LSTM_with_PSO-TF2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM_with_PSO_and_Keras_on_KDD (2).ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsFmsqafu6co",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "206749c9-8e44-4128-cba2-f56d013693a4"
      },
      "source": [
        "!pip install --upgrade pyswarm"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: pyswarm in /usr/local/lib/python3.7/dist-packages (0.6)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.7/dist-packages (from pyswarm) (1.19.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzET-UbHHij9"
      },
      "source": [
        "### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZIdcD_BbiIi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe4923c0-cc29-4112-83f9-d7b170fd7347"
      },
      "source": [
        "# !pip install pyswarms\n",
        "\n",
        "import pandas as pd # Data Management Framework\n",
        "import numpy as np # Mathematical Computation\n",
        "from matplotlib import pyplot as plt # Graphs\n",
        "from sklearn.metrics import confusion_matrix, classification_report # Evalution Perameter\n",
        "import seaborn as sns # Graph\n",
        "%matplotlib inline\n",
        "from sklearn.preprocessing import * # Data Preprocessing\n",
        "from sklearn.model_selection import train_test_split # Training and testing Split \n",
        "import gc # Memory Management\n",
        "import warnings # warnings ignore\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Natural Inspired Algorithms\n",
        "from pyswarm import pso\n",
        "\n",
        "# Deep learning Framework\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.layers import Conv1D\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Activation\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow import keras\n",
        "import tensorflow\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "# from tensorflow.keras.utils import np_utils\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from google.colab import drive \n",
        "drive.mount('/content/drive')\n",
        "\n",
        "tensorflow.random.set_seed(42)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJkOR7GBEZWW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71574507-4b08-46a8-a873-42d9e2c25c4b"
      },
      "source": [
        "import os\n",
        "os.getcwd() \n",
        "os.chdir('/content/drive/MyDrive/pso/')\n",
        "!pwd"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/pso\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRmkQv2nm2Xv",
        "outputId": "be71f9bd-c224-4c0a-f266-ac696bb8a693"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCN19ZUiIGru",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9c7ef73-9a77-42b9-8b18-2aafda9508d0"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " attack_type.csv\n",
            " CICIDS2017_New_Approach_PSO.ipynb\n",
            " CICIDS2017.rar\n",
            "'Copy of CICIDS2017_New_Approach_PSO.ipynb'\n",
            "'Field Names.csv'\n",
            " KDDTest.csv\n",
            " KDDTrain.csv\n",
            " model.png\n",
            " New_Approach_PSO.ipynb\n",
            "'Nueral_Network_with_PSO_and_Keras_on_KDD (2).ipynb'\n",
            " Nueral_Network_with_PSO_and_Keras_on_KDD.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbbUghRuHe4k"
      },
      "source": [
        "### Data Reading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4avFesRVM_i"
      },
      "source": [
        "### Read the dataset\n",
        "cols = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes','dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n",
        "'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
        "'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate',\n",
        "'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n",
        "'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
        "'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'attack_type', 'difficulty_level']\n",
        "\n",
        "train = pd.read_csv(\"/content/drive/MyDrive/pso/KDDTrain.csv\", names = cols)\n",
        "test = pd.read_csv(\"/content/drive/MyDrive/pso/KDDTest.csv\", names = cols)"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQFz04BRlV9K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "3ff36f56-eb4b-4f89-900d-d53676e8918a"
      },
      "source": [
        "# Train dataset display\n",
        "print(f\"Training dataset: {train.shape}\")\n",
        "train.head()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training dataset: (125973, 43)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
              "      <th>srv_diff_host_rate</th>\n",
              "      <th>dst_host_count</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack_type</th>\n",
              "      <th>difficulty_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>491</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>150</td>\n",
              "      <td>25</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
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              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>26</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>5</td>\n",
              "      <td>5</td>\n",
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              "      <td>0.2</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>30</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>30</td>\n",
              "      <td>32</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration protocol_type  ... attack_type difficulty_level\n",
              "0         0           tcp  ...      normal               20\n",
              "1         0           udp  ...      normal               15\n",
              "2         0           tcp  ...     neptune               19\n",
              "3         0           tcp  ...      normal               21\n",
              "4         0           tcp  ...      normal               21\n",
              "\n",
              "[5 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLNMXpdxlhFH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "ab0d139a-f8ac-4daa-8631-f62f8493134a"
      },
      "source": [
        "# Test dataset display\n",
        "print(f\"Testing dataset: {test.shape}\")\n",
        "test.head()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing dataset: (22543, 43)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
              "      <th>srv_diff_host_rate</th>\n",
              "      <th>dst_host_count</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack_type</th>\n",
              "      <th>difficulty_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>229</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>10</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>12983</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>134</td>\n",
              "      <td>86</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>icmp</td>\n",
              "      <td>eco_i</td>\n",
              "      <td>SF</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3</td>\n",
              "      <td>57</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>saint</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>tcp</td>\n",
              "      <td>telnet</td>\n",
              "      <td>RSTO</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>29</td>\n",
              "      <td>86</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.71</td>\n",
              "      <td>mscan</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration protocol_type  ... attack_type difficulty_level\n",
              "0         0           tcp  ...     neptune               21\n",
              "1         0           tcp  ...     neptune               21\n",
              "2         2           tcp  ...      normal               21\n",
              "3         0          icmp  ...       saint               15\n",
              "4         1           tcp  ...       mscan               11\n",
              "\n",
              "[5 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6W_BBPNoKI6h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "93d7146f-2984-43cf-9c54-85c4b6afbbd6"
      },
      "source": [
        "train.describe()"
      ],
      "execution_count": 110,
      "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",
