Build_Hierarchy_Complete.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mccarthyajb/HL-NTAC/blob/main/Build_Hierarchy_Complete.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItPEsplPVrS_",
        "outputId": "86a28382-3832-4c57-d39a-adbe8d570783"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not connected to a GPU\n",
            "Your runtime has 13.6 gigabytes of available RAM\n",
            "\n",
            "Not using a high-RAM runtime\n"
          ]
        }
      ],
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)\n",
        "\n",
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Not using a high-RAM runtime')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "\n",
        "\n",
        "def plot_cm(cm,title,savename):\n",
        "\n",
        "  print(cm)\n",
        "  plt.figure(figsize=(20,20),dpi=300)\n",
        "  plt.matshow(cm, cmap=\"OrRd\" )\n",
        "  plt.title(title, pad=150)\n",
        "  plt.colorbar()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  ax = plt.gca()\n",
        "  from matplotlib.ticker import MultipleLocator; ax.xaxis.set_major_locator(MultipleLocator(1)); ax.yaxis.set_major_locator(MultipleLocator(1))\n",
        "  l_col_list = list(roughfine_labels)\n",
        "  # roughfine_labels)\n",
        "\n",
        "  # new_orders = np.argsort(roughfine_labels)\n",
        "  # cm[new_orders][:,new_orders]\n",
        "\n",
        "  import string\n",
        "  for i in range(0,len(l_col_list)):\n",
        "    pretty_string = ''.join(filter(lambda x: x in string.printable, l_col_list[i]))\n",
        "    l_col_list[i] = pretty_string\n",
        "\n",
        "  ax.set_xticklabels([''] + l_col_list, rotation=90)\n",
        "  ax.set_yticklabels([''] + l_col_list)\n",
        "\n",
        "  # savename='ConfusionMatrix-Multiclass-Original.png'\n",
        "  basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "  filename=basedir+savename\n",
        "  plt.savefig(filename,dpi=300,bbox_inches='tight')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "Xw7_XATiv1Pg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_classification_report(predicted_labels, orig_labels, texfilename, caption):\n",
        "\n",
        "  print(predicted_labels)\n",
        "  print(orig_labels)\n",
        "\n",
        "\n",
        "  from matplotlib.ticker import MultipleLocator; ax.xaxis.set_major_locator(MultipleLocator(1)); ax.yaxis.set_major_locator(MultipleLocator(1))\n",
        "  l_col_list = list(outcome)\n",
        "  import string\n",
        "  for i in range(0,len(l_col_list)):\n",
        "    pretty_string = ''.join(filter(lambda x: x in string.printable, l_col_list[i]))\n",
        "    l_col_list[i] = pretty_string\n",
        "\n",
        "  print(classification_report(\n",
        "        predicted_labels,orig_labels,\n",
        "        target_names=l_col_list,\n",
        "        labels=range(0,(len(outcome)))))\n",
        "\n",
        "  DICT_CLASS_REPORT = classification_report(\n",
        "        predicted_labels,orig_labels,\n",
        "        target_names=l_col_list,\n",
        "        labels=range(0,(len(outcome))),output_dict=True\n",
        "    )\n",
        "\n",
        "  print(DICT_CLASS_REPORT)\n",
        "  macro_f1 = DICT_CLASS_REPORT['macro avg']['f1-score']\n",
        "  print(macro_f1)\n",
        "\n",
        "  class_report_df = pd.DataFrame(DICT_CLASS_REPORT)\n",
        "  class_report_df = class_report_df.round(decimals=2)\n",
        "  class_report_df = class_report_df.transpose()\n",
        "  basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "  filename= basedir + texfilename\n",
        "  print(class_report_df.to_latex(caption=caption))\n",
        "  with open(filename, 'w') as f:\n",
        "    f.write(class_report_df.to_latex(caption=caption))"
      ],
      "metadata": {
        "id": "Dgb-V5NvwkdN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_cm_theta(cm,title,savename): # for JSMA theta and gamma values\n",
        "  # theta=0\n",
        "  # gamma=0\n",
        "  print(cm)\n",
        "  plt.figure(figsize=(20,20),dpi=300)\n",
        "  plt.matshow(cm, cmap=\"OrRd\" )\n",
        "  title = title + ' (Theta=' +str(theta) + ' Gamma=' +str(gamma)+')'\n",
        "  plt.title(title, pad=150)\n",
        "  plt.colorbar()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  ax = plt.gca()\n",
        "  from matplotlib.ticker import MultipleLocator; ax.xaxis.set_major_locator(MultipleLocator(1)); ax.yaxis.set_major_locator(MultipleLocator(1))\n",
        "  l_col_list = list(roughfine_labels)\n",
        "  import string\n",
        "  for i in range(0,len(l_col_list)):\n",
        "    pretty_string = ''.join(filter(lambda x: x in string.printable, l_col_list[i]))\n",
        "    l_col_list[i] = pretty_string\n",
        "\n",
