Action_classification_task_based_on_Internet_Firewall_logs.ipynb

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  "metadata": {
    "colab": {
      "name": "Action classification task based on Internet Firewall logs",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python",
      "language": "python",
      "name": "conda-env-python-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.13"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2VgPk2mTThF"
      },
      "source": [
        "# **Action classification task based on Internet Firewall logs**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bN5NPujBTThV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b671a5f-a2c6-416c-f774-c5917151bea2"
      },
      "source": [
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00542/log2.csv"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-12 10:58:18--  https://archive.ics.uci.edu/ml/machine-learning-databases/00542/log2.csv\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2876998 (2.7M) [application/x-httpd-php]\n",
            "Saving to: ‘log2.csv’\n",
            "\n",
            "log2.csv            100%[===================>]   2.74M  5.41MB/s    in 0.5s    \n",
            "\n",
            "2021-11-12 10:58:19 (5.41 MB/s) - ‘log2.csv’ saved [2876998/2876998]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORfD1EUiZuLK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f4342bb-d23f-401a-e592-d3ab6e66b26c"
      },
      "source": [
        "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/Classification_evaluation_security_dataset_L2/log2.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-12 10:58:25--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/Classification_evaluation_security_dataset_L2/log2.csv\n",
            "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 198.23.119.245\n",
            "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|198.23.119.245|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2811465 (2.7M) [text/csv]\n",
            "Saving to: ‘log2.csv.1’\n",
            "\n",
            "log2.csv.1          100%[===================>]   2.68M  7.21MB/s    in 0.4s    \n",
            "\n",
            "2021-11-12 10:58:26 (7.21 MB/s) - ‘log2.csv.1’ saved [2811465/2811465]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OK6PuHMwS1Ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11e5e736-066e-4c96-ffa4-1969caec71ac"
      },
      "source": [
        "!pip install scikit-plot\n",
        "!pip install imblearn"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-plot\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.1.0)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.19.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.4.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n",
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.7/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (from imblearn) (0.4.3)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (0.22.2.post1)\n",
            "Requirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.19.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20->imbalanced-learn->imblearn) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdVcijxmTTha"
      },
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import scikitplot as sk\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn import *\n",
        "import time\n",
        "\n",
        "%matplotlib inline\n",
        "sns.set(style = \"darkgrid\")\n",
        "plt.rcParams['figure.figsize'] = (15, 5)\n",
        "plt.style.use('ggplot')\n",
        "seed = 42\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(action = \"ignore\", category = FutureWarning)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwMoaJLV13Z3"
      },
      "source": [
        "pd.set_option(\"precision\", 3)\n",
        "pd.options.display.float_format = '{:.3f}'.format"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wamAf3HLm_FS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "cce07824-75a0-41a5-9652-7ec1f882ca2e"
      },
      "source": [
        "df = pd.read_csv('log2.csv', header = 0)\n",
        "df.head(5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Source Port</th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>NAT Source Port</th>\n",
              "      <th>NAT Destination Port</th>\n",
              "      <th>Action</th>\n",
              "      <th>Bytes</th>\n",
              "      <th>Bytes Sent</th>\n",
              "      <th>Bytes Received</th>\n",
              "      <th>Packets</th>\n",
              "      <th>Elapsed Time (sec)</th>\n",
              "      <th>pkts_sent</th>\n",
              "      <th>pkts_received</th>\n",
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              "      <th>2</th>\n",
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            ],
            "text/plain": [
              "   Source Port  Destination Port  ...  pkts_sent  pkts_received\n",
              "0        57222                53  ...          1              1\n",
              "1        56258              3389  ...         10              9\n",
              "2         6881             50321  ...          1              1\n",
              "3        50553              3389  ...          8              7\n",
              "4        50002               443  ...         13             18\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53xc7fWwoOMD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d258663c-0442-46d2-ec69-eb3a76b4390a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(65532, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uuAnHe3nvCh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2d6e6dc-d554-48ee-a658-d130de620e12"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Source Port', 'Destination Port', 'NAT Source Port',\n",
              "       'NAT Destination Port', 'Action', 'Bytes', 'Bytes Sent',\n",
              "       'Bytes Received', 'Packets', 'Elapsed Time (sec)', 'pkts_sent',\n",
              "       'pkts_received'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCV1-dAJ13Z6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5920fe2-31ce-4203-ff82-2c35d1755c79"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 65532 entries, 0 to 65531\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   Source Port           65532 non-null  int64 \n",
            " 1   Destination Port      65532 non-null  int64 \n",
            " 2   NAT Source Port       65532 non-null  int64 \n",
            " 3   NAT Destination Port  65532 non-null  int64 \n",
            " 4   Action                65532 non-null  object\n",
            " 5   Bytes                 65532 non-null  int64 \n",
            " 6   Bytes Sent            65532 non-null  int64 \n",
            " 7   Bytes Received        65532 non-null  int64 \n",
            " 8   Packets               65532 non-null  int64 \n",
            " 9   Elapsed Time (sec)    65532 non-null  int64 \n",
            " 10  pkts_sent             65532 non-null  int64 \n",
            " 11  pkts_received         65532 non-null  int64 \n",
            "dtypes: int64(11), object(1)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGuHLQMl13Z8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "60600e25-e52c-453b-8445-7b9ebe6ce69b"