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              "\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>duration</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
              "      <th>srv_diff_host_rate</th>\n",
              "      <th>dst_host_count</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>difficulty_level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>125973.00000</td>\n",
              "      <td>1.259730e+05</td>\n",
              "      <td>1.259730e+05</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.0</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "      <td>125973.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>287.14465</td>\n",
              "      <td>4.556674e+04</td>\n",
              "      <td>1.977911e+04</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>0.022687</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.204409</td>\n",
              "      <td>0.001222</td>\n",
              "      <td>0.395736</td>\n",
              "      <td>0.279250</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>0.001103</td>\n",
              "      <td>0.302192</td>\n",
              "      <td>0.012669</td>\n",
              "      <td>0.000413</td>\n",
              "      <td>0.004096</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.009423</td>\n",
              "      <td>84.107555</td>\n",
              "      <td>27.737888</td>\n",
              "      <td>0.284485</td>\n",
              "      <td>0.282485</td>\n",
              "      <td>0.119958</td>\n",
              "      <td>0.121183</td>\n",
              "      <td>0.660928</td>\n",
              "      <td>0.063053</td>\n",
              "      <td>0.097322</td>\n",
              "      <td>182.148945</td>\n",
              "      <td>115.653005</td>\n",
              "      <td>0.521242</td>\n",
              "      <td>0.082951</td>\n",
              "      <td>0.148379</td>\n",
              "      <td>0.032542</td>\n",
              "      <td>0.284452</td>\n",
              "      <td>0.278485</td>\n",
              "      <td>0.118832</td>\n",
              "      <td>0.120240</td>\n",
              "      <td>19.504060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2604.51531</td>\n",
              "      <td>5.870331e+06</td>\n",
              "      <td>4.021269e+06</td>\n",
              "      <td>0.014086</td>\n",
              "      <td>0.253530</td>\n",
              "      <td>0.014366</td>\n",
              "      <td>2.149968</td>\n",
              "      <td>0.045239</td>\n",
              "      <td>0.489010</td>\n",
              "      <td>23.942042</td>\n",
              "      <td>0.036603</td>\n",
              "      <td>0.045154</td>\n",
              "      <td>24.399618</td>\n",
              "      <td>0.483935</td>\n",
              "      <td>0.022181</td>\n",
              "      <td>0.099370</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.002817</td>\n",
              "      <td>0.096612</td>\n",
              "      <td>114.508607</td>\n",
              "      <td>72.635840</td>\n",
              "      <td>0.446456</td>\n",
              "      <td>0.447022</td>\n",
              "      <td>0.320436</td>\n",
              "      <td>0.323647</td>\n",
              "      <td>0.439623</td>\n",
              "      <td>0.180314</td>\n",
              "      <td>0.259830</td>\n",
              "      <td>99.206213</td>\n",
              "      <td>110.702741</td>\n",
              "      <td>0.448949</td>\n",
              "      <td>0.188922</td>\n",
              "      <td>0.308997</td>\n",
              "      <td>0.112564</td>\n",
              "      <td>0.444784</td>\n",
              "      <td>0.445669</td>\n",
              "      <td>0.306557</td>\n",
              "      <td>0.319459</td>\n",
              "      <td>2.291503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>4.400000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>2.760000e+02</td>\n",
              "      <td>5.160000e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42908.00000</td>\n",
              "      <td>1.379964e+09</td>\n",
              "      <td>1.309937e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7479.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7468.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>511.000000</td>\n",
              "      <td>511.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           duration     src_bytes  ...  dst_host_srv_rerror_rate  difficulty_level\n",
              "count  125973.00000  1.259730e+05  ...             125973.000000     125973.000000\n",
              "mean      287.14465  4.556674e+04  ...                  0.120240         19.504060\n",
              "std      2604.51531  5.870331e+06  ...                  0.319459          2.291503\n",
              "min         0.00000  0.000000e+00  ...                  0.000000          0.000000\n",
              "25%         0.00000  0.000000e+00  ...                  0.000000         18.000000\n",
              "50%         0.00000  4.400000e+01  ...                  0.000000         20.000000\n",
              "75%         0.00000  2.760000e+02  ...                  0.000000         21.000000\n",
              "max     42908.00000  1.379964e+09  ...                  1.000000         21.000000\n",
              "\n",
              "[8 rows x 39 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWvZPuXilk8i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e3f1d97-6686-4cf7-c5b0-79fbf207ec60"
      },
      "source": [
        "%%time\n",
        "# Drop the diffficulty level column\n",
        "train.drop('difficulty_level', axis=1, inplace=True)\n",
        "test.drop('difficulty_level', axis=1, inplace=True)\n",
        "gc.collect()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 270 ms, sys: 32.5 ms, total: 303 ms\n",
            "Wall time: 8.29 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNU462pTP8ae"
      },
      "source": [
        "le = LabelEncoder()\n",
        "\n",
        "# attack type\n",
        "train[\"attack_type\"] = le.fit_transform(train[\"attack_type\"])\n",
        "test[\"attack_type\"] = le.fit_transform(test[\"attack_type\"])"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfwaBC1nmqGC"
      },
      "source": [
        "# protocol type\n",
        "train[\"protocol_type\"] = le.fit_transform(train[\"protocol_type\"])\n",
        "test[\"protocol_type\"] = le.fit_transform(test[\"protocol_type\"])\n",
        "\n",
        "# service\n",
        "train[\"service\"] = le.fit_transform(train[\"service\"])\n",
        "test[\"service\"] = le.fit_transform(test[\"service\"])\n",
        "\n",
        "# flag\n",
        "train[\"flag\"] = le.fit_transform(train[\"flag\"])\n",
        "test[\"flag\"] = le.fit_transform(test[\"flag\"])"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJL0c2GUJHVv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "44ff14f8-ca13-4bf6-a9b5-302f52e5fbba"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 114,
      "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>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
              "      <th>srv_diff_host_rate</th>\n",
              "      <th>dst_host_count</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>9</td>\n",
              "      <td>491</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>150</td>\n",
              "      <td>25</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>44</td>\n",
              "      <td>9</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>26</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>30</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>32</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration  protocol_type  ...  dst_host_srv_rerror_rate  attack_type\n",
              "0         0              1  ...                      0.00           11\n",
              "1         0              2  ...                      0.00           11\n",
              "2         0              1  ...                      0.00            9\n",
              "3         0              1  ...                      0.01           11\n",
              "4         0              1  ...                      0.00           11\n",
              "\n",
              "[5 rows x 42 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9Mxt7wqpSqy"
