk11zk1dvnx5nMKNppx37+K6e/f99/Dgg3aM49VXrWDK55/bvbubb1ZyJ8lEY9PYsVC8uB2ZbtsW+vVTcieSHMKOT/ffb/fsqlaFWbOsPYqSO5GUE7rIysHA6UBZ4ERggnPuPO/9mpxP8t53ADoAlCxZ0ic6yFQW19077+HTT+0YxyefwKGHWu+6J5+Ef/87xl9MJKGiOTZt327ntV9+Gc4803bvzj8/dFQism/iNz61aAF58+o1XCTFxTPBWwyclOPPJ2a9L6dFwFfe+23AL865OdigNSWOcaWNuO3ebdtmjUxbtIDp0+GYY2zCWKOGlVYXSW7pOTYtWgTVqsHEibZK/+67ug8rknzCjk/nnnvAn0JEwovnEc0pwOnOuVOdc4cAdwBDdnrOYGwFCudcYezYwfw4xpRWsnfvGjWK4e7dpElw6qlwzz2W6L3/PixYYGf0ldxJaki/sWnoULjgAqt+162bFUBScieSjNJvfBKRmIvbDp73frtzrjYwEsgFdPLe/+CcawpM9d4PyXqsvHNuFpAB1PPer4xXTOkke/fuzDPh9ttj9EkzMmyXLlcuGD4cKlSAg+LaaUMk5tJqbNq6FZ57Dlq1sjt3ffvaoCAiSSmtxicRiZs9JnjOueuBYd77zH395N774cDwnd7XOMfvPVAn601iaNAg273r2TOGu3e9elkzvb59oVKlGH1Skf23v+NTWoxN8+dbBbwpU+Cxx3bcrRGRuNPcSURC2pvtl9uBuc65N5xzxeIdkBy4uOzebd1qXdJLlLCS6iLJQePTP+nXz35W586FAQPsvp2SO5FE0tgkIsHsMcHz3t8NlAB+Bro45yZnld49PO7RyX4ZNAhmzrR8LGa7dx062F27V1/VsUxJGhqfdrJpEzz6qK3snH02fPedtSkRkYTS2CQiIe3VTN17vw7oD/QBjgNuAr51zj0ex9hkP8Rl9279enjpJShTBsqXj9EnFYkNjU9ZZs2Ciy+2xZj69WHCBOtLKSJBaGwSkVD25g7eDcADwL+BbsDF3vtlzrnDgFlA6/iGKPsie/cupnfv3noLli2DwYPBuRh9UpEDp/EJ60fZuTM8/rhVxhwxAir+Y99jEUkQjU0iEtLeVNG8BXjTez8h5zu99xudcw/FJyzZH3HZvVu5Epo3hypVoFSpGH1SkZhJ7/Hpzz+hZk1b0bnySujRA/6vvTuPsqwu78X9eWkEVNB7BfReZhNxQJM4IEH9xZiILr0xDtGoGMUB5ZKocWRJM4PNkIBDFBBQEUGNAyi2qKAgXGcmUSIiBogR1KuA4ISAyPf3x6m6qZDuprpr7zqn9nmetXpRZ59d3/P2Pl1v94f37L232GLcVQHT3puAsZpPwDsoyY9nH1TV3ZPcr7X2/dbaOX0Vxtqbnd594AMdTu+OOGL0j8gVKzpaEDp1UKa1P11yyej/5Fx1VXLIIck++3T4gw8s0EGZ1t4EjN18zsH7WJK5l/n93cw2Jsjs9O6BDxxdGb0T1147uvrei16UPOxhHS0KnZq+/tRa8s53JjvvnNx8c3Luucn++wt3MFmmrzcBE2M+E7z1W2u3zT5ord1WVRv0WBPr4PTTe5jeHXLI6ObmBx/c0YLQuenqTz/7WbL77qMf+Kc9bXTu3Wabjbsq4L+art4ETJT5TPCumzlZOElSVc9Icn1/JbG2epnefe97yYknJnvu6Up8TLLp6U9f/ero3naf/nTy1rcmK1cKdzC5pqc3ARNnPhO8PZN8sKqOTlJJrkmyW69VsVZOPz259NKOp3f77z+6MfK++3a0IPRi+P3pjjuSf/zHZL/9km23Tb7yleTRjx53VcCaDb83ARPrLgNea+2qJDtX1cYzj3/Ve1XMWy/Tu298I/noR0f/oLzf/TpaFLo3+P70k58ku+2WfO5zyXOfO7rH3b3vPe6qgLsw+N4ETLT5TPBSVX+R5KFJNqqZ+6C11g7psS7maXZ6d8opHU7v9tknuc99kje+saMFoT+D7U/nnJO88IXJTTclxx+fvOIV7kMJS8hgexMw8e7yHLyqOi7J85K8OqOPGfx1km17rot56GV6d+65yVlnJcuXmxQw8QbZn26/fTQ9f9KTkv/+35MLLkj22EO4gyVkkL0JWDLmc5GVx7bWdktyY2vt4CSPSfLAfstiPmand/vvn6w/r1nsXWhtFOy23DJ55Ss7WBB6N6z+dM01oxuWH3po8tKXJhdemPzBH4y7KmDtDas3AUvKfGLBLTP/vbmqtkhyQ5L/2V9JzMcdd4zuYtDp9G7lyuT885N3vzu5+907WhR6NZz+tHLlKNTddlvywQ8mL3jBuCsC1t1wehOw5Mwn4H2qqv5bkiOTfCNJS/LuXqviLn3yk8m3vjU6966T6d3vfjc69+6BD0xe8pIOFoRFsfT70623Jm96U/JP/zS6DcJHPpJsv/24qwIWZun3JmDJWmM0qKr1kpzTWrspyWlVdUaSjVprP1+U6lilXs69++AHk+98Z3T1zE4SI/RrEP3pyitHP8QXX5y8+tXJkUcmG2447qqABRhEbwKWtDWeg9dauyPJMXMe36pBjd/s9K6zc+9uvTU54IDkkY9Mnv3sDhaE/i35/vThD49+5q6+OvnEJ5J3vEO4gwFY8r0JWPLmc5GVc6rq2VUu4TYJZqd322/f4fTu+OOTf//35PDDk/Xm80cCJsbS60833zy65cGuu44uoPLNbybPfOa4qwK6tfR6EzAY8/nX/P9O8rEkt1bVL6rql1X1i57rYjU6n9798pfJihWjK/c96UkdLAiLaun1p5/9bHQJ3OXLk/POS7bZZtwVAd1ber0JGIy7jAittU0WoxDu2tzp3a67drTo29+eXHddcthh7rPFkrMk+9NWWyXf+97oHnfAIC3J3gQMxl0GvKp6/Kq2t9a+2H05rMnKlaPp3ckndzS9u/765KijRh8P23nnDhaExbVk+5NwB4O2ZHsTMAjziQl7zfl6oyQ7Jbk4yZ/3UhGr1FoP07sjjviPj2jC0qQ/AZNIbwLGZj4f0fzLuY+rauskb++tIlbpk58cXYuhs+ndNdckRx+d7LZb8tCHdrAgLD79CZhEehMwTutyycRrkzyk60JYvV6md4ccMlr4oIM6WhAmgv4ETCK9CVg08zkH751J2szD9ZI8PMk3+iyK/2x2evf+93c0vbviiuTEE5NXvSrZbrsOFoTx0J+ASaQ3AeM0n7hw0Zyvb0/yz621r/RUD3cyO717wAOSF7ygo0X33z+5+92TffftaEEYG/0JmER6EzA28wl4pya5pbX2uySpqmVVdY/W2s39lkbSw/TuoouSj31sFPLue98OFoSx0p+ASaQ3AWMzn3Pwzkly9zmP757k7H7KYa5epnf77JNsumnyhjd0tCCMlf4ETCK9CRib+cyENmqt/Wr2QWvtV1V1jx5rYsbKlR1P777wheTzn0/e8pbk3vfuYEEYO/0JmER6EzA285ng/bqqHjn7oKoeleQ3/ZVE8h8XuOxsetdasnx5stVWyd/9XQcLwkTQn4BJpDcBYzOfudBrk3ysqn6UpJL8jyTP67Uqup/effKTyQUXJO95T7LRRh0sCBNBfwImkd4EjM18bnR+YVU9OMmDZjZd0Vr7bb9lTbfOp3e/+93o3LsHPSh58Ys7WBAmg/4ETCK9CRinu/yIZlW9Msk9W2vfbq19O8nGVeUzfj2and7tt19H07tTTkkuvzxZsaKjBWEy6E/AJNKbgHGazzl4r2it3TT7oLV2Y5JX9FfSdJu9cubv/37yN3/TwYK33poceGDyqEclz352BwvCRNGfgEmkNwFjM59xzrKqqtZaS0b3ckmyQb9lTa9PfSq55JLkpJM6GrYdd1zygx+Mzr2r6mBBmCj6EzCJ9CZgbOYTIc5M8pGqOn7m8f9O8tn+Sppes+fedTa9++Uvk0MPTf78z5NddulgQZg4+hMwifQmYGzmE/DelGSPJHvOPL40o6tB0bHOp3dve1ty3XXJYYeZ3jFU+hMwifQmYGzu8hy81todSc5P8v0kOyX58ySX91vW9Ol8enf99clRRyXPelbyx3/cwYIwefQnYBLpTcA4rXZOVFUPTLLrzK/rk3wkSVprf7Y4pU2X2end+97X0fTu8MOTX/96dOVMGBj9CZhEehMwCdYUJb6b5EtJntZauzJJqup1i1LVlJk7vXvhCztY8JprkmOOSXbbLdlhhw4WhImjPwGTSG8Cxm5NH9H8qyQ/TnJuVb27qp6YxIlcPZid3nV237uDD/6P1AjDpD8Bk0hvAsZutQGvtXZ6a+35SR6c5Nwkr01y36p6V1U9ebEKHLrOp3ff/e7oc55/+7fJttt2sCBMHv0JmER6EzAJ5nORlV+31j7UWvvLJFsluSSjq0PRgTPO6Hh6t//+yT3ukeyzTweLwWTTn4BJpDcB43SXAW+u1tqNrbUTWmtP7KugadL59O7CC5NTT03e8IbkvvftYEFYOvQnYBLpTcBi62JmxDo644zkG9/o8MqZ++yTbLpp8vrXd7AYAACw1Ah4YzI7vfu93+toenfOOcnZZydvfWtyr3t1sCAAALDUCHhjMju9O/HEDqZ3rY2md1tvPbq4CgAAMJUEvDHofHp3+unJBRck731vstFGHSwIAAAsRQLeGMyd3t3tbgtc7Pbbk333TR784NGNzQEAgKkl4C2y1kb3Ie9senfKKcnll4+untnJlVoAAIClSiJYZJ/+dHLxxR1N7265ZfRZzx13TP7qr7ooDwAAWMIEvEXU+bl3xx2X/OAHo3PvqjpYEAAAWMoEvEU0O71773s7mN798pfJoYcmT3xisssundQHAAAsbeuNu4BpMTu9u//9kxe9qIMF3/rW5Prrk8MP72AxAABgCEzwFkmn07vrrkuOOmp03t2jH91JfQAAwNJngrcIOp/eHX54cvPNyYoVHSwGAAAMhQneIvjMZzqc3v3gB8kxxyQvfnHykId0Uh8AADAMJng963x6d/DBo/8edFAHiwEAAENigtezz3wmueiijqZ3l1+enHRS8prXJNts00V5AADAgJjg9ajz6d1++yX3uEeyfHkHiwEAAENjgtej2ende97TwfTuwguTj398lBg337yL8gAAgIExwevJ7PRuu+2S3XbrYMHly5PNNkte//oOFgMAAIbIBK8nnU7vzj47Oeec5G1vSzbZpJP6AACA4THB60Fro4tddjK9a200vdtmm2TPPbsoDwAAGCgTvB589rOjU+Y6md59/OOjUeCJJyYbbdRJfQAAwDCZ4HWs03Pvbr99dOXMhzyko8twAgAAQ2aC17HZ6d27393B9O7kk5Pvfjc57bRkfW8VAACwZiZ4Hep0enfLLaPFHv3o5FnPWnhxAADA4BkLdWju9G6DDRa42LvelVxzTfK+9yVVndQHAAAMmwleRzqd3v3iF8mhhya77JI88YkdVAcAAEwDE7yOnHlmh9O7t741ueGG5LDDOqkNAACYDiZ4Heh0enfddclb3pI8+9mj8+8AAADmyQSvA2eemVxwQXLCCR1M7w47LLn55mTFik5qAwAApocJ3gLNTu+23TZ58YsXuNi//3ty7LHJS16SPPjBHVQHAABMExO8Bep0enfQQaMrZh54YBelAQAAU8YEbwE6nd595zujG5u/8pXJNtt0UR4AADBlTPAWoNPp3X77Jfe8Z7J8eSe1AQAA08cEbx11Or274ILkE59I3vjGZLPNuigPAACYQiZ46+isszqa3rWW7L13svnmyete11l9AADA9BHw1sHs9G6bbTqY3p19dnLuucnb355sskkX5QEAAFNKwFsHZ52VnH9+cvzxHUzvli8fJcU99+ysPgAAYDoJeGtp7vTuJS9Z4GKnnZZcfHHyvvclG27YQXUAAMA0E/DWUmfTu9tvH105c4cdkhe9qLP6AACA6SXgrYVOp3fvf39yxRXJxz+eLFvWQXUAAMC0E/DWQmfTu1tuGSXFnXZKnvnMrsoDAACmnIA3T60lBx/c0fTu2GOTa69NTj45qeqiPAAAAAFvvj73ueTrX0+OO26B07tf/CI57LDkSU9K/uzPOqsPAABgvXEXsBTMnnu39dbJS1+6wMXe8pbkhhtGIQ8AAKBDJnjz0Nn07qc/HQW85zwn2XHHzuoDAABIep7gVdVTquqKqrqyqvZew37PrqpWVROXejqd3h166OgCKytWdFEasI6G0JuAYdKfgIXqLeBV1bIkxyR5apIdkuxaVTusYr9Nkrwmyfl91bIQs9O7ffdd4PTu+98fjQBf+tLkQQ/qqjxgLQ2lNwHDoz8BXehzgrdTkitba1e31m5L8uEkz1jFfm9O8g9JbumxlnXS6fTuoINGV8w88MAOKgMWYMn3JmCw9CdgwfoMeFsmuWbO42tntv0/VfXIJFu31j69poWqao+quqiqLrruuuu6r3Q1Pv/5jqZ3l12WnHJK8qpXJVtt1Vl9wDpZ8r0JGCz9CViwsV1Fs6rWS/LWJG+4q31baye01nZsre24+eab919cOp7e7bdfcs97Jnuv9qP0wISY9N4ETC/9CZiPPgPeD5NsPefxVjPbZm2S5GFJzquq7yfZOcnKSTlZ+POfT772tWSffRY4vfv615PTT0/22ivZbLPO6gPW2ZLuTcCg6U/AgvUZ8C5Msn1V3b+qNkjy/CQrZ59srf28tbZZa2271tp2Sb6e5OmttYt6rGleOpvetZYsX55svnnyutd1VR6wMEu2NwGDpz8BC9ZbwGut3Z7kVUnOSnJ5ko+21i6rqkOq6ul9vW4X5k7vNtxwgQudd97oI5obb9xVecACLOXeBAyb/gR0oVpr465hrey4447toov6+x9VrSWPe1xy7bXJv/7rAgLeHXckO+2UXH99csUVC0yKMGxVdXFrbUl/xKjv3gSMh/4ETKI19ab1F7uYSTc7vXvXuxaYyU47Lbn44uSkk4Q7AABgUYztKpqTqLXk4IM7OPfu9ttHH8vcYYfkhS/srD4AAIA1McGb4+yzk69+NTn22AUO3U46Kfne90ZXz1y2rKvyAAAA1sgEb8bslTO32ip52csWsNBvfjNaaOedk6c7HxoAAFg8JngzOpveHXts8sMfJh/4QFLVWX0AAAB3xQQvHU7vfv7z5LDDkic/OXnCEzqqDgAAYH5M8NLh9O6oo5Kf/WwU8gAAABbZ1E/wOpve/eQnydveljz3ucmjHtVVeQAAAPM29RO8c87paHp36KHJLbckb35zZ7UBAACsjame4HU2vfv+95Pjjhst8sAHdlQdAADA2pnqCd455yRf+UpyzDELnN4deGCy3nrJAQd0VhsAAMDamtoJ3uz0bsstk913X8BC3/52csopyatfPRoFAgAAjMnUTvA6m97tt1+yySbJ3nt3VhsAAMC6mMoJXmfTu699LfnkJ5O99ko23bSr8gAAANbJVE7wOpnetZYsX57c977Ja1/baX0AAADrYuoCXmfTu899Lvk//yd5xzuSjTfuqjwAAIB1NnUB7wtfGE3vjj56AdO7O+4YTe+22y7ZY48uywMAAFhnUxXwOpvenXpqcsklycknL/AKLQAAAN2ZqoD3hS8kX/7yaHq30UbruMhvfzu6cuZDH5q84AWd1gcAALAQUxPwOpvenXRS8q//Orp65rJlHVUHAACwcFMT8DqZ3v3mN6OU+JjHJH/5l12WBwAAsGBTEfA6m94dfXTyox8lH/pQUtVVeQAAAJ2YioDXyfTuppuSww9PnvKU5E//tNP6AAAAurDeuAvoW2vJwQcnW2yxwOndUUclN96YHHZYZ7UBAAB0afATvHPPTb70peSd71zA9O4nP0ne9rbkec9LHvGITusDAADoyqAneLPn3m2xRfLyly9goRUrkltvTd785q5KAwAA6NygJ3idTO/+7d+S448ffb5z++07rQ8AAKBLg53gdTa9O/DA0f3uDjigq9IAAAB6MdgJXifTu3/5l+QDH0je+MbRPRYAAAAm2GAneJdemmy33QKnd/vtl9zrXsnee3dVFgAAQG8GG/Be+9rku99dwPTuq19NVq5M9toruc99Oq0NAACgD4MNeEmy4Ybr+I2tJcuXJ/e7X/Ka13RaEwAAQF8Gew7egpx1VvLFLyZHH51svPG4qwEAAJiXQU/w1skdd4ymd/e/f/KKV4y7GgAAgHkzwbuzj30s+eY3k1NOSTbYYNzVAAAAzJsJ3ly//e3oypkPe1iy667jrgYAAGCtmODNdeKJyZVXjq6euWzZuKsBAABYKyZ4s26+OTnkkOSxj02e9rRxVwMAALDWTPBmHX108qMfJf/8z0nVuKsBAABYayZ4SXLTTckRRyRPfWry+MePuxoAAIB1IuAlyZFHJjfemBx66LgrAQAAWGcC3v/9v8nb3548//nJIx4x7moAAADWmYC3YkVy223Jm9887koAAAAWZLoD3tVXJ8cfn+y+e/KAB4y7GgAAgAWZ7oB34IHJ+usnBxww7koAAAAWbHoD3qWXJh/8YPL3f59sscW4qwEAAFiw6Q14++6b3OteyZveNO5KAAAAOjGdAe8rX0nOOGMU7u5zn3FXAwAA0InpC3itJcuXJ/e73+jjmQAAAAOx/rgLWHRnnpl86UvJMcck97znuKsBAADozHRN8O64YzS9u//9k5e/fNzVAAAAdGq6Jngf+UjyrW8lH/hAssEG464GAACgU9Mzwfvtb5P990/+8A+TXXcddzUAAACdm54J3nvfm1x1VfKpTyXrTU+uBQAApsd0JJ2bb04OOSR53OOSv/iLcVcDAADQi+mY4L3zncmPfzw6B69q3NUAAAD0YvgTvBtvTI44Ivlf/yv5kz8ZdzUAAAC9GX7AO/LI5KabkkMPHXclAAAAvRp2wPvxj5O3v3101cyHP3zc1QAAAPRq2AFvxYrR7REOOWTclQAAAPRuuAHvqquSE05IXv7y5AEPGHc1AAAAvRtuwDv++ORudxvd3BwAAGAKDDfgHX548uUvJ1tsMe5KAAAAFsVwA96yZckjHznuKgAAABbNcAMeAADAlBHwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAWA9eFAAAMl0lEQVQEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABqLXgFdVT6mqK6rqyqraexXPv76qvlNVl1bVOVW1bZ/1ACR6EzC59CdgoXoLeFW1LMkxSZ6aZIcku1bVDnfa7ZIkO7bW/jDJqUn+sa96ABK9CZhc+hPQhT4neDslubK1dnVr7bYkH07yjLk7tNbOba3dPPPw60m26rEegERvAiaX/gQsWJ8Bb8sk18x5fO3MttXZPclnV/VEVe1RVRdV1UXXXXddhyUCU0hvAiaV/gQs2ERcZKWqXphkxyRHrur51toJrbUdW2s7br755otbHDC19CZgUulPwOqs3+PaP0yy9ZzHW81s+0+qapck+yb509barT3WA5DoTcDk0p+ABetzgndhku2r6v5VtUGS5ydZOXeHqnpEkuOTPL219tMeawGYpTcBk0p/Ahast4DXWrs9yauSnJXk8iQfba1dVlWHVNXTZ3Y7MsnGST5WVd+sqpWrWQ6gE3oTMKn0J6ALfX5EM621zyT5zJ22HTDn6136fH2AVdGbgEmlPwELNREXWQEAAGDhBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgRDwAAAABkLAAwAAGAgBDwAAYCAEPAAAgIEQ8AAAAAZCwAMAABgIAQ8AAGAgBDwAAICBEPAAAAAGQsADAAAYCAEPAABgIAQ8AACAgeg14FXVU6rqiqq6sqr2XsXzG1bVR2aeP7+qtuuzHoBEbwIml/4ELFRvAa+qliU5JslTk+yQZNeq2uFOu+2e5MbW2gOSvC3JP/RVD0CiNwGTS38CutDnBG+nJFe21q5urd2W5MNJnnGnfZ6R5P0zX5+a5IlVVT3WBKA3AZNKfwIWbP0e194yyTVzHl+b5I9Xt09r7faq+nmSTZNcP3enqtojyR4zD39VVVfMfH3vJD+fs+udH29257V6cufX7et772rfNT2/uudWtd1xnf/zjuu67zv3+W3XsZ51MY7etKptQ3q/57Pf2v4cres2x3XNzzmua39ch96fhvxez2dff8ev2/c6rv18bzf/dmqt9fIryXOSvGfO4xclOfpO+3w7yVZzHl+VZLO1eI0T7uLxRX39/tb0un19713tu6bnV/fcqrY7ro7ruI9rz7//Re9NQ3+/57Pf2v4cres2x9Vx7fu49vz792+njr/X3/GO67Qc17m/+vyI5g+TbD3n8VYz21a5T1Wtn1EqvWEtXuNTd/F4sSzkddfme+9q3zU9v7rnVrXdcZ3/847ruu87rt//OHrT6rYthsV4v+ez39r+HC1k22JwXPuxFI5rn/zbqfvv9Xd8P9/ruPbzvZ3826lm0mDnZprO95I8MaNmdGGSF7TWLpuzzyuT/EFrbc+qen6Sv2qtPbfDGi5qre3Y1XqMOK79cFwXxyT0ppnX8H73wHHth+O6OCahP3mv++G49sNxXbXezsFro8+FvyrJWUmWJTmxtXZZVR2S0Th1ZZL3Jjmlqq5M8rMkz++4jBM6Xo8Rx7UfjusimJDelHi/++K49sNxXQQT0p+81/1wXPvhuK5CbxM8AAAAFlevNzoHAABg8Qh4AAAAAyHgAQAADISABwAAMBBTG/Cq6iFVdVxVnVpVfzvueoaiqp5ZVe+uqo9U1ZPHXc9QVNXvVdV7q+rUcddCv/SmfuhN/dCbpov+1A/9qR/T3J+WZMCrqhOr6