        "  ax.set_xticklabels([''] + l_col_list, rotation=90)\n",
        "  ax.set_yticklabels([''] + l_col_list)\n",
        "\n",
        "  # savename='ConfusionMatrix-Multiclass-Original.png'\n",
        "  basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "  filename=basedir+savename\n",
        "  plt.savefig(filename,dpi=300,bbox_inches='tight')\n",
        "  plt.show()\n",
        "\n",
        "def unique(list1):\n",
        "  x = np.array(list1)\n",
        "  return (np.unique(x).tolist)"
      ],
      "metadata": {
        "id": "T_7iV7LT-W2Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_cm_hier(cm,title,labels,savename): # for JSMA theta and gamma values\n",
        "  # theta=0\n",
        "  # gamma=0\n",
        "  print(cm)\n",
        "  plt.figure(figsize=(20,20),dpi=300)\n",
        "  plt.matshow(cm, cmap=\"OrRd\" )\n",
        "  title = title \n",
        "  plt.title(title, pad=150)\n",
        "  plt.colorbar()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  ax = plt.gca()\n",
        "  from matplotlib.ticker import MultipleLocator; ax.xaxis.set_major_locator(MultipleLocator(1)); ax.yaxis.set_major_locator(MultipleLocator(1))\n",
        "  l_col_list = list(labels)\n",
        "  # import string\n",
        "  # for i in range(0,len(l_col_list)):\n",
        "  #   pretty_string = ''.join(filter(lambda x: x in string.printable, l_col_list[i]))\n",
        "  #   l_col_list[i] = pretty_string\n",
        "\n",
        "  ax.set_xticklabels([''] + l_col_list, rotation=90)\n",
        "  ax.set_yticklabels([''] + l_col_list)\n",
        "\n",
        "  # savename='ConfusionMatrix-Multiclass-Original.png'\n",
        "  basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "  filename=basedir+savename\n",
        "  plt.savefig(filename,dpi=300,bbox_inches='tight')\n",
        "  plt.show()\n",
        "\n",
        "def plot_cm_thetahier(cm,title,labels,savename): # for JSMA theta and gamma values\n",
        "  # theta=0\n",
        "  # gamma=0\n",
        "  print(cm)\n",
        "  plt.figure(figsize=(20,20),dpi=300)\n",
        "  plt.matshow(cm, cmap=\"OrRd\" )\n",
        "  title = title + ' (Theta=' +str(theta) + ' Gamma=' +str(gamma)+')'\n",
        "  plt.title(title, pad=150)\n",
        "  plt.colorbar()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  ax = plt.gca()\n",
        "  from matplotlib.ticker import MultipleLocator; ax.xaxis.set_major_locator(MultipleLocator(1)); ax.yaxis.set_major_locator(MultipleLocator(1))\n",
        "  l_col_list = list(labels)\n",
        "  # import string\n",
        "  # for i in range(0,len(l_col_list)):\n",
        "  #   pretty_string = ''.join(filter(lambda x: x in string.printable, l_col_list[i]))\n",
        "  #   l_col_list[i] = pretty_string\n",
        "\n",
        "  ax.set_xticklabels([''] + l_col_list, rotation=90)\n",
        "  ax.set_yticklabels([''] + l_col_list)\n",
        "\n",
        "  # savename='ConfusionMatrix-Multiclass-Original.png'\n",
        "  basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "  filename=basedir+savename\n",
        "  plt.savefig(filename,dpi=300,bbox_inches='tight')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "JbFcBqQv8Nrw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IX7-IPH85Arq"
      },
      "source": [
        "# SDAV Extra - Violin Plots\n",
        "\n",
        "This notebook will demonstrate the use of Violin Plots. This visualisation technique, similar to a box plot, is well suited for comparing multiple distributions, and show a curved distribution plot for each feature under consideration. Below shows a simple violin plot example using Seaborn."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "4lYJo_Iv5Arr",
        "outputId": "392f6afd-d7d0-4f5b-928e-f8062f64d70e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting lime\n",
            "  Downloading lime-0.2.0.1.tar.gz (275 kB)\n",
            "\u001b[K     |████████████████████████████████| 275 kB 4.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from lime) (3.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lime) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from lime) (1.5.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from lime) (4.64.0)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from lime) (1.0.2)\n",
            "Requirement already satisfied: scikit-image>=0.12 in /usr/local/lib/python3.7/dist-packages (from lime) (0.18.3)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (2021.11.2)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (1.3.0)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (2.4.1)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (2.6.3)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (7.1.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (1.4.3)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->lime) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->lime) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->lime) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->lime) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->lime) (3.1.0)\n",
            "Building wheels for collected packages: lime\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283857 sha256=bbee579254e9589cfa710a3f1a25ed312cc32f6d2d497c1acb3c6d151adb8c27\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/cb/e5/ac701e12d365a08917bf4c6171c0961bc880a8181359c66aa7\n",
            "Successfully built lime\n",
            "Installing collected packages: lime\n",
            "Successfully installed lime-0.2.0.1\n",
            "Mounted at ./mount\n",
            "Drive Mounted\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#!pip install seaborn\n",
        "!pip install lime\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('./mount')\n",
        "print(\"Drive Mounted\")\n",
        "\n",
        "tips = sns.load_dataset(\"tips\")\n",
        "ax = sns.violinplot(x=\"day\", y=\"total_bill\", data=tips)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8smbGmxq5Aru"
      },
      "source": [
        "## Case Study: Comparing Benign and DDoS behaviours in network traffic analysis using CICIDS2017\n",
        "\n",
        "Let's consider an example where we want to look at benign and malicious network traffic. We will use to CICIDS2017 dataset for this. This dataset captures in the region of 80 numerical features that characterise network activity. Each data instance has been labelled as either benign or as an attack type (we focus on DDoS here, however other attacks are present in the full dataset). What data attributes set these two classes apart? We can use violin plots to judge this visually over the entire dataset.\n",
        "\n",
        "### Load in dataset and clean it up\n",
        "\n",
        "First we will load in the data set, and we will remove all Not-a-Number and Infinity values that may be present. We will also remove columns that contain only zeros (i.e., no separating features)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "gsgMZTkq5Arv",
        "outputId": "b0fa4474-f975-4255-86e2-246299ac224f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Read in df1 - DDoS\n",
            "Read in df2 - PortScan\n",
            "Read in df3 - Botnet\n",
            "Read in df4 - Benign (Normal Human Activities)\n",
            "Read in df5 - Infiltration\n",
            "Read in df6 - Web Attacks\n",
            "Read in df7 - Brute Force\n",
            "Read in df8 - DoS/DDoS/HeartBleed\n",
            "Before Concat\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Destination Port   Flow Duration   Total Fwd Packets  \\\n",
              "0                   54865               3                   2   \n",
              "1                   55054             109                   1   \n",
              "2                   55055              52                   1   \n",
              "3                   46236              34                   1   \n",
              "4                   54863               3                   2   \n",
              "...                   ...             ...                 ...   \n",
              "692698                 53           32215                   4   \n",
              "692699                 53             324                   2   \n",
              "692700              58030              82                   2   \n",
              "692701                 53         1048635                   6   \n",
              "692702                 53           94939                   4   \n",
              "\n",
              "         Total Backward Packets  Total Length of Fwd Packets  \\\n",
              "0                             0                           12   \n",
              "1                             1                            6   \n",
              "2                             1                            6   \n",
              "3                             1                            6   \n",
              "4                             0                           12   \n",
              "...                         ...                          ...   \n",
              "692698                        2                          112   \n",
              "692699                        2                           84   \n",
              "692700                        1                           31   \n",
              "692701                        2                          192   \n",
              "692702                        2                          188   \n",
              "\n",
              "         Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