      },
      "source": [
        "df.describe().T\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Source Port</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>49391.969</td>\n",
              "      <td>15255.713</td>\n",
              "      <td>0.000</td>\n",
              "      <td>49183.000</td>\n",
              "      <td>53776.500</td>\n",
              "      <td>58638.000</td>\n",
              "      <td>65534.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination Port</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>10577.386</td>\n",
              "      <td>18466.027</td>\n",
              "      <td>0.000</td>\n",
              "      <td>80.000</td>\n",
              "      <td>445.000</td>\n",
              "      <td>15000.000</td>\n",
              "      <td>65535.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NAT Source Port</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>19282.973</td>\n",
              "      <td>21970.690</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>8820.500</td>\n",
              "      <td>38366.250</td>\n",
              "      <td>65535.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NAT Destination Port</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>2671.050</td>\n",
              "      <td>9739.162</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>53.000</td>\n",
              "      <td>443.000</td>\n",
              "      <td>65535.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bytes</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>97123.950</td>\n",
              "      <td>5618438.909</td>\n",
              "      <td>60.000</td>\n",
              "      <td>66.000</td>\n",
              "      <td>168.000</td>\n",
              "      <td>752.250</td>\n",
              "      <td>1269359015.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bytes Sent</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>22385.797</td>\n",
              "      <td>3828138.650</td>\n",
              "      <td>60.000</td>\n",
              "      <td>66.000</td>\n",
              "      <td>90.000</td>\n",
              "      <td>210.000</td>\n",
              "      <td>948477220.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bytes Received</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>74738.153</td>\n",
              "      <td>2463207.712</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>79.000</td>\n",
              "      <td>449.000</td>\n",
              "      <td>320881795.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Packets</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>102.866</td>\n",
              "      <td>5133.002</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>1036116.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Elapsed Time (sec)</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>65.834</td>\n",
              "      <td>302.462</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>15.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>10824.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pkts_sent</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>41.400</td>\n",
              "      <td>3218.871</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>747520.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pkts_received</th>\n",
              "      <td>65532.000</td>\n",
              "      <td>61.467</td>\n",
              "      <td>2223.332</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>327208.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         count      mean  ...       75%            max\n",
              "Source Port          65532.000 49391.969  ... 58638.000      65534.000\n",
              "Destination Port     65532.000 10577.386  ... 15000.000      65535.000\n",
              "NAT Source Port      65532.000 19282.973  ... 38366.250      65535.000\n",
              "NAT Destination Port 65532.000  2671.050  ...   443.000      65535.000\n",
              "Bytes                65532.000 97123.950  ...   752.250 1269359015.000\n",
              "Bytes Sent           65532.000 22385.797  ...   210.000  948477220.000\n",
              "Bytes Received       65532.000 74738.153  ...   449.000  320881795.000\n",
              "Packets              65532.000   102.866  ...     6.000    1036116.000\n",
              "Elapsed Time (sec)   65532.000    65.834  ...    30.000      10824.000\n",
              "pkts_sent            65532.000    41.400  ...     3.000     747520.000\n",
              "pkts_received        65532.000    61.467  ...     2.000     327208.000\n",
              "\n",
              "[11 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjchGbdx13aA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "171f43ef-c0f8-4418-94ad-1366d3595ee3"
      },
      "source": [
        "[df[i].unique() for i in df if df[i].dtypes == \"object\"]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['allow', 'drop', 'deny', 'reset-both'], dtype=object)]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyrfDQcMQGj-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "a8920a18-a72c-41ac-936a-5500f528836f"
      },
      "source": [
        "df.columns = df.columns.str.lower().str.replace(\" \",\"_\")\n",
        "df.head(7)"
      ],
      "execution_count": 12,
      "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>source_port</th>\n",
              "      <th>destination_port</th>\n",
              "      <th>nat_source_port</th>\n",
              "      <th>nat_destination_port</th>\n",
              "      <th>action</th>\n",
              "      <th>bytes</th>\n",
              "      <th>bytes_sent</th>\n",
              "      <th>bytes_received</th>\n",
              "      <th>packets</th>\n",
              "      <th>elapsed_time_(sec)</th>\n",
              "      <th>pkts_sent</th>\n",
              "      <th>pkts_received</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57222</td>\n",
              "      <td>53</td>\n",
              "      <td>54587</td>\n",
              "      <td>53</td>\n",
              "      <td>allow</td>\n",
              "      <td>177</td>\n",
              "      <td>94</td>\n",
              "      <td>83</td>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56258</td>\n",
              "      <td>3389</td>\n",
              "      <td>56258</td>\n",
              "      <td>3389</td>\n",
              "      <td>allow</td>\n",
              "      <td>4768</td>\n",
              "      <td>1600</td>\n",
              "      <td>3168</td>\n",
              "      <td>19</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6881</td>\n",
              "      <td>50321</td>\n",
              "      <td>43265</td>\n",
              "      <td>50321</td>\n",
              "      <td>allow</td>\n",
              "      <td>238</td>\n",
              "      <td>118</td>\n",
              "      <td>120</td>\n",
              "      <td>2</td>\n",
              "      <td>1199</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50553</td>\n",
              "      <td>3389</td>\n",
              "      <td>50553</td>\n",
              "      <td>3389</td>\n",
              "      <td>allow</td>\n",