      },
      "source": [
        "X = train.iloc[:, train.columns != 'attack_type'] # Features\n",
        "y = train['attack_type'].values # Target Variable"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZswZNqshL3-j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0136c6c7-d564-47bb-923f-1d26b1ead30b"
      },
      "source": [
        "print(sorted(train[\"attack_type\"].unique()))"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhbQi2jfpo3Y"
      },
      "source": [
        "# Train test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jn7NB9HXrIX7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3abe5fa-5983-4f1e-ac3a-f732eed690b3"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100778, 41)\n",
            "(25195, 41)\n",
            "(100778,)\n",
            "(25195,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFHNAuvBrmfp"
      },
      "source": [
        "# scale the data to improve the performance   \n",
        "scaler = StandardScaler()\n",
        "\n",
        "# then we call the transform method to scale both the training and testing data\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.fit_transform(X_test)"
      ],
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AcUuRg7xfH4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa116907-1d34-47a2-a947-22a0b89ca7a2"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100778, 41)\n",
            "(25195, 41)\n",
            "(100778,)\n",
            "(25195,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcMR1onSLC7v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c31bdc36-e7ae-473e-b9d4-7c78e67e8feb"
      },
      "source": [
        "print(\"Rows :\", X_train.shape[0], \"Columns :\" , X_train.shape[1])"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows : 100778 Columns : 41\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fG2GEHSYF95F",
        "outputId": "d2c386eb-b3d8-4d8a-d56e-245f3805274f"
      },
      "source": [
        "X_train = np.reshape(X_train, (X_train.shape[0],X_train.shape[1],1))\n",
        "X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n",
        "print(X_train.shape, y_train.shape)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100778, 41, 1) (100778,)\n",
            "(25195, 41, 1) (25195,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFXXsUX0KbX_"
      },
      "source": [
        "## Model Design"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Whh27jSg5wp9"
      },
      "source": [
        "#used when model won't get improve then execution will stop( to Halt the Training of Neural Networks At the Right Time)\n",
        "# patience: Number of epochs with no improvement after which training will be stopped, monitor: Quantity to be monitored, to discover the training epoch on which training was stopped, \n",
        "# the “verbose” argument can be set to 1\n",
        "EarlyStopper = EarlyStopping(patience=4, monitor='val_loss', mode='min')"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H13QigHiG7e_",
        "outputId": "c341bbd1-d4eb-4dd3-b9a3-1e885a26b13d"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(layers.LSTM(50, return_sequences=True, input_shape = (X_train.shape[1], 1)))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(23, activation='softmax'))\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=[\"accuracy\"])\n",
        "model.summary()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_21\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_22 (LSTM)               (None, 41, 50)            10400     \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 2050)              0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 23)                47173     \n",
            "=================================================================\n",
            "Total params: 57,573\n",
            "Trainable params: 57,573\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0Oh5g9f1xv0",
        "cellView": "form"
      },
      "source": [
        "#@title Neural Network Code commented\n",
        "# input_dim = X_train.shape[1]\n",
        "# model = Sequential()\n",
        "# # input layer=41, hidden layer=128, 64, output layer=23\n",
        "# # relu: to manage non-linear data, softmax=to get probablity\n",
        "# model.add(layers.Dense(128, input_dim=input_dim, activation='relu'))\n",
        "# model.add(layers.Dense(64, activation='relu'))\n",
        "# model.add(layers.Dense(23, activation='softmax'))\n",
        "\n",
        "# # to improve performance. Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments.\n",
        "# opt = keras.optimizers.Adam(learning_rate=0.001)\n",
        "\n",
        "# model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=[\"accuracy\"])\n",
        "# print(model.summary())"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49zthS0QNanB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "7a790a1b-893c-4a71-b407-72a2a4557915"
      },
      "source": [
        "keras.utils.plot_model(model, show_shapes=True,  show_layer_names=True)"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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E9uuvv+IqR5WVlX19fZs2bbK0tESDvqvq6+uvWLEiNzeXz+cHBARQ6+Wr1wmAChnpj1eNjY0bN268e/dub2/vzZs3Hz16hP8Ni1WipCNUhBC6c+fOvn37vv32WzabTYjYv3+/1K20Y2yZTroCG9xyX1/f8+fPqfJx5ubmCKHz5893d3fX1NQMnhApKCiop6fn9OnToq9dSKjXCcBbQnSIgCzDrb766itcZFtLS2vZsmV1dXXOzs76+vrq6urjxo2LjY3Fb93euHFj4sSJXC53wYIF0dHRuDjCpEmT/vWvf+3Zs0dXVxchZGxs/OOPP+bm5uIG9fX1jx49Kvnsb5pDMSUlRepWyQ4cOIAHePJ4PA8Pj8zMTByzjY1NbW3toUOHdHR0EEITJ068d+8eSZKBgYFsNtvMzIzFYuno6CxdurS2tpZq7cWLF++//z6Hw7GwsPj8888jIiIQQtbW1njYk+jNefbs2ZkzZ/h8flJS0uCorl69am9vr6amhhAyMTHZvXv3qAX2zTffSJjgs7CwEDcYGRk5ZswYPT09X19fPATYysqKGt1FkuTMmTOjo6PFrqunpycyMtLc3JzFYhkaGnp7e1dVVe3duxf3zk+YMEHGSncIhlsBBhj8OYR6rHJiQpnOITEtsMWLFz948EBBjUNiBUww+HMIZQPlx9gynUoPjOpGuH37Nn46Vm48AIwyZiXWu3fvEm82kvq4imsZDBYZGVlTU3Pv3r1169aJzv0JFGrjxo3URxq/dEM5f/58dHR0QUGBpaUl3mHt2rWiO7i5ufH5fHV1dXt7e0XMSTVc3d3dtra2ogPyMKFQmJaWJl+poCGPPXny5N69e0WfRYqKiqjbKOF1JylEH1+hK0BGDCnTORhDAouNjVVTU5swYYKiazAi6AoQgXuBSkpKqquru7u7qfU7d+50d3dva2vDi1ZWVmPHjkUInT59WvTwkpIST09PeiOXG56AC8/SSrl37978+fMRQo6OjsNtUMKx6enprq6u1CvjQqHwyZMnly5dWrx4MUzNMqqSk5N7enpIknz48KGPj4+yw/n/GBJYUlLSwMBAfX39KNRgVCga6yiOTklGLpeLZxCgpjvcs2dPbm7usWPHRF9BzMjIUFNTCwwMVPq0AkO6cuXKb7/9Jrby1q1bUVFRQUFBM2bMGG6Dko8NCQlxdHRcvHgxfheUIAg8g4CNjY188SOmdQUAwCg01lFUSknG+/fv79ixIz4+nsPhiK53dnYODQ1taGjYtm3bKIcklUAgiIiISE9PF1vv6OhYUFCwevVq6v8M2Uk9Ni4urqKiYvBJ5QaJFbzlSJJMTU3FVWn09fWXLl1K1SUYVh1FJdaKlFtGRgZJkh4eHoM3JSUlTZ48+bvvvjt//vyQx0q4b5JrV6I3lIWUUWxs7ObNm/HreaNGX1/f1dU1PT0df68fOUis4C0XFxcXHR0dGxvb1NR06dKlx48fu7i4PH/+HCGUkZEh+p5oZmZmfHw8tZienu7u7o7rKN6/fz84ONjf37+rqyskJKSuru7GjRv9/f0ffPABrjY5rKbQf0duCIVChV57cXHxlClThpxij8vlHj58WE1NLSAgAFdvECPhvm3atCksLEwgEPD5/Ly8vNraWktLy4CAAGo0SFRU1L59+9LS0p4+feru7r5q1apffvlFloAvX75cW1u7atWqEVy0nGbOnNnQ0HDr1i1aWoPECt5mAoEgNTV12bJla9as0dXVdXBwOHjwYEtLy6FDh+RrULm1Ioels7Pz4cOHEt7ycHJyCgsLq6uri4qKEtsk430bsnal1LKQbyIQCEJDQ7OysuS63JHCPapveslouCCxgrdZVVVVR0fHnDlzqDVz587V0NAY/PatHJRYK1IWTU1NJElKnhE6KSlpypQpmZmZZWVlouuHe99Ea1fKXRYyJibms88+MzMzk7qnIuAbhR/JRw4SK3ibtba2IoTE6pzp6em1t7fT0j5ja0UihLq7uxFCkn/q4XA4OTk5BEGsX79eIBBQ60dy36iykNRo0EePHkmdd66srKyysnLDhg1S21cQ/Do1vmkjB4kVvM309PQQQmLpgK46ioytFYnhTCH1NTwnJ6fw8PCamhrRVzlGct+ospCi4zrLy8slH5WdnX3hwgU1NTWci3Eju3fvJghCxv7ZEert7UX/vWkjB4kVvM2mTZumra0t+i/z2rVrvb29s2fPxosjqaPI2FqRmJGREUEQsoxUTUxMtLW1xdM+YlLvmwTylYXMyckRTcT4ewB+QUC0R0Jx8I3CBaFGDhIreJtxOJytW7cWFhYeOXKkra2tsrIyKCjI1NQ0MDAQ7zDcOoqMrRU5GI/Hs7S0fPLkidQ9cYcAnjiHWiP5vklu7U1lIf38/IyNjRXxyuzIW8Y3ysHBgZ6ARP+XgFdagWpBMrzSKhQKU1JSbGxs2Gy2vr6+l5dXdXU1tXVYBR5Hp1bkYLK/0mpmZia6Jjg4mM1md3V14cXCwkI8SMDAwGDLli1ih0dERIi+0irhvkmtXTlkWUiSJL28vBBCO3fulHotok+sWHl5+fz586kpfExMTJydnS9evIi3Sm5Z8rHYkiVLzMzMhEIhtSYkJETuV1ohsQIVJktipZGySjLKnVhrampYLJaMxW1HwcDAgIuLS3Z2NtNabmlp4XA4+/fvF105ksQKXQEADIPSSzJKJhAIzp49W1NTg3+Ksba2TkhISEhI6OjoUHZoaGBgoKioqL29nfZiciNvOS4ubsaMGcHBwQghkiQbGxvLysrwqxzygcQKwNvj5cuXuAjL+vXr8Zro6GhfX18/Pz+l11spLS0tKCgoKSmRPLR29FtOTU2tqKg4c+YMnr/9xIkTuAhLcXGx/DGJPr5CVwBQLWgUuwKUWJJx5DMInD17NjIykq543iZFRUXJycl4Qim5Df4cjupUpgCoruTk5OTkZGVHISc3Nzc3NzdlR8FEnp6enp6etDcLXQEAAEAzSKwAAEAzSKwAAEAzSKwAAECzIX68Onbs2OjHAYB8pFb3eAvgty3hH6YqER0iMKwZFAAAAGBiw60IkqY5XgBQKDzxCTy1AZUAfawAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzSKwAAEAzgiRJZccAwBB+/PHH7OxsoVCIFx8+fIgQsrCwwItqamr/5//8n9WrVystPgDeDBIrYKjbt287OjpK2OHWrVvTp08ftXgAkB0kVsBctra21dXVQ26ytrauqakZ5XgAkBH0sQLmWrt2LZvNHryezWavW7du9OMBQEbwxAqY68GDB9bW1kN+RGtqaqytrUc/JABkAU+sgLksLS1nzZpFEIToSoIg5syZA1kVMBkkVsBon3zyibq6uugadXX1Tz75RFnxACAL6AoAjNbU1GRqakoNukIIqampNTY2GhsbKzEqACSDJ1bAaEZGRq6urtRDq7q6+nvvvQdZFTAcJFbAdGvXrhX9XrV27VolBgOALKArADBdW1uboaFhb28vQojNZjc1Nenp6Sk7KAAkgSdWwHQ6OjqLFi1isVgsFmvx4sWQVQHzQWIFKmDNmjUDAwMDAwNQHACoBOgKACqgu7vbwMCAJMmWlhYul6vscACQhmQSHx8fZd8PAIDq8fHxUXb2+h2Wsm+IuHnz5oWFhSk7CjA8K1asCA0NdXJyUtwpKioqCIKQXO9K0dLS0hBC8PlkGvz3wiiMS6zjx49fvny5sqMAw7NixQonJyeF/sUtW7YMIcRiKfMTm5+fjxCCzyfT4L8XRmFcYgVgSMpNqQAMC4wKAAAAmkFiBQAAmkFiBQAAmkFiBQAAmkFiBUpz5swZXV3dU6dOKTuQ0XD+/Pno6OiCggJLS0uCIAiCEKsm4+bmxufz1dXV7e3tb9y4oaw4Kd3d3ba2ttu3bxdbLxQK09LSnJ2d5WhzyGNPnjy5d+/egYEB+WNlHkisQGnIP8xbf7t27crIyIiJifH29n7w4IGVldXYsWOPHDlSXFxM7XPu3Ln8/Hx3d/eqqqpZs2YpMVosNjZ28EyONTU17777bnh4eFdX13AbfNOxHh4eHA5n4cKFra2tI4qYSSCxAqVZsmTJ69ev3d3dFX0igUAg3xMWLfbs2ZObm3vs2DE+n0+tzMjIUFNTCwwMfP36tbICk+DKlSu//fab2Mpbt25FRUUFBQXNmDFjuA1KPjYkJMTR0XHx4sX9/f1yRswwkFjB2y87O7upqUkpp75///6OHTvi4+M5HI7oemdn59DQ0IaGhm3btiklMAkEAkFERER6errYekdHx4KCgtWrV2tqag63TanHxsXFVVRUDD6pioLECpSjrKzM3NycIIivv/4aIZSVlaWlpcXj8U6cOPHRRx/p6OiMHz/+6NGjeOeMjAwOh2NkZLRx40ZTU1MOh+Ps7Hzt2jW8NTg4WENDw8TEBC9u3rxZS0uLIIiWlhaEUGho6NatW2trawmCwFMQ/vTTTzo6Ort37x6Fy8zIyCBJ0sPDY/CmpKSkyZMnf/fdd+fPnx/yWJIkU1NTp06dqqmpqa+vv3Tp0rt37+JNkm8XQmhgYGDnzp3m5uZcLnf69Ol5eXmyxxwbG7t582ZDQ8PhXOhI6evru7q6pqenvx0dRJBYgXIsWLDgypUr1OKmTZvCwsIEAgGfz8/Ly6utrbW0tAwICOjr60MIBQcH+/v7d3V1hYSE1NXV3bhxo7+//4MPPnj8+DFCKCMjQ/Q108zMzPj4eGoxPT3d3d3dysqKJMn79+8jhPDvJKLzaClOcXHxlClTeDze4E1cLvfw4cNqamoBAQGdnZ2Dd4iLi4uOjo6NjW1qarp06dLjx49dXFyeP3+OpN0uhFBUVNS+ffvS0tKePn3q7u6+atWqX375RZaAL1++XFtbu2rVqhFctJxmzpzZ0NBw69at0T817SCxAmZxdnbW0dExNDT08/Pr7Oysr6+nNrFYLPz4Zmdnl5WV1d7enpOTI8cplixZ0tbWtmPHDvqiHlpnZ+fDhw+trKzetIOTk1NYWFhdXV1UVJTYJoFAkJqaumzZsjVr1ujq6jo4OBw8eLClpeXQoUOiuw15u7q7u7Oysry8vLy9vfX09LZv385ms2W5VwKBIDQ0NCsrS67LHSkbGxuEUGVlpVLOTi9IrIChNDQ0EELUI5iYOXPm8Hg86qsxMzU1NZEkOeTjKiUpKWnKlCmZmZllZWWi66uqqjo6OubMmUOtmTt3roaGBtUBIkb0dlVXV3d1dU2bNg1v4nK5JiYmstyrmJiYzz77zMzMTOqeioBvFH4kV3WQWIGq0tTUbG5uVnYUknR3dyOEJP/Uw+FwcnJyCIJYv369QCCg1uOxR9ra2qI76+nptbe3Sz0v7ljYvn078V+PHj2SOkCqrKyssrJyw4YNUttXEFzCHN80VQeJFaikvr6+1tbW8ePHKzsQSXCmkDr03cnJKTw8vKamJjExkVqJp/YSS6MyXjL+3SktLU209HJ5ebnko7Kzsy9cuKCmpoZzMW5k9+7dBEHI2D87Qni+yLdjhghIrEAllZaWkiQ5b948vMhisd7UaaBERkZGBEHIMlI1MTHR1tb25s2b1Jpp06Zpa2uLZrRr16719vbOnj1bamsTJkzgcDgVFRXDijYnJ0c0EeNvA7GxsSRJivZIKA6+UcbGxqNwLkWDxApUhlAofPXqVX9//+3bt0NDQ83Nzf39/fEma2vrly9fFhUV9fX1NTc3P3r0SPTAMWPGNDY21tXVtbe39/X1lZSUjM5wKx6PZ2lp+eTJE6l74g4BdXV10TVbt24tLMPPhOwAACAASURBVCw8cuRIW1tbZWVlUFCQqalpYGCgLK2tW7fu6NGjWVlZbW1tAwMDT548efr0KULIz8/P2NhYEa/MjrxlfKMcHBzoC0p5RmUCGFn5+Pgwbe4aIAuEUF5e3rAOOXDgAB55yuPxPDw8MjMz8W8XNjY2tbW1hw4d0tHRQQhNnDjx3r17JEkGBgay2WwzMzMWi6Wjo7N06dLa2lqqtRcvXrz//vscDsfCwuLzzz+PiIhACFlbW9fX15MkeePGjYkTJ3K53AULFjx79uzMmTN8Pj8pKWm4lynH5zM4OJjNZnd1deHFwsJCPEjAwMBgy5YtYjtHRER4enpSi0KhMCUlxcbGhs1m6+vre3l5VVdX401Sb1dPT09kZKS5uTmLxTI0NPT29q6qqiJJ0svLCyG0c+dOqZGLPrFi5eXl8+fPNzU1xanDxMTE2dn54sWLeKvkliUfiy1ZssTMzEwoFEqNTQwD8wYkVkADORLrcAUGBo4ZM0ahp5BKjs9nTU0Ni8X64YcfFBTScA0MDLi4uGRnZzOt5ZaWFg6Hs3//fjmOZWDegK4AoDJUsQCStbV1QkJCQkJCR0eHsmNBAwMDRUVF7e3tfn5+TGs5Li5uxowZwcHB9AamLKqXWPfv349/Ezh48KBSAkhISLCzs9PR0dHU1LS2tv7iiy9E/81I3iqBaEE5ExOTNWvWvGnPW7du+fn5WVhYaGpqGhgYODo6JiUl4U1+fn6ERKdPnxY90ZsGyaemphIEoaamZmtre+nSpeHcHiAuOjra19fXz89P6fVWSktLCwoKSkpKJA+tHf2WU1NTKyoqzpw5w2az6Q1MaZT9yPw7Mj7S19TUIIS++eabUQhpMFdX18zMzBcvXrS1teXl5bHZ7EWLFsm4VSorKytdXV0JO9y+fZvH44WEhDx8+FAgEFRXV3/xxRcLFy7EW1esWHHu3LnW1ta+vj78Y4WHh0dvb29nZ2dTU1NAQMCpU6eoEyGETExMent7xU7R398/ceJEhBDVrFRIwV0B0dHReAD8pEmT8vPzFXciyUbylfPs2bORkZH0xvN2KCoqSk5O7u/vl7sF6AoYPYqrFKetrY37+/h8/vLly728vH766Sf80rrUrSO3f/9+PT299PT0SZMmcTicyZMnJyYmUkP/CIKYP3++rq4uNacpQRBsNpvH4xkaGoqN1Jk9e/azZ8+KiorETlFQUKCsd2/eJDk5uaenhyTJhw8f+vj4KDscebi5ue3Zs0fZUTCRp6dndHS06IiIt8Bbm1gVVynu9OnToh8CAwMDhBD1WovkrSP34sWL169fv3z5klqjoaFBFeE/evSohO9igYGBH3/8MbW4adMmhNA333wjtltqaurWrVvpChiAP6C3IbFevHjxnXfe4fF4Ojo6Dg4ObW1tYpXi0tPTtbS01NTUZs+ebWxszGaztbS0Zs2a5eLigodS6+npffHFF/KdvaGhgcvlWlhYyLJ15AXr5s6d29nZ+ec///ny5ctyN4L9+c9/njp16j//+U/RQvGXL1/u6upyc3MbYeMA/JGpfGLt7Oz08PDw8fF5+fJlTU3N5MmTe3t7xSrFhYaGRkREkCT5zTffPHz48NmzZ+++++7Nmzejo6Nv3rz58uXLTz/9NCUlRY56ZV1dXT///HNAQADuAZS6deQF67744os5c+bcunVrwYIF9vb2+/btE316Ha6NGzcihER/Bvzqq6/Cw8PlbhAAgN6CxFpXV9fW1mZvb8/hcIyNjQsKCvC37yHZ2dnxeLyxY8euXLkSIWRubm5gYMDj8fBP8HKUSkpOTjY1NaV+lJe6deQF67hc7pUrV/7nf/7H1tb2zp07kZGRU6dOvXjxonytffrpp1paWn/9619x+Y8HDx5cv35dKbU4AXibsJQdwEhZWloaGRmtWbMmJCTE399/0qRJshyFHyGpCXbwII/hvmxeWFh47Nixc+fOic5lJOPWkWCz2cHBwcHBwdeuXduzZ09RUZGvr291dbW+vv5wm9LV1V21atW3336bm5u7bt26tLS0TZs2aWho4HIYwyK1xsdbAL9zeezYMWUHAn7nyZMnjCvHo+RRCb8n33Cr33777eOPP2axWARBrFixAr8+6O3tjbsCsF27diGE2tvb8SKexOLmzZt4ERe/GNbrMUePHp07d25DQ4McWyWQOtxqsKCgIIRQQUGB2Ho83Er0FUmxEz18+JD877W/8847r169MjY2fvnyJUmSuKjSsIZbAaBEMNyKfvb29qdOnWpsbIyMjMzLy9u/f7+iz3jgwIEjR478/PPP48aNG+5WOVy6dCktLQ3/2dvbW2wmSzw9vdwDD2bMmDFv3rx///vfgYGBvr6+cjz2Yop+pZUJGDheEpAkycAReCrfFdDY2Nja2mpnZ2doaPjll1+eO3fuzp07ijsdSZJRUVGvXr0qKiqixorKuFVuv/76q5aWFv5zT0/PnTt3pk+fTm3Fv+mLrhmuTZs2Xb169fjx4/irAABghFT+ibWxsXHjxo13797t7e29efPmo0ePcI1OsUpxdJ3uzp07+/bt+/bbb9lstuirovgxWfJWhNBwC9b19fU9f/68tLSUSqwIIS8vr2PHjrW2tr5+/frEiRNRUVGenp4jSazLly83MDDw8vKytLSUuxEAAEX1EmtqauqCBQsQQtu2bfP29jY0NBwYGHB2dubxeB9//PHGjRu3bNmCEAoKCjIyMrKzs1u8ePGuXbtSUlIQQg4ODmVlZXv37sXDjBYtWvT3v/89Ly9v0aJFCKHg4ODc3FzJZycl9idK3irZP/7xD2tr69ra2tevX1MZGc/qfPLkSWrYf0hIyNy5c2NiYkxMTIyMjCIjI4OCgsQmN25vb3d1dbW3t0cInTp1ysbGJjk5efCJ5s6d+/nnnyOENDU1169fT70UsGPHDjyt2z//+U97e3uxuZgAAFIRI8kFtPP19UUI5efnKzsQMDwEQeTl5YnOQf1Wgs8nMzHw70X1nlgBAIDhILH+zt27dyXU3KO9iiUA4K0EifV3bG1tJYzqkNoDC4Co8+fPR0dHixbAxWPjKG5ubnw+X11d3d7eXhHzUA1Xd3e3ra3t9u3bxdYLhcK0tLThlotLSkoSezSZNm2a6A5lZWXz58/n8XimpqaRkZE9PT14/cmTJ/fu3auKdc0pkFgBUIhdu3ZlZGTExMR4e3s/ePDAyspq7NixR44cKS4upvY5d+5cfn6+u7t7VVXVrFmzlBgtFhsbK1qRB6upqXn33XfDw8NpLNKGEKqqqnJzc1u4cGFzc3NhYeH333+PX3VBCHl4eHA4nIULF7a2ttJ4xtEEiRWoABqr6yquUK+oPXv25ObmHjt2TPSF5oyMDDU1tcDAQKVPJTCkK1eu/Pbbb2Irb926FRUVFRQUNGPGDDnaFHubUbT9xMREExOT+Ph4LS0tJyenyMjIw4cPU/U6QkJCHB0dFy9eLPY6jKqAxApUAI3VdRVXqJdy//79HTt2xMfHczgc0fXOzs6hoaENDQ3btm1TaAByEAgEERER6enpYusdHR0LCgpWr16tqalJ4+n6+/uLi4tdXV0JgsBrPvroI5IkT5w4Qe0TFxdXUVExOCSVAIkVjBKSJFNTU6dOnaqpqamvr7906VLq8SQ4OBiP2MWLmzdv1tLSIgiipaUFISRWXTcjI4PD4RgZGW3cuNHU1JTD4Tg7O1+7dk2OphAdFXIHy8jIIEnSw8Nj8KakpKTJkyd/991358+fH+5dysrK0tLS4vF4J06c+Oijj3R0dMaPH4+rXmADAwM7d+40NzfncrnTp08XG90sWWxs7ObNmw0NDYdzofJ78OBBR0eHubk5tQbPFXT79m1qjb6+vqura3p6OqOGhMoIEisYJXFxcdHR0bGxsU1NTZcuXXr8+LGLi8vz588RQhkZGaJjYDMzM+Pj46lFseq6wcHB/v7+XV1dISEhdXV1N27c6O/v/+CDD/D8N8NqCtFRIXew4uLiKVOmDDmVA5fLPXz4sJqaWkBAQGdn5+AdJNylTZs2hYWFCQQCPp+fl5dXW1traWkZEBBAvVgYFRW1b9++tLS0p0+furu7r1q16pdffpEl4MuXL9fW1iqiXGR0dLS+vr6GhoaFhcXSpUuvX7+O1z979gwhJNpPwuFwuFwuvlLKzJkzGxoa5CiUrHSQWMFoEAgEqampy5YtW7Nmja6uroODw8GDB1taWg4dOiRfgywWCz/W2dnZZWVltbe35+TkyNHOyCvkiuns7Hz48CF+/hqSk5NTWFhYXV1dVFSU2CYZ75Kzs7OOjo6hoaGfn19nZ2d9fT1CqLu7Oysry8vLy9vbW09Pb/v27Ww2W5Z7IhAIQkNDs7Ky5LpcST799NOTJ08+fvy4o6Pj6NGj9fX1rq6uVVVVCCE8AEBsnis2m43rAlPwG4CVlZW0x6ZokFjBaKiqquro6JgzZw61Zu7cuRoaGtRX+JGYM2cOj8eTo065IjQ1NZEkKXkW6KSkpClTpmRmZoq9Ljzcu4TLCuMn1urq6q6uLmo8E5fLNTExkeWexMTEfPbZZ4qYPnLChAkzZ87U1tbW0NCYN29eTk6OQCDIzMxECOHeZ7Efpnp7e6lpMTF8G8UeY1UCJFYwGvC4GW1tbdGVenp6uPDryGlqajY3N9PS1Ah1d3cjhCT/1MPhcHJycgiCWL9+vegz2kjuEu5Y2L59OzVo9NGjR1IHSJWVlVVWVm7YsEFq+yPn4OCgrq5+7949hBDuBG9ra6O2dnV1dXd3m5qaih6C8yy+paoFEisYDXp6egghsQTR2tpKS+H3vr4+upoaOZwLpA5ud3JyCg8Pr6mpSUxMpFaO5C7h353S0tJEhzdJndYhOzv7woULampqOBfjRnbv3k0QhIz9s7ITCoVCoRD/l2NhYcHn8x89ekRtxV3eYkXa8EwWYo+xKgESKxgN06ZN09bWFv23eu3atd7e3tmzZ+NFFosld3XH0tJSkiRxucgRNjVyRkZGBEHIMlI1MTHR1tYWz+CASb1LEuD5hisqKoYVbU5Ojmgixk/9sbGxJEmK9kjI58MPPxRdvH79OkmSTk5OCCEWi7V48eJLly5RPxuWlJQQBCE2lALfRmNj4xFGMvogsYLRwOFwtm7dWlhYeOTIkba2tsrKyqCgIFNT08DAQLyDtbX1y5cvi4qK+vr6mpubRZ9l0FDVdYVC4atXr/r7+2/fvh0aGmpubu7v7y9HU8OtkCsVj8eztLTEs2NJhjsERH/AkXqXJLe2bt26o0ePZmVltbW1DQwMPHnyBE/P4+fnZ2xsrIhXZiW33NDQkJub29ra2tfXV15evmHDBnNzc+r1qh07djx//nzXrl2dnZ3l5eUpKSn+/v5TpkwRbQHfRgcHB9ojVzj550NQAJj6QkUhGaZmEQqFKSkpNjY2bDZbX1/fy8ururqa2vrixYv333+fw+FYWFh8/vnnERERCCFra+v6+nqSJG/cuDFx4kQul7tgwYJnz54FBgay2WwzMzMWi6Wjo7N06dLa2lr5mjpz5gyfz09KSpLlMmX8fAYHB7PZbDz3GkmShYWFeJCAgYHBli1bxHaOiIgQnZdMwl3KzMzEP+bY2NjU1tYeOnRIR0cHITRx4sR79+6RJNnT0xMZGWlubs5isQwNDb29vauqqkiS9PLyQgjt3LlTauSiT6xYeXn5/Pnzqa5PExMTZ2fnixcv4q2SW966dauVlZWWlhaLxRo/fnxAQEBjY6PoDhcvXnznnXc0NTVNTU0jIiK6u7vFWliyZImZmZlQKJQcNgPzBiRWQANZEiuNAgMDx4wZM2qno8g+2SWLxRrW3JQKNTAw4OLikp2drUItkyTZ0tLC4XD2798vdU8G5g3oCgAqicmlj6ytrRMSEhISEjo6OpQdCxoYGCgqKmpvb6e96KXiWsbi4uJmzJgRHBysiMYVDRIrAPSLjo729fX18/