qdV9e07bX9KVV1RVVdW1d5rWqO1dnlrbc8kz03yuD7rXSo6Oq6nt9ZekWTPJM/rs96loqPjenVrbfd+K2Wh9KZ+6E390Jumi/7UD/2pH/rTwizJ2yRU1eOT/CrJya21h81sW5bRzUGflOTajG4OumtG95E5/E5LvKy19tOqenqSv01ySmvtQ4tV/6Tq6rjOfN9bknywtfaNRSp/YnV8XE9trT1nsWpn7ehN/dCb+qE3TRf9qR/6Uz/0p4Xp7UbnfWqtfbGqtrvT5p2SXNlauzpJqurDSZ7RWjs8ydNWs87KJCur6tNJpr5JdXFcq6qSHJHksxrUSFd/Xpl8elM/9KZ+6E3TRX/qh/7UD/1pYZbkRzRXY8sk18x5fO3MtlWqqidU1Tuq6vgkn+m7uCVsrY5rklcn2SXJc6pqzz4LW+LW9s/rplV1XJJHVNXyvoujU3pTP/SmfuhN00V/6of+1A/9aZ6W5ASvC62185KcN+YyBqe19o4k7xh3HUPTWrsho8/mM3B6Uz/0pn7oTdNFf+qH/tSPae5PQ5rg/TDJ1nMebzWzjYVxXPvhuE4P73U/HNd+OK7TxfvdD8e1H47rPA0p4F2YZPuqun9VbZDk+UlWjrmmIXBc++G4Tg/vdT8c1344rtPF+90Px7Ufjus8LcmAV1X/nORrSR5UVddW1e6ttduTvCrJWUkuT/LR1tpl46xzqXFc++G4Tg/vdT8c1344rtPF+90Px7UfjuvCLMnbJAAAAPBfLckJHgAAAP+VgAcAADAQAh4AAMBACHgAAAADIeABAAAMhIAHAAAwEAIea1RVv1qE19izqnbr+3Xu9JrPrKodFvM1ge7oTcCk0p8YN/fBY42q6lettY07WGdZa+13XdTUxWtW1UlJzmitnbqYNQHd0JuASaU/MW4meMxbVe1VVRdW1aVVdfCc7adX1cVVdVlV7TFn+6+q6i1V9a0kj5l5fGhVfauqvl5V95vZ76CqeuPM1+dV1T9U1QVV9b2q+pOZ7feoqo9W1Xeq6hNVdX5V7biKGr8/8/3fSPLXVfWKmZq/VVWnzazz2CRPT3JkVX2zqn5/5teZM7+PL1XVg/s9mkBX9CZgUulPjIOAx7xU1ZOTbJ9kpyQPT/Koqnr8zNMva609KsmOSf6+qjad2X7PJOe31v6otfblmcdfb639UZIvJnnFal5u/dbaTklem+TAmW1/l+TG1toOSfZP8qg1lHtDa+2RrbUPJ/l4a+3RM695eZLdW2tfTbIyyV6ttYe31q5KckKSV8/8Pt6Y5Ni1OT7AeOhNwKTSnxiX9cddAEvGk2d+XTLzeOOMmtYXM2pMz5rZvvXM9huS/C7JaXPWuC3JGTNfX5zkSat5rY/P2We7ma//vyT/lCSttW9X1aVrqPUjc75+WFWtSPLfZmo+6847V9XGSR6b5GNVNbt5wzWsD0wOvQmYVPoTYyHgMV+V5PDW2vH/aWPVE5LskuQxrbWbq+q8JBvNPH3LnT7H/dv2Hyd9/i6r//N36zz2WZNfz/n6pCTPbK19q6pekuQJq9h/vSQ3tdYevg6vBYyX3gRMKv2JsfARTebrrCQvm/k/NqmqLavqvknundH4/+aZz17v3NPrfyXJc2dee4ckfzDP79skyY+r6m5J/mbO9l/OPJfW2i+S/FtV/fXM+lVVf9RV4UCv9CZgUulPjIWAx7y01j6X5ENJvlZV/5Lk1Ix+yM9Msn5VXZ7kiCRf76mEY5NsXlXfSbIiyWVJfj6P79s/yfkZNbnvztn+4SR7VdUlVfX7GTWw3WdOar4syTO6LB7oh94ETCr9iXFxmwSWhKpaluRurbVbZprK2Uke1Fq7bcylAVNMbwImlf40vZyDx1JxjyTnznxcoJL8nQYFTAC9CZhU+tOUMsEDAAAYCOfgAQAADISABwAAMBACHgAAwEAIeAAAAAMh4AEAAAzE/w9eMZwXkBkbHgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaoQQhLt0kF0",
        "outputId": "23adfc57-b8de-447e-8fcc-66dba47bf1a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "learning_rates = [0.001, 0.01, 0.1, 0.2, 0.4]\n",
        "n_estimators_list = [100, 500, 1000]\n",
        "\n",
        "i = 0\n",
        "plt.figure(figsize=(15,5))\n",
        "\n",
        "for est in n_estimators_list:\n",
        "\n",
        "  i = i + 1\n",
        "  scores = list()\n",
        "  score_tests = list()\n",
        "\n",
        "  for rate in learning_rates:\n",
        "\n",
        "    params = {'loss': 'deviance', ##  loss{‘deviance’, ‘exponential’}, default=’deviance’\n",
        "          'learning_rate': rate, ##  learning_ratefloat, default=0.1\n",
        "          'n_estimators': est, ##  number of iterations, int, default=100\n",
        "          'max_depth': 3, ##  int, default=3\n",
        "          'subsample': 1, ## float, default=1.0\n",
        "          'min_samples_split': 2 ## int or float, default=2\n",
        "          }\n",
        "    clf_cv = ensemble.GradientBoostingClassifier(**params)\n",
        "    clf_cv.fit(X1_train, y1_train)\n",
        "    cv_score = clf_cv.score(X1_train, y1_train)\n",
        "    scores.append(np.mean(cv_score))\n",
        "    score_test = clf_cv.score(X1_test, y1_test)\n",
        "    score_tests.append(score_test)\n",
        "\n",
        "  plt.subplot(1,3,i)\n",
        "  plt.semilogx(learning_rates, scores, 'b')\n",
        "  plt.semilogx(learning_rates, score_tests, 'r')\n",
        "  plt.title('N estimators = {}'.format(est))\n",
        "  plt.xlabel('learning rate')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.ylim(0,1.1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTASS1uYJMzx",
        "outputId": "857aeaba-5922-4fcb-fda1-5a8142575411",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "subsamples = [0.2, 0.6, 1]\n",
        "n_estimators_list = [100, 500, 1000]\n",
        "\n",
        "i = 0\n",
        "plt.figure(figsize=(15,5))\n",
        "\n",
        "for est in n_estimators_list:\n",
        "\n",
        "  i = i + 1\n",
        "  scores = list()\n",
        "  score_tests = list()\n",
        "\n",
        "  for sub in subsamples:\n",
        "\n",
        "    params = {'loss': 'deviance', ##  loss{‘deviance’, ‘exponential’}, default=’deviance’\n",
        "          'learning_rate': 0.2, ##  learning_ratefloat, default=0.1\n",
        "          'n_estimators': est, ##  number of iterations, int, default=100\n",
        "          'max_depth': 3, ##  int, default=3\n",
        "          'subsample': sub, ## float, default=1.0\n",
        "          'min_samples_split': 2 ## int or float, default=2\n",
        "          }\n",
        "    clf_cv = ensemble.GradientBoostingClassifier(**params)\n",
        "    clf_cv.fit(X_train, y_train)\n",
        "    cv_score = clf_cv.score(X_train, y_train)\n",
        "    scores.append(np.mean(cv_score))\n",
        "    score_test = clf_cv.score(X_test, y_test)\n",
        "    score_tests.append(score_test)\n",
        "\n",
        "  plt.subplot(1,3,i)\n",
        "  plt.plot(subsamples, scores, 'b')\n",
        "  plt.plot(subsamples, score_tests, 'r')\n",
        "  plt.title('n_estimators = {}'.format(est))\n",
        "  plt.xlabel('sub samples')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.ylim(0,1.1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVTIoYNgKmDl"
      },
      "source": [
        "Based on the accuracy plot, max_depth=3, learning_rate=0.2, n_estimators=1000, subsample=0.6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oynJgr5F-oU"
      },
      "source": [
        "GB classifer training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sE9RNXUlgTj",
        "outputId": "9955decf-5153-416c-c834-8ea7cc67fd1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "params = {'loss': 'deviance', ##  loss{‘deviance’, ‘exponential’}, default=’deviance’\n",
        "          'learning_rate': 0.2, ##  learning_ratefloat, default=0.1\n",
        "          'n_estimators': 1000, ##  number of iterations, int, default=100\n",
        "          'max_depth': 3, ##  int, default=3\n",
        "          'subsample': 0.6, ## float, default=1.0\n",
        "          'min_samples_split': 2 ## int or float, default=2\n",
        "          }\n",
        "clf_GB = ensemble.GradientBoostingClassifier(**params)\n",
        "print(cross_val_score(clf_GB, X1_train, y1_train, cv=5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.65406427 0.7221172  0.65028355 0.62121212 0.66098485]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWmV016QK3h2",
        "outputId": "67144221-5658-44dd-fdb0-85e68c5a5ffd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        }
      },
      "source": [
        "params = {'loss': 'deviance', ##  loss{‘deviance’, ‘exponential’}, default=’deviance’\n",
        "          'learning_rate': 0.2, ##  learning_ratefloat, default=0.1\n",
        "          'n_estimators': 1000, ##  number of iterations, int, default=100\n",
        "          'max_depth': 3, ##  int, default=3\n",
        "          'subsample': 0.6, ## float, default=1.0\n",
        "          'min_samples_split': 2 ## int or float, default=2\n",
        "          }\n",
        "clf_GB = ensemble.GradientBoostingClassifier(**params)\n",
        "clf_GB.fit(X1_train, y1_train)\n",
        "preds_GB = clf_GB.predict(X1_test)\n",
        "print(classification_report(y1_test, preds_GB))\n",
        "cm_test_GB = confusion_matrix(y1_test, preds_GB)\n",
        "plot_confusion_matrix(cm_test_GB, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      0.56      0.72        16\n",
            "           2       0.74      0.86      0.79        29\n",
            "           3       0.71      0.86      0.77        14\n",
            "           4       0.62      0.71      0.67         7\n",
            "           5       0.82      0.50      0.62        18\n",
            "           6       0.54      0.57      0.55        23\n",
            "           7       1.00      0.67      0.80         3\n",
            "           8       0.68      0.74      0.71        23\n",
            "           9       0.70      1.00      0.82         7\n",
            "\n",
            "    accuracy                           0.71       140\n",
            "   macro avg       0.76      0.72      0.72       140\n",
            "weighted avg       0.73      0.71      0.70       140\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mgshnWLLJsK"
      },
      "source": [
        "GB blind well predictin"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmEU1GXkLo1h",
        "outputId": "6ced1355-ad13-49b2-f902-a229052dee2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "pred_GB_test_well = clf_GB.predict(X_test_well)\n",
        "print(classification_report(y_test_well, pred_GB_test_well, target_names=['SS', 'CSiS', 'FSiS', 'SiSH', 'MS', 'WS','D','PS', 'BS']))\n",
        "cm_GB = confusion_matrix(y_test_well, pred_GB_test_well)\n",
        "plot_confusion_matrix(cm_GB, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in true_divide\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          SS       0.50      0.07      0.12        89\n",
            "        CSiS       0.34      0.76      0.47        89\n",
            "        FSiS       0.77      0.54      0.63       117\n",
            "        SiSH       0.05      0.14      0.08         7\n",
            "          MS       0.11      0.11      0.11        19\n",
            "          WS       0.66      0.61      0.63        71\n",
            "           D       0.83      0.29      0.43        17\n",
            "          PS       0.48      0.50      0.49        40\n",
            "          BS       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.46       449\n",
            "   macro avg       0.41      0.34      0.33       449\n",
            "weighted avg       0.55      0.46      0.45       449\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zi3gdQk1efVf"
      },
      "source": [
        "3.3. Random forest (RF) parameter optimization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1TTVj4Cej3g",
        "outputId": "fab1721f-f6b1-4d69-8a5c-9e49bc9c20c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "max_depths = [2, 3, 4]\n",
        "n_estimators_list = [100, 500, 1000, 2000, 5000]\n",
        "\n",
        "i = 0\n",
        "plt.figure(figsize=(15,5))\n",
        "\n",
        "for depth in max_depths:\n",
        "\n",
        "  i = i + 1\n",
        "  scores = list()\n",
        "  score_tests = list()\n",
        "\n",
        "  for est in n_estimators_list:\n",
        "\n",
        "    params = {'n_estimators': est, ##  number of iterations, int, default=100\n",