              "      <td>3327</td>\n",
              "      <td>1438</td>\n",
              "      <td>1889</td>\n",
              "      <td>15</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50002</td>\n",
              "      <td>443</td>\n",
              "      <td>45848</td>\n",
              "      <td>443</td>\n",
              "      <td>allow</td>\n",
              "      <td>25358</td>\n",
              "      <td>6778</td>\n",
              "      <td>18580</td>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>51465</td>\n",
              "      <td>443</td>\n",
              "      <td>39975</td>\n",
              "      <td>443</td>\n",
              "      <td>allow</td>\n",
              "      <td>3961</td>\n",
              "      <td>1595</td>\n",
              "      <td>2366</td>\n",
              "      <td>21</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>60513</td>\n",
              "      <td>47094</td>\n",
              "      <td>45469</td>\n",
              "      <td>47094</td>\n",
              "      <td>allow</td>\n",
              "      <td>320</td>\n",
              "      <td>140</td>\n",
              "      <td>180</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   source_port  destination_port  ...  pkts_sent  pkts_received\n",
              "0        57222                53  ...          1              1\n",
              "1        56258              3389  ...         10              9\n",
              "2         6881             50321  ...          1              1\n",
              "3        50553              3389  ...          8              7\n",
              "4        50002               443  ...         13             18\n",
              "5        51465               443  ...         12              9\n",
              "6        60513             47094  ...          3              3\n",
              "\n",
              "[7 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GzIE-4dBQPEg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "116e2de6-9847-41e6-d796-e18749981d48"
      },
      "source": [
        "Counter(df.action)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'allow': 37640, 'deny': 14987, 'drop': 12851, 'reset-both': 54})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63dQAaURQZUl"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "lab = preprocessing.LabelEncoder()\n",
        "df.action = lab.fit_transform(df.action)\n",
        "\n",
        "data_out = df.drop(\"action\", axis = 1)\n",
        "\n",
        "\n",
        "quant_1 = data_out.quantile(0.25)\n",
        "quant_3 = data_out.quantile(0.75)\n",
        "diff = quant_3 - quant_1\n",
        "data = df[~((df < (quant_1 - 1.5 * diff)) |(df > (quant_3 + 1.5 * diff))).any(axis = 1)]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vOwPIutT9lC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bebb3a09-3fc3-4bbd-9fa8-031f4ca945d6"
      },
      "source": [
        "data.shape, Counter(data.action)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((34531, 12), Counter({0: 16649, 1: 5020, 2: 12851, 3: 11}))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heqLYeSTRCXP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "9f1f0001-6e30-4689-df75-b328b9665278"
      },
      "source": [
        "fig = sns.countplot(data.action)\n",
        "for p in fig.patches:\n",
        "    fig.annotate(format(p.get_height(), '.0f'), \n",
        "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
        "                   ha = 'center', va = 'center', \n",
        "                   xytext = (0, 9), size = 12,\n",
        "                   textcoords = 'offset points')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWe8cL2cRKTW"
      },
      "source": [
        "feature, target = data.drop(\"action\", axis = 1).values, data[\"action\"].values\n",
        "sm = SMOTE(random_state = seed)\n",
        "feature, target = sm.fit_resample(feature, target)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwDnh9T8W12c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07ad9440-1e95-43cb-df24-2b9a5c50ef9d"
      },
      "source": [
        "Counter(target)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0: 16649, 1: 16649, 2: 16649, 3: 16649})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfgjZ8woSXaP"
      },
      "source": [
        "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(feature, target,\n",
        "                                                                test_size = 0.3,\n",
        "                                                                random_state = seed,\n",
        "                                                                stratify = target,\n",
        "                                                                shuffle = True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0nZeIBEadqh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "019cb48f-562d-48b4-daba-33bd27bf1736"
      },
      "source": [
        "xtrain.shape, xtest.shape, ytrain.shape, ytest.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((46617, 11), (19979, 11), (46617,), (19979,))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_da1RoqDSfOP"
      },
      "source": [
        "xtrain = preprocessing.StandardScaler().fit_transform(xtrain)\n",
        "xtest = preprocessing.StandardScaler().fit_transform(xtest)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trtqWSjpSjEI"
      },
      "source": [
        "AUTOTUNE = os.cpu_count()\n",
        "Classifiers = {\n",
        "    \"Linear_Regressor\": linear_model.LogisticRegression(C = 10, solver = 'liblinear'), \n",
        "   \n",
        "    \"Random_Forest\": ensemble.RandomForestClassifier(random_state = seed, n_jobs = AUTOTUNE), \n",
        "    \"Ada_Boost\": ensemble.AdaBoostClassifier(tree.DecisionTreeClassifier(random_state = seed),\n",
        "                                             random_state = seed,\n",
        "                                             learning_rate = 0.1),  \n",
        "    \n",
        "    \"Ex_Tree_Classifier\": ensemble.ExtraTreesClassifier(random_state = seed, n_jobs = AUTOTUNE),  \n",
        "    \n",
        "    \"kNN\": neighbors.KNeighborsClassifier(n_jobs = AUTOTUNE), \n",
        "    \"Decision_Tree\": tree.DecisionTreeClassifier(random_state = seed)  \n",
        "}"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTfmkmZgSn7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "37f7e75c-e647-44f7-a074-073d6db7edcc"
      },
      "source": [
        "start_time = time.time()\n",
        "kfold = StratifiedKFold(n_splits = 10)\n",
        "for name, classifier in Classifiers.items():\n",
        "    sk.estimators.plot_learning_curve(classifier, xtrain, ytrain,\n",
        "                                      cv = kfold,\n",
        "                                      title = name,\n",
        "                                      random_state = seed,\n",
        "                                      n_jobs = -1)\n",
        "print(\"Elapsed time:\", round(time.time() - start_time, 2), 'sec\\n')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elapsed time: 429.57 sec\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNEWFAxmUgAh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f72d98d3-b10a-4df4-9788-678525a3fb7a"