NTer2V0tLSgoKCkpISyUNrGdUyQig1NbWiouLMmTNsNpv2xkcBJFagYmJiYnJycl6/fm1hYXH8+HFlh/NGu3fvDg4O/vLLL5UbxsKFC3/88UeqeIJKtHzixImenp7S0lK5J2NXOpWf/hr80SQnJ4vOjchkbm5ubm5uyo5C9Xh6enp6eio7ihGBJ1YAAKAZJFYAAKAZJFYAAKAZJFYAAKAZ4368unr1qq+vr7KjAMOWlpaWn5+v7CgU6+rVqwgh+HwyzdWrV6lKEQzBrMSKCzQAlePj46PoU+BiJTNnzlT0iSRg2r9egM2bN49pqYMgVXA+GfAHhCdcOXbsmLIDAUA66GMFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACaQWIFAACasZQdAABD6+rq6unpoRZ7e3sRQq9evaLWaGpq8ng8JUQGgDQESZLKjgGAIWRlZW3evFnCDpmZmZs2bRq1eACQHSRWwFDNzc2mpqYDAwNDblVXV3/69KmhoeEoRwWALKCPFTCUoaHhwoUL1dXVB29SV1f/y1/+AlkVMBYkVsBca9asGfIbFUmSa9asGf14AJARdAUA5mpvbzc0NBT9CQvT0NBobm7W0dFRSlQASAVPrIC5+Hy+u7s7m80WXclisTw9PSGrAiaDxAoYbfXq1f39/aJrBgYGVq9erax4AJAFdAUARuvt7TUwMGhvb6fWaGtrt7S0aGpqKjEqACSDJ1bAaBoaGr6+vhoaGniRzWavWLECsipgOEisgOlWrVqFX7tCCPX19a1atUq58QAgFXQFAKYTCoUmJibNzc0IIQMDg2fPng05uBUA5oAnVsB0ampqq1at0tDQYLPZq1evhqwKmA8SK1ABK1eu7O3thX4AoCqYUt2qvLz88ePHyo4CMBRJkmPHjkUIPXz4sK6uTtnhAIaaMGGCk5OTsqNACCFEMoOPj4+y7wQAQLX5+PgoO5P9P0x5YkUI+fj45OfnKzsKID+CIPLy8pYvX66Ixu/cuYMQsrOzU0Tjw+Lr64sQgs8q0+C/F4ZgUGIFQAImpFQAZAQ/XgEAAM0gsQIAAM0gsQIAAM0gsQIAAM0gsQIAAM0gsQIlO3PmjK6u7qlTp5QdiKKcP38+Ojq6oKDA0tKSIAiCINauXSu6g5ubG5/PV1dXt7e3v3HjhrLipHR3d9va2m7fvl1svVAoTEtLc3Z2HlZrSUlJxO9NmzZNdIeysrL58+fzeDxTU9PIyEhqwoiTJ0/u3bv3TbNJMhwkVqBk5FtdBmjXrl0ZGRkxMTHe3t4PHjywsrIaO3bskSNHiouLqX3OnTuXn5/v7u5eVVU1a9YsJUaLxcbGVldXi62sqal59913w8PDu7q6aDxXVVWVm5vbwoULm5ubCwsLv//++6CgILzJw8ODw+EsXLiwtbWVxjOODkisQMmWLFny+vVrd3d3RZ9IIBAM92lrhPbs2ZObm3vs2DE+n0+tzMjIUFNTCwwMfP369WgGI6MrV6789ttvYitv3boVFRUVFBQ0Y8YMOdr84YcfRN9KEm0/MTHRxMQkPj5eS0vLyckpMjLy8OHDd+/exVtDQkIcHR0XL14sNosE80FiBX8U2dnZTU1No3a6+/fv79ixIz4+nsPhiK53dnYODQ1taGjYtm3bqAUjI4FAEBERkZ6eLrbe0dGxoKBg9erV9JYY7+/vLy4udnV1JQgCr/noo49Ikjxx4gS1T1xcXEVFxeCQGA4SK1CmsrIyc3NzgiC+/vprhFBWVpaWlhaPxztx4sRHH32ko6Mzfvz4o0eP4p0zMjI4HI6RkdHGjRtNTU05HI6zs/O1a9fw1uDgYA0NDRMTE7y4efNmLS0tgiBaWloQQqGhoVu3bq2trSUIwtraGiH0008/6ejo7N69W0GXlpGRQZKkh4fH4E1JSUmTJ0/+7rvvzp8/P+SxJEmmpqZOnTpVU1NTX19/6dKl1EOc5FuEEBoYGNi5c6e5uTmXy50+fXpeXp7sMcfGxm7evNnQ0HA4Fyq/Bw8edHR0mJubU2usrKwQQrdv36bW6Ovru7q6pqenq1aXESRWoEwLFiy4cuUKtbhp06awsDCBQMDn8/Py8mpray0tLQMCAvr6+hBCwcHB/v7+XV1dISEhdXV1N27c6O/v/+CDD3BdtIyMDNEyBZmZmfHx8dRienq6u7u7lZUVSZL3799HCOFfRYRCoYIurbi4eMqUKTweb/AmLpd7+PBhNTW1gICAzs7OwTvExcVFR0fHxsY2NTVdunTp8ePHLi4uz58/R9JuEUIoKipq3759aWlpT58+dXd3X7Vq1S+//CJLwJcvX66trVVEYcbo6Gh9fX0NDQ0LC4ulS5dev34dr3/27BlCSLSfhMPhcLlcfKWUmTNnNjQ03Lp1i/bAFAcSK2AiZ2dnHR0dQ0NDPz+/zs7O+vp6ahOLxcKPcnZ2dllZWe3t7Tk5OXKcYsmSJW1tbTt27KAv6v+vs7Pz4cOH+PlrSE5OTmFhYXV1dVFRUWKbBAJBamrqsmXL1qxZo6ur6+DgcPDgwZaWlkOHDonuNuQt6u7uzsrK8vLy8vb21tPT2759O5vNluX+CASC0NDQrKwsuS5Xkk8//fTkyZOPHz/u6Og4evRofX29q6trVVUVQggPABCrXM5mswUCgegaGxsbhFBlZSXtsSkOJFbAaHgaQepxTMycOXN4PB71NZk5mpqaSJIc8nGVkpSUNGXKlMzMzLKyMtH1VVVVHR0dc+bModbMnTtXQ0OD6vQQI3qLqquru7q6qPFMXC7XxMRElvsTExPz2WefmZmZSd1zuCZMmDBz5kxtbW0NDY158+bl5OQIBILMzEyEEO59Fvthqre3l8vliq7Bt1HsMZbhILEC1aapqYmnw2KU7u5uhJDkn3o4HE5OTg5BEOvXrxd9RsOji7S1tUV31tPTE50D/E1wx8L27dupQaOPHj2SOkCqrKyssrJyw4YNUtsfOQcHB3V19Xv37iGEcId4W1sbtbWrq6u7u9vU1FT0EJxn8S1VFZBYgQrr6+trbW0dP368sgMRh3OB1MHtTk5O4eHhNTU1iYmJ1Eo9PT2EkFgalfEy8e9OaWlposObysvLJR+VnZ194cIFNTU1nItxI7t37yYIQsb+WdkJhUKhUIj/y7GwsODz+Y8ePaK24u7v6dOnix6C5+gVe4xlOEisQIWVlpaSJDlv3jy8yGKx3tRpMMqMjIwIgpBlpGpiYqKtre3NmzepNdOmTdPW1hbNaNeuXevt7Z09e7bU1iZMmMDhcCoqKoYVbU5Ojmgixt8AYmNjSZIU7ZGQz4cffii6eP36dZIk8QQqLBZr8eLFly5don5CLCkpIQhCbCgFvo3GxsYjjGQ0QWIFKkYoFL569aq/v//27duhoaHm5ub+/v54k7W19cuXL4uKivr6+pqbm0UfhRBCY8aMaWxsrKura29v7+vrKykpUdxwKx6PZ2lp+eTJE6l74g4B0R9wOBzO1q1bCwsLjxw50tbWVllZGRQUZGpqGhgYKEtr69atO3r0aFZWVltb28DAwJMnT54+fYoQ8vPzMzY2VsQrs5JbbmhoyM3NbW1t7evrKy8v37Bhg7m5OfV61Y4dO54/f75r167Ozs7y8vKUlBR/f/8pU6aItoBvo4ODA+2RK5DCJn0ZHh8fH+bMVwPkgxDKy8sb1iEHDhzAHW08Hs/DwyMzMxP/UmFjY1NbW3vo0CEdHR2E0MSJE+/du0eSZGBgIJvNNjMzY7FYOjo6S5cura2tpVp78eLF+++/z+FwLCwsPv/884iICISQtbV1fX09SZI3btyYOHEil8tdsGDBs2fPzpw5w+fzk5KShnuZMn5Wg4OD2Wx2V1cXXiwsLMSDBAwMDLZs2SK2c0REhKenJ7UoFApTUlJsbGzYbLa+vr6Xl1d1dTXeJPUW9fT0REZGmpubs1gsQ0NDb2/vqqoqkiS9vLwQQjt37pQauegTK1ZeXj5//nyq69PExMTZ2fnixYt4q+SWt27damVlpaWlxWKxxo8fHxAQ0NjYKLrDxYsX33nnHU1NTVNT04iIiO7ubrEWlixZYmZmJhQKJYfNqBwCiRXQRo7EOlyBgYFjxoxR6CmkkvGzWlNTw2KxxN7mVKKBgQEXF5fs7GwVapkkyZaWFg6Hs3//fql7MiqHQFcAUDGqUu7I2to6ISEhISGho6ND2bGggYGBoqKi9vZ2Pz8/VWkZi4uLmzFjRnBwsCIaVxzVS6w9PT0hISEmJiY8Hu8vf/kL/pXg4MGDyo5rCH19fcnJydbW1hoaGnp6etOmTaurq5N6lGh9OTGTJk1CCO3fv5/JVw0o0dHRvr6+fn5+Sq+3UlpaWlBQUFJSInloLaNaRgilpqZWVFScOXOGzWbT3rhCqV5i/eqrr3766ae7d++mp6dv3LhR9IVIplmxYsXf/va3H3/8saur6z//+Y+VlZUsDy9UfTldXV38taK/v7+rq+v58+f4s7tt2zYmX7XixMTE5OTkvH792sLC4vjx48oORya7d+8ODg7+8ssvlRvGwoULf/zxR6qQgkq0fOLEiZ6entLSUn19fdobVzTVS6xFRUVz5szR09P77LPPfHx8ZDxKrGTcKFSQy83NLSoqys/P/9Of/sRisUxNTU+cOCFW4ldG6urqXC7XyMho8uTJwzpw9K9aoZKTk3t6ekiSfPjwoex/9Urn5ua2Z88eZUehejw9PaOjo8VeeFUVqpdYnzx5Isf3ArGScaNQQe6bb76ZNWsWvWNEioqKhrX/6F81AACpVmL93//9QvXthgAAIABJREFUX2tr66dPn/71r38lCELsnT/sX//6l52dna6uLofDcXBwOHv2LBpUMm5wBbkhK61Jrc8mQW9v79WrVyVUBaa3Zh1DrhoAgKlSYv3ggw/u379vbGz86aefkiQ5ZH/l8+fPV6xYUVdX19jYqK2tvXr1ajSoZNzgCnJDVlqTWp9NgsbGxt7e3l9//fX999/HlUOnTp2amZlJ/rem5HBr1v3888/79+9/01aGXDUAAFOlxCoLHx+fXbt26evrjxkzxsPD48WLF1IrdEittCahhN2b4KRvaGi4e/fuqqqq58+fL126dMuWLX//+9/xDrLUrHv9+jU1HmDhwoXMv2oAAMZSdgAKhLtipQ57lL3SmuQSdqJwjQl7e3vqx6L4+Phvvvnm0KFD+HFSFrq6utQ0aqWlpTKWw1DiVSOE0tLS8vPzZdlTdV29ehUh5Ovrq+xAwO9cvXqVqhqhdG/bE2txcfF7771naGioqan5xRdfyHKIfJXWJMMv/+FJQTANDY2JEyfW1tbK1+B7770nYYokhlw1AAB7q55Y6+vrvby8li1b9v33348bN+7AgQOyZBmq0lpoaChdkWhra9vY2Ny5c0d0ZX9/v66uLl2noDDnqhFCYWFhovOjvJXws+pb/2Cuchj1HeKtemKtrKzs6+vbtGmTpaUlh8Ohpn6UTL5Ka1KtWLHi5s2bDx48wItdXV2PHj1SRIUeRl01AAC9ZYkVT/d4/vz57u7umpoa0aksxErGiS6qq6u/qdLaSISHh0+cONHf37++vv7FixeRkZECgYCa4IjGmnWMumoAAEIqVTawrq5u5syZCCEWizVr1qzjx49/9dVXuPytlpbWsmXLSJKMjIwcM2aMnp6er68vnlHZysqqvr5erGSc2OKQldak1meT6vHjxytXrtTX19fU1HznnXdKSkqoTRJq1l2+fJl6w8rExGThwoViOzD2qpHiq1sxAaOqKAEKo/5eCJIZs3VDv9VbgCCIvLw86GMFSsGov5e3qisAAACYABKrPO7evTtkWT9MQYUpwVvg/Pnz0dHRopUh165dK7qDm5sbn89XV1e3t7dXxDQqskhISLCzs9PR0dHU1LS2tv7iiy/E3nIsKyubP38+j8czNTWNjIzs6emhNiUlJYn9cxArPPSmY0+ePLl3715VKbYrFSRWedja2kroXsnNzVV2gICJdu3alZGRERMTQ1WGHDt27JEjR4qLi6l9zp07l5+f7+7uXlVVNWvWLKXE+fPPP2/ZsqWurq6lpSU5OTk9PV10JFNVVZWbm9vChQubm5sLCwu///57agIrqSQc6+HhweFwFi5cSL0Uo9IgsQKVQWPZw9GvoLhnz57c3Nxjx47x+XxqZUZGhpqaWmBgoNIrYYvS1tbGU+Dw+fzly5d7eXn99NNPjx8/xlsTExNNTEzi4+O1tLScnJwiIyMPHz4s+s6e2Gw0v/32G7VJ8rEhISGOjo6LFy/u7+8fzetVBEisQGXQWPZwlCso3r9/f8eOHfHx8RwOR3S9s7NzaGhoQ0ODhNfqRt/p06dFq6AaGBgghPBbef39/cXFxa6urtRw6Y8++ogkyRMnTkhtVpZj4+LiKioq0tPTabwcpYDECkYVSZKpqalTp07V1NTU19dfunQp9cASHBysoaFB1aLfvHmzlpYWQRD4zWCxsocZGRkcDsfIyGjjxo24fpizszM1hndYTSG6qzgOlpGRQZKkh4fH4E1JSUmTJ0/+7rvvzp8/P+SxEu6Y1BqPQ1aGHK6GhgYul2thYYEQevDgQUdHBx46jeGpZ2/fvi21HVmO1dfXd3V1TU9PZ8hoJfkpahzXMDFqDBqQD5JhHOvOnTs1NDR++OGH1tbW27dvz5o1y8DA4NmzZ3jr6tWrjY2NqZ1TUlIQQs3NzXjR29sblz3EAgMDtbS07ty5093dXVVVNXfuXD6fj2e6Hm5Tp0+f5vP5CQkJslymHJ9VS0tLOzs7sZVWVlYPHz4kSfLKlStqamqTJk3q6OggSbKkpER0KmzJdyw2NhYhdOHChdevXzc1Nbm4uGhpafX29uKt27Zt09TUPH78+KtXr2JiYtTU1K5fvz6syDs7O/l8fnBwMF68ePEiQiglJUV0Hy6XS422TkxMHD9+vJ6eHpvNnjRpkqen57///W8Zj8Wio6MRQjdv3hxWnCTDcgg8sYLRIxAIUlNTly1btmbNGl1dXQcHh4MHD7a0tBw6dEi+BlksFn6Us7Ozy8rKam9vF618KDtZqjjKrbOz8+HDh/jpbEhOTk5hYWF1dXXUi3kUGe/YkDUepVaGlEVycrKpqWlSUhJexD/ii02XwmazBQIB/vOnn3568uTJx48fd3R0HD16tL6+3tXVtaqqSpZjMRsbG4RQZWXlsOJkGkisYPRUVVV1dHTMmTOHWjN37lwNDQ3R13DlNmfOHB6PN2TlQ+VqamoiSVLyJKZJSUlTpkzJzMwsKysTXT/cOyZa41H2ypBvUlhYeOzYsbNnz1I/uOE+YrEfl3p7e7lcLv7zhAkTZs6cqa2traGhMW/evJycHIFAkJmZKcuxGL5Rz58/lz1OBoLECkYPHkkjNqeOnp5ee3s7Le1rampKrfA9+rq7u9F/S/S+CYfDycnJIQhi/fr1ok9wI7ljI6wMmZubu2fPntLSUjzpOoa7rdva2qg1XV1d3d3duE7mYA4ODurq6vfu3ZP9WJxn8U1TXZBYwejR09NDCIklhdbW1vHjx4+88b6+PrqaohfOFFKHvjs5OYWHh9fU1CQmJlIrR3LHqMqQon1/5eXlssR84MCBI0eO/Pzzz+PGjRNdb2FhwefzHz16RK3B0/xMnz59yHaEQqFQKMT/qch4bG9vL/rvTVNdkFjB6Jk2bZq2trboVAjXrl3r7e2dPXs2XmSxWHJPrlVaWkqSJFVDfiRN0cvIyIggCFlGqiYmJtra2t68eZNaI/WOSSBfZUiSJCMjIysrK4uKigbP18lisRYvXnzp0iVquraSkhKCIKgBDx9++KHo/vi3MicnJ1mOxfCNwmWGVBckVjB6OBzO1q1bCwsLjxw50tbWVllZGRQUZGpqGhgYiHewtrZ++fJlUVFRX19fc3Oz6NMNGlQFESEkFApfvXrV399/+/bt0NBQc3Nzf39/OZqisYrjYDwez9LS8smTJ1L3xB0Coj/vSL1jklt7U2VIPz8/Y2PjIV+ZvXPnzr59+7799ls2my36Zio1l+WOHTueP3++a9euzs7O8vLylJQUf3//KVOm4K0NDQ25ubmtra19fX3l5eUbNmwwNzenXq+SfCyGb5QiKhePKmUMRRgCo4ZKAPkgGYZbCYXClJQUGxsbNputr6/v5eVVXV1NbX3x4sX777/P4XAsLCw+//zziIgIhJC1tTUeRCVW9jAwMJDNZpuZmbFYLB0dnaVLl9bW1srXlIQqjoPJ8VkNDg5ms9ldXV14sbCwEA8SMDAw2LJli9jOERERosOtJNwxqTUeh6wMSZKkl5cXQmjnzp2DQ33Tz/Giw6QuXrz4zjvvaGpqmpqaRkREdHd3U5u2bt1qZWWlpaXFYrHGjx8fEBDQ2Ngo2r6EY7ElS5aYmZkJhcJh3WGSYTkEEiugjSyJlUb4tctROx1Fjs9qTU0Ni8USe9dTiQYGBlxcXLKzs5UdiLiWlhYOh7N//345jmVUDoGuAKDCVKUYkrW1dUJCQkJCgliZKKUYGBgoKipqb29nYBm2uLi4GTNmBAcHKzuQkYLECsBoiI6O9vX19fPzU3q9ldLS0oKCgpKSEslDa0dfampqRUXFmTNn8BTuKg0SK1BJMTExOTk5r1+/trCwOH78uLLDkcnu3buDg4O//PJL5YaxcOHCH3/8kSqkwBAnTpzo6ekpLS3V19dXdiw0eKumvwZ/HMnJycnJycqOYtjc3Nzc3NyUHQUTeXp6enp6KjsK2sATKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0E3Zbyj8Pz4+Psq+EwAA1cacN68Ikhlzy5SXl1PTQAIwWFpaGkIoLCxM2YEA5powYQKupKV0TEmsAEi2fPlyhNCxY8eUHQgA0kEfKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0AwSKwAA0Iyl7AAAGNq1a9du3bpFLT548AAhdOjQIWqNo6Pjn/70JyVEBoA0BEmSyo4BgCGcPn3a3d1dXV1dTU0NIYQ/qARBIISEQuHAwMCpU6c+/vhjJUcJwFAgsQKG6uvrMzAwaGtrG3Krjo5Oc3OzhobGKEcFgCygjxUwFJvNXrly5ZCpU8ImAJgAEitgrpUrV/b29g5e39fXt2rVqtGPBwAZQVcAYC6hUDhu3Ljnz5+LrTc0NHz27BnuewWAgeCjCZhLTU1t7dq1Yl/5NTQ0/P39IasCJoNPJ2C0wb0Bvb29K1euVFY8AMgCugIA09nY2Ny/f59atLS0rK2tVWI8AEgFT6yA6dasWcNms/GfNTQ0Pv30U+XGA4BU8MQKmO7+/fs2NjbUYnV19eTJk5UYDwBSwRMrYDpra2tHR0eCIAiCcHR0hKwKmA8SK1ABn3zyibq6urq6+ieffKLsWACQDroCgApobGycMGECSZKPHz82MzNTdjgASMHExJqamlpeXq7sKACzlJaWIoTee+89JccBGMbJySk8PFzZUYhjYldAeXn51atXlR0FGKmrV6/S+Pdobm4+ceJEulqj0ZMnT44fP67sKP6grl69ysyHMIbWY503b15+fr6yowAj4uvrixCi6+/x5cuXCKExY8bQ0hqNjh07tmLFCvi4KgX+jDEQQxMrAGIYmFIBeBMmdgUA8H/bO/ugpq70j58LeQ8JxAqY8mJDolAV36pdiDq0w5StZUQQ1Ey1W9aRQauNWHQFEURAfMECAwvjtGWzO8UVqTJo1SjrsrHrlGHaEYSNqyIVECjyUjCBBAnk7h9nen/5BUhCCOSGns9f3HPOfe5zTuLjyXPP+R4EwqFBgRWBQCBsDAqsCAQCYWNQYEUgEAgbgwIrglzcvHnT1dX122+/tbcjM8WdO3eSk5OvXLni5+cH9+l+9NFHhg3CwsI4HI6zs/PSpUvv379vFyczMjKWLFnC5XLpdLpIJPrTn/40ODho2ODevXvr1q1jsVh8Pv/IkSOvXr0iqrKysrD/z7Jlyyy599q1a2fOnBkbG5uFDs40KLAiyAUJd6zYkOPHjxcUFBw9ejQ6Ovqnn34SCoWvvfZaaWnpjRs3iDZVVVXffPPNpk2blErl6tWr7eJndXX1/v37W1paent7s7Oz8/PzDRc2KZXKsLCw0NDQnp6eioqKv/zlL3v37rXQsol7IyIiGAxGaGjowMCA7bs0y+DkIyYmJiYmxt5eIKYLyT9HjUYTHBw8fTuXLl2y8N/RqVOnFi9erNVqiRKhUHjhwgUnJycvL6+BgQGiXC6Xb968efq+WU14ePjo6ChxuW3bNgBAW1sbvNy+fbtAINDr9fAyJycHw7D//ve/8DIzM/Prr7+ezLLpe3Ecl0qlwcHBOp3OEj9J+x1DM1bEb5SSkpLu7u5Ze9zTp09TU1NPnDjBYDAMy8VicUJCQkdHx6FDh2bNGbNcv37d2dmZuJw/fz4AQKPRAABGR0dv3LgREhKCYRis3bhxI47jV69eNWvWknvT09Pr6+vz8/Nt2J3ZBwVWBIm4d++er68vhmF//vOfAQDFxcVsNpvFYl29enXjxo1cLtfb2/vixYuwcUFBAYPB8PDw2LNnD5/PZzAYYrG4trYW1kqlUhqNtmDBAni5b98+NpuNYVhvby8AICEhITExsbm5GcMwkUgEALh16xaXyz158uQMda2goADH8YiIiPFVWVlZixcv/uqrr+7cuTPhvTiO5+bmvvnmm3Q6ncfjRUZGPnr0CFaZHiIAwNjYWFpamq+vL5PJXL58OZxfT5WOjg4mkykQCAAAP/300+DgoK+vL1ErFAoBAA0NDWbtWHIvj8cLCQnJz8/HHTkphAIrgkSsX7/++++/Jy4/+eSTgwcParVaDodz6dKl5uZmPz+/uLg4nU4HAJBKpbGxsRqN5sCBAy0tLffv3x8dHX3vvfeeP38OACgoKIA/YCFFRUUnTpwgLvPz8zdt2iQUCnEch+e+wHcmer1+hrp248YNf39/Fos1vorJZP71r391cnKKi4sbGhoa3yA9PT05OTklJaW7u/u77757/vz5hg0b4OG1pocIAJCUlHT27Nm8vLyff/5506ZNH3744Y8//jglzzUaTXV1dVxcHDzVsaurCwDA4XCIBgwGg8lkGh6mm5yczOPxaDSaQCCIjIz84YcfYLkl9wIAVq1a1dHR8eDBgyn5SSpQYEU4AGKxmMvluru7SySSoaGhtrY2oopCocCp3JIlS4qLi9VqtUwms+IR4eHhKpUqNTXVdl7/H0NDQ8+ePYOzswkJDg4+ePBgS0tLUlKSUZVWq83Nzd2yZcvOnTtdXV0DAwPPnz/f29v7xRdfGDabcIiGh4eLi4ujoqKio6Pd3NyOHTtGpVKnOj7Z2dl8Pj8rKwtewpf4hokCAACVStVqtfDvjz/++Nq1a8+fPx8cHLx48WJbW1tISIhSqbTkXgg8MKKxsXFKfpIKFFgRjgScNBHTMSPWrFnDYrGIn8nkobu7G8fxCaerBFlZWf7+/kVFRffu3TMsVyqVg4ODa9asIUrWrl1Lo9GIpIcRhkP0+PFjjUZDrHZiMpkLFiyY0vhUVFSUl5ffvn2bmGbCHPHo6Khhs5GRESaTCf/28fFZtWqVi4sLjUYLCgqSyWRarbaoqMiSeyFwoIymsY4FCqyIOQWdTu/p6bG3F8YMDw8DAOh0uok2DAZDJpNhGLZr1y7DGRxce+Ti4mLY2M3NTa1Wm30uTCwcO3aMWFLa2toK30FZQllZ2enTpxUKxRtvvEEUwrS1SqUiSjQazfDwMJ/Pn9BIYGCgs7PzkydPLL8Xxlk4aA4KCqyIuYNOpxsYGPD29ra3I8bASGF26TvUbG5qasrMzCQK3dzcAABGYdTCbrq7uwMA8vLyDFcCWShgWlhYWFpaWl1d/frrrxuWCwQCDofT2tpKlMAk9fLlyye0o9fr9Xo9/E/FwntHRkbAr4PmoKDAipg7KBQKHMeDgoLgJYVCmSxpMMt4eHhgGPby5UuzLTMzMwMCAurq6oiSZcuWubi4GL5xqq2tHRkZeeutt8xa8/HxYTAY9fX1U/IWx/EjR440NjZWVlYazZQBABQK5YMPPvjuu++IF31yuRzDMGLBw+9//3vD9j/88AOO48HBwZbcC4ED5enpOSW3SQUKrAjHRq/X9/f3j46ONjQ0JCQk+Pr6xsbGwiqRSPTLL79UVlbqdLqenh7DiRIAYN68eZ2dnS0tLWq1WqfTyeXymVtuxWKx/Pz82tvbzbaECQHD1zsMBiMxMbGioqK0tFSlUjU2Nu7du5fP58fHx1ti7Y9//OPFixeLi4tVKtXY2Fh7e/vPP/8MAJBIJJ6enhNumX348OHZs2e//PJLKpVquDP13LlzsEFqauqLFy+OHz8+NDRUU1OTk5MTGxvr7+8Pazs6OsrKygYGBnQ6XU1Nze7du319fYntVabvhcCBCgwMNNtB8jK7+xEsgrS7KRBTworPsbCwEKbhWCxWREREUVERfI+xaNGi5ubmL774gsvlAgAWLlz45MkTHMfj4+OpVKqXlxeFQuFyuZGRkc3NzYS1vr6+d999l8FgCASCTz/99PDhwwAAkUgEdxDdv39/4cKFTCZz/fr1XV1dN2/e5HA4WVlZU+2mhTuvpFIplUrVaDTwsqKiAi4SmD9//v79+40aHz582HDnlV6vz8nJWbRoEZVK5fF4UVFRjx8/hlVmh+jVq1dHjhzx9fWlUCju7u7R0dFKpRLH8aioKABAWlraeFcnex2fk5NDtLl79+7bb79Np9P5fP7hw4eHh4eJqsTERKFQyGazKRSKt7d3XFxcZ2enoX0T90LCw8O9vLyI3VkmIG2sQIEVMVPMwucYHx8/b968GX2EWSwMrE1NTRQKxcRez1lmbGxsw4YNJSUl9nbEmN7eXgaDce7cOUsakzZWoFQAwrFxFDEkkUiUkZGRkZFhJBNlF8bGxiorK9VqtUQisbcvxqSnp69cuVIqldrbkWmBAisCMUskJydv3bpVIpFY8hZrRlEoFFeuXJHL5aaX1s4+ubm59fX1N2/epFKp9vZlWsyRwLp7924Oh4Nh2FRfgM40er0+Ly9PLBYblZvVu5wMQx1PCI1G8/DweOedd3Jycvr7+2egEyTl6NGjMpns5cuXAoHAUQ6gPnnypFQqPXXqlH3dCA0NvXDhAiGkQBKuXr366tUrhULB4/Hs7cu0sXcuYgKsy5tA4Ym6urqZcMk6njx5sm7dOgDAihUrjKpCQkKKior6+vpUKtWlS5eoVOr7779vuWWhUOjq6orjOHwn/q9//Ss2NhbDMD6fD1e3kAHS5r9si+WygQibQ9rv2ByZsZKQBw8eJCUl7d27d+XKleNrXVxc4IsXDoezbdu2qKioW7duQfWQKYFhmJub2zvvvCOTycrLy1+8eBEeHm73X5oIxG+cuRNYCYVHkrBixYorV67s2LFjwo2MJvQurSYmJiY2Nra7u/v8+fPTsYNAIKaJAwdWHMdzcnL8/f3pdLqrqytcpUgwoQylWfFKuMKOxWJxudzAwEC4qdkmipamMdS7BNPQBoVr4+VyObx0rEFAIOYO9s5FTICFeZOUlBQMwz7//PP+/n6NRgPlc4gc66FDh+h0+uXLl/v7+48ePerk5ASTjykpKQCAf/7zny9fvuzu7t6wYQObzR4ZGcFxfHBwkMvlnjlzRqvVdnV1bdmypaenx4QpC/nd7343PsdqyNDQEIfDkUqlRMn169c5HE5GRsZktxA5ViNgEPTx8SHDIJA2/2VbUI7VjpD2O0bGL4Qlg6XRaFgs1nvvvUeUGL680mq1LBZLIpEQjel0+ieffIL/GlOIc4dgOH769CmO4//5z38AANevXzd8kAlTFmI2sKakpCxevFilUlluc7LAiuM4zLriJBgE0n7pbQsKrHaEtN8xil2mydPn6dOnGo0mNDR0wlrLZSgNxSv9/Pw8PDx27tx54MCB2NhYKJU2fUVL00C9y6qqKkNZdasZGhrCcRxuaiTDIFy+fJlsue8Z4jfSTRISExNjbxcmwFEDK5RpgKpo4yFkKI8dO0YUTqYXScBkMqurq5OSkk6ePJmRkbFt2zaZTGadKQspKyvLzc1VKBRGymxWA1UvAwICADkGISgo6ODBg1Z1xWGoqanJz89HSWe7kJeXZ28XJsZRAyuUIocnPYyHkKFMSEiYktmlS5d+++23PT09ubm5p0+fXrp0KdzzZ4UpsxQWFt6+fbu6unq8MpvV3Lp1CwCwceNGQI5B8Pb2Njx4aq6Sn5//W+gmCfnmm2/s7cLEOOqqgGXLljk5Od29e3fCWutkKDs7Ox8+fAgAcHd3P3Xq1OrVqx8+fGidKdPgJvUuraarqysvL8/b23vXrl2A9IOAQMxhHDWwQgG0y5cvl5SUqFSqhoYGw7PVTMhQmqCzs3PPnj2PHj0aGRmpq6trbW0NCgqyzpRpzOpdWqINiuP44OAglFbr6em5dOnSunXrnJ2dKysrYY6V5IOAQMxl7PvubEIsfNOnVqt379792muvubi4rF+/Pi0tDQDg7e394MEDfBIZStPilS0tLWKxmMfjOTs7v/766ykpKaOjo5OZMuteTU3NunXriETkggULxGLx3bt3cQv0Lk1og167dm358uUsFotGozk5OYFfN1+9/fbbGRkZfX19ho3tOwikfWNrW9CqADtC2u8YhuP4bMTvqbB161ZA4uwJwkJ+I59jeXn59u3bSfjv6LcAab9jjpoKQCAQCNKCAqs1PHr0CJscEooHIxyFO3fuJCcnG4pDfvTRR4YNwsLCOByOs7Pz0qVLJzyxahYwLXp55syZgIAAJpPJZrMDAgJSU1OJ866vXbt25swZR9Emnw6OutzKvgQEBKCffgibc/z48bq6ugsXLnA4nOjoaJFINDAwUFpaKpFIwsPDYZuqqqpbt26dP3++srLSXn5WV1fv379fIpFQqVS5XL5z587GxkZCoeLf//53XFzcH/7wByaTKZfLd+zYUVtbW1VVBQCIiIh49uxZaGhoZWUlPNZ7roJmrAgHRqvVjhcRt7sp6zh9+nRZWVl5ebnhHryCggInJ6f4+HhSSUGaFr2k0Wj79u1zd3d3cXHZunVrZGTkP/7xD2INyYEDB1asWPHBBx+Mjo7arwczDgqsCAempKSku7ubbKas4OnTp6mpqSdOnIA7XwjEYnFCQkJHR8ehQ4fs5dt4TIteVlRUGPbCy8sLAGCYK0hPT6+vr8/Pz58ld+0BCqwIO4PjeG5u7ptvvkmn03k8XmRkJKFCIJVKaTQacYLIvn372Gw2hmG9vb0AgISEhMTExObmZgzDRCJRQUEBg8Hw8PDYs2cPn89nMBhisbi2ttYKU2Aayo3WUVBQgON4RETE+KqsrKzFixd/9dVXd+7cmfBeEwNoViLSJmqQRqKXRjQ1Nbm5uS1cuJAo4fF4ISEh+fn5czmfZselXpNB2rVpiClh4eeYlpZGo9G+/vrrgYGBhoaG1atXz58/v6urC9bu2LHD09OTaJyTkwMAgEqGOI5HR0cLhUKiNj4+ns1mP3z4cHh4WKlUrl27lsPhtLW1WWHL56nMAAAFjUlEQVTKrHIjgU3Wsfr5+S1ZssSoUCgUPnv2DMfx77//3snJ6Y033hgcHMRxXC6Xb968mWhmegBNSETi05bExCcSvYSMjIy0t7cXFhbS6fTxh34nJycDWxykRNpYgWasCHui1Wpzc3O3bNmyc+dOV1fXwMDA8+fP9/b2Gu6jmxIUCgXO3ZYsWVJcXKxWq2UymRV2wsPDVSpVamqqdW5MiaGhoWfPngmFwskaBAcHHzx4sKWlJSkpyajKwgEUi8VcLtfd3V0ikQwNDbW1tQEAhoeHi4uLo6KioqOj3dzcjh07RqVSpzpc2dnZfD4/KyvLqNzHx8fb2zs9Pf3s2bPbt283ql20aBEAYLKdMnMAFFgR9kSpVA4ODq5Zs4YoWbt2LY1GI37CT4c1a9awWCwbajzOEN3d3TiOmz6JOisry9/fv6io6N69e4blUx1AQ4nI6UtiQtHL27dvjxe9fP78eXd399///ve//e1vq1atMspfw86+ePHC8mc5FiiwIuzJwMAAAMBIicbNzU2tVtvEPp1O7+npsYmpmWN4eBgAMOHZaAQMBkMmk2EYtmvXLq1WS5RPZwAJNUhiCXZra6vlB6+VlZWdPn1aoVBA0V4jqFSqu7t7WFhYWVmZUqnMzs42rGUymeDXjs9JUGBF2BO4mNEoCgwMDHh7e0/fuE6ns5WpGQVGGbPL5oODgz/77LOmpqbMzEyicDoDSAhLGiYHa2pqLPG5sLCwtLS0urrarJSwSCRydnZWKpWGhSMjI+DXjs9JUGBF2JNly5a5uLj8+OOPREltbe3IyMhbb70FLykUCvzdagUKhQLH8aCgoOmbmlE8PDwwDLNkpWpmZmZAQEBdXR1RYnYATWCdGiRuUvSyr6/vww8/NCxpamoaGxvz8fExLISd9fT0nNKjHQgUWBH2hMFgJCYmVlRUlJaWqlSqxsbGvXv38vn8+Ph42EAkEv3yyy+VlZU6na6np6e1tdXw9nnz5nV2dra0tKjVahg09Xp9f3//6OhoQ0NDQkKCr68vPLl2qqYsUW60FSwWy8/PDx6KYRqYEDBcQ2p2AE1bm0wNUiKReHp6Trhl1rToJZvNrqqqqq6uVqlUOp2urq7u448/ZrPZn332maER2NnAwECzTjoqdlmLYBrSLqFATAkLP0e9Xp+Tk7No0SIqlcrj8aKioh4/fkzU9vX1vfvuuwwGQyAQfPrpp/CQc5FIBBdR3b9/f+HChUwmc/369V1dXfHx8VQq1cvLi0KhcLncyMjI5uZm60yZUG40wibLraRSKZVK1Wg08LKiogIuEpg/f/7+/fuNGh8+fNhwuZWJATQtEYlPrgYZFRUFAEhLSxvvqlnRy4iICIFA4OLiQqfThUKhRCJpbGw0MhIeHu7l5QXVhKcDaWMFCqyImWL2P0e4z3I2n4jbKLA2NTVRKJTx6z3txdjY2IYNG0pKSmbCeG9vL4PBOHfu3PRNkTZWoFQAYk7hoMpJIpEoIyMjIyPDcOunvRgbG6usrFSr1TOk05aenr5y5UqpVDoTxkkCCqwIBClITk7eunWrRCKxu96KQqG4cuWKXC43vbTWOnJzc+vr62/evEmlUm1unDygwIqYIxw9elQmk718+VIgEFy+fNne7ljDyZMnpVLpqVOn7OtGaGjohQsXCF0FG3L16tVXr14pFAoej2dz46QC6bEi5gjZ2dlGq9AdkbCwsLCwMHt7MVNs3rx58+bN9vZiNkAzVgQCgbAxKLAiEAiEjUGBFYFAIGwMCqwIBAJhY0j68qq9vb28vNzeXiCmBdy2OOc/R6haMue7SU7a29tJKrJj7x0KExATE2PvUUEgEI4BOXdeYfgcPnYGgUAg7AHKsSIQCISNQYEVgUAgbAwKrAgEAmFjUGBFIBAIG/M/5CMcarEqD3sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JJYR6k_NC0L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29008349-5c22-4156-fc07-903f5a0169c0"