        "          'max_depth': depth, ##  int, default=3\n",
        "          }\n",
        "    clf_cv = ensemble.RandomForestClassifier(**params)\n",
        "    clf_cv.fit(X1_train, y1_train)\n",
        "    cv_score = clf_cv.score(X1_train, y1_train)\n",
        "    scores.append(np.mean(cv_score))\n",
        "    score_test = clf_cv.score(X1_test, y1_test)\n",
        "    score_tests.append(score_test)\n",
        "\n",
        "  plt.subplot(1,4,i)\n",
        "  plt.plot(n_estimators_list, scores, 'b')\n",
        "  plt.plot(n_estimators_list, score_tests, 'r')\n",
        "  plt.title('max depth = {}'.format(depth))\n",
        "  plt.xlabel('n_estimators')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.ylim(0,1.1)\n",
        "\n",
        "scores = list()\n",
        "score_tests = list()\n",
        "for est in n_estimators_list:\n",
        "  clf_cv = ensemble.RandomForestClassifier(n_estimators=est)\n",
        "  clf_cv.fit(X1_train, y1_train)\n",
        "  cv_score = clf_cv.score(X1_train, y1_train)\n",
        "  scores.append(np.mean(cv_score))\n",
        "  score_test = clf_cv.score(X1_test, y1_test)\n",
        "  score_tests.append(score_test)\n",
        "plt.subplot(1,4,4)\n",
        "plt.plot(n_estimators_list, scores, 'b')\n",
        "plt.plot(n_estimators_list, score_tests, 'r')\n",
        "plt.title('max depth = {}'.format('None'))\n",
        "plt.xlabel('n_estimators')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0,1.1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hk7BqAZboRBR"
      },
      "source": [
        "Max_depth can be default (None), and n_estimator = 2000 gives best accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbzYALUOIxbS"
      },
      "source": [
        "RF classifier training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86kjZr3_lQgt",
        "outputId": "961ea566-051a-4454-a2e0-dc58949f0140",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        }
      },
      "source": [
        "clf_RF = ensemble.RandomForestClassifier(n_estimators=2000)\n",
        "print(cross_val_score(clf_RF, X1_train, y1_train, cv=5))\n",
        "clf_RF.fit(X1_train, y1_train)\n",
        "preds_RF = clf_RF.predict(X1_test)\n",
        "print(classification_report(y1_test, preds_RF))\n",
        "cm_test_RF = confusion_matrix(y1_test, preds_RF)\n",
        "plot_confusion_matrix(cm_test_RF, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.70888469 0.75614367 0.70510397 0.73106061 0.71022727]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      0.62      0.77        16\n",
            "           2       0.74      0.90      0.81        29\n",
            "           3       0.73      0.79      0.76        14\n",
            "           4       0.56      0.71      0.63         7\n",
            "           5       0.88      0.39      0.54        18\n",
            "           6       0.64      0.70      0.67        23\n",
            "           7       1.00      1.00      1.00         3\n",
            "           8       0.67      0.78      0.72        23\n",
            "           9       0.88      1.00      0.93         7\n",
            "\n",
            "    accuracy                           0.74       140\n",
            "   macro avg       0.79      0.77      0.76       140\n",
            "weighted avg       0.76      0.74      0.73       140\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6k395woLEbg"
      },
      "source": [
        "RF blind well prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lX1PB7i8LYWi",
        "outputId": "2c6bd487-c2d2-40ec-97fb-92532bbcc2c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "source": [
        "preds_RF_test_well = clf_RF.predict(X_test_well)\n",
        "print(classification_report(y_test_well, preds_RF_test_well))\n",
        "cm_RF = confusion_matrix(y_test_well, preds_RF_test_well)\n",
        "plot_confusion_matrix(cm_RF, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.38      0.06      0.10        89\n",
            "           2       0.36      0.79      0.49        89\n",
            "           3       0.85      0.63      0.73       117\n",
            "           4       0.07      0.14      0.09         7\n",
            "           5       0.38      0.16      0.22        19\n",
            "           6       0.67      0.65      0.66        71\n",
            "           7       0.92      0.71      0.80        17\n",
            "           8       0.53      0.62      0.57        40\n",
            "\n",
            "    accuracy                           0.53       449\n",
            "   macro avg       0.52      0.47      0.46       449\n",
            "weighted avg       0.57      0.53      0.50       449\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRgVLWGNoKLj"
      },
      "source": [
        "3.4. KNN Parameter optimzation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPHKJt2WoNUz",
        "outputId": "e23fbaf2-d44b-462d-e790-1c23ab51b345",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        }
      },
      "source": [
        "neighbor_list = [5, 7, 10]\n",
        "weight_list = ['uniform', 'distance']\n",
        "i=0\n",
        "\n",
        "for weight in weight_list:\n",
        "  scores = list()\n",
        "  score_tests = list()\n",
        "  i = i + 1\n",
        "\n",
        "  for neighbor in neighbor_list:\n",
        "    clf_cv = KNeighborsClassifier(n_neighbors=neighbor, weights=weight)\n",
        "    clf_cv.fit(X1_train, y1_train)\n",
        "    scores.append(clf_cv.score(X1_train, y1_train))\n",
        "    score_tests.append(clf_cv.score(X1_test, y1_test))\n",
        "    print(scores)\n",
        "\n",
        "  plt.subplot(1,3,i)\n",
        "  plt.plot(neighbor_list, scores, 'b')\n",
        "  plt.plot(neighbor_list, score_tests, 'r')\n",
        "  plt.title('Weight = {}'.format(weight))\n",
        "  plt.xlabel('Number of neighbors')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.ylim(0,1.1)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.6662883087400681]\n",
            "[0.6662883087400681, 0.6348846008323874]\n",
            "[0.6662883087400681, 0.6348846008323874, 0.6008323874385169]\n",
            "[0.9973514945138101]\n",
            "[0.9973514945138101, 0.9973514945138101]\n",
            "[0.9973514945138101, 0.9973514945138101, 0.9973514945138101]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sc+bMmXzpS18CGCvpYrerVRIk3SJifWEgItZLanNvCkldgauATwBLgUckTY+IZ4qmGQVcAhwSEW9IGrTV76ATkWDgwHQbXybGI9LG3nJB8+yzMGNGcdBsYMCAHenfvxAuO/LSS+uZOrVl4JTumNfc3MzkyZO5//772WuvveYDk9yuVkmQvCbp+IiYDiDpBOD1Cp53ELAoIl7InncLcAJpA23BOcBVEfEGQES8ujXFW0sSDBqUbgcdtPnjxUHz+c8PZP/9p9Oz5/G8+CI8/PDPWbVqAKXbWAtBM348/OAHMHfuXEaOHMmIESMg9TbcrlZRkJwH3CxpKiBgCXB6Bc9ryqYtWAocXDLNBwAk/Ya0+nNpRMwsnZGkc4FzAfbYY48KXtrKKQ6an/3saj796U+zfPn5RAR77DGU2bN/TN++5Xs0q7M9hpYtW8bQoUOLZ+t2tYp2SPsj8GFJvbPhNVV+/VHAYcAQYI6k/SLizZIargWuhbRRroqv32nttddePPzww6xZk5qzd9E6zG67wcGl0bB13K6dTEUH7Un6JLAv0EPZsfgRcVkbT1sGFC+6hmTjii0Ffh8R/wsslvQ86QvY9hY/y+2ee+5h/vz5rFu37v1x3/zmN1t9TlNTE0uWFHc03a5WwRnSJF1NOt7mAtKqzURgzwrm/QgwStLwbOPsqcD0kmn+h7TUQtIAUpf4hUqLt2133nnnceutt/K9732PiOD222/npZdeavN548ePZ+HChSxevBjS98HtahARrd6AJ0v+9gZ+1dbzsmmPBZ4H/gh8PRt3GXB8dl/Af5A21D0FnNrWPA888MCw/Pbbb78Wf1evXh0f+9jHKnruPffcE6NGjQpgndt1+wPMiwr+v4tvlazaFPq9ayXtDqwCBlcYUr8AflEy7ptF9wP42+xm7aiwI1rPnj1Zvnw5/fv3Z8WKFRU999hjj+XYY49F0tMR8S/gdu3sKgmSuyT1A64AHiP95HddTauymjvuuON48803+drXvsYBBxyAJM4555x6l2UNqtUgkdQFmBVpa/udku4GekTEW+1SndXExo0bmTBhAv369eOkk07iU5/6FOvWrWPneh+KbA2r1Y2tkc6KdlXR8HsOkcbXpUsXJk+e/P5w9+7dHSKWSyXn9Zol6SQVfve17cKECRO48847CxvFzXKpJEi+QDpI7z1Jb0taLentGtdlNXbNNdcwceJEunfvTt++fenTpw99t/WcBNbpVbJnq8+7tx1avXq7OUumdQCVnCHt0HLjo+RER9ZY5swp33ylJzoyq0QlP/9+reh+D9JRvY8Cf1mTiqxdXHHFFe/fX7duHXPnzuXAAw/kwQcfTIcJv/kmLFu26bbDDvDZz9axYquK1avTtW4L7frGG3DhhblnW8mqzXHFw5KGAlNyv3I1NDen07z37FnvShrH+vWwYgV3XXJJiy/Ukl69uOg3v0kXT162bPMzJI0e7SDpyJqb0zVsi8O/3K10lVaCL34xLShy2JYr7S0F9sn1qtUyfz78+Z+nU4rtsQfsuWf6W3p/0KDt/8K/5XoR5W6vljk1SPfuDBk8mGffeQcmTIDjj4emppa3wRXtzGy1sHr1ltuzsDBYuXLTBZgKunVL7dbUlK6T8olPbN6uTU25QwQq20byPdLerJB+5fkQaQ/X+uvfH779bXjpJXj5ZXj+ebj/flhTcqaD7t03hUq5wBkyhPfPOdgRZb2IVgNi+fLNexGQQrbwhRk3Lv3dfXcumD4d9ekDvXqxcccdefyJJzjgkEPgppva//11Vs3NKQBKQ6GtXgSkywsU2nXMmPIBMWhQu125rZIeSfEZeTeQLoz1mxrVs3WamuCSS1qOKyyZX3453QohU7h/773pn7J0/4nddmsZMKWB079/9Xs1EWkdtdwSpoJexGYBUXrbffc0XRnjiq492q1bNyaddhqHFF/b1PJprRdRuK1cmS7xWKy0F3HkkeXbtVev+ryvLagkSO4A1kVEM6RzsUrqGRFr23hefUjp1Oq77JJWe8pZvz41ZHHIFIKmcJ3O0qV7z56t92qamlpeGLjavYjS26675gq2k08+mR49etC1a1cgnYt17dq19PT2ptZt2FDZtojSXjGka4sU2m/s2M3DoZ17EdVUSZDMAo4ACp/MTsB9tDyzfGPZccd03Ybhw8s/XrguRGnIFO4/8UT6MhUrXFS4f/+0pCm95B607EWMH18+IAYP3mIvopomTJjAAw888P6Z0d59912OPPJIfvvb39b8tdu0bh18/OP1rqKlDRtSu26pF1EIgv32g6OOaoheRDVVEiQ9ouj0ihGxJrvK3var+LoQBxxQfpp162Dp0s1DZtUq+MhHNl/SVKEXUU3r1q1rcXrF3r17s7b0ojn1IqXPqiPp0iX1cMuF/8CBDdmLqKZKguQdSQdExGMAkg4EyvTHO5kePWDkyHRrQL169eKxxx7jgCwoH330UXbaaac6V5Xp3j1dO8MaRiVBchFwu6TlpDNf/Rnp1IvWwKZMmcLEiRPZfffdiQhWrlzJrbfeWu+yrEFVskPaI5L2BkZnoxZEOqmvNbDx48fz3HPPsWDBAgBGjx7NDlXYn8A6p0pO/jwZ6BURT0fE00BvSX9T+9Kslq666ireeecdxo4dy9ixY1mzZg3f//73612WNahKthCdE0XXI4l09TSfk6/BXXfddfTr1+/94V122YXrrvMZNG3bVBIkXYtPapRd07fNa/9ax9bc3NzipEbNzc2sX7++lWeYbVklG1tnArdKuiYb/gLgTeoN7uijj+aUU07hC1/4ApBOdHTMMcfUuSprVJUEyd+Trs96Xjb8JOmXG2tgl19+Oddeey1XX301AB/84AdZuXJlnauyRtXmqk12AujfAy+SzkXyl8CztS3Laq1Lly4cfPDBDBs2jLlz5/Lggw+yzz4d46Buazxb7JFI+gAwKbu9DtwKEBGHt09pVgvPP/8806ZNY9q0aQwYMIBTTkm7BM2ePbvOlVkja23V5jngV8CnImIRgKQvt0tVVjN77703f/EXf8Hdd9/NyGyv3CuvvLLOVVmja23V5q+AFcBsSddJmkDas9Ua2E9/+lMGDx7M4YcfzjnnnMOsWbN8SQrLbYtBEhH/ExGnAnsDs0m7yg+S9ANJR7ZXgVZdJ554IrfccgvPPfcchx9+OFOmTOHVV1/li1/8Ivfdd1+9y7MGVcnG1nci4r+zc7cOAf5A+iXHGlivXr047bTTuOuuu1i6dCn7778/l19+eb3Lsga1Vcc+R8QbEXFtREyoVUHW/nbZZRfOPfdcZs2aVe9SrEF17pMomFlV1DRIJB0taYGkRZIubmW6kySFpHG1rMeqY+bMmYwePRpgrNvVoIZBkh2TcxVwDDAGmCRpTJnp+gBfIu30Zh1cc3MzkydPZkY68dB83K5GbXskBwGLIuKFiFgP3AKcUGa6fwIuB9bVsBarkrlz5zJy5EhGjBgB6TIlbleraZA0AUuKhpdm494n6QBgaETc09qMJJ0raZ6kea+VO6mytZtly5YxdOjQ4lFuV6vfxlZJXYD/AL7S1rTZL0XjImLcwIEDa1+cbTO3a+dUyyBZBhQvuoZk4wr6AGOBhyS9CHwYmO4Ncx1bU1MTS5YUdzTdrrZt1/6t1CPAKEnDSV+0U4HTCg9GxFvAgMKwpIeAr0bEPKzDGj9+PAsXLmTx4sWQDplwu1rteiQRsQE4H7iXdNqB2yJivqTLJB1fq9e12urWrRtTp07lqKOOAtgXt6sBarQDtsaNGxfz5nnh1hFIejQiqrLK4nbtOLalXb1nq5nl5iAxs9wcJGaWm4PEzHJzkJhZbg4SM8vNQWJmuTlIzCw3B4mZ5eYgMbPcHCRmlpuDxMxyc5CYWW4OEjPLzUFiZrk5SMwsNweJmeXmIDGz3BwkZpabg8TMcnOQmFluDhIzy81BYma5OUjMLDcHiZnl5iAxs9wcJGaWm4PEzHJzkJhZbg4SM8utpkEi6WhJCyQtknRxmcf/VtIzkp6UNEvSnrWsx6pj5syZjB49GmCs29WghkEiqStwFXAMMAaYJGlMyWR/AMZFxAeBO4B/rVU9Vh3Nzc1MnjyZGTNmAMzH7WrUtkdyELAoIl6IiPXALcAJxRNExOyIWJsNPgwMqWE9VgVz585l5MiRjBgxAiBwuxq1DZImYEnR8NJs3JacDcwo94CkcyXNkzTvtddeq2KJtrWWLVvG0KFDi0e5Xa1jbGyV9BlgHHBFuccj4tqIGBcR4wYOHNi+xdk2c7t2Ht1qOO9lQPGia0g2rgVJRwBfBz4eEe/VsB6rgqamJpYsKe5oul2ttj2SR4BRkoZL2hE4FZhePIGk/YFrgOMj4tUa1mJVMn78eBYuXMjixYsBhNvVqGGQRMQG4HzgXuBZ4LaImC/pMknHZ5NdAfQGbpf0uKTpW5iddRDdunVj6tSpHHXUUQD74nY1QBFR7xq2yrhx42LevHn1LsMASY9GxLhqzMvt2nFsS7t2iI2tZtbYHCRmlpuDxMxyc5CYWW4OEjPLzUFiZrk5SMwsNweJmeXmIDGz3BwkZpabg8TMcnOQmFluDhIzy81BYma5OUjMLDcHiZnl5iAxs9wcJGaWm4PEzHJzkJhZbg4SM8vNQWJmuTlIzCw3B4mZ5eYgMbPcHCRmlpuDxMxyc5CYWW4OEjPLzUFiZrk5SMwst5oGiaSjJS2QtEjSxWUe7y7p1uzx30saVst6rDpmzpzJ6NGjAca6XQ1qGCSSugJXAccAY4BJksaUTHY28EZEjASuBC6vVT1WHc3NzUyePJkZM2YAzMftatS2R3IQsCgiXoiI9cAtwAkl05wA/Fd2/w5ggiTVsCbLae7cuYwcOZIRI0YABG5XA7rVcN5NwJKi4aXAwVuaJiI2SHoL6A+8XjyRpHOBc7PBNZIW1KDeAaWvW2cdtZ5dgL6SXgL2pOO3a3HtHUFHqgXK17Pn1s6klkFSNRFxLXBtLV9D0ryIGFfL19gaHbUeSScDR0fE57Pxn93WebZHu0LH+iw7Ui1QvXpquWqzDBhaNDwkG1d2GkndgJ2BVTWsyfJzu9pmahkkjwCjJA2XtCNwKjC9ZJrpwOey+ycDD0ZE1LAmy8/tapup2apNtm58PnAv0BX4UUTMl3QZMC8ipgPXAz+RtAj4E+lLWS8172JvpQ5ZTwO2K3Ssz7Ij1QJVqkdeUJhZXt6z1cxyc5CYWW4OEkBSP0l3SHpO0rOSPlLner4sab6kpyVNk9SjnV//R5JelfR00bhdJd0vaWH2d5f2rGlbuF03e/2atauDJPlPYGZE7A38OfBsvQqR1ARcCIyLiLGkDZrtvbHyRuDoknEXA7MiYhQwKxvu6NyuLd1Ijdq10weJpJ2BQ0m/NBAR6yPizfpWRTdgp2wfjJ7A8vZ88YiYQ/q1pVjxbu//BZzYnjVtLbfr5mrZrp0+SIDhwGvADZL+IOmHknrVq5iIWAb8G/AysAJ4KyLuq1c9RXaLiBXZ/ZXAbvUspgJu18pUpV0dJGkpcQDwg4jYH3iHOnbbs3XUE0j/CLsDvSR9pl71lJPtXNbR9xtwu26lPO3qIEkHnS2NiN9nw3eQvoD1cgSwOCJei4j/BX4KfLSO9RS8ImkwQPb31TrX0xa3a2Wq0q6dPkgiYiWwRNLobNQE4Jk6lvQy8GFJPbND7ydQx42ERYp3e/8c8PM61tImt2vFqtKu3rMVkPQh4IfAjsALwJkR8UYd6/lH4BRgA/AH4PMR8V47vv404DDSIeavAN8C/ge4DdgDeAn464go3XDXobhdN3v9mrWrg8TMcuv0qzZmlp+DxMxyc5CYWW4OEjPLzUFiZrk1RJBICkn/XjT8VUmXVmneN2YnNK4pSROzI1BnV2Fel0k6oo1pLpX01TLjhxUf/VlvbtvN5tWQbdsQQQK8B/yVpAH1LqRYdvBVpc4GzomIw/O+bkR8MyIeyDufbaF04bNqctsWadS2bZQg2UA6t+SXSx8oXepIWpP9PUzSLyX9XNILkr4j6dOS5kp6StJeRbM5QtI8Sc9L+lT2/K6SrpD0iKQnJX2haL6/kjSdMntKSpqUzf9pSZdn474JfAy4XtIVJdMfJumhovNm3Jzt+YikA7P38ANt2hwAAANeSURBVKike4t2ZX7/PUs6Nnveo5K+K+nuotmPyeb9gqQLi8Z3y17n2ex1e2bzmpAd4PaU0rkrumfjX5R0uaTHgImSLpT0TPa53FJB+7XGbbs9tG1EdPgbsAboC7xIurTBV4FLs8duBE4unjb7exjwJjAY6E66RMI/Zo99CZhS9PyZpFAdRTpGowfpwk3fyKbpDswjHXB1GOkAsOFl6tydtCv0QNJBYw8CJ2aPPUQ6F0Xpcw4D3iJd1qEL8DvSF3MH4LfAwGy6U0gnWn7/PWd1LinUAkwD7s7uX5o9vztpT8ZV2TyHkQ7MOiSb7kfZ51mY1wey8T8GLsruvwj8XVHNy4Hu2f1+blu3baP0SIiIt0kfwIVtTVvkkYhYEWk35D8ChcO2nyJ96AW3RcTGiFhI2pV6b+BI4HRJjwO/J10pblQ2/dyIWFzm9cYDD0U6MGsDcDPpnBhtmRsRSyNiI/B4VttoYCxwf1bDN0hfyGJ7Ay8U1TKt5PF7IuK9iHiddDBW4RDxJRHxm+z+TaQv92jSQWXPZ+P/q6T2W4vuPwncrHT06oYK3l+r3LaN37YNcaW9IlOAx4AbisZtIFtFk9SFdFxFQfFxDBuLhjfS8r2XHicQgIALIuLe4gckHUZaalVTcZ3NWW0C5kdEntMDlpsvlH+/bSl+z58kfRGPA74uab/snysPt23++UKd2rZheiQAkQ4muo20cavgReDA7P7xpC7e1pooqUu2bj0CWEC6bssXJe0AIOkDavvEOHOBj0saoLThahLwy22oh6yGgcrOMyppB0n7lplmhKRh2fApFc57D206f+lpwK+zeQ2TNDIb/9lytWf/0EMjYjbw96TVkd4Vvu4WuW0bu20bKkgy/05aLyy4jtTATwAfYduWKC+TvigzgPMiYh3pqNFngMeUflK7hjZ6cJHONHUxMBt4Ang0IrbpsOyIWE9aV748e2+PU3L+ioh4F/gbYKakR4HVpHXytiwAJkt6lnRR8B9k7/lM4HZJT5GW7FeXeW5X4KZsmj8A343qncLQbbtpmoZqWx/92+Ak9Y6INdmvAVcBCyPiynrXZfk1Uts2Yo/EWjon22A3n9QVvabO9Vj1NEzbukdiZrm5R2JmuTlIzCw3B4mZ5eYgMbPcHCRmltv/ARyIVlTIPyQYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xtMh4_uns4D"
      },
      "source": [
        "Using weight has a better KNN modeling score."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKyPsM8rJDcL"
      },
      "source": [
        "KNN classifer training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9ZjRFianXB-",
        "outputId": "2d293106-ac2f-4736-f125-ddc36ffb1faa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "clf_knn = KNeighborsClassifier(weights='distance')\n",
        "print(cross_val_score(clf_knn, X1_train, y1_train, cv=5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.53497164 0.51228733 0.5557656  0.48295455 0.52462121]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ov-gaZeqr15s",
        "outputId": "3aa83931-50fb-4595-9528-07aca8db8646",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        }
      },
      "source": [
        "clf_knn = KNeighborsClassifier(weights='distance')\n",
        "clf_knn.fit(X1_train, y1_train)\n",
        "preds_knn = clf_knn.predict(X1_test)\n",
        "print(classification_report(y1_test, preds_knn))\n",
        "cm_test_knn = confusion_matrix(y1_test, preds_knn)\n",
        "plot_confusion_matrix(cm_test_knn, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.78      0.44      0.56        16\n",
            "           2       0.49      0.59      0.53        29\n",
            "           3       0.45      0.71      0.56        14\n",
            "           4       0.14      0.14      0.14         7\n",
            "           5       0.60      0.33      0.43        18\n",
            "           6       0.45      0.39      0.42        23\n",
            "           7       0.50      0.33      0.40         3\n",
            "           8       0.54      0.57      0.55        23\n",
            "           9       0.55      0.86      0.67         7\n",
            "\n",
            "    accuracy                           0.50       140\n",
            "   macro avg       0.50      0.48      0.47       140\n",
            "weighted avg       0.52      0.50      0.49       140\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzvO4MVVK9fs"
      },
      "source": [
        "KNN blind well prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lk5MXpkBta3K",
        "outputId": "f67feb15-8b9c-4133-e3ce-a4c91605afdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "preds_knn_test_well = clf_knn.predict(X_test_well)\n",
        "print(classification_report(y_test_well, preds_knn_test_well))\n",
        "cm_knn = confusion_matrix(y_test_well, preds_knn_test_well)\n",
        "plot_confusion_matrix(cm_knn, target_list, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in true_divide\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.58      0.33      0.42        89\n",
            "           2       0.35      0.61      0.45        89\n",
            "           3       0.72      0.51      0.60       117\n",
            "           4       0.08      0.14      0.11         7\n",
            "           5       0.00      0.00      0.00        19\n",
            "           6       0.62      0.51      0.56        71\n",
            "           7       0.22      0.24      0.23        17\n",
            "           8       0.29      0.38      0.33        40\n",
            "           9       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.44       449\n",
            "   macro avg       0.32      0.30      0.30       449\n",
            "weighted avg       0.51      0.44      0.46       449\n",
            "\n",
            "Normalized confusion matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv_KTLh7QHXg"
      },
      "source": [
        "**4. Model performance evaluation**\n",
        "\n",
        "I will use the diagnosis of confusion matrix from train data set to evaluate the model performance. The diagnosis of confusion matrix points how much percentage of the stone is correctly predicted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTWXowRcLy9n",
        "outputId": "c5601066-9785-4d9a-9288-72f50ef0dfd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "### To create a data frame recording the correct prediction (normalized) of facies for each machine learning algorithm\n",
        "\n",
        "mod_test_list = ['SVM', 'GB', 'RF','KNN']\n",
        "cm_test_list = [cm_test_SVM, cm_test_GB, cm_test_RF, cm_test_knn]\n",
        "face_test_list = ['SS', 'CSiS', 'FSiS', 'SiSH', 'MS', 'WS','D','PS']\n",
        "pred_test_df = pd.DataFrame(index=target_list, columns=mod_test_list)\n",
        "\n",
        "for mod in mod_test_list:\n",
        "  col_index = int(mod_test_list.index(mod))\n",
        "  cm = cm_test_list[col_index]\n",
        "\n",
        "  for face in face_test_list:\n",
        "    row_index = target_list.index(face)\n",
        "    #print(face, row_index, col_index)\n",
        "    pred_test_df.iloc[row_index, col_index] = cm[row_index][row_index]/sum(cm[row_index])\n",
        "\n",
        "### add the accuracy factor\n",
        "df_1 = pd.DataFrame([[0.72, 0.74, 0.74, 0.50]], index=['Accuracy'], columns=mod_test_list)    \n",
        "pred_test_df = pred_test_df.append(df_1)\n",
        "\n",
        "print(pred_test_df.head(10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               SVM        GB        RF       KNN\n",
            "SS          0.6875       0.5     0.625    0.4375\n",
            "CSiS      0.862069  0.862069  0.896552  0.586207\n",
            "FSiS      0.928571  0.785714  0.785714  0.714286\n",
            "SiSH      0.571429  0.714286  0.714286  0.142857\n",
            "MS        0.555556  0.555556  0.388889  0.333333\n",
            "WS         0.73913   0.73913   0.73913  0.391304\n",
            "D         0.333333  0.666667         1  0.333333\n",
            "PS        0.826087  0.826087  0.782609  0.565217\n",
            "BS             NaN       NaN       NaN       NaN\n",
            "Accuracy      0.72      0.74      0.74       0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFJXGW-DMZUh",
        "outputId": "a16bad02-513d-4af0-c1d9-62fee8681e53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "X_ind = np.arange(pred_test_df.shape[0])\n",
        "(pred_df_index_list) = ['SS', 'CSiS', 'FSiS', 'SiSH', 'MS', 'WS','D','PS','BS', 'Accuracy']\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(X_ind, pred_test_df['SVM'], color='b', width=0.15)\n",
        "plt.bar(X_ind+0.15, pred_test_df['GB'], color='r', width=0.15)\n",
        "plt.bar(X_ind+0.3, pred_test_df['RF'], color='g', width=0.15)\n",
        "plt.bar(X_ind+0.45, pred_test_df['KNN'], color='y', width=0.15)\n",
        "plt.xticks(X_ind, pred_df_index_list)\n",
        "plt.xlabel('Facies')\n",
        "plt.ylabel('Correct predictions')\n",
        "plt.legend(labels=mod_test_list)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XJnP55hSocw"
      },
      "source": [
        "1. All four algorithms predict 0 BS, which is consistent with the blind test well.\n",
        "2. SVM, GB, and RF present very close accuracy score, while KNN's is much lower than the others.\n",
        "3. RF could correctly predict much more Dolomite(D) than the other algorithms, while SVM, GB, and RF have the same correct prediction for other facies, such as Wackstone(WS).\n",
        "4. we can use the prediction performance using blind well for further evaluation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzp7EWiRowvE"
      },
      "source": [
        "**5. Calssifier evluation using blind test well**\n",
        "\n",
        "I will use the same method shown in item4 for evaluation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKnYgdM-asnM",