      },
      "source": [
        "from sklearn.metrics import log_loss\n",
        "\n",
        "start_time = time.time()\n",
        "logging_cols=[\"Classifier\", \"Test accuracy\", \"Train accuracy\", \"Log_loss\", \"Correct_cases\", \"Incorrect_cases\"]\n",
        "logging = pd.DataFrame(columns = logging_cols)\n",
        "\n",
        "for name_c , classif_c in Classifiers.items():\n",
        "    classif_c.fit(xtrain, ytrain)\n",
        "    predict = classif_c.predict(xtest)\n",
        "    accuracy = classif_c.score(xtest, ytest)\n",
        "    train_pred = classif_c.predict_proba(xtest)\n",
        "    lg_ls = log_loss(ytest, train_pred)\n",
        "    train_test_acc = classif_c.score(xtrain, ytrain)\n",
        "    correct = (ytest == predict).sum()\n",
        "    incorrect = (ytest != predict).sum()\n",
        "    \n",
        "    print(\"=\"*45)\n",
        "    print('****    Estimations    ****')\n",
        "    print(f\"Test accuracy:  {accuracy*100:.2f}%    Name: {name_c}\")\n",
        "    print(f\"Train accuracy:  {train_test_acc*100:.2f}%\")\n",
        "    print(f\"Log_loss:  {lg_ls:.4f}\")\n",
        "    print(f\"Correct   points:  {correct}\")\n",
        "    print(f\"Incorrect points:  {incorrect}\")\n",
        "    sk.metrics.plot_confusion_matrix(ytest, predict, title = name_c, cmap = plt.cm.Greens)\n",
        "    logging_entry = pd.DataFrame([[name_c, accuracy*100, train_test_acc*100, lg_ls, correct, incorrect]], columns = logging_cols)\n",
        "    logging = logging.append(logging_entry)\n",
        "print(\"\\nElapsed time:\", round(time.time() - start_time, 2), 'sec\\n')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=============================================\n",
            "****    Estimations    ****\n",
            "Test accuracy:  90.09%    Name: Linear_Regressor\n",
            "Train accuracy:  89.61%\n",
            "Log_loss:  0.2822\n",
            "Correct   points:  17999\n",
            "Incorrect points:  1980\n",
            "=============================================\n",
            "****    Estimations    ****\n",
            "Test accuracy:  74.67%    Name: Random_Forest\n",
            "Train accuracy:  99.98%\n",
            "Log_loss:  0.3297\n",
            "Correct   points:  14919\n",
            "Incorrect points:  5060\n",
            "=============================================\n",
            "****    Estimations    ****\n",
            "Test accuracy:  81.09%    Name: Ada_Boost\n",
            "Train accuracy:  99.98%\n",
            "Log_loss:  3.2288\n",
            "Correct   points:  16200\n",
            "Incorrect points:  3779\n",
            "=============================================\n",
            "****    Estimations    ****\n",
            "Test accuracy:  99.13%    Name: Ex_Tree_Classifier\n",
            "Train accuracy:  99.98%\n",
            "Log_loss:  0.1019\n",
            "Correct   points:  19805\n",
            "Incorrect points:  174\n",
            "=============================================\n",
            "****    Estimations    ****\n",
            "Test accuracy:  99.45%    Name: kNN\n",
            "Train accuracy:  99.56%\n",
            "Log_loss:  0.0946\n",
            "Correct   points:  19869\n",
            "Incorrect points:  110\n",
            "=============================================\n",
            "****    Estimations    ****\n",
            "Test accuracy:  73.76%    Name: Decision_Tree\n",
            "Train accuracy:  99.98%\n",
            "Log_loss:  9.0639\n",
            "Correct   points:  14736\n",
            "Incorrect points:  5243\n",
            "\n",
            "Elapsed time: 31.58 sec\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_vVp-p8eypC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "96ed774a-dcdc-4367-eba7-06a5b1f010a9"
      },
      "source": [
        "logging"
      ],
      "execution_count": 25,
      "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>Classifier</th>\n",
              "      <th>Test accuracy</th>\n",
              "      <th>Train accuracy</th>\n",
              "      <th>Log_loss</th>\n",
              "      <th>Correct_cases</th>\n",
              "      <th>Incorrect_cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear_Regressor</td>\n",
              "      <td>90.090</td>\n",
              "      <td>89.607</td>\n",
              "      <td>0.282</td>\n",
              "      <td>17999</td>\n",
              "      <td>1980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random_Forest</td>\n",
              "      <td>74.673</td>\n",
              "      <td>99.976</td>\n",
              "      <td>0.330</td>\n",
              "      <td>14919</td>\n",
              "      <td>5060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ada_Boost</td>\n",
              "      <td>81.085</td>\n",
              "      <td>99.976</td>\n",
              "      <td>3.229</td>\n",
              "      <td>16200</td>\n",
              "      <td>3779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ex_Tree_Classifier</td>\n",
              "      <td>99.129</td>\n",
              "      <td>99.976</td>\n",
              "      <td>0.102</td>\n",
              "      <td>19805</td>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>kNN</td>\n",
              "      <td>99.449</td>\n",
              "      <td>99.558</td>\n",
              "      <td>0.095</td>\n",
              "      <td>19869</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision_Tree</td>\n",
              "      <td>73.757</td>\n",
              "      <td>99.976</td>\n",
              "      <td>9.064</td>\n",
              "      <td>14736</td>\n",
              "      <td>5243</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Classifier  Test accuracy  ...  Correct_cases  Incorrect_cases\n",
              "0    Linear_Regressor         90.090  ...          17999             1980\n",
              "0       Random_Forest         74.673  ...          14919             5060\n",
              "0           Ada_Boost         81.085  ...          16200             3779\n",
              "0  Ex_Tree_Classifier         99.129  ...          19805              174\n",
              "0                 kNN         99.449  ...          19869              110\n",
              "0       Decision_Tree         73.757  ...          14736             5243\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lh5-IzqktzLW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "outputId": "66acdcd3-eefe-4c4c-d952-d5553c9e058e"
      },
      "source": [
        "sns.set_color_codes(\"muted\")\n",
        "graph = sns.barplot(x = 'Classifier',\n",
        "                    y = 'Test accuracy',\n",
        "                    data = logging,          \n",
        "                    palette = 'hls')\n",
        "\n",
        "for p in graph.patches:\n",
        "    graph.annotate(format(p.get_height(), '.2f'), \n",
        "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
        "                   ha = 'center', va = 'center', \n",
        "                   xytext = (0, -12), size = 12,\n",
        "                   textcoords = 'offset points')\n",
        "\n",
        "plt.xlabel('\\nClassifier`s name')\n",