      },
      "source": [
        "history = model.fit(X_train, y_train, epochs=20, batch_size=32, verbose=1,validation_data=(X_test, y_test), callbacks=[EarlyStopper])"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "3150/3150 [==============================] - 38s 12ms/step - loss: 0.1198 - accuracy: 0.9684 - val_loss: 0.0622 - val_accuracy: 0.9815\n",
            "Epoch 2/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0424 - accuracy: 0.9869 - val_loss: 0.0491 - val_accuracy: 0.9861\n",
            "Epoch 3/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0338 - accuracy: 0.9895 - val_loss: 0.0353 - val_accuracy: 0.9912\n",
            "Epoch 4/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0287 - accuracy: 0.9911 - val_loss: 0.0362 - val_accuracy: 0.9896\n",
            "Epoch 5/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0248 - accuracy: 0.9924 - val_loss: 0.0324 - val_accuracy: 0.9908\n",
            "Epoch 6/20\n",
            "3150/3150 [==============================] - 35s 11ms/step - loss: 0.0222 - accuracy: 0.9928 - val_loss: 0.0299 - val_accuracy: 0.9920\n",
            "Epoch 7/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0204 - accuracy: 0.9935 - val_loss: 0.0276 - val_accuracy: 0.9928\n",
            "Epoch 8/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0188 - accuracy: 0.9940 - val_loss: 0.0271 - val_accuracy: 0.9925\n",
            "Epoch 9/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0179 - accuracy: 0.9941 - val_loss: 0.0275 - val_accuracy: 0.9927\n",
            "Epoch 10/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0170 - accuracy: 0.9942 - val_loss: 0.0256 - val_accuracy: 0.9931\n",
            "Epoch 11/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0232 - val_accuracy: 0.9936\n",
            "Epoch 12/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0156 - accuracy: 0.9949 - val_loss: 0.0243 - val_accuracy: 0.9925\n",
            "Epoch 13/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0148 - accuracy: 0.9950 - val_loss: 0.0239 - val_accuracy: 0.9936\n",
            "Epoch 14/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0135 - accuracy: 0.9956 - val_loss: 0.0233 - val_accuracy: 0.9930\n",
            "Epoch 15/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0139 - accuracy: 0.9954 - val_loss: 0.0230 - val_accuracy: 0.9943\n",
            "Epoch 16/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0135 - accuracy: 0.9954 - val_loss: 0.0226 - val_accuracy: 0.9942\n",
            "Epoch 17/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0129 - accuracy: 0.9958 - val_loss: 0.0224 - val_accuracy: 0.9947\n",
            "Epoch 18/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0122 - accuracy: 0.9959 - val_loss: 0.0252 - val_accuracy: 0.9934\n",
            "Epoch 19/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0122 - accuracy: 0.9961 - val_loss: 0.0235 - val_accuracy: 0.9937\n",
            "Epoch 20/20\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0116 - accuracy: 0.9961 - val_loss: 0.0235 - val_accuracy: 0.9942\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsRkaS0_UpdR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26745691-4b7b-482f-8f98-35a3b1e73e0d"
      },
      "source": [
        "train_loss, train_acc = model.evaluate(X_train, y_train, verbose=0)\n",
        "print(f\"Train_Accuracy:{train_acc} Train Loss: {train_loss}\")\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(f\"Test Accuracy:{test_acc} Test Loss: {test_loss}\")\n",
        "y_pred = np.argmax(model.predict(X_test, verbose=0), axis=1)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train_Accuracy:0.9965171217918396 Train Loss: 0.010684876702725887\n",
            "Test Accuracy:0.9942448735237122 Test Loss: 0.023549742996692657\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeLiOGg_UvZa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7995c0c-4799-4aa2-95e6-9dc4fc4e71cf"
      },
      "source": [
        "print(y_pred)\n",
        "print(y_test)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[11  9  9 ... 11 11  9]\n",
            "[11  9  9 ... 11 11  9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Os7JBZ7gALcC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ffcda149-f64f-470b-e705-9d996b7bfef2"
      },
      "source": [
        "# Plot the graph accuracy: a ratio of correctly predicted observation to the total observations, Precision is the ratio of \n",
        "# correctly predicted positive observations to the total predicted positive observations. High precision relates to the low false positive rate. \n",
        "# Recall is the ratio of correctly predicted positive observations to the all observations in actual class.\n",
        "# F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, \n",
        "# but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. \n",
        "# Accuracy works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall.\n",
        "plt.figure(figsize = (15,6))\n",
        "plt.subplot(121)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.subplot(122)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# predict probabilities for test set\n",
        "\n",
        "Target_names = ['back', 'buffer_overflow', 'ftp_write', 'guess_passwd', \n",
        "                'imap', 'ipsweep', 'land', 'loadmodule', 'multihop',\n",
        "                'neptune', 'nmap', 'normal', 'perl', 'phf', \n",
        "                'pod', 'portsweep', 'rootkit', 'satan', 'smurf', \n",
        "                'spy', 'teardrop', 'warezclient', 'warezmaster']\n",
        "print(\"Classification Report:\\n\")\n",
        "print()\n",
        "print(classification_report(y_test, y_pred))\n",
        "# confusion matrix\n",
        "print(\"Confusion Matrix:\\n\")\n",
        "print()\n",
        "matrix = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize = (15,7) )\n",
        "sns.heatmap(matrix, annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Classification Report:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99       185\n",
            "           1       1.00      0.22      0.36         9\n",
            "           3       1.00      0.91      0.95        11\n",
            "           4       0.25      1.00      0.40         1\n",
            "           5       0.98      0.98      0.98       733\n",
            "           6       0.00      0.00      0.00         3\n",
            "           7       0.00      0.00      0.00         0\n",
            "           8       0.00      0.00      0.00         0\n",
            "           9       1.00      1.00      1.00      8228\n",
            "          10       0.99      0.91      0.95       313\n",
            "          11       0.99      1.00      1.00     13422\n",
            "          12       1.00      1.00      1.00         1\n",
            "          13       1.00      1.00      1.00         1\n",
            "          14       1.00      0.93      0.96        43\n",
            "          15       1.00      1.00      1.00       573\n",
            "          16       0.00      0.00      0.00         1\n",
            "          17       0.99      0.97      0.98       738\n",
            "          18       1.00      0.99      1.00       534\n",
            "          19       0.00      0.00      0.00         1\n",
            "          20       1.00      1.00      1.00       188\n",
            "          21       0.98      0.89      0.93       202\n",
            "          22       0.25      0.12      0.17         8\n",
            "\n",
            "    accuracy                           0.99     25195\n",
            "   macro avg       0.70      0.68      0.67     25195\n",