              "0                                  0                       6   \n",
              "1                                  6                       6   \n",
              "2                                  6                       6   \n",
              "3                                  6                       6   \n",
              "4                                  0                       6   \n",
              "...                              ...                     ...   \n",
              "692698                           152                      28   \n",
              "692699                           362                      42   \n",
              "692700                             6                      31   \n",
              "692701                           256                      32   \n",
              "692702                           226                      47   \n",
              "\n",
              "         Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
              "0                            6                      6.0   \n",
              "1                            6                      6.0   \n",
              "2                            6                      6.0   \n",
              "3                            6                      6.0   \n",
              "4                            6                      6.0   \n",
              "...                        ...                      ...   \n",
              "692698                      28                     28.0   \n",
              "692699                      42                     42.0   \n",
              "692700                       0                     15.5   \n",
              "692701                      32                     32.0   \n",
              "692702                      47                     47.0   \n",
              "\n",
              "         Fwd Packet Length Std  ...   min_seg_size_forward  Active Mean  \\\n",
              "0                      0.00000  ...                     20          0.0   \n",
              "1                      0.00000  ...                     20          0.0   \n",
              "2                      0.00000  ...                     20          0.0   \n",
              "3                      0.00000  ...                     20          0.0   \n",
              "4                      0.00000  ...                     20          0.0   \n",
              "...                        ...  ...                    ...          ...   \n",
              "692698                 0.00000  ...                     20          0.0   \n",
              "692699                 0.00000  ...                     20          0.0   \n",
              "692700                21.92031  ...                     32          0.0   \n",
              "692701                 0.00000  ...                     20          0.0   \n",
              "692702                 0.00000  ...                     20          0.0   \n",
              "\n",
              "         Active Std   Active Max   Active Min  Idle Mean   Idle Std  \\\n",
              "0               0.0            0            0        0.0        0.0   \n",
              "1               0.0            0            0        0.0        0.0   \n",
              "2               0.0            0            0        0.0        0.0   \n",
              "3               0.0            0            0        0.0        0.0   \n",
              "4               0.0            0            0        0.0        0.0   \n",
              "...             ...          ...          ...        ...        ...   \n",
              "692698          0.0            0            0        0.0        0.0   \n",
              "692699          0.0            0            0        0.0        0.0   \n",
              "692700          0.0            0            0        0.0        0.0   \n",
              "692701          0.0            0            0        0.0        0.0   \n",
              "692702          0.0            0            0        0.0        0.0   \n",
              "\n",
              "         Idle Max   Idle Min   Label  \n",
              "0               0          0  BENIGN  \n",
              "1               0          0  BENIGN  \n",
              "2               0          0  BENIGN  \n",
              "3               0          0  BENIGN  \n",
              "4               0          0  BENIGN  \n",
              "...           ...        ...     ...  \n",
              "692698          0          0  BENIGN  \n",
              "692699          0          0  BENIGN  \n",
              "692700          0          0  BENIGN  \n",
              "692701          0          0  BENIGN  \n",
              "692702          0          0  BENIGN  \n",
              "\n",