        "plt.title('Estimations (Test accuracy) of the classifiers')\n",
        "plt.show()\n",
        "\n",
        "sns.set_color_codes(\"muted\")\n",
        "graph = sns.barplot(x = 'Classifier',\n",
        "                    y = 'Train accuracy',\n",
        "                    data = logging,          \n",
        "                    palette = 'hls')\n",
        "\n",
        "for p in graph.patches:\n",
        "    graph.annotate(format(p.get_height(), '.2f'), \n",
        "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
        "                   ha = 'center', va = 'center', \n",
        "                   xytext = (0, -12), size = 12,\n",
        "                   textcoords = 'offset points')\n",
        "\n",
        "plt.xlabel('\\nClassifier`s name')\n",
        "plt.title('Estimations (Train accuracy) of the classifiers')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vyffff6+WLVtqwoQJ6tatm5KTk2vd57BTp05au3btCd/fTp06afXq1SdsIzIy0nX+23E//fTTSes6bsOGDRoxYoT69++vlJQURUdHKycnp0bbklznQtYlJCREAwYM0OLFi/X+++/rqquuUlhYWI1tKioqtG/fvpOG6Hbt2mnTpk2y2WyuZTt27FBpaWmNUW53uNNf69KlSxcdOXJEVqu1Vt/4/Wjqb53qPfo9dz5z0om/E6T6f0ZOpXPnztq3b59CQkJqHbs7V2ht0aKFrrnmGj3++ON6/fXXtX79es4TBHBSBEEAPuH4VMDf/+d0OrV37149//zz2rhxo/bv368ffvhB33//vesco5YtWyooKEiZmZkqKChwe+TLXcnJyfr666+1efNm/fLLL3rkkUdqBYvExERt27ZN2dnZKioqqnPEKC0tTV27dtW9996r77//Xjt37tSkSZNktVo1YsQIt+s5Hih27Nih/fv361//+pfKy8trTDn9vb59+2rLli0nvbLocW3atNHQoUP1yCOPaOnSpdq7d6927NihDz74QHPnzpUkffXVV3rrrbe0detW5ebm6quvvlJeXp7rPUlMTJQkffPNNyoqKlJ5eXmdbSUnJ6uoqEiLFy92nZv47rvv1thm7Nix2rt3r+677z5t2bJF2dnZ+vzzz/XDDz9IOnaO4cqVK/XUU09px44dysrK0ocffqisrCxJx6axZmVl6Z133lF2drbef/99ff7556d8HY7X9/HHH+vnn3/W9u3bNXHixBoBqk2bNho4cKCmTZumZcuWKTs7W5s3b9aCBQtq7OdPf/qTVq5cqczMTF1//fW12vnPf/4js9l80umdo0aNcl19cufOndq4caPuv/9+9ejRQz169HDreI5zp7/WJS0tTX369NFdd92lr776Svv27dPWrVv19ttv1whhv3eq9+j3TvWZO9V3QkM+I6cyaNAgJSYm6tZbb1VmZqZycnL0448/6vXXXz9lwHzxxRf1xRdfKCsrS3v27NHHH3+soKCgk4ZnAGBqKACfsHHjxjrPs1q7dq0CAwO1d+9eTZw4UUVFRWrRooUuvfRSTZ48WdKx6Z5Tp07Vyy+/rDfffFNxcXH65ptvGq22Bx98UA8//LBGjx6tkJAQXX/99bryyitrXNhjzJgx2rlzpwYPHqyjR49q4cKFtc4h9PPz0+zZs/XMM8/otttuk81mU9euXTV//vx6TQkMDw/X/Pnz9fzzz8tms6l169Z6/PHHT3ihDunYOWyRkZH68ssvNXDgwFO28cQTT2j+/PmaM2eOcnJyFBwcrPbt22vkyJGuGhYuXOi6mE98fLzuuOMODRs2TNKxc6RGjx6tRx99VEVFRTWu7vhbl112mW6//Xa9+OKLOnr0qHr27KlJkybVOJ8sJSVFb7/9tl544QXdcMMN8vPzU/v27fXwww+7jm3u3LmaNWuW3nvvPZlMJnXs2NEVqvr06aO//vWvmjNnjqZPn67LLrtM48aN0+OPP37K1+GZZ57R1KlTNWzYMEVFRdV5m45nnnlGs2fP1syZM5Wfn6+IiAhdeeWVNbbp2rWrOnTooKqqKl144YW12vnXv/6lgQMHKjg4+IS1REVFud736667TmazWZdcckmN21C4q67+2qpVq1M+z8/PT6+99ppmzZqlp59+Wvn5+QoPD1dqaqrGjh17wued6j36vVN95k71ndCQz8ipWCwWvf3223rppZf04IMP6vDhw2rZsqW6du2qjIyMkz7XbDbr5Zdf1v79+2UwGHTeeedp3rx5Pn2vTgCn5uf8/YkNAAA0wNKlS7VgwQJ9+OGHrnP94B1VVVXq16+fxo4dq7/85S811h04cECDBg3S0qVL3QpjAADfwIggAKBRDB48WIWFhcrPz3frnCacPofDocOHD2vRokWqqKjQ0KFDa22zf/9+PfHEE4RAAEANjAgCANBM5eTkqH///oqOjnbdrBwAAHcQBAEAAADAx3DVUAAAAADwMQRBAAAAAPAxBEEAAAAA8DFn/VVDc3Nzm7oEAAAAAGgSCQkJdS5nRBAAAAAAfAxBEAAAAAB8DEEQAAAAAHwMQRAAAAAAfAxBEAAAAAB8DEEQAAAAAHyMV24fsXDhQq1bt04FBQWaPn26kpKSJB27tcPs2bNVVlamkJAQjR8/XvHx8adcBwAAAABoOK+MCPbq1UvTpk1TdHR0jeXz5s3TlVdeqZkzZ+rKK6/U3Llz3VoHAAAAAGg4rwTB1NRURUVF1VhWUlKi3bt3Kz09XZKUnp6u3bt368iRIyddBwAAAAA4PV6ZGlqXQ4cOKSIiQgbDsSxqMBjUsmVLFRYWStIJ14WFhdWrnYSEhMYtHAAAAACauSYLgt6Sm5vb1CUAAAAAQJM40cBYkwXByMhIFRUVyeFwyGAwyOFw6PDhw4qKipLT6TzhOgAAznalTqnEbm/qMtBA4UajQv28115pqVRaQn9pjkLDjQoNbeoq4KuaLAiGh4erbdu2yszMVN++fZWZmank5GTX1M+TrQMA4GxWYrdrfs7Bpi4DDTQmMVah/kavtVdaYtfC+fSX5mj0mFiFhnqvrwC/5ZUgOH/+fK1fv17FxcV64oknFBoaqhdeeEG33HKLZs+erSVLlig4OFjjx493Pedk6wAAAAAADefndDqdTV2EJ3GOIACgucmpZkSwORuTGKtEL44I5uYwIthcjR4Tq4RERgThWSc6R9Art48AAAAAAJw5CIIAAAAA4GMIggAAAADgY876+wgCAIDGV567Xzvffkule3fLHBqmc68foegLe0qSclcsV/ZnH8tWUqzw9ilKHXOrLC1b1rmfnK++UN7qlSrL2afY3hfpvLG3/9rG/hxt//scVeQfO/8ttG2y2v95tIJbJXr+ANFoysr3a/vOt3SkdLdM5jClnDtCsdHH+kpO7nJlZX8sm61YLcJT1Dn1VgVY6u4rx5UfzdOaDQ8oNrqXuna8U5JUdPgnbdj0tIxGs2u789rfqFbxfT13YEAzRxAEAAD14rDbteXlF5RwWX+df/+DKt6xXZtnzlDPaYmyFh9W1pL31X3yQwqMjdOudxdq2+uzdMEDj9S5L0uLlmozcIiKtm6Ww2arsc7csqU6jbtHAZFRktOp/V9/oW1zZqnXE8964zDRCBwOu37Y8oJaJ/RXj/MfVFHxdv2weYZCeibKaj2sXVnvq2f3hxQUGKcduxZq87ZZ6nVB3X3luO0731RY6Dm1llssLXRpn1meOhTgrMPUUAAAUC9HD+TKVnxYrf9wtfwMBrXs2Enh7dsrb02mDm36QTE9eym4VaIM/v5qO/Balfy8wzWq93vRPXoq+oIeMgWH1FpnCgpWYFS0/Pz85HQ6JYPhhPvBman8aK6stsNq0/pq+fkZFNmyk1qEt1duXqYKDv2g2JheCglOlMHgr3PaXqvDJTt0tOLE7/GBg2vl7x+syJadvHgUwNmJEUEAAHD6nMemcgZGx6jmjamOPSjL2afAmNgG7XrVnbfIbq2U0+lU8pChp18rmlxZeY6CAmOOd4//+V9fKdunoMDafaW6+qh+2f2Bep4/RTkHvq213mY7ouWZd8hotCgm6kK1O2eY/I0BnjkA4CzAiCAAAKiXoLh4mcLClP35J3JUV6to62YV/7xddqtVEV26qWDDdyrbly27zaY9yz6S/PxqTfusj4xX5ynj1XnqMOovCmnTtvEOBB4XHBQvsylMe7I/kcNRrcKizSoq3i673aqoiG7KK/hOpWXZsttt+u+ejyT5ye6ou6/s2v2BWsVfqoCAyDraSVCfnk/r0otnq8f5U3SkdLd+/uUdDx8d0LwRBAEAQL0Y/P3V5a6JOrR5k1b/dZyy//2ZYnqmKSAiQhGdOqvtkKHaOuslrb3/HgVERckYECBLRMRptWm0BCjh0v7aPm+ObEdKGulI4GkGg7+6d5mogkOb9O3qcdqT/ZniYtIUEBChyIjOatd2qDZtfUkr196jwIAo+RsDFGCp3VeOlO7RoaKtatv66jrbsVhaKCQ4UX5+BgUFxqjDuSN0sGC9pw8PaNaYGgoAAOotpHVSjQvAfP/kY4q7OEOSlNj/D0rs/wdJ0tG8A9rz8TIFt2p92m06nU45bFZZDx+WOSz8tPcH7wgNSapxAZh13z+mhLhjfSUp8Q9KSjzWV8qPHlDWnmUKCa7dV4qKt6uyslAr1t4tSbLbK+V0OrRmw3716flUHa3+77xSACdEEAQAAPVWti9bgXFxksOp/d98JVtJseLT+8peZVPFwYMKbpUoa9Eh/fzWG0q84kqZgoPr3I/DbpfTYZfT4ZDT4ZC9yiY/g1EGo1FF27bIFBKqkNZJslsrlfXhYvkHBysoIcHLR4vTUVqWraDAOElOZe//SlZbsVrF95XdbtPRioMKCU5UpfWQtv38hpISr5TJVLuvtE7op/iYi1yP9+z7VBWVBerYYYwk6dDhbQoKjFGAJUqV1iLtzFqkmKgLvXWIQLNEEAQAAPWWtyZTB1Yul9NuV3iHFHW77wEZTCZVHS3XT6/PVkV+vvwDAhSX3lfn/N8w1/P2fLJMJTt3qNvEyZKkvR8v1Z5lH7rWH1y7Wm0H/5+ShwxV9dGj2vWPBbIeLpLBZFboOeeq28TJMprMterBmSs3L1M5B5bL6bSrZXiKLuz2gAwGk6qqyrX5p9mqqMiX0T9AreL6qv05v/aVrD3LdLhkhy7sNllGo0VGo8W1zmgMkMFgltkcJkkqLd2rLT+9pqrqcplNIYqJ6qH251zv9WMFmhM/51k+bp6bm9vUJQAAUC851XbNz+E2Cc3VmMRYJfobvdZebo5dC+fTX5qj0WNilZDovb4C35RwglkUXCwGAAAAAHwMQRAAAAAAfAxBEAAAAAB8DEEQAAAAAHwMQRAAAAAAfAxBEAAAAAB8DEEQAAAAAHwMN5SvS2mp7KUlTV0FGsAYGi6FhjZ1GQAAAMAZjSBYB3tpiQ4unN/UZaABYkePkZEgCAAAAJwUU0MBAAAAwMcQBAEAAADAxxAEAQAAAMDHEAQBAAAAwMcQBAEAAADAx3DV0GZof1m53tq+U7uPlCrMZNaIlHPVMzZakrT1UJHe2r5LhyoqdW54mG7rcp6iAwPq3E9BRYVe37JD/y05osjAAN14Xnt1joyQJFU5HFq087/6Li9fNrtDfeJjdENqe/kb+LcDAAAAoLnjr/pmxu5w6IUftqh7dKTm9svQzZ1S9NqWn3Sg/KhKbTa9tGmrhrVL1uv90nVOeKhe+XHbCfc168ef1DYsRK/3S9f17ZI1c9M2HbHZJEn/ytqr3SWleq5PL83I6K3dR8q0NGuvtw4TAAAAgAcRBJuZ3PKjOmy16eo2rWXw81OnyJZq3yJcmbl52nCwUIkhweodFyOz0aj/OzdZ2aVlyi0rr7WfA+VHtedIqYa2S5bZaFSvuBi1DgnW+oMFkqQfCg7pyjaJCjGbFGY268qkRK3Yf8DbhwsAAADAAwiCZ4mcsnLllJUrKTTEtSzA36jYoEDl1BEEc8rKFRMUqED/X2cHJ4WGaP9vtnXWeIZTRZVWHa2q9kD1AAAAALyJINjMxAcHKcxs0id7slXtcGhzYZG2FxXLarer0m5XkH/N0z4D/Y2qsNtr7afSbq8RAiUpyOSviupj23aNitC/9+7TEZtNxVar/l92jiTJ6qi9LwAAAADNCxeLaWb8DQZN7N5FC7bv1Ce7s5UcFqq0uBj5GwwKMBpVUV1zxK6i2q5Ao7HWfuretlqB/se2HXJOGx2tqtaUNRvkbzDossQE7TlSpnCz2XMHBwAAAMArCILNUFJoiB7pdYHr8WPrvldGQpz85KeVub+ex1dZbVf+0QolhgTX2kdiSLAKKir/F/6OdYO9pWXqEx8rSTIbjbqxYwfd2LGDJOmbfblKDguVwc/Pk4cGAAAAwAuYGtoMZZeWyWa3y2q369Pd2Sq22tS3Vbx6xEYpp6xc6/PyZbPb9dF/96h1aIgS6giC8cFBSgoN0Yf/3SOb3a4NBwu0r7Rcvf53G4qiSqsOV1rldDq1q7hEH2Xt0dB2yd4+VAAAAAAewIhgM5SZm6flOQdkdzqV0jJcD1zYTSaDQSazWX89v7Pe2r5Lr27ZrnbhYWjWMbIAACAASURBVLqrW0fX897Y9rMk6eZOKZKku7p11OtbdujWbzIVFWDRPed3Utj/pn4ePFqhOVu264jNpsgAi4a3P0ddoyK8f7AAAAAAGh1BsBn6c0o7/TmlXZ3rOkdGaHp67zrXHQ+Ax0UHBurhXt3r3Pa8iBaaeclFp1coAAAAgDMSU0MBAAAAwMcQBAEAAADAxxAEAQAAAMDHEAQBAAAAwMcQBAEAAADAxxAEAQAAAMDHnBG3j/j+++/13nvvyel0SpKuu+469e7dW7m5uZo9e7bKysoUEhKi8ePHKz4+vomrBYCGKVWpSuylTV0GGiDcGKpQhTZ1