        "outputId": "908f1dbe-6d38-453d-f9f4-3b17314a15be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "### To create a data frame recording the correct prediction (normalized) of facies of blind test well for each machine learning algorithm\n",
        "\n",
        "mod_list = ['SVM', 'GB', 'RF','KNN']\n",
        "cm_list = [cm_SVM, cm_GB, cm_RF, cm_knn]\n",
        "face_list = ['SS', 'CSiS', 'FSiS', 'SiSH', 'MS', 'WS','D','PS']\n",
        "pred_df = pd.DataFrame(index=target_list, columns=mod_list)\n",
        "\n",
        "for mod in mod_list:\n",
        "  col_index = int(mod_list.index(mod))\n",
        "  cm = cm_list[col_index]\n",
        "\n",
        "  for face in face_list:\n",
        "    row_index = target_list.index(face)\n",
        "    #print(face, row_index, col_index)\n",
        "    pred_df.iloc[row_index, col_index] = cm[row_index][row_index]/sum(cm[row_index])\n",
        "\n",
        "\n",
        "print(pred_df.head(10))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            SVM         GB         RF       KNN\n",
            "SS    0.0786517  0.0674157  0.0561798  0.325843\n",
            "CSiS   0.719101   0.764045   0.786517  0.606742\n",
            "FSiS   0.538462   0.538462   0.632479  0.512821\n",
            "SiSH   0.285714   0.142857   0.142857  0.142857\n",
            "MS     0.105263   0.105263   0.157895         0\n",
            "WS     0.535211   0.605634   0.647887  0.507042\n",
            "D      0.294118   0.294118   0.705882  0.235294\n",
            "PS          0.6        0.5      0.625     0.375\n",
            "BS          NaN        NaN        NaN       NaN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTg0Se5YuR0Z",
        "outputId": "4537e26c-ff0d-4466-a69f-1512c0407b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "### add the accuracy factor and percentage of most correct prediction\n",
        "df_1 = pd.DataFrame([[0.46, 0.49, 0.53, 0.44],[0.125,0.125,0.375,0.375]], index=['Accuracy','prec'], columns=mod_list)    \n",
        "pred_df = pred_df.append(df_1)\n",
        "pred_df.head(13)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>SVM</th>\n",
              "      <th>GB</th>\n",
              "      <th>RF</th>\n",
              "      <th>KNN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SS</th>\n",
              "      <td>0.0786517</td>\n",
              "      <td>0.0674157</td>\n",
              "      <td>0.0561798</td>\n",
              "      <td>0.325843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CSiS</th>\n",
              "      <td>0.719101</td>\n",
              "      <td>0.764045</td>\n",
              "      <td>0.786517</td>\n",
              "      <td>0.606742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FSiS</th>\n",
              "      <td>0.538462</td>\n",
              "      <td>0.538462</td>\n",
              "      <td>0.632479</td>\n",
              "      <td>0.512821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SiSH</th>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MS</th>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.157895</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WS</th>\n",
              "      <td>0.535211</td>\n",
              "      <td>0.605634</td>\n",
              "      <td>0.647887</td>\n",
              "      <td>0.507042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.294118</td>\n",
              "      <td>0.294118</td>\n",
              "      <td>0.705882</td>\n",
              "      <td>0.235294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PS</th>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.625</td>\n",
              "      <td>0.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BS</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.46</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prec</th>\n",
              "      <td>0.125</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.375</td>\n",
              "      <td>0.375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                SVM         GB         RF       KNN\n",
              "SS        0.0786517  0.0674157  0.0561798  0.325843\n",
              "CSiS       0.719101   0.764045   0.786517  0.606742\n",
              "FSiS       0.538462   0.538462   0.632479  0.512821\n",
              "SiSH       0.285714   0.142857   0.142857  0.142857\n",
              "MS         0.105263   0.105263   0.157895         0\n",
              "WS         0.535211   0.605634   0.647887  0.507042\n",
              "D          0.294118   0.294118   0.705882  0.235294\n",
              "PS              0.6        0.5      0.625     0.375\n",
              "BS              NaN        NaN        NaN       NaN\n",
              "Accuracy       0.46       0.49       0.53      0.44\n",
              "prec          0.125      0.125      0.375     0.375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIuT9wpPlIwz",
        "outputId": "59ff058d-0391-4741-fbe1-b16c22617c8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "X_ind = np.arange(pred_df.shape[0])\n",
        "(pred_df_index_list) = ['SS', 'CSiS', 'FSiS', 'SiSH', 'MS', 'WS','D','PS','BS','Accuracy','perc']\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.bar(X_ind, pred_df['SVM'], color='b', width=0.15)\n",
        "plt.bar(X_ind+0.15, pred_df['GB'], color='r', width=0.15)\n",
        "plt.bar(X_ind+0.3, pred_df['RF'], color='g', width=0.15)\n",
        "plt.bar(X_ind+0.45, pred_df['KNN'], color='y', width=0.15)\n",
        "plt.xticks(X_ind, pred_df_index_list)\n",
        "plt.xlabel('Facies')\n",
        "plt.ylabel('Correct predictions')\n",
        "plt.legend(labels=mod_list)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVHHsd0hGGeJ"
      },
      "source": [
        "Based on the performance of blind well test, RF overall has the highest accuracy score, and shows most correct prediction at three facies (WS, D, and PS). KNN amazingly also shows most correct prediction at three facies (SS, FSiS, and MS), and the accuracy score is close to the others. In additional, the accuracy score is much lower than those from train data set. So there is still a room for improvement. Perhaps introducing more wells could help. Improving data quality could be another option. On the other hand, we have not labeled the importance of each stone, since non-marine stones usually correspond to high probability of oil and gas. After labeling the importance of stones, we would have better evaluation criteria for performance of the algorithms."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sIkqUjc6nYZr"
      },
      "source": [
        "**6. Plot the predicted facies for comparison**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnqUW93rcw-9",
        "outputId": "644116fa-3b2e-413e-e947-4e47b6905fdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "blind = test_well.copy()\n",
        "blind['predict_SVM'] = pred_test_well\n",
        "blind['predict_GB'] = pred_GB_test_well\n",
        "blind['predict_RF'] = preds_RF_test_well\n",
        "blind['predict_KNN'] = preds_knn_test_well\n",
        "blind.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Facies</th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "      <th>predict_SVM</th>\n",
              "      <th>predict_GB</th>\n",
              "      <th>predict_RF</th>\n",
              "      <th>predict_KNN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>937</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2774.5</td>\n",
              "      <td>98.36</td>\n",
              "      <td>0.642</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>18.685</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>938</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2775.0</td>\n",
              "      <td>97.57</td>\n",
              "      <td>0.631</td>\n",
              "      <td>7.9</td>\n",
              "      <td>16.745</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.984</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>939</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2775.5</td>\n",
              "      <td>98.41</td>\n",
              "      <td>0.615</td>\n",
              "      <td>12.8</td>\n",
              "      <td>14.105</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.968</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>940</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2776.0</td>\n",
              "      <td>85.92</td>\n",
              "      <td>0.597</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.385</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.952</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>941</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2776.5</td>\n",
              "      <td>83.16</td>\n",
              "      <td>0.592</td>\n",
              "      <td>12.3</td>\n",
              "      <td>13.345</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.935</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Facies Formation Well Name  ...  predict_GB  predict_RF  predict_KNN\n",
              "937       2     A1 SH   SHANKLE  ...           3           3            2\n",
              "938       2     A1 SH   SHANKLE  ...           3           3            2\n",
              "939       2     A1 SH   SHANKLE  ...           2           2            3\n",
              "940       2     A1 SH   SHANKLE  ...           3           3            2\n",
              "941       2     A1 SH   SHANKLE  ...           2           3            2\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u15UBUiHcbNW"
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "\n",
        "facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
        "       '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
        "\n",
        "def compare_facies_plot(logs, compare1, compare2, compare3, compare4, facies_colors):\n",
        "      #make sure logs are sorted by depth\n",
        "    logs = logs.sort_values(by='Depth')\n",
        "    cmap_facies = colors.ListedColormap(\n",
        "            facies_colors[0:len(facies_colors)], 'indexed')\n",
        "    \n",
        "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
        "    \n",
        "    cluster0 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
        "    cluster1 = np.repeat(np.expand_dims(logs[compare1].values,1), 100, 1)\n",
        "    cluster2 = np.repeat(np.expand_dims(logs[compare2].values,1), 100, 1)\n",
        "    cluster3 = np.repeat(np.expand_dims(logs[compare3].values,1), 100, 1)\n",
        "    cluster4 = np.repeat(np.expand_dims(logs[compare4].values,1), 100, 1)\n",
        "    \n",
        "    f, ax = plt.subplots(nrows=1, ncols=10, figsize=(15, 12))\n",
        "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