              "[2827876 rows x 71 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>Flow Duration</th>\n",
              "      <th>Total Fwd Packets</th>\n",
              "      <th>Total Backward Packets</th>\n",
              "      <th>Total Length of Fwd Packets</th>\n",
              "      <th>Total Length of Bwd Packets</th>\n",
              "      <th>Fwd Packet Length Max</th>\n",
              "      <th>Fwd Packet Length Min</th>\n",
              "      <th>Fwd Packet Length Mean</th>\n",
              "      <th>Fwd Packet Length Std</th>\n",
              "      <th>...</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>Active Mean</th>\n",
              "      <th>Active Std</th>\n",
              "      <th>Active Max</th>\n",
              "      <th>Active Min</th>\n",
              "      <th>Idle Mean</th>\n",
              "      <th>Idle Std</th>\n",
              "      <th>Idle Max</th>\n",
              "      <th>Idle Min</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>54865</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55054</td>\n",
              "      <td>109</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>55055</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46236</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54863</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>692698</th>\n",
              "      <td>53</td>\n",
              "      <td>32215</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>112</td>\n",
              "      <td>152</td>\n",
              "      <td>28</td>\n",
              "      <td>28</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>692699</th>\n",
              "      <td>53</td>\n",
              "      <td>324</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>84</td>\n",
              "      <td>362</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>692700</th>\n",
              "      <td>58030</td>\n",
              "      <td>82</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>6</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>15.5</td>\n",
              "      <td>21.92031</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>692701</th>\n",
              "      <td>53</td>\n",
              "      <td>1048635</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>256</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>692702</th>\n",
              "      <td>53</td>\n",
              "      <td>94939</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>226</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2827876 rows × 71 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c6789c9-bde7-476a-9852-45259bf620b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6c6789c9-bde7-476a-9852-45259bf620b1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c6789c9-bde7-476a-9852-45259bf620b1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Load in the dataset\n",
        "df = pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')\n",
        "df1=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')\n",
        "print(\"Read in df1 - DDoS\")\n",
        "\n",
        "df2=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017/Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv')\n",
        "print(\"Read in df2 - PortScan\")\n",
        "    \n",
        "df3=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017//Friday-WorkingHours-Morning.pcap_ISCX.csv')\n",
        "print(\"Read in df3 - Botnet\")\n",
        "    \n",
        "df4=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017//Monday-WorkingHours.pcap_ISCX.csv')\n",
        "print(\"Read in df4 - Benign (Normal Human Activities)\")\n",
        "    \n",
        "df5=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017/Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX.csv')\n",
        "print(\"Read in df5 - Infiltration\")\n",
        "    \n",
        "df6=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017//Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv')\n",
        "print(\"Read in df6 - Web Attacks\")\n",
        "    \n",
        "df7=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017//Tuesday-WorkingHours.pcap_ISCX.csv')\n",
        "print(\"Read in df7 - Brute Force\")\n",
        "    \n",
        "df8=pd.read_csv('mount/My Drive/Colab Notebooks/CICIDS2017//Wednesday-workingHours.pcap_ISCX.csv')\n",
        "print(\"Read in df8 - DoS/DDoS/HeartBleed\")\n",
        "\n",
        "#frames = [df1, df2, df3, df4]\n",
        "frames = [df1, df2, df3, df4, df5, df6, df7, df8]\n",
        "print(\"Before Concat\")\n",
        "df = pd.concat(frames)\n",
        "\n",
        "#df = pd.read_csv('./CICIDS2017/TrafficLabelling/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')\n",
        "#df = pd.read_csv('./CICIDS2017/TrafficLabelling/Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv')\n",