GQAANJomD4JOp1OzZs3StGnTlJSUpL179+qRRx5Rz549NW/ePF155ZXq27evVq5cqblz52rq1KlNXTIANEiJvVTzDy5s6jLQAGNiRyvUSBAEAJw9zoipoX5+fjp69Kgkqby8XC1btlRpaal2796t9PR0SVJ6erp2796tI0eONGWpAAAAANDsNfmIoJ+fnyZMmKDnn39eFotFFRUVevDBB3Xo0CFFRETIYDiWVQ0Gg1q2bKnCwkKFhYW5vf+EhIR615RfWFDv5+DMYDFbFNOA9xzwhoL8wqYuAQ1ksZiVEOO975aCvHyvtYXGZzFblBAX47X2CgvoL82V2WJRQoL3+grwW00eBO12u5YuXar7779fqamp2rFjh1588UXdddddjbL/3Nzc+tdkszZK2/A+q83aoPcc8Aar3dbUJaCBrFabV79brNV2r7WFxuft30U2K/2lubJZ+bsFnneigbEmnxq6Z88eFRUVKTU1VZKUmpqqgIAAmUwmFRUVyeFwSJIcDocOHz6sqKiopiwXAAAAAJq9Jg+CkZGRKioqcv1rSE5OjoqLixUfH6+2bdsqMzNTkpSZmank5OR6TQsFAAAAANTW5FNDW7RoobFjx2rGjBmu8wHvuOMOhYSE6JZbbtHs2bO1ZMkSBQcHa/z48U1cLQCcvSoKKrTzzZ90ZFexDCaDonvFqt0NqTIYDdrx920q3lGkiryjSr2ls+IvaXXC/TiqHPr5zZ9UsC5PRotRra9JVtKAtq71uctzlP1xlmzFNoWntFDqrZ1laRnghSMEAADHNXkQlKSMjAxlZGTUWt6qVSs9/fTTTVARAPienW/+JHOYWX1mX6rqo9X68ZmNyv1ynxKvaqOQpFDFpMUpa9HOU+5n95JfVJFXrotevkS2Yqs2PbVBwa2CFdktWod/KlLW+7vU/aGeCowL0q6FO7Rt1mZd8EgvLxwhAAA4rsmnhgIAzgyVBRWKSYuT0WyUpYVFEd2iVL6/TJKU+IckRXSOlMF06l8beaty1XbIuTIFmxTcKkTxlyUqb+Wx6f+HfihQTK9YBSeGyOBvUNtrz1HJjsOqOHjUo8cGAABqIggCACRJiVe10cG1ebJb7bIWVarox0JFdK3fBbqqyqtkK7YqpM2vN18PSQpVeU6Z67Hzt0/434OyfWUCAADeQxAEAEiSWqS2VHlOmVaN/Vpr7lqh0OQwRfWo3/2t7JXVkiRj4K9nHvgH+buWR3SLUsF3eSrLLpXdZteej/4r+UkOG5e/BwDAmwiCAAA5HU79+Nz3iu4Zo77zL1f6nMtUXV6l//7z1OcE/pYx4FgAtFdUu5ZVV1S7lkd0jlTboe209aVNWnvPSgVEBcoY4C9LBBeLAQDAmwiCAABVlVfJeqhSiX9IksFkkCnUrLhLWqnox8J67ccUbJK5hUVl2aWuZWXZpQpODHE9TvxDktJeyFD6a5cpulesnA6ngluH1LU7AADgIQRBAIDMoWYFRAdq/1f75LA7VFVepbxVua6A5qh2yG6zy+mUnPb//exw1rmvuIwE7VmaparyKpXnlunA8hzF9U2QJNltdpXtK5XT6VRlYYV+fmObEq9MkinY5LVjBQAAZ8jtIwAATa/zX8/XL//YoeyPd8vP4KcWnSLU/oZUSdKPz25U8fbDkqQju4r18xs/6fyHeqplxwjlrc7V3mVZ6v23dElS8tB2+vnNn7T27hUymI1KGpisyG7Rko7dY/Cn2ZtVkV8h/wCj4vq20jnD2jfNAQMA4MMIggAASVJo2zB1f7ju+/mdaLkkxV2coLiLE1yPDSaDzru1s867tXOtbU3BJvV69uLTLxYAAJwWgiBwGpwqld1e0tRloIGMxnD5KfTUGwIAAJxlCILAabDbS5RzcH5Tl4EGSowdI38jQRAAAPgegiAAAADgC0qdspdw39bmyhhulEL9Gm1/BEEAAADAB9hL7Do4P6epy0ADxY5JlDG08eIbt48AAAAAAB9DEAQAAAAAH0MQBAAAAAAfwzmCwFns9jErazy22ezqd0UrjfpLhxrLl324W0uX7NF9D3ZTp84RJ9zfF//epy//naMjR2yKjAzQ3RO7KC4+SJ8s26NPlmW7tnM4nKqudmjmaxcrNNTcuAcFAACA00YQBM5ic+b3df1cWVmtv965Rj17xdTYJv9ghTasK1CLFicPbCuW52rVtwf01/u6KqFVkAryKxUUfOwr5JrBbXXN4LaubZcu2a2fdxQTAgEAAM5QTA0FfMTG9QUKCzOpQ2p4jeVvv7VTw0acK6P/ib8OHA6nln24RyNGtVOrxGD5+fkpJjZQISGmWts6nU6tWZWnizPiGv0YAAAA0DgYEQR8xOpVeeqTESc/v1/vP7NhXb78/Q3qdn6k3j7Jcw8XWXW4yKqcnHL9/fUdMhr91Cc9ToP/r60Mhpr3s9m5o0RHjlSpR69oDx0JAAA4G41ZeXuNxza7TVe06qe/dBilnPL9mrP97zpYkS9JSg5tq9Ht/6zE4Fb13pckWe1WvfvLe/quYIPsDruSQlrr0Qse9MBRnbkIgoAPKCyo1M/bizXmllTXsoqKan3wXpbuf7DbKZ9fVGSVJG3bUqQnn+2po0erNf3ZHxURYdEl/RJqbLt6VZ569IpWQABfLwAAwH3z+85x/VxZXak71/xVvWJ6SpJamlvqnk7jFBUQKaec+mL/15q1bY6e7fVEvfclSW/8/JbsToee7/WUQkwh2luWXdduzmpMDQV8wJrMPHVICVd0TKBr2bIle9QnPVZR0YEneeYxZvOxr4qrr0lSULBJUdGBurRfgjb/eKjGdlarXRvW5TMtFAAAnJb1BRsVZgpTavixC9wFm4IUHRglPz8/OZ1OGWRwjQ7Wd1+55Qf0feEm3Zxyo8LMYTL4GZQc2tZTh3LG4p/sAR+wJjNPAwa2qbHsp22HdbjIqm++ypUklR6x6bWXt+nqgUn64++2jYsPkr+/n/z06zRQv5ozQiVJ/9lYoOAQk1I7tmj8gwAAAD5jVd5qZcT1qXFKiyTdsupOVdqtcjqdGpo8pEH7+m9plqICIrVk91JlHlyjFuZwDW07RL1iejT6cZzJCILAWW7XzhIdPmxVz941z9mbNOV82e0O1+Npj3yvEaPaqUu32rePsFiM6pUWo88/yVZS2xBVHK3Wt98c0NXXtK6x3eqVebo4I7bWlzYAAIC7CioLtb34Z92SOqbWunkZr6rSbtWqvNWKskQ2aF9FlYeVU75fvaJ7aHafF7Wr5Bc9v+UltQpOUKvghJPs7ezC1FDgLLd6VZ4u7BGtwMCa/+4TEmpSeAuL6z+DwU9Bwf6uc/sWvPGzFrzxs2v7UX/pIEuAURPHr9GTj/1HaX1ilHFJvGv94SKrtv9UrD7pTAsFAAANl5m3RinhHRQTWPeF5wKMFvVPuFRzts9Tie1IvfdlNppk9DNqSJuB8jf467yWqerYIlVbirY26nGc6RgRBM5yN96c4tZ202deVOPxX373vMAgf91xV6cTPr9lhEVvvH1pvesDAAD4rcy8NRrYZsBJt3E6nbI6bDpsPaxwc1i99tU6uHWt7X57+ouvYEQQAAAAwBlhZ8kuHbYeVu/onjWWbynapj2le+VwOnS0ukL/+GWRgv2DlRB04qmcJ9pXaosOigqI1L+yP5XdYdfPxbv0U/EOdY3o4pFjOlMxIggAAADgjLAqb7V6RF+oQP+aVzU/Wn1UC3b9Q0XWwzIbTDo39BxN7jZRZqNJkrRszyfaUbJTk7tNPOW+/A3+mtjlbs3b8aY+3vupIgOidPt5Y5UQHC9f4lYQ/Oyzz5Senq6wsBMPuwIAAADA6bg55cY6l/eO6aneMT3rXCdJg9teo8Fu7kuSEoNbadqFD9e/wLOIW0Fw69at+uc//6lOnTqpb9++6tmzp0wmk6drAwAAAAB4gFtBcNKkSSotLdXq1av16aefat68eerdu7f69u2rjh07erpGAAAAAEAjcvscwdDQUF111VW66qqrtHfvXs2aNUvLly9XVFSU+vfvrwEDBiggIMCTtQIAAAAAGkG9LhazZcsWrVq1Shs2bNC5556r8ePHKyoqSp999pmefvppPf74456qEwAAAADQSNwKggsXLtSaNWsUFBSkvn37asaMGYqIiHCtb9++vW666SaPFQkAAAAAaDxuBcGqqirdd999ateuXd078ffXs88+26iFAQAAAAA8w60geO2118psNtdYVlZWJpvN5hoZbNWqVeNXBwAAAABodAZ3Nnr++edVVFRUY1lRUZGmT5/ukaIAAAAAAJ7jVhDMzc1VUlJSjWVJSUnav3+/R4oCAAAAAHiOW0EwLCxMeXl5NZbl5eUpNDTUI0UBAAAAADzHrXMEL7vsMs2YMUPDhw9XbGys8vLy9N5776lfv36erg8AAAAA0MjcCoJDhgyRv7+/3n77bR06dEiRkZHq16+frrnmGk/XBwAAAABoZG4FQYPBoEGDBmnQoEGergcAAAAA4GFuBUFJqq6uVm5uro4cOVJjeefOnRu9KAAAAACA57gVBHfs2KEXXnhBVVVVqqioUGBgoCorKxUZGalZs2Z5ukYAAAAAQCNyKwguWLBAgwYN0jXXXKObbrpJb775pj744INaN5lvKJvNpgULFmjLli0ymUzq0KGDbrvtNuXm5mr27NkqKytTSEiIxo8fr/j4+EZpEwAAAAB8lVtBMDc3VwMGDKixbMiQIRo3blyjnDf4j3/8QyaTSTNnzpSfn5+Ki4slSfPmzdOVV16pvn37auXKlZo7d66mTp162u0BAAAAgC9z6z6CQUFBqqiokCS1aNFCOTk5KisrU2Vl5WkXUFlZqZUrV2r48OHy8/NztVFSUqLdu3crPT1dkpSenq7du3fXOkcRAAAAAFA/bo0I9u7dWz/88IPS09N12WWXadq0aTIajUpLSzvtAo7fmH7x4sXatm2bAgICNHz4cJnNZkVERMhgOJZVDQaDWrZsqcLCQoWFhbm9/4SEhHrXlF9YUO/n4MxgMVsU04D3vKHy8ukrzZnZYlFcjPf6S0F+odfaQuOyWMxK8GZfycv3WltofBazRQlxMV5rr7CA/tJcmS0WJSR4r6/kF+R5rS00PovFrJiEuEbbn1tB8MYbb3T9PGjQIHXo0EEVFRXq1q3baRfgcDh08OBBJScn64YbbtCuXbv03HPPaeLEiae9b+nYtNb6stusjdI2vM9qszboPW+oajt9pTmzWb3bX6x2m9faQuOyWm3e7SvVdq+1hcbn7d9FNiv9pbny9u8hu7Xaa22h8TX0d9GJBsZOOTXU4XDorrvuUlVVlWtZamqqunfv7hqtOx1RUVEyGo26+OKLJUnt27dXaGiozGazioqK5HA4XHUcPnxYUVFRp90mAAAAAPiyUyY5g8Egg8FQIwg2prCwMHXq1EmbN2+WJNe9CuPj49W2bVtlZmZKkjIzM5WcnFyvaaEAAAAAgNrcmho6YMAAvfjii7r22msVERHhuqiLJMXGxp52Ebfccotee+01LVy4UP7+/ho/fryCg4N1yy23aPbs2VqyZImCg4M1fvz4024LAAAAAHydW0Fw/vz5kuQatfut995777SLiI2N1WOPPVZreatWrfT000+f9v4BAAAAAL9yKwg2RtgDAAAAAJwZTv9qLwAAAACAZsWtEcFHH320xnmBvzVt2rRGLQgAAAAA4FluBcF+/frVeFxcXKzly5crIyPDI0UBAAAAADzHrSB46aWX1lqWlpamV199Vdddd11j1wQAAAAA8KAGnyMYERGhvXv3NmYtAAAAAAAvcGtE8Jtvvqnx2Gazad26derQoYNHigIAAAAAeI5bQXDVqlU1HlssFqWkpOiPf/yjR4oCAAAAAHiOW0Fw6tSpnq4DAAAAAOAlbp0juGLFilrnA+7Zs0crV670SFEAAAAAAM9xKwi+9957ioyMrLEsKipKixYt8khRAAAAAADPcSsIVlRUKCgoqMayoKAglZeXe6QoAAAAAIDnuBUEExMT9d1339VYtn79eiUmJnqkKAAAAACA57h1sZiRI0fqmWee0Zo1axQXF6e8vDxt2bJFDz74oKfrAwAAAAA0MreCYGpqqmbMmKHMzEwVFhaqXbt2uvHGGxUVFeXp+gAAAAAAjcytIFhVVaUWLVpoyJAhrmXV1dWqqqqSyWTyWHEAAAAAgMbn1jmCTz75pLKysmosy8rK0lNPPeWRogAAAAAAnuNWEMzOzlb79u1rLGvXrl2tewsCAAAAAM58bgXBoKAglZSU1FhWUlIii8XikaIAAAAAAJ7jVhDs3bu3Zs6cqezsbFmtVmVnZ2vWrFm66KKLPF0fAAAAAKCRuXWxmOHDh2vhwoWaMmWKqqqqZDabdemll2rEiBGerg8AAAAA0MjcCoJms1ljx47VzTffrNLSUoWGhsrPz8/TtQEAAAAAPMCtIHhcZWWlKisrVVFR4VoWGxvb6EUBAAAAADzHrSCYk5Ojl19+uc6rhL733nuNXhQAAAAAwHPculjM3//+d3Xq1Enz589XUFCQ3nzzTV1xxRUaN26cp+sDAAAAADQyt4Lg3r17NXLkSAUHB8vpdCooKEijRo1iNBAAAAAAmiG3gqDJZJLdbpckhYaGqrCwUE6nU2VlZR4tDgAAAADQ+Nw6RzA1NVVr167VpZdeqrS0ND399NMymUzq1KmTp+sDAAAAADQyt4LgxIkTXT+PGDFCrVu3VmVlpfr27euxwgAAAAAAnlGv20dIksFgIAACAAAAQDPm1jmCAAAAAICzB0EQAAAAAHwMQRAAAAAAfIxbQXD+/Pl1Ln/rrbcasxYAAAAAgBe4FQRXrFhR5/KVK1c2ajEAAAAAAM876VVDv/nmG0mS3W53/Xxcfn6+QkNDPVcZAAAAAMAjThoEV61aJUmqrq52/XxceHi4xo0b57nKAAAAAAAecdIgOHXqVEnSokWLNHz4cK8UBAAAAADwLLfOERwwYIAqKyslSQ6HQ8uXL9eKFSvkcDg8WhwAAAAAoPG5FQSfffZZHThwQJL07rvv6uOPP9Ynn3yihQsXerQ4AAAAAEDjcysIHjhwQG3btpUkZWZmasqUKZo6darWrFnjydoAAAAAAB5w0nMEjzMYDKqurtaBAwcUFBSkqKgoORwO13RRAAAAAEDz4VYQPP/88/Xiiy+qtLRUffr0kSTl5OQoIiLCo8UBAAAAABqfW0Hw9ttv14oVK2Q0GtW3b19JUmlpqYYNG9aoxSxevFiLFy/W9OnTlZSUpJ07d2revHmy2WyKjo7WXXfdpfDw8EZtEwAAAAB8jVtB0GQy6fLLL5fD4VBJSYlatmypTp06NWohWVlZ2rVrl6KjoyUduzrpK6+8onHjxik1NVVLlizRO