            "weighted avg       0.99      0.99      0.99     25195\n",
            "\n",
            "Confusion Matrix:\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wb55c8FVX0In"
      },
      "source": [
        "### PSO with NN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B21R_k94shgV"
      },
      "source": [
        "#fine_tuning,nnunits,dropout,learning_rate\n",
        "lb=[49,0.01]\n",
        "ub=[50,0.02]"
      ],
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbrmvjgQsF26"
      },
      "source": [
        "from tensorflow.keras import backend as K\n",
        "\n",
        "def model_design(x): \n",
        "  \n",
        "  print(f\"Units : {int(x[0])}, Learning Rate : {x[1]:.2f}\")\n",
        "  K.clear_session()\n",
        "  input_dim = X_train.shape[1]\n",
        "\n",
        "  model = Sequential()\n",
        "  model.add(layers.LSTM(int(x[0]), return_sequences=True, input_shape = (X_train.shape[1], 1)))\n",
        "  model.add(layers.Flatten())\n",
        "  model.add(layers.Dense(23, activation='softmax'))\n",
        "\n",
        "  if x[1]< 0.003:\n",
        "    learning_rate = 0.001\n",
        "  elif x[1]< 0.0075:\n",
        "    learning_rate = 0.005\n",
        "  elif x[1]< 0.015:\n",
        "    learning_rate = 0.01\n",
        "  elif x[1]< 0.035:\n",
        "    learning_rate = 0.02\n",
        "  elif x[1]< 0.075:\n",
        "    learning_rate = 0.05\n",
        "  elif x[1]< 0.125:\n",
        "    learning_rate = 0.1\n",
        "  elif x[1]< 0.175:\n",
        "    learning_rate = 0.15\n",
        "  else:\n",
        "    learning_rate = 0.2\n",
        "\n",
        "  opt = keras.optimizers.Adam(learning_rate=0.001)\n",
        "  model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=[\"accuracy\"])\n",
        "  return model"
      ],
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALdS2EA1txbW"
      },
      "source": [
        "EarlyStopper = EarlyStopping(patience=4, monitor='val_loss', mode='min')\n",
        "count = 0"
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlT3Vh95t5Fr"
      },
      "source": [
        "def best_model(x):\n",
        "  model = model_design(x)\n",
        "  \n",
        "  history = model.fit(X_train, y_train, epochs=2, batch_size=32, verbose=1, validation_data=(X_test, y_test),callbacks=[EarlyStopper], workers=8, use_multiprocessing=True)\n",
        "  \n",
        "  train_loss, train_acc = model.evaluate(X_train, y_train, verbose=0)\n",
        "  print(f\"Train_Accuracy:{train_acc} Train Loss: {train_loss}\")\n",
        "  \n",
        "  test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
        "  print(f\"Test Accuracy:{test_acc} Test Loss: {test_loss}\")\n",
        "\n",
        "\n",
        "  if test_acc > 0.987:\n",
        "    # Plot the graph\n",
        "    plt.figure(figsize = (15,6))\n",
        "    plt.subplot(121)\n",
        "    plt.plot(history.history['accuracy'])\n",
        "    plt.plot(history.history['val_accuracy'])\n",
        "    plt.plot(np.argmax(history.history[\"val_accuracy\"]), np.max(history.history[\"val_accuracy\"]), marker=\"x\", color=\"r\", label=\"best model\")\n",
        "    plt.title('model accuracy')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'val'], loc='upper left')\n",
        "    plt.subplot(122)\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.plot(np.argmin(history.history[\"val_loss\"]), np.min(history.history[\"val_loss\"]), marker=\"x\", color=\"r\", label=\"best model\")\n",
        "    plt.title('model Loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'val'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "    # predict probabilities for test set\n",
        "    y_pred = np.argmax(model.predict(X_test, verbose=0), axis=1)\n",
        "  \n",
        "    print(classification_report(y_test, y_pred))\n",
        "    # confusion matrix\n",
        "    matrix = confusion_matrix(y_test, y_pred)\n",
        "    plt.figure(figsize = (15,7) )\n",
        "    sns.heatmap(matrix, annot=True)\n",
        "    plt.show()\n",
        "    global count\n",
        "    count = count+1\n",
        "    model.save(f\"model-{count}-{round(test_acc, 3)}-{round(test_loss, 3)}\")\n",
        "    np.savetxt(f\"data-{count}.csv\", x, delimiter=',')\n",
        "\n",
        "  return test_loss"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wlGbeZ0uWfn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a84a9e0-ef0f-4124-fd7a-3f2a233a205c"
      },
      "source": [
        "xopt, fopt = pso(best_model, lb, ub)\n",
        "print (\"Best position\"+str(xopt))\n",
        "print (\"Loss:\" + str(fopt))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Units : 49, Learning Rate : 0.02\n",
            "Epoch 1/2\n",
            "3150/3150 [==============================] - 38s 12ms/step - loss: 0.1179 - accuracy: 0.9689 - val_loss: 0.0584 - val_accuracy: 0.9836\n",
            "Epoch 2/2\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0418 - accuracy: 0.9874 - val_loss: 0.0503 - val_accuracy: 0.9859\n",
            "Train_Accuracy:0.9885391592979431 Train Loss: 0.038839783519506454\n",
            "Test Accuracy:0.9858702421188354 Test Loss: 0.05032128468155861\n",
            "Units : 49, Learning Rate : 0.02\n",
            "Epoch 1/2\n",
            "3150/3150 [==============================] - 38s 11ms/step - loss: 0.1158 - accuracy: 0.9700 - val_loss: 0.0637 - val_accuracy: 0.9826\n",
            "Epoch 2/2\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0422 - accuracy: 0.9872 - val_loss: 0.0498 - val_accuracy: 0.9865\n",
            "Train_Accuracy:0.9888368248939514 Train Loss: 0.03834116458892822\n",
            "Test Accuracy:0.9865449666976929 Test Loss: 0.04978514090180397\n",
            "Units : 49, Learning Rate : 0.01\n",
            "Epoch 1/2\n",
            "3150/3150 [==============================] - 114s 12ms/step - loss: 0.1189 - accuracy: 0.9692 - val_loss: 0.0619 - val_accuracy: 0.9824\n",
            "Epoch 2/2\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0418 - accuracy: 0.9869 - val_loss: 0.0504 - val_accuracy: 0.9860\n",
            "Train_Accuracy:0.9880132675170898 Train Loss: 0.03944821655750275\n",
            "Test Accuracy:0.9860289692878723 Test Loss: 0.05044792592525482\n",
            "Units : 49, Learning Rate : 0.01\n",
            "Epoch 1/2\n",
            "3150/3150 [==============================] - 40s 12ms/step - loss: 0.1164 - accuracy: 0.9698 - val_loss: 0.0593 - val_accuracy: 0.9828\n",
            "Epoch 2/2\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0426 - accuracy: 0.9869 - val_loss: 0.0504 - val_accuracy: 0.9861\n",
            "Train_Accuracy:0.9887872338294983 Train Loss: 0.03864789381623268\n",
            "Test Accuracy:0.9861083626747131 Test Loss: 0.05035063251852989\n",
            "Units : 49, Learning Rate : 0.02\n",
            "Epoch 1/2\n",
            "3150/3150 [==============================] - 38s 12ms/step - loss: 0.1146 - accuracy: 0.9695 - val_loss: 0.0632 - val_accuracy: 0.9821\n",
            "Epoch 2/2\n",
            "3150/3150 [==============================] - 36s 12ms/step - loss: 0.0417 - accuracy: 0.9874 - val_loss: 0.0489 - val_accuracy: 0.9867\n",
            "Train_Accuracy:0.9889261722564697 Train Loss: 0.03772841766476631\n",
            "Test Accuracy:0.9867036938667297 Test Loss: 0.04893209785223007\n",
            "Units : 49, Learning Rate : 0.01\n",
            "Epoch 1/2\n",
            "3150/3150 [==============================] - 39s 12ms/step - loss: 0.1195 - accuracy: 0.9686 - val_loss: 0.0575 - val_accuracy: 0.9836\n",
            "Epoch 2/2\n",
            "3150/3150 [==============================] - 36s 11ms/step - loss: 0.0430 - accuracy: 0.9868 - val_loss: 0.0525 - val_accuracy: 0.9853\n",
            "Train_Accuracy:0.9861775636672974 Train Loss: 0.04259311407804489\n",
            "Test Accuracy:0.9852748513221741 Test Loss: 0.05253145471215248\n",
            "Units : 49, Learning Rate : 0.02\n",
            "Epoch 1/2\n",
            "3150/3150 [==============================] - 38s 12ms/step - loss: 0.1176 - accuracy: 0.9692 - val_loss: 0.0595 - val_accuracy: 0.9819\n",
            "Epoch 2/2\n",
            " 458/3150 [===>..........................] - ETA: 27s - loss: 0.0477 - accuracy: 0.9844"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WRcbn1xDWDX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}