        "#df = pd.read_csv('./CICIDS2017/TrafficLabelling/Friday-WorkingHours-Morning.pcap_ISCX.csv')\n",
        "#df = pd.read_csv('./CICIDS2017/TrafficLabelling/Monday-WorkingHours.pcap_ISCX.csv')\n",
        "# Remove NaN and Inf\n",
        "df = df[~df.isin([np.nan, np.inf, -np.inf]).any(1)]\n",
        "# Remove columns with all zero values\n",
        "df = df.loc[:, (df != 0).any(axis=0)]\n",
        "# Output table\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37PsvhHm5Arx",
        "outputId": "bc7c12e3-0b5d-4fd6-d640-63560c1c41e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index([' Destination Port', ' Flow Duration', ' Total Fwd Packets',\n",
            "       ' Total Backward Packets', 'Total Length of Fwd Packets',\n",
            "       ' Total Length of Bwd Packets', ' Fwd Packet Length Max',\n",
            "       ' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
            "       ' Fwd Packet Length Std', 'Bwd Packet Length Max',\n",
            "       ' Bwd Packet Length Min', ' Bwd Packet Length Mean',\n",
            "       ' Bwd Packet Length Std', 'Flow Bytes/s', ' Flow Packets/s',\n",
            "       ' Flow IAT Mean', ' Flow IAT Std', ' Flow IAT Max', ' Flow IAT Min',\n",
            "       'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max',\n",
            "       ' Fwd IAT Min', 'Bwd IAT Total', ' Bwd IAT Mean', ' Bwd IAT Std',\n",
            "       ' Bwd IAT Max', ' Bwd IAT Min', 'Fwd PSH Flags', ' Fwd URG Flags',\n",
            "       ' Fwd Header Length', ' Bwd Header Length', 'Fwd Packets/s',\n",
            "       ' Bwd Packets/s', ' Min Packet Length', ' Max Packet Length',\n",
            "       ' Packet Length Mean', ' Packet Length Std', ' Packet Length Variance',\n",
            "       'FIN Flag Count', ' SYN Flag Count', ' RST Flag Count',\n",
            "       ' PSH Flag Count', ' ACK Flag Count', ' URG Flag Count',\n",
            "       ' CWE Flag Count', ' ECE Flag Count', ' Down/Up Ratio',\n",
            "       ' Average Packet Size', ' Avg Fwd Segment Size',\n",
            "       ' Avg Bwd Segment Size', ' Fwd Header Length.1', 'Subflow Fwd Packets',\n",
            "       ' Subflow Fwd Bytes', ' Subflow Bwd Packets', ' Subflow Bwd Bytes',\n",
            "       'Init_Win_bytes_forward', ' Init_Win_bytes_backward',\n",
            "       ' act_data_pkt_fwd', ' min_seg_size_forward', 'Active Mean',\n",
            "       ' Active Std', ' Active Max', ' Active Min', 'Idle Mean', ' Idle Std',\n",
            "       ' Idle Max', ' Idle Min', ' Label'],\n",
            "      dtype='object')\n",
            "Length:  71\n"
          ]
        }
      ],
      "source": [
        "# What columns are left?\n",
        "print (df.columns)\n",
        "# How many columns?\n",
        "print (\"Length: \", len(df.columns))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMNMIjX25Arz"
      },
      "source": [
        "### Normalise each column\n",
        "\n",
        "Each column has its own range of values - some are quite narrow, some are quite large. We often normalise data to make it easier to work with and draw comparisons - this essentially means scaling it to be within a fixed range. Here, we want to normalise each feature indepedently - essentially meaning that each column will have a minimum value of zero and a maximum value of one, and all values for that particular feature will be scaled within this range.\n",
        "\n",
        "We will use the sci-kit learn library to achieve this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "s2ZmxzNu5Ar0",
        "outputId": "0e741d02-4344-4de5-94ed-88f3de9ed0f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
              "0                     0.002581                 0.001010   \n",
              "1                     0.002581                 0.001010   \n",
              "2                     0.002581                 0.001010   \n",
              "3                     0.002581                 0.001010   \n",
              "4                     0.002581                 0.001010   \n",
              "...                        ...                      ...   \n",
              "692698                0.012043                 0.004713   \n",
              "692699                0.018065                 0.007070   \n",
              "692700                0.000000                 0.002609   \n",
              "692701                0.013763                 0.005386   \n",
              "692702                0.020215                 0.007911   \n",
              "\n",
              "         Fwd Packet Length Std  Bwd Packet Length Max   Bwd Packet Length Min  \\\n",
              "0                     0.000000               0.000000                0.000000   \n",