++8ozvvvLNR2wUAAAAAX+PWxWLKy8s1c+ZMjRw5UnfffbckaePGjVq0aFGjFFFVVaU33nhDY8eOdS3LysqS2WxWamqqJOmKK67Q2rVrG6U9AAAAAPBlbo0Izps3T8HBwXr11Vc1ceJESVKHDh20cOHCRrnR/HvvvaeMjAzFxMS4lhUWFioqKsr1OCwsTE6nU2VlZQoJCXF73wkJCfWuJ7+woN7PwZnBYrYopgHveUPl5dNXmjOzxaK4GO/1l4L8Qq+1hcZlsZiV4M2+kpfvtbbQ+CxmixLiYk69YSMpLKC/NFdmi0UJCd7rK/kFeV5rC43PYjErJiGu0fbnVhDcsmWLXn/9dfn7/7p5WFiYSkpKTruAnTt3KisrSyNHjjztfdUlNze33s+x26weqATeYLVZG/SeN1S1nb7SnNms3u0vVrvNa22hcVmtNu/2lWq719pC4/P27yKblf7SXHn795DdWu21ttD4Gvq76EQDY24FwaCgIJWWlqply5auZYWFhTUeN9RPP/2k/fv3a/z48ZKkQ4cO6amnntLVV1+twsJf//X8yJEj8vPzq9doIAAAAACgtpMGwczMTKWnp6t///6aMWOGhg8fLqfTqZ07d+qf//ynrrjiitMuYMiQIRoyZIjr8bhx4zR58mQlJibq66+/1o4dO5Samqovv/xSF1100Wm3BwAAAAC+7qRBcN68eUpPT9fgwYNlNpv1xhtvyG6367XXXtPll1+uAQMGeKwwg8Gg8ePHa+7cuaqqqnLdPgIAAAAAcHpOGgSdTqckyc/PTwMGDPBo8Dtu9uzZrp9TUlI0Y8YMj7cJAAAAAL7kpEHQ4XBo69atJ91B586dG7UgAAAAAIBnnTQIVlVVac6cOa6Rwd/z8/PTrFmzPFIYAAAAAMAzThoEAwICCHoAAAAAcJYxNHUBAAAAAADvOmkQPNGUUAAAAABA83XSILhw4UJv1QEAAAAA8BKmhgIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAjyEIAgAAAICPIQgCAAAAgI8hCAIAAACAj/Fv6gJKS0s1a9Ys5eXlyd/fX/Hx8br11lsVFhamnTt3at68ebLZbIqOjtZdd92l8PDwpi4ZAAAAAJq1Jh8R9PPz06BBgzRz5kzNmDFDsbGxeuedd+RwOPTKK6/o5ptv1syZM3XeeefpnXfeaepyAQAAAKDZa/IgGBISok6dOrket2/fXoWFhcrKypLZbFZqaqok6YorrtDatWubqkwAAAAAOGs0+dTQ33I4HPryyy914YUXqrCwUFFRUa51YWFhcjqdKisrU0hIiNv7TEhIqHcd+YUF9X4OzgwWs0UxDXjPGyovn77SnJktFsXFeK+/FOQXeq0tNC6LxawEb/aVvHyvtYXGZzFblBAX47X2CgvoL82V2WJRQoL3+kp+QZ7X2kLjs1jMikmIa7T9nVFBcP78+bJYLLrqqqu0fv36Rtlnbm5uvZ9jt1kbpW14n9VmbdB73lDVdvpKc2azere/WO02r7WFxmW12rzbV6rtXmsLjc/bv4tsVvpLc+Xt30N2a7XX2kLja+jvohMNjDX51NDjFi5cqLy8PE2YMEEGg0FRUVEqLPz1X8+PHDkiPz+/eo0GAgAAAABqOyOC4Lvvvqvdu3fr/vvvl8lkkiSdc845stls2rFjhyTpyy+/1EUXXdSUZQIAAADAWaHJp4bu27dPS5cuVXx8vB5++GFJUkxMjO6//36NHz9ec+fOVVVVlev2EQAAAACA09PkQbB169Z6//3361yXkpKiGTNmeLkiAAAAADi7nRFTQwEAAAAA3kMQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAAAfQxAEAAAAAB9DEAQAAAAAH0MQBAAAAP5/e/ce33P9/3/8tvfeO723OU9ZNsYszJkwp+SQIpKkkFOSY86H1kcXh5F85DBy+DCj5RTKSCnlUBYVSpglJMMwwxrbe5v33vv9sa/3z7KxaZje9+tf2+vwfD7f79fr9Xw+H6/n8/V6i9gZBYIiIiIiIiJ2RoGgiIiIiIiInVEgKCIiIiIiYmcUCIqIiIiIiNgZBYIiIiIiIiJ2RoGgiIiIiIiInVEgKCIiIiIiYmcUCIqIiIiIiNgZBYIiIiIiIiJ2RoGgiIiIiIiInVEgKCIiIiIiYmcUCIqIiIiIiNgZBYIiIiIiIiJ2RoGgiIiIiIiInVEgKCIiIiIiYmcUCIqIiIiIiNgZBYIiIiIiIiJ2xvigC3AncXFxzJ8/n2vXruHh4cGQIUMoU6bMgy6WiIiIiIjIQ6vQjwguWbKENm3aEBoaSps2bVi8ePGDLpKIiIiIiMhDrVAHgn/99RcnT56kSZMmADRp0oSTJ0+SlJT0gEsmIiIiIiLy8CrUU0MvXbpEiRIlMBiy4lWDwUDx4sVJSEigSJEieUrD29s7/xl7e+NTr37+9xM75I2vj84VyRtvvKnvU+9BF0MeAt5AfV+fB10MeUh4e0O9+jpfJA+8wae+74MuhRQShXpEUERERERERApeoQ4ES5YsyeXLl7FarQBYrVauXLlCqVKlHnDJREREREREHl6FOhAsWrQo5cuXJyoqCoCoqCj8/PzyPC1UREREREREbuWQmZmZ+aALcTtnz55l/vz5JCcn4+7uzpAhQ+7uuT8REREREREBHoJAUERERERERApWoZ4aKiIiIiIiIgVPgaCIiIiIiIidUSAoIiIiQhsbFAAAG7ZJREFUIiJiZxQIioiIiIiI2BkFgiIiIiIiInbG+KAL8LAaPHgw48aNw9fX17Zs2rRp9OnTh0cfffQBliyrbE5OTjg5OWGxWHjuuedo2bLlAy2TZHevj1FO5+e9MHHiRBISEnBzcwPA29ubESNG3NM8k5OT+eabb3j++efvaT7/FteuXaN///60atWKPn365LjNxIkTad++PXXr1s13+jt37mT58uV4eXkBYDAY6NGjB9WqVftH5c4tr4CAAP2EENnrkBvGjBlD6dKl85zG22+/zfXr17FYLJw7dw4fHx8A/Pz8GDRoUIGX+YbExERWrFjBb7/9hru7OwaDgZYtW9KqVSvmz59PxYoVeeaZZwosvxMnTvD5558zdOhQALZu3coXX3yBs7MzY8aMYcGCBUyYMKHA8pO86dKlCxEREbi6umZbvnbtWtavX8/UqVOpVKmSbVlqaio9e/YkOjqaSZMm0a1bNzp27AhAdHQ0H330Ee+99959/xz27OZ6KDU1FR8fH55//nkef/zxu0pv69atpKen89xzz+W6zccff4yPjw+NGjW622LfIjY2lnnz5gFZbabZbLa1aS1btizQ+qiwUSBYgIKDg+9bXhkZGTg6Oua6fuTIkfj6+hIbG8u4ceOoXbs2JUqUuC953wtWqxUHBwccHBzua7730r08RvdTnz597iqAgLs7l5KTk9m0aZMCwTyKiooiICCA77//nh49emA0Fny1X716dUaNGgXAzz//zNKlS5k9e3aB57Nz5048PT0VCP6fG3XI3Xr33XcBiI+PJzg4mBkzZtyyTUHX92lpaUyYMIHmzZszaNAgDAYDKSkpfP/99wWWx99VrFjRFgQCbNmyhSFDhuDv7w9wV0Hgg2gH7YmXlxerVq3K9dgUL16czz//nNatW+Pu7n6fSyc3u7ke+vHHH5k2bRr/+c9/bEF8fjz99NN33Obll1/Od7p34uvra6v/du7cyf79+21t2s3+jde9AsECdPMozMSJE6lYsSK///47V65cISgoiO7duwNw5coVwsPDSUhIID09ncaNG9OpUycAIiIiiImJwWKx4OnpycCBA/Hy8rI11E8++STR0dG0bNkyTxeMr68vHh4eXL58mRIlShAXF8fy5cu5evUqFouFtm3b8tRTTwHwww8/sGbNGpydnWnYsCFr1qyx3a3r0qULnTt35pdffqFmzZp06NCBiIgITp06xfXr1wkMDKRXr14YDAbWrVvH999/j5OTEw4ODkyYMAGj0cj8+fM5ffo0RqORMmXKMHLkSAAiIyPZtWsXkNVgv/baa7i6urJ27VrOnDlDSkoKCQkJTJkyBQ8Pj3tx6B6om4/RkSNH+OKLL7BYLAD06NGD6tWrA1nnV7NmzTh48CCJiYm0b9/edpcqJiaGsLAwAKpWrcrNPw96/Phxli1bRlpaGi4uLvTp0wd/f3/bOdWyZUsOHDhAeno6Q4cOZevWrRw/fhxnZ2fGjh1LsWLF8v2Z7pTnzefxE088keP1YLVaCQ8P5/Dhwzg5OeHq6kpISAhLly4lOTmZMWPG4OLiwpQpU/7pIfhX27FjB6+++iqRkZHs3buXoKAgzpw5w4IFC0hNTcXX15fr16/btv/ss8/YvXs3GRkZODk50a9fP8qXL5/n/FJSUrJ1zA4cOMCqVauwWq0UKVKEN954wzZrIrdrf+/evaxZswaDwYDVauW1114jPj6eEydOsGzZMtasWUOPHj2oUaNGwXxJ/xJnz55lypQpTJ48GS8vL9atW8fZs2cZPnx4vtIZPHgwjRo14vDhw/j6+tKvXz9Wr17NkSNHsFgstmWurq6kpKTk2hbkJCoqCg8PD1544QXbMpPJROvWrW/Z9tChQ6xZs4br16+TkZFBp06daNy4MUC+2pmbR4tmz57N+fPn+eCDD6hQoQKvvPIKwcHBLF26FIBjx46xatUqUlJSgKxOZ506de66DZY7s1qtREREkJiYyODBgwFo0KABhw4d4sCBA9SqVeuWfYoXL05AQAAbN26kW7du97vIkosGDRpw/PhxPvvsM4YOHXrbemP58uWcOHECg8FA5cqV6du3b7aR36NHjxIeHo7VarVd/02aNMk2ayA1NZXw8HBOnDgBQLNmzWw3iW/XD8+r/PRZgNv2sQsrBYL3UEJCApMmTSI1NZU333yTFi1aUKZMGT744ANefPFFqlatisViYfLkyfj7+1OjRg06duxIz549Adi2bRsrV660NeJXr17F39/ftj4vfvvtNzw9PSlfvjwZGRmEhoYydOhQHnvsMcxmM2+99RYBAQG4u7uzePFipk6dSpkyZdi8efMtaTk7OzNt2jQAFi1aRNWqVRkwYABWq5W5c+eyfft2GjZsyOeff87ixYtxdnbGbDbj7OzM/v37MZvNtlGCa9euAfDLL7+wa9cuQkJCcHNzY/78+axfv55XX30VyGqUp0+fTpEiRe7+QBRyNx+jRx55hMaNG+Pg4EBcXByTJ09m0aJFtm3T0tKYOnUq8fHxjBo1iubNm+Po6MicOXMYOnQogYGB7N69m6+++goAi8XCzJkzGTRoENWrV+fgwYPMnDnTNgXi6tWrVK5cmW7durFp0yYmT57MxIkTKV++PGFhYXz55Ze88sorty3/jY45QNu2bWnatOkd87z5PA4JCcnxevD09CQ6OppZs2ZhMBhs50zfvn1zHb2Q7E6dOsW1a9eoVq0aiYmJ7Nixg6CgIObNm8ezzz5L8+bN+f3333nnnXds+zz55JO0b98egIMHD7JkyRKmTp1623wOHTrEmDFjSE1NJSkpyTY74q+//mLevHlMmjSJsmXLsn37dubOncu7775722t/7dq19O/fn4CAAKxWK6mpqQQGBvLtt9/e9RTWf6NZs2bZpoY6Ojry3nvv0bVrV+bMmUOXLl2Iioqy1dn5ZTabbft+8sknmEwm2/8rVqxgw4YNdO3alYiIiBzbglatWuWY7smTJ/M8UuDn50dISAgGg4HExETeeustatasCZCvduZmI0aMYPDgwbZRjPj4eNu65ORklixZQnBwMMWLF+fKlSsEBwczc+ZM4O7aYLm99PR05s+fT+nSpRk2bFi2WT9du3Zl9erVtmP+d506dWL06NE8++yz96u4kgeVKlVi3759bNy4Mdd6Y/ny5bi6ujJjxgwMBgNJSUm3pLNx40bat29PkyZNyMzMtN2cudn69euxWq28//77mM1mxo8fj6+vL7Vr1wZy74fnR177LIGBgbn2sR977LH8fo33jQLBeygoKAiDwYDJZOKxxx7jwoULFC9enCNHjmQ76VNTUzlz5gw1atTgwIEDfPXVV6SmppKRkZEtPScnJ4KCgvKU96xZs8jMzOT8+fOMHDkSo9HImTNnOHv2LHPmzLFtZ7FYOHv2LAaDAT8/P9sF0qJFCyIiIrKl2bx5c9vf+/bts931gazKvGTJkphMJh599FE++OADatSoQd26dXFzc6NcuXKcOXOGsLAwAgMDqVOnDpDVgWzUqBEmkwmAVq1asWzZMls+tWvX/tcGgTkdowsXLhAaGsrly5dxdHQkMTGRxMRE26jcjbvhpUuXxsPDg0uXLmGxWHBxcSEwMBCARo0asXjxYiDr7pTRaLSNKtaoUQOj0UhcXByurq64urrajoWfnx8lS5a0jf5UqFCBgwcP3vFz/H1qaGxs7G3zvPk8Tk1NzfV6aN68ORaLhUWLFlGtWjVbOSXvtm/fTrNmzXBwcKBBgwaEh4dz8eJFTp8+TbNmzQAICAjINr3wjz/+YMOGDVy7dg0HBwfOnTt3x3xunhoaHR3NnDlzCA0N5dixY5QvX56yZcsCWXVIWFgYZrP5ttd+YGAgH374IQ0aNKBWrVr3/FnXh1VOU0ObNWvGoUOHmDFjBpMnT7Z9v/l14/yArPrebDbzww8/AFntRrly5WzrcmoLCkJSUhILFy7k/PnztptBcXFx+Pv756udyaujR48SHx9vmzIL4ODgwPnz5/H09MxXGyx58+6779KoUSM6dOhwy7o6deqwYcMG9uzZk+O+xYoVo1WrVnzyySc6LoXIjRlJt6s39u/fz/Tp020zB3Lq5wUGBvLpp59y4cIFatSokeMNpEOHDtGnTx8cHBwwmUw0btyYQ4cO2QLBnPrh+Q0E89pnKVGiRK59bAWCdurmh/gNBgMZGRm2C2TatGm3PKtz8eJFPvzwQ6ZNm0bp0qU5evQoc+fOta13dXXN8zNyNzoIe/bsYcGCBTz++ONkZmbi6emZ40jKvn377pjmzQ90Z2ZmMmbMGB555JFbtps6dSpHjx7l8OHDvPXWW7z99tuUK1eOWbNm2aZ6rF69mvfffz9fef7b5HSMQkND6dGjB/Xr18dqtdKjRw/S09Nt++R0TuUkr+fJ39PLa/r/xM3n8e2uB8gKlqOjozl06BArV65k+vTpBV6efyuLxUJUVBROTk589913QNbzDTt37rztPjNnzmTSpElUqFCBy5cvM2DAgHzlGxgYSEZGBqdPn77rsvfu3ZvY2FgOHz7M7NmzadeuXa4jTJKdxWLhzJkzuLu7k5iYeNfp/L3uff3113N8AdDt2oKc+Pn5