        "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
        "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
        "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
        "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
        "    im0 = ax[5].imshow(cluster0, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im1 = ax[6].imshow(cluster1, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im2 = ax[7].imshow(cluster2, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im3 = ax[8].imshow(cluster3, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im4 = ax[9].imshow(cluster4, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "            \n",
        "    divider = make_axes_locatable(ax[9])\n",
        "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
        "    cbar=plt.colorbar(im4, cax=cax)\n",
        "    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
        "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
        "                                ' PS ', ' BS ']))\n",
        "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
        "    \n",
        "    for i in range(len(ax)-5):\n",
        "        ax[i].set_ylim(ztop,zbot)\n",
        "        ax[i].invert_yaxis()\n",
        "        ax[i].grid()\n",
        "        ax[i].locator_params(axis='x', nbins=3)\n",
        "    \n",
        "    ax[0].set_xlabel(\"GR\")\n",
        "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
        "    ax[1].set_xlabel(\"ILD_log10\")\n",
        "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
        "    ax[2].set_xlabel(\"DeltaPHI\")\n",
        "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
        "    ax[3].set_xlabel(\"PHIND\")\n",
        "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
        "    ax[4].set_xlabel(\"PE\")\n",
        "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
        "    ax[5].set_xlabel('Facies')\n",
        "    ax[6].set_xlabel(compare1)\n",
        "    ax[7].set_xlabel(compare2)\n",
        "    ax[8].set_xlabel(compare3)\n",
        "    ax[9].set_xlabel(compare4)\n",
        "    \n",
        "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
        "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([]); ax[6].set_yticklabels([])\n",
        "    ax[7].set_yticklabels([]); ax[8].set_yticklabels([]); ax[9].set_yticklabels([])\n",
        "    ax[5].set_xticklabels([])\n",
        "    ax[6].set_xticklabels([])\n",
        "    ax[7].set_xticklabels([])\n",
        "    ax[8].set_xticklabels([])\n",
        "    ax[9].set_xticklabels([])\n",
        "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_SG6pWseEEH",
        "outputId": "2e633764-0d11-47e1-e8b1-026f9cd33b33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        }
      },
      "source": [
        "compare_facies_plot(blind, 'predict_SVM', 'predict_GB', 'predict_RF', 'predict_KNN', facies_colors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 11 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IK36NcuSdVcm"
      },
      "source": [
        "A-B test to evaluate the difference in F1 score:\n",
        "\n",
        "1. Get the F1 score 100 times for each algorithms  \n",
        "2. A-B test on the average F1 between RF and others\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuAEFHGkXcOC",
        "outputId": "f6abb71a-9e3a-4dcc-b307-507b379c1484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "list0 = []\n",
        "for i in range(0,100,1):\n",
        "  X_train0, X_test0, y_train0, y_test0 = train_test_split(X, y, test_size=0.05)\n",
        "  clf0 = ensemble.RandomForestClassifier(n_estimators=2000)\n",
        "  clf0.fit(X_train0, y_train0)\n",
        "  list0.append(clf0.score(X_test0, y_test0))\n",
        "  print(i, clf0.score(X_test0, y_test0))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.7642857142857142\n",
            "1 0.7214285714285714\n",
            "2 0.7357142857142858\n",
            "3 0.75\n",
            "4 0.7428571428571429\n",
            "5 0.7642857142857142\n",
            "6 0.7142857142857143\n",
            "7 0.75\n",
            "8 0.7428571428571429\n",
            "9 0.7928571428571428\n",
            "10 0.7142857142857143\n",
            "11 0.7785714285714286\n",
            "12 0.7285714285714285\n",
            "13 0.7928571428571428\n",
            "14 0.7071428571428572\n",
            "15 0.7642857142857142\n",
            "16 0.6642857142857143\n",
            "17 0.7214285714285714\n",
            "18 0.7714285714285715\n",
            "19 0.7642857142857142\n",
            "20 0.7214285714285714\n",
            "21 0.7214285714285714\n",
            "22 0.75\n",
            "23 0.7428571428571429\n",
            "24 0.7142857142857143\n",
            "25 0.8142857142857143\n",
            "26 0.7428571428571429\n",
            "27 0.75\n",
            "28 0.7571428571428571\n",
            "29 0.7285714285714285\n",
            "30 0.7285714285714285\n",
            "31 0.7428571428571429\n",
            "32 0.7857142857142857\n",
            "33 0.7285714285714285\n",
            "34 0.7214285714285714\n",
            "35 0.7571428571428571\n",
            "36 0.7357142857142858\n",
            "37 0.7357142857142858\n",
            "38 0.8\n",
            "39 0.7285714285714285\n",
            "40 0.7428571428571429\n",
            "41 0.7642857142857142\n",
            "42 0.7357142857142858\n",
            "43 0.65\n",
            "44 0.7642857142857142\n",
            "45 0.7785714285714286\n",
            "46 0.7428571428571429\n",
            "47 0.7642857142857142\n",
            "48 0.7285714285714285\n",
            "49 0.8071428571428572\n",
            "50 0.7785714285714286\n",
            "51 0.7071428571428572\n",
            "52 0.7071428571428572\n",
            "53 0.7714285714285715\n",
            "54 0.7\n",
            "55 0.7071428571428572\n",
            "56 0.7428571428571429\n",
            "57 0.6857142857142857\n",
            "58 0.7142857142857143\n",
            "59 0.7428571428571429\n",
            "60 0.7785714285714286\n",
            "61 0.6714285714285714\n",
            "62 0.7928571428571428\n",
            "63 0.7642857142857142\n",
            "64 0.7357142857142858\n",
            "65 0.7571428571428571\n",
            "66 0.7928571428571428\n",
            "67 0.8071428571428572\n",
            "68 0.7214285714285714\n",
            "69 0.7714285714285715\n",
            "70 0.7357142857142858\n",
            "71 0.7142857142857143\n",
            "72 0.7642857142857142\n",
            "73 0.7428571428571429\n",
            "74 0.7357142857142858\n",
            "75 0.7571428571428571\n",
            "76 0.7571428571428571\n",
            "77 0.6928571428571428\n",
            "78 0.8214285714285714\n",
            "79 0.7071428571428572\n",
            "80 0.7214285714285714\n",
            "81 0.7357142857142858\n",
            "82 0.7\n",
            "83 0.6857142857142857\n",
            "84 0.6857142857142857\n",
            "85 0.7785714285714286\n",
            "86 0.7928571428571428\n",
            "87 0.7357142857142858\n",
            "88 0.7571428571428571\n",
            "89 0.7928571428571428\n",
            "90 0.7428571428571429\n",
            "91 0.7714285714285715\n",
            "92 0.7642857142857142\n",
            "93 0.7785714285714286\n",
            "94 0.8571428571428571\n",
            "95 0.8071428571428572\n",
            "96 0.6857142857142857\n",
            "97 0.6785714285714286\n",
            "98 0.8071428571428572\n",
            "99 0.7571428571428571\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7FrYyDdr9P3",
        "outputId": "5f823987-8973-4347-caae-98ee5e57a7b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "df0 = pd.DataFrame(list0, columns=['RF'])\n",
        "df0.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f36d82a0a20>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPnUlEQVR4nO3dfYwc9X3H8c+nOA+Io2BqsrIckiMSJDWcQuQVfYgq3SltYkApRK0oVovsQntR1USR6v7hJFJjNYpkVUD6T6SKCGRaJblGCU1QoE0R4bASpVLviOkZEOEhl4arsetgHg5ZTU2+/WPHZX05e/ZmH2a/t++XtLqZ38zsfvervc/NzezsOiIEAMjnl+ouAABQDQEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAGOkWJ70fYJ28u2X7C93/ZYsWy/7Z8Vy07d/qDumoEzIcAxij4cEWOSrpL0PkmfbFv2NxEx1nb7x3pKBMoR4BhZEfGCpG+rFeRAOgQ4Rpbtt0u6RtIzddcCVEGAYxR9w/arkn4i6aikz7Qt+0vbLxW3Y/WUB3SGAMcouiEizpc0Kek9kja1LbstIi4sbptW3RoYEgQ4RlZEPCJpv6Tbai4FqGRD3QUANftbSYu231t3IcBasQeOkRYR/y3p7yX9Vd21AGtlvtABAHJiDxwAkiLAASApAhwAkioNcNuX2H7Y9hO2H7f9iWJ8r+0l2weL27X9LxcAcErpSUzbmyVtjohHbZ8vaV7SDZJulLQcER2/h3bTpk0xPj5eqdDXXntN5513XqVtRwl9KkePOkOfyg2qR/Pz88ci4uKV46XvA4+Iw5IOF9Ov2n5S0pYqRYyPj2tubq7KppqdndXk5GSlbUcJfSpHjzpDn8oNqke2f7zq+FreRmh7XNIBSVdK+gtJuyS9ImlO0u6IOL7KNtOSpiWp0Whsm5mZWVvlheXlZY2NjVXadpTQp3L0qDP0qdygejQ1NTUfEc2V4x0HePGh949I+lxE3Gu7IemYpJD0WbUOs9xytvtoNpvBHnh/0ady9Kgz9KncAPfAVw3wjt6FYvtNkr4u6UsRca8kRcSRiHg9In4u6YuSru5lwQCAs+vkXSiWdJekJyPijrbxzW2rfUTSod6XBwA4k04+zOr9km6WtGD7YDH2KUk7bF+l1iGURUkf7UuFAIBVdfIulO9K8iqLHuh9OQCATnElJgAkRYADQFIEOAAkxTfyYKiM77m/74+xe+Kkdq14nMV91/X9cYFeYw8cAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJLaUHcBwDAY33N/bY+9uO+62h4bubEHDgBJEeAAkBQBDgBJEeAAkFRpgNu+xPbDtp+w/bjtTxTjF9l+0PbTxc+N/S8XAHBKJ3vgJyXtjoitkn5d0p/b3ippj6SHIuIySQ8V8wCAASkN8Ig4HBGPFtOvSnpS0hZJ10u6p1jtHkk39KtIAMAvckR0vrI9LumApCsl/WdEXFiMW9LxU/MrtpmWNC1JjUZj28zMTKVCl5eXNTY2VmnbUZK9TwtLL/f9MRrnSkdO9P1hOjax5YK6S1hV9tfSIAyqR1NTU/MR0Vw53nGA2x6T9Iikz0XEvbZfag9s28cj4qzHwZvNZszNza2x9JbZ2VlNTk5W2naUZO/TIC6o2T1xUrcvDM81bMN6IU/219IgDKpHtlcN8I7ehWL7TZK+LulLEXFvMXzE9uZi+WZJR3tVLACgXCfvQrGkuyQ9GRF3tC26T9LOYnqnpG/2vjwAwJl08n/k+yXdLGnB9sFi7FOS9kn6qu1bJf1Y0o39KREAsJrSAI+I70ryGRZ/oLflAAA6xZWYAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJDU8HwoMobGID6TG0D32AMHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIigAHgKQIcABIqjTAbd9t+6jtQ21je20v2T5Y3K7tb5kAgJU62QPfL2n7KuOfj4iritsDvS0LAFCmNMAj4oCkFwdQCwBgDRwR5SvZ45K+FRFXFvN7Je2S9IqkOUm7I+L4GbadljQtSY1GY9vMzEylQpeXlzU2NlZp21HSiz4tLL3co2qGU+Nc6ciJuqt4w8SWC+ouYVX8zpUbVI+mpqbmI6K5crxqgDckHZMUkj4raXNE3FJ2P81mM+bm5tZWeWF2dlaTk5OVth0lvejT+J77e1PMkNo9cVK3L2you4z/t7jvurpLWBW/c+UG1SPbqwZ4pXehRMSRiHg9In4u6YuSru62QADA2lQKcNub22Y/IunQmdYFAPRH6f+Rtr8iaVLSJtvPS/qMpEnbV6l1CGVR0kf7WCMAYBWlAR4RO1YZvqsPtQAA1oArMQEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIiwAEgKQIcAJIank+1B0ZUXV+gMaxfJIHOsQcOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEkR4ACQFAEOAEmVBrjtu20ftX2obewi2w/afrr4ubG/ZQIAVupkD3y/pO0rxvZIeigiLpP0UDEPABig0gCPiAOSXlwxfL2ke4rpeyTd0OO6AAAlqh4Db0TE4WL6BUmNHtUDAOiQI6J8JXtc0rci4spi/qWIuLBt+fGIWPU4uO1pSdOS1Gg0ts3MzFQqdHl5WWNjY5W2HSW96NPC0ss9qmY4Nc6Vjpyou4r6TWy54KzL+Z0rN6geTU1NzUdEc+X4hor3d8T25og4bHuzpKNnWjEi7pR0pyQ1m82YnJys9ICzs7Oquu0o6UWfdu25vzfFDKndEyd1+0LVl/76sfiHk2ddzu9cubp7VPUQyn2SdhbTOyV9szflAAA61cnbCL8i6fuS3m37edu3Ston6XdsPy3pt4t5AMAAlf4fGRE7zrDoAz2uBQCwBlyJCQBJEeAAkBQBDgBJEeAAkBQBDgBJEeAAkBQBDgBJEeAAkBQBDgBJEeAAkBQBDgBJEeAAkBQfijzExit8LvfuiZPr/vO8AbSwBw4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUAQ4ASRHgAJAUX+jQgSpfrAAMu7LXdT+/HGRx33V9ud9Rwx44ACRFgANAUgQ4ACRFgANAUl2dxLS9KOlVSa9LOhkRzV4UBQAo14t3oUxFxLEe3A8AYA04hAIASXUb4CHpX23P257uRUEAgM44IqpvbG+JiCXbb5P0oKSPR8SBFetMS5qWpEajsW1mZqbSYy0vL2tsbKxyrd1YWHq5lsetonGudORE3VUMN3rUmX72aWLLBf254wEbVC5NTU3Nr3aOsasAP+2O7L2SliPitjOt02w2Y25urtL9z87OanJyslpxXcp0JebuiZO6fYELbM+GHnWmn31aL1diDiqXbK8a4JUPodg+z/b5p6YlfVDSoeolAgDWops/rw1J/2T71P18OSL+pSdVAQBKVQ7wiHhO0nt7WAsAYA14GyEAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJFX5W+kHbWHpZe3ac3/dZQDA0GAPHACSIsABICkCHACSIsABICkCHACSIsABICkCHACSSvM+cADrx/g6uaZj98TJjq9PWdx3Xc8fnz1wAEiKAAeApAhwAEiKAAeApAhwAEiqqwC3vd32U7afsb2nV0UBAMpVDnDb50j6gqRrJG2VtMP21l4VBgA4u272wK+W9ExEPBcRP5M0I+n63pQFACjjiKi2of37krZHxJ8U8zdL+rWI+NiK9aYlTRez75b0VMVaN0k6VnHbUUKfytGjztCncoPq0Tsj4uKVg32/EjMi7pR0Z7f3Y3suIpo9KGldo0/l6FFn6FO5unvUzSGUJUmXtM2/vRgDAAxANwH+75Ius32p7TdLuknSfb0pCwBQpvIhlIg4aftjkr4t6RxJd0fE4z2r7Bd1fRhmRNCncvSoM/SpXK09qnwSEwBQL67EBICkCHAASGooAryTS/Jt32j7CduP2/5y2/jrtg8Wt3V7ErWsR7Y/39aHH9p+qW3ZTttPF7edg618sLrsE6+l1vJ32H7Y9g9s/4fta9uWfbLY7inbHxps5YNVtU+2x22faHst/V3fioyIWm9qnQB9VtK7JL1Z0mOStq5Y5zJJP5C0sZh/W9uy5bqfwzD0aMX6H1frpLIkXSTpueLnxmJ6Y93Padj6xGvptHXulPRnxfRWSYtt049JeoukS4v7Oafu5zSEfRqXdGgQdQ7DHngnl+T/qaQvRMRxSYqIowOusW5r/diCHZK+Ukx/SNKDEfFi0b8HJW3va7X16aZPo6KTHoWkXy6mL5D0X8X09ZJmIuJ/IuJHkp4p7m896qZPAzMMAb5F0k/a5p8vxtpdLuly29+z/W+22wPorbbnivEb+l1sTTrpkSTJ9jvV2jv6zlq3XQe66ZPEa+mUvZL+yPbzkh5Q6z+VTrddL7rpkyRdWhxaecT2b/WryCxfarxBrcMok2pd8XnA9kREvKTWZwQs2X6XpO/YXoiIZ2ustW43SfpaRLxedyFDbrU+8Vpq2SFpf0Tcbvs3JP2D7SvrLmoInalPhyW9IyJ+anubpG/YviIiXul1AcOwB97JJfnPS7ovIv63+Nfth2oFuiJiqfj5nKRZSe/rd8E1WMvHFtyk0w8LjNJHHnTTJ15Lb7hV0lclKSK+L+mtan1oE6+l063ap+IQ00+L8Xm1jqVf3pcqh+BkwQa1TqxdqjdOFlyxYp3tku4ppjep9a/Nr6h1Uu4tbeNP6ywnrbLeOulRsd57JC2quECrGLtI0o+KXm0spi+q+zkNYZ94Lb2xzj9L2lVM/6pax3Yt6QqdfhLzOa3fk5jd9OniU31R6yToUr9+52pvVPEkr1Vrr/pZSZ8uxv5a0u8W05Z0h6QnJC1IuqkY/81i/rHi5611P5e6elTM75W0b5Vtb1HrhNMzkv647ucyjH3itXTa79tWSd8renFQ0gfbtv10sd1Tkq6p+7kMY58k/Z6kx4uxRyV9uF81cik9ACQ1DMfAAQAVEOAAkBQBDgBJEeAAkBQBDgBJEeAAkBQBDgBJ/R/JUhJ9KgEs3gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7rKTxd_zQGD",
        "outputId": "f5a2a4a9-d18f-4420-8669-a7546ea8ec25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "list0 = []\n",
        "for i in range(0,100,1):\n",
        "  X_train0, X_test0, y_train0, y_test0 = train_test_split(X, y, test_size=0.05)\n",
        "  clf0 = KNeighborsClassifier(weights='distance')\n",
        "  clf0.fit(X_train0, y_train0)\n",
        "  list0.append(clf0.score(X_test0, y_test0))\n",
        "  print(i, clf0.score(X_test0, y_test0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.4857142857142857\n",
            "1 0.55\n",
            "2 0.5571428571428572\n",
            "3 0.4714285714285714\n",
            "4 0.5214285714285715\n",
            "5 0.4857142857142857\n",
            "6 0.4857142857142857\n",
            "7 0.5071428571428571\n",
            "8 0.4928571428571429\n",
            "9 0.5428571428571428\n",
            "10 0.5071428571428571\n",
            "11 0.5357142857142857\n",
            "12 0.6\n",
            "13 0.5\n",
            "14 0.5857142857142857\n",
            "15 0.4785714285714286\n",
            "16 0.5642857142857143\n",
            "17 0.5928571428571429\n",
            "18 0.5214285714285715\n",
            "19 0.5285714285714286\n",
            "20 0.5071428571428571\n",
            "21 0.5214285714285715\n",
            "22 0.5071428571428571\n",
            "23 0.5571428571428572\n",
            "24 0.6285714285714286\n",
            "25 0.5\n",
            "26 0.5714285714285714\n",
            "27 0.5071428571428571\n",
            "28 0.4714285714285714\n",
            "29 0.4642857142857143\n",
            "30 0.4857142857142857\n",
            "31 0.5785714285714286\n",
            "32 0.55\n",
            "33 0.4642857142857143\n",
            "34 0.5928571428571429\n",
            "35 0.5357142857142857\n",
            "36 0.5142857142857142\n",
            "37 0.4928571428571429\n",
            "38 0.5571428571428572\n",
            "39 0.5571428571428572\n",
            "40 0.5714285714285714\n",
            "41 0.5928571428571429\n",
            "42 0.5071428571428571\n",
            "43 0.6071428571428571\n",
            "44 0.5142857142857142\n",
            "45 0.5071428571428571\n",
            "46 0.5285714285714286\n",
            "47 0.5785714285714286\n",
            "48 0.55\n",
            "49 0.5214285714285715\n",
            "50 0.4928571428571429\n",
            "51 0.5285714285714286\n",
            "52 0.4714285714285714\n",
            "53 0.55\n",
            "54 0.5714285714285714\n",
            "55 0.5857142857142857\n",
            "56 0.6214285714285714\n",
            "57 0.5071428571428571\n",
            "58 0.5642857142857143\n",
            "59 0.5857142857142857\n",
            "60 0.5214285714285715\n",
            "61 0.55\n",
            "62 0.5357142857142857\n",
            "63 0.5285714285714286\n",
            "64 0.55\n",
            "65 0.5071428571428571\n",
            "66 0.5428571428571428\n",
            "67 0.5785714285714286\n",
            "68 0.5571428571428572\n",
            "69 0.5928571428571429\n",
            "70 0.5357142857142857\n",
            "71 0.5857142857142857\n",
            "72 0.4714285714285714\n",
            "73 0.5285714285714286\n",
            "74 0.4928571428571429\n",
            "75 0.55\n",
            "76 0.5428571428571428\n",
            "77 0.5428571428571428\n",
            "78 0.5642857142857143\n",
            "79 0.5071428571428571\n",
            "80 0.5357142857142857\n",
            "81 0.5285714285714286\n",
            "82 0.6285714285714286\n",
            "83 0.5142857142857142\n",
            "84 0.55\n",
            "85 0.5428571428571428\n",
            "86 0.45714285714285713\n",
            "87 0.4928571428571429\n",
            "88 0.5571428571428572\n",
            "89 0.44285714285714284\n",
            "90 0.5571428571428572\n",
            "91 0.5285714285714286\n",
            "92 0.5285714285714286\n",
            "93 0.6142857142857143\n",
            "94 0.44285714285714284\n",
            "95 0.44285714285714284\n",
            "96 0.55\n",
            "97 0.55\n",
            "98 0.5142857142857142\n",
            "99 0.5571428571428572\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mneeebrzs0Z",
        "outputId": "d510568a-d637-478c-b077-c678973dd224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "df1 = pd.DataFrame(list0, columns=['KNN'])\n",
        "df1.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f36da3b70f0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3-oO2yJ0Jjv"
      },
      "source": [
        "from scipy import stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8_CH-l_0Kop",
        "outputId": "1228a344-020f-4311-f763-cb3a8f60bec1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "stats.ttest_ind(df0['RF'],df1['KNN'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=37.85139665039692, pvalue=1.3333523854178673e-92)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3JA1XT20t7b",
        "outputId": "fb117f57-ea91-43ac-e7cb-bae35fd706e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "list0 = []\n",
        "for i in range(0,100,1):\n",
        "  X_train0, X_test0, y_train0, y_test0 = train_test_split(X_stnd, y, test_size=0.05)\n",
        "  clf0 = SVC(C=10, gamma=1)\n",
        "  clf0.fit(X_train0, y_train0)\n",
        "  list0.append(clf0.score(X_test0, y_test0))\n",
        "  print(i, clf0.score(X_test0, y_test0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.7571428571428571\n",
            "1 0.7357142857142858\n",
            "2 0.7785714285714286\n",
            "3 0.6571428571428571\n",
            "4 0.7428571428571429\n",
            "5 0.7357142857142858\n",
            "6 0.7285714285714285\n",
            "7 0.7714285714285715\n",
            "8 0.7285714285714285\n",
            "9 0.7071428571428572\n",
            "10 0.7928571428571428\n",
            "11 0.7285714285714285\n",
            "12 0.7571428571428571\n",
            "13 0.7142857142857143\n",
            "14 0.8\n",
            "15 0.7357142857142858\n",
            "16 0.7142857142857143\n",
            "17 0.7071428571428572\n",
            "18 0.8\n",
            "19 0.7785714285714286\n",
            "20 0.7642857142857142\n",
            "21 0.7071428571428572\n",
            "22 0.7571428571428571\n",
            "23 0.7928571428571428\n",
            "24 0.6928571428571428\n",
            "25 0.7571428571428571\n",
            "26 0.7571428571428571\n",
            "27 0.7714285714285715\n",
            "28 0.7\n",
            "29 0.8071428571428572\n",
            "30 0.7571428571428571\n",
            "31 0.8\n",
            "32 0.7642857142857142\n",
            "33 0.7285714285714285\n",
            "34 0.7285714285714285\n",
            "35 0.7285714285714285\n",
            "36 0.7785714285714286\n",
            "37 0.7285714285714285\n",
            "38 0.8071428571428572\n",
            "39 0.7714285714285715\n",
            "40 0.7857142857142857\n",
            "41 0.7785714285714286\n",
            "42 0.7071428571428572\n",
            "43 0.7357142857142858\n",
            "44 0.7928571428571428\n",
            "45 0.7571428571428571\n",
            "46 0.7714285714285715\n",
            "47 0.7571428571428571\n",
            "48 0.7857142857142857\n",
            "49 0.7928571428571428\n",
            "50 0.7571428571428571\n",
            "51 0.7785714285714286\n",
            "52 0.7785714285714286\n",
            "53 0.7714285714285715\n",
            "54 0.7857142857142857\n",
            "55 0.7285714285714285\n",
            "56 0.7142857142857143\n",
            "57 0.8571428571428571\n",
            "58 0.7571428571428571\n",
            "59 0.7785714285714286\n",
            "60 0.8071428571428572\n",
            "61 0.7928571428571428\n",
            "62 0.7357142857142858\n",
            "63 0.7857142857142857\n",
            "64 0.7714285714285715\n",
            "65 0.7785714285714286\n",
            "66 0.7214285714285714\n",
            "67 0.75\n",
            "68 0.7714285714285715\n",
            "69 0.7857142857142857\n",
            "70 0.7785714285714286\n",
            "71 0.6785714285714286\n",
            "72 0.7571428571428571\n",
            "73 0.7428571428571429\n",
            "74 0.7642857142857142\n",
            "75 0.7857142857142857\n",
            "76 0.7785714285714286\n",
            "77 0.7071428571428572\n",
            "78 0.7428571428571429\n",