sWPHjjxtGxYWRr169Rg9ejQODg4MGzaM69evYzAYCrSduVm5cuWYNGnSLcvj4+Pz1QZL3lStWpVff/2VNm3a4OLicsv67t27s3DhwlwDvQ4dOjB8+HAqVKhwr4sqeXTixAl8fHy4ePFirvVGXrRr1466dety6NAhwsPDqVmz5h1nKP1dQfRp8tpnOX36dK597MJMPx9xn7m5uVGlShUiIyNtyxISEkhMTMRsNmM0GilWrBhWq5Wvv/76H+cXFBREzZo1iYyMxNvbGxcXF1unELKeKUlJScHf35+TJ09y/vx5gNt2FgHq1atHZGQkVqsVyLpzGx8fj9lsJikpiapVq9KlSxd8fHw4ffo0ly5dwmAwUL9+fXr16kVSUhLXrl2jevXq7N69G7PZTGZmJtu2bbO7535uPkbJycm2t/7t2LEj27NbufH29iY9PZ2YmBgg61nP5ORk2zqLxcLhw4cBOHz4MBkZGff0ZRv5yfN210NSUhJpaWnUqlWL7t27YzKZuHDhAiaTibS0tHsSpP6b7N27F29vbxYtWsT8+fOZP38+48ePZ9euXfj6+hIVFQVkPc8ZGxsLZI3mWK1WSpUqBWS9wS2/YmNjMZvNlC5dmoCAAP7880/Onj0LwLfffoufnx9ubm63vfbj4uLw9fW1TTW+8fyHm5tbjtOD5P/76KOP8PPzY/z48SxZsoRLly794zTr1q3L5s2bbTelzGYzZ86cAXJvC3LTuHFjkpKS2LRpk61TlZKSwjfffHPLtsnJyXh5eeHg4MDBgwdt7VN+25m8CggI4Ny5c7a6C7Kuj5ufuZaC1aVLF6pXr87UqVNzvLYrV65MmTJlbM8S/53JZOK5557jk08+uddFlTzYu3cvW7dutU3hz63eqFu3brY6IKepoXFxcTz66KO0bt2atm3bcvz48Vu2qV69Otu3byczMxOz2czu3bvvaR/ydn2W2/WxCzONCP4DISEh2d4eZDab87Tf0KFDWb58uW0qlZubGwMHDsTX15eGDRsyYsQIihQpQu3atW2d+3+iW7dujBs3jueff55x48axfPlyNm3ahNVqpWjRoowYMYJixYrRr18/pk2bhouLC3Xq1MHR0RFnZ+cc0+zduzcrVqxgzJgxQNZdl969e+Po6MjMmTNJT08nMzMTPz8/6tevT3R0NKtWrQKyHgzv2LEjJUqUoESJEpw6dYrx48cDWdMRX3zxxX/8mR82N45R3759mTFjBh4eHtSsWRNPT8877uvk5MSwYcMICwvDwcGBKlWq2DryRqORUaNGZXtxy41pqPdKfvPM7XpIS0vjf//7n+1B8Vq1alGpUiUMBgNNmjRh9OjRuLu762UxudixYwdNmzbNtiwgIIDMzEx69uzJihUr2LhxIz4+Pra3J5pMJrp06UJwcDAeHh40bNgwT3ndeEYQsu6YDho0yDbV580332Tu3LlkZGRQpEgR3nzzTSBr2ndu1/7KlStt0wHd3d1to5KtWrUiIiKCTZs26WUxZH9GEKBp06YcOXKEqVOn4uzszEsvvURoaCgTJkz4R2+669ixI+vWrSM4ONj29ubOnTtTtmzZXNuC3H7GwtXVlYkTJ7JixQqGDBmCq6srRqORNm3a3LJt9+7dCQsLY+3atVSsWNE2DTYlJSVf7UxepjcDeHh4MHbsWFasWMGHH36IxWKhdOnSjBs37m6+Nsmjjh074uzsTEhICP/5z39uWd+1a1fGjh2b6/7PPPMMW7ZsuZdFlNu4UQ+lpqZStmxZgoODqVSpEn5+frnWG7169bK1+46OjlSpUoXXXnstW7pbtmwhOjoao9GIk5NTjj9/1LlzZ5YuXcro0aOBrDowp5cLFaTc+izFihXLtY9dmDlk6laX/B+z2Wz7PbgdO3awfft2QkJCHnCpRERERESkoGlEUGy2bNnCnj17sFqteHh40L9//wddJBERERERuQc0IviQWr9+PT/++OMty8ePH0/RokUfQInk32rbtm18+eWXtywfPHhwvn5fTh5ef/31V45TcBs0aEDnzp0fQIkkPx5EezF9+nQSEhKyLStVqpSmWYrIA6W6KTsFgiIiIiIiInZGbw0VERERERGxMwoERURERERE7IwCQRERERERETujQFBERERERMTOKBAUERERERGxMwoERURERERE7IwCQRERERERETujQFBERERERMTOKBAUERERERGxMwoERURERERE7IwCQRERERERETujQFBERERERMTOKBAUERERERGxMwoERURERERE7IwCQRERERERETujQFBERERERMTOKBAUERG5D44dO0ZUVNSDLoaIiAigQFBERB4ia9euZe7cufcs/ZEjRxIdHQ1AZmYmCxYsoE+fPgQHBxMTE8OwYcPuOm1fX182bdrEH3/8UVDFFRERuWsOmZmZmQ+6ECIiIjdERUWxefNmzp49i5ubG+XLl6dTp05UrlyZtWvXcv78eYYOHXrPyxETE0NoaChz5szB1dW1QNKMi4tj4cKFTJgwAaPRWCBpioiI3A21QiIiUmhs3ryZyMhI+vXrR82aNTEajRw4cIC9e/dSuXLl+1qWixcv4uXlVSBBYEZGBo6Ojnh7exMSElIApRMREflnFAiKiEihkJKSwscff8ygQYNo0KCBbXm9evWoV69ejvvMmjWLmJgY0tPTKV++PK+//jo+Pj4A/Pzzz3z00UdcunQJNzc32rVrR4cOHUhKSmLBggX89ttvODg44OPjw8SJEzEYDAwePJj+/fuTkJDA0qVLsVgs9OjRg/bt2xMYGMi8efNYtGgRAJcvXyY8PJyYmBhcXV1p164dbdu2BbKmsJ4+fRonJyf2799Pz549admyZY6f4fjx44SFhXHu3DmcnZ1p0qQJvXr1umW76Oho5s2bR7t27di4cSMGg4GuXbvy1FNP2T7vmjVruHDhAiaTiaeeeoouXboAEB8fz5AhQxg4cCBr164lNTWVrl27UqFCBRYtWkRCQgJNmzalb9++tvy2b9/OZ599RmJiIv7+/rzxxht4eXnl97CKiEghpUBQREQKhd9//53r169Tv379PO9Tq1YtBg4ciNFoZOXKlcydO5cZM2YAsGjRIkaMGEGVKlW4du0a8fHxQNaoY4kSJQgLCwOyXuLi4OCQLd0WLVpgMBjYtm2bbQTvxrODAFarlenTp/PEE08wfPhwLl26REhICN7e3tSqVQuAffv2MWLECIYMGYLFYsn1Myxbtoy2bdvSrFkzUlNTiY2NzXXbxMREUlJSWLRoEQcPHmTWrFk88cQTeHh44OLiwpAhQyhbtiynT59mypQplC9fPtv3eezYMUJDQ4mJieG///0vNWvW5J133iEjI4OxY8cSFBRE1apV2bt3Lxs2bGDcuHGUKVOGyMhIQkNDmTJlSp6PjYiIFG56WYyIiBQKV69exdPTE0dHxzzv06JFC9zc3HBycuKll17i1KlTpKSkAODo6MiZM2dISUnBw8ODChUq2JYnJiaSkJCA0WikSpUqtwSCd3LixAmSkpLo3LkzRqORRx55hJYtW7J7927bNgEBAdSvXx+DwYCzs3OuaRmNRs6fP09SUhKurq4EBATkuq2jo6Mtzzp16uDq6kpcXBwAgYGB+Pr6YjAYKFeuHI0bN+bIkSPZ9u/cuTPOzs7UrFkTFxcXmjRpQtGiRSlRogSVK1fm5MmTAHz99de88MILlC1bFkdHR1544QX+/PNPLl68mK/vSURECi+NCIqISKHg6enJ1atXbc/T3YnVamX16tX88MMPJCUl2YK5pKQkTCYTo0aN4tNPP2XVqlX4+vrSvXt3AgIC6NChA+vWrbONbrVq1YqOHTvmq6wXL17kypUr9O7dO1t5qlSpYvu/ZMmSeUprwIABfPzxx4wYMYLSpUvTuXNn6tatm+O2fw+UXVxcSE1NBbJG+1atWkVsbCwWiwWLxULDhg2z7V+0aFHb387Ozrf8fyOtixcvsmzZMiIiImzrMzMzuXz5sqaHioj8SygQFBGRQiEgIAAnJyf27t17SwCTk6ioKPbt28c777yDl5cXKSkp9OnTx7be39+fsWPHYrFY+PLLL5k9ezYLFy7Ezc2Nnj170rNnT2JjY5k8eTIVK1akevXqeS5rqVKlKF26dIH8lEWZMmUYPnw4VquVn376iVmzZrF06dJ8v6Rm7ty5tGnThuDgYJydnVm+fDlJSUl3VaZSpUrRqVMnmjZtelf7i4hI4aepoSIiUiiYTCa6dOnC0qVL+emnn0hLS8NisfDLL7+wYsWKW7Y3m80YjUY8PDxIS0tj9erVtnUWi4Vdu3aRkpKC0WjEZDLZRgz379/P+fPnyczMxGQyYTAY8j011N/fHzc3NyIjI0lPT8dqtRIbG8vx48fz/bm/++47kpKSMBgMmEwmAAyG/DfPZrMZDw8PnJ2dOX78+D/68frWrVsTGRnJ6dOngawX+ezZs+eu0xMRkcJHI4IiIlJotG/fnmLFivHpp58yb948XF1dqVChAp06dbpl2yeffJJff/2VAQMG4OHhwcsvv8zWrVtt67/77jvCw8OxWq14e3vbfnvw3LlzhIeHk5SUhLu7O08//TTVqlXLVzkNBgPjxo0jIiKCwYMHY7FY8Pb25uWXX873Zz5w4AARERGkpaXh5eXFsGHDbvtMYW5ef/11IiIiCA8Pp2rVqgQFBZGcnJzvdADq169Pamoqc+bMISEhAZPJRPXq1QkKCrqr9EREpPDRD8qLiIiIiIjYGU0NFRERERERsTMKBEVEREREROyMAkERERERERE7o0BQRERERETEzigQFBERERERsTMKBEVEREREROyMAkERERERERE7o0BQRERERETEzigQFBERERERsTP/D+X9IgdsqA26AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIFR1-sLoKmJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "56232381-c67d-4feb-d948-7736d34b47d5"
      },
      "source": [
        "sns.set_color_codes(\"muted\")\n",
        "graph = sns.barplot(x = 'Classifier',\n",
        "                    y = 'Log_loss',\n",
        "                    data = logging,          \n",
        "                    palette = 'hls')\n",
        "\n",
        "for p in graph.patches:\n",
        "    graph.annotate(format(p.get_height(), '.2f'), \n",
        "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
        "                   ha = 'center', va = 'center', \n",
        "                   xytext = (0, -12), size = 12,\n",
        "                   textcoords = 'offset points')\n",
        "\n",
        "plt.xlabel('\\nClassifier`s name')\n",
        "plt.title('Estimations (Log_loss) of the classifiers')\n",
        "plt.show()\n",
        "\n",
        "graph = sns.barplot(x = 'Classifier',\n",
        "                    y = 'Correct_cases',\n",
        "                    data = logging,          \n",
        "                    palette = 'hls')\n",
        "\n",
        "for p in graph.patches:\n",
        "    graph.annotate(format(p.get_height(), '.2f'), \n",
        "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
        "                   ha = 'center', va = 'center', \n",
        "                   xytext = (0, -12), size = 12,\n",
        "                   textcoords = 'offset points')\n",
        "\n",
        "plt.xlabel('\\nClassifier`s name')\n",
        "plt.title('Estimations (Correct_cases) of the classifiers')\n",
        "plt.show()\n",
        "\n",
        "graph = sns.barplot(x = 'Classifier',\n",
        "                    y = 'Incorrect_cases',\n",
        "                    data = logging,          \n",
        "                    palette = 'hls')\n",
        "\n",
        "for p in graph.patches:\n",
        "    graph.annotate(format(p.get_height(), '.2f'), \n",
        "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
        "                   ha = 'center', va = 'center', \n",
        "                   xytext = (0, -12), size = 12,\n",
        "                   textcoords = 'offset points')\n",
        "\n",
        "plt.xlabel('\\nClassifier`s name')\n",
        "plt.title('Estimations (Incorrect_cases) of the classifiers')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}