            "79 0.7571428571428571\n",
            "80 0.7857142857142857\n",
            "81 0.7071428571428572\n",
            "82 0.7428571428571429\n",
            "83 0.7357142857142858\n",
            "84 0.7785714285714286\n",
            "85 0.7142857142857143\n",
            "86 0.7642857142857142\n",
            "87 0.7214285714285714\n",
            "88 0.7214285714285714\n",
            "89 0.7357142857142858\n",
            "90 0.7071428571428572\n",
            "91 0.7714285714285715\n",
            "92 0.7571428571428571\n",
            "93 0.7285714285714285\n",
            "94 0.7928571428571428\n",
            "95 0.7071428571428572\n",
            "96 0.7428571428571429\n",
            "97 0.7428571428571429\n",
            "98 0.7928571428571428\n",
            "99 0.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQCcEy9n37vp",
        "outputId": "2384a42c-7d1c-4aab-8cbe-d4a0d2ea3155",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "df2 = pd.DataFrame(list0, columns=['SVM'])\n",
        "df2.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f36f2315278>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEHd9mLP4Bcw",
        "outputId": "031efaf4-0c4c-4da5-a356-2c89fb7fde38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "stats.ttest_ind(df0['RF'],df2['SVM'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-1.7043710576071207, pvalue=0.08988042427111864)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "du12vqVp4bs6",
        "outputId": "826362d5-b2ff-41cd-bf10-11f70b4d3b17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "list0 = []\n",
        "params = {'loss': 'deviance', ##  loss{‘deviance’, ‘exponential’}, default=’deviance’\n",
        "          'learning_rate': 0.2, ##  learning_ratefloat, default=0.1\n",
        "          'n_estimators': 1000, ##  number of iterations, int, default=100\n",
        "          'max_depth': 3, ##  int, default=3\n",
        "          'subsample': 0.6, ## float, default=1.0\n",
        "          'min_samples_split': 2 ## int or float, default=2\n",
        "          }\n",
        "for i in range(0,100,1):\n",
        "  X_train0, X_test0, y_train0, y_test0 = train_test_split(X, y, test_size=0.05)\n",
        "  clf0 = ensemble.GradientBoostingClassifier(**params)\n",
        "  clf0.fit(X_train0, y_train0)\n",
        "  list0.append(clf0.score(X_test0, y_test0))\n",
        "  print(i, clf0.score(X_test0, y_test0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.6928571428571428\n",
            "1 0.7214285714285714\n",
            "2 0.6285714285714286\n",
            "3 0.6571428571428571\n",
            "4 0.6785714285714286\n",
            "5 0.6928571428571428\n",
            "6 0.6571428571428571\n",
            "7 0.7642857142857142\n",
            "8 0.6357142857142857\n",
            "9 0.5214285714285715\n",
            "10 0.7571428571428571\n",
            "11 0.6142857142857143\n",
            "12 0.7214285714285714\n",
            "13 0.65\n",
            "14 0.6142857142857143\n",
            "15 0.6785714285714286\n",
            "16 0.6928571428571428\n",
            "17 0.6357142857142857\n",
            "18 0.7142857142857143\n",
            "19 0.65\n",
            "20 0.65\n",
            "21 0.7285714285714285\n",
            "22 0.7357142857142858\n",
            "23 0.6\n",
            "24 0.6428571428571429\n",
            "25 0.6785714285714286\n",
            "26 0.7357142857142858\n",
            "27 0.6071428571428571\n",
            "28 0.6785714285714286\n",
            "29 0.05\n",
            "30 0.6571428571428571\n",
            "31 0.7071428571428572\n",
            "32 0.6214285714285714\n",
            "33 0.6714285714285714\n",
            "34 0.65\n",
            "35 0.6857142857142857\n",
            "36 0.6928571428571428\n",
            "37 0.5928571428571429\n",
            "38 0.5928571428571429\n",
            "39 0.6571428571428571\n",
            "40 0.65\n",
            "41 0.6285714285714286\n",
            "42 0.6714285714285714\n",
            "43 0.6571428571428571\n",
            "44 0.65\n",
            "45 0.6928571428571428\n",
            "46 0.6642857142857143\n",
            "47 0.6642857142857143\n",
            "48 0.014285714285714285\n",
            "49 0.7142857142857143\n",
            "50 0.6928571428571428\n",
            "51 0.6857142857142857\n",
            "52 0.65\n",
            "53 0.6928571428571428\n",
            "54 0.65\n",
            "55 0.6214285714285714\n",
            "56 0.6928571428571428\n",
            "57 0.6142857142857143\n",
            "58 0.5714285714285714\n",
            "59 0.7142857142857143\n",
            "60 0.6785714285714286\n",
            "61 0.6642857142857143\n",
            "62 0.7357142857142858\n",
            "63 0.6285714285714286\n",
            "64 0.7071428571428572\n",
            "65 0.6357142857142857\n",
            "66 0.65\n",
            "67 0.6285714285714286\n",
            "68 0.6928571428571428\n",
            "69 0.7071428571428572\n",
            "70 0.7142857142857143\n",
            "71 0.6357142857142857\n",
            "72 0.7428571428571429\n",
            "73 0.6642857142857143\n",
            "74 0.40714285714285714\n",
            "75 0.7071428571428572\n",
            "76 0.7071428571428572\n",
            "77 0.7571428571428571\n",
            "78 0.6357142857142857\n",
            "79 0.6857142857142857\n",
            "80 0.7214285714285714\n",
            "81 0.6571428571428571\n",
            "82 0.7142857142857143\n",
            "83 0.6357142857142857\n",
            "84 0.7071428571428572\n",
            "85 0.7071428571428572\n",
            "86 0.6428571428571429\n",
            "87 0.7357142857142858\n",
            "88 0.7642857142857142\n",
            "89 0.4857142857142857\n",
            "90 0.6642857142857143\n",
            "91 0.55\n",
            "92 0.7\n",
            "93 0.7214285714285714\n",
            "94 0.6214285714285714\n",
            "95 0.7\n",
            "96 0.6571428571428571\n",
            "97 0.6214285714285714\n",
            "98 0.7071428571428572\n",
            "99 0.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sL9XlP0eQsQ9",
        "outputId": "357fd3b6-5cd0-4277-f7c8-731c81a88aec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "df3 = pd.DataFrame(list0, columns=['GB'])\n",
        "df3.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1221f07a20>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LgykpKTgqXGF"
      },
      "source": [
        "list_RF = [0.764285714,0.721428571,0.735714286,0.75,0.742857143,0.764285714,0.714285714,0.75,0.742857143,0.792857143,\n",
        "           0.714285714,0.778571429,0.728571429,0.792857143,0.707142857,0.764285714,0.664285714,0.721428571,0.771428571,0.764285714,0.721428571,\n",
        "           0.721428571,0.75,0.742857143,0.714285714,0.814285714,0.742857143,0.75,0.757142857,0.728571429,0.728571429,0.742857143,0.785714286,\n",
        "           0.728571429,0.721428571,0.757142857,0.735714286,0.735714286,0.8,0.728571429,0.742857143,0.764285714,0.735714286,0.65,0.764285714,0.778571429,\n",
        "           0.742857143,0.764285714,0.728571429,0.807142857,0.778571429,0.707142857,0.707142857,0.771428571,0.7,0.707142857,0.742857143,0.685714286,0.714285714,\n",
        "           0.742857143,0.778571429,0.671428571,0.792857143,0.764285714,0.735714286,0.757142857,0.792857143,0.807142857,0.721428571,0.771428571,0.735714286,0.714285714,\n",
        "           0.764285714,0.742857143,0.735714286,0.757142857,0.757142857,0.692857143,0.821428571,0.707142857,0.721428571,0.735714286,0.7,0.685714286,0.685714286,0.778571429,\n",
        "           0.792857143,0.735714286,0.757142857,0.792857143,0.742857143,0.771428571,0.764285714,0.778571429,0.857142857,0.807142857,0.685714286,0.678571429,0.807142857,0.757142857]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVPOLpdqryzU"
      },
      "source": [
        "list_GB = [0.692857142857142,0.721428571428571,0.628571428571428,0.657142857142857,0.678571428571428,0.692857142857142,0.657142857142857,0.764285714285714,0.635714285714285,0.521428571428571,0.757142857142857,\n",
        "           0.614285714285714,0.721428571428571,0.65,0.614285714285714,0.678571428571428,0.692857142857142,0.635714285714285,0.714285714285714,0.65,0.65,0.728571428571428,0.735714285714285,0.6,0.642857142857142,\n",
        "           0.678571428571428,0.735714285714285,0.607142857142857,0.678571428571428,0.05,0.657142857142857,0.707142857142857,0.621428571428571,0.671428571428571,0.65,0.685714285714285,0.692857142857142,0.592857142857142,\n",
        "           0.592857142857142,0.657142857142857,0.65,0.628571428571428,0.671428571428571,0.657142857142857,0.65,0.692857142857142,0.664285714285714,0.664285714285714,0.0142857142857142,0.714285714285714,0.692857142857142,\n",
        "           0.685714285714285,0.65,0.692857142857142,0.65,0.621428571428571,0.692857142857142,0.614285714285714,0.571428571428571,0.714285714285714,0.678571428571428,0.664285714285714,0.735714285714285,0.628571428571428,\n",
        "           0.707142857142857,0.635714285714285,0.65,0.628571428571428,0.692857142857142,0.707142857142857,0.714285714285714,0.635714285714285,0.742857142857142,0.664285714285714,0.407142857142857,0.707142857142857,0.707142857142857,\n",
        "           0.757142857142857,0.635714285714285,0.685714285714285,0.721428571428571,0.657142857142857,0.714285714285714,0.635714285714285,0.707142857142857,0.707142857142857,0.642857142857142,0.735714285714285,0.764285714285714,\n",
        "           0.485714285714285,0.664285714285714,0.55,0.7,0.721428571428571,0.621428571428571,0.7,0.657142857142857,0.621428571428571,0.707142857142857,0.55]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyAM7d-6tLwW"
      },
      "source": [
        "df_RF = pd.DataFrame(list_RF, columns=['RF'])\n",
        "df_GB = pd.DataFrame(list_GB, columns=['GB'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsJHktp2tFI7",
        "outputId": "0eb420f2-32c3-4d72-a9bf-c6e1b08a0fe5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from scipy import stats\n",
        "stats.ttest_ind(df_RF['RF'],df_GB['GB'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=8.330960491186465, pvalue=1.3183937146753801e-14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Vbp33Qltnm1"
      },
      "source": [
        "Ttest shows difference between between RF and GF is significant pvalue=1.3183937146753801e-14\n",
        "\n",
        "Ttest shows difference between between RF and SVM is **NOT** significant pvalue=pvalue=0.08988042427111864\n",
        "\n",
        "Ttest shows difference between between RF and KNN is significant pvalue=pvalue=1.3333523854178673e-92"
      ]
    }
  ]
}