              "1                     0.000000               0.000307                0.002072   \n",
              "2                     0.000000               0.000307                0.002072   \n",
              "3                     0.000000               0.000307                0.002072   \n",
              "4                     0.000000               0.000000                0.000000   \n",
              "...                        ...                    ...                     ...   \n",
              "692698                0.000000               0.003891                0.026243   \n",
              "692699                0.000000               0.009268                0.062500   \n",
              "692700                0.003076               0.000307                0.002072   \n",
              "692701                0.000000               0.006554                0.044199   \n",
              "692702                0.000000               0.005786                0.039019   \n",
              "\n",
              "         Bwd Packet Length Mean   Bwd Packet Length Std  Flow Bytes/s  \\\n",
              "0                      0.000000                     0.0      0.113636   \n",
              "1                      0.001034                     0.0      0.111968   \n",
              "2                      0.001034                     0.0      0.112020   \n",
              "3                      0.001034                     0.0      0.112072   \n",
              "4                      0.000000                     0.0      0.113636   \n",
              "...                         ...                     ...           ...   \n",
              "692698                 0.013102                     0.0      0.111925   \n",
              "692699                 0.031204                     0.0      0.112511   \n",
              "692700                 0.001034                     0.0      0.112115   \n",
              "692701                 0.022067                     0.0      0.111921   \n",
              "692702                 0.019481                     0.0      0.111923   \n",
              "\n",
              "         Flow Packets/s   Flow IAT Mean  ...   Subflow Fwd Bytes  \\\n",
              "0              0.444444    1.333333e-07  ...        9.323764e-07   \n",
              "1              0.336391    1.016667e-06  ...        4.661882e-07   \n",
              "2              0.339744    5.416666e-07  ...        4.661882e-07   \n",
              "3              0.343137    3.916666e-07  ...        4.661882e-07   \n",
              "4              0.444444    1.333333e-07  ...        9.323764e-07   \n",
              "...                 ...             ...  ...                 ...   \n",
              "692698         0.333364    5.379999e-05  ...        8.702180e-06   \n",
              "692699         0.335391    1.008333e-06  ...        6.526635e-06   \n",
              "692700         0.339431    4.500000e-07  ...        2.408639e-06   \n",
              "692701         0.333335    1.248483e-03  ...        1.491802e-05   \n",
              "692702         0.333344    1.583400e-04  ...        1.460723e-05   \n",
              "\n",
              "         Subflow Bwd Packets   Subflow Bwd Bytes  Init_Win_bytes_forward  \\\n",
              "0                   0.000000        0.000000e+00                0.000519   \n",
              "1                   0.000003        9.153974e-09                0.000458   \n",
              "2                   0.000003        9.153974e-09                0.000458   \n",
              "3                   0.000003        9.153974e-09                0.000488   \n",
              "4                   0.000000        0.000000e+00                0.000504   \n",
              "...                      ...                 ...                     ...   \n",
              "692698              0.000007        2.319007e-07                0.000000   \n",
              "692699              0.000007        5.522898e-07                0.000000   \n",
              "692700              0.000003        9.153974e-09                0.015366   \n",
              "692701              0.000007        3.905696e-07                0.000000   \n",
              "692702              0.000007        3.447997e-07                0.000000   \n",
              "\n",
              "         Init_Win_bytes_backward   act_data_pkt_fwd   min_seg_size_forward  \\\n",
              "0                       0.000000           0.000005                    1.0   \n",
              "1                       0.003922           0.000000                    1.0   \n",
              "2                       0.003922           0.000000                    1.0   \n",
              "3                       0.005035           0.000000                    1.0   \n",
              "4                       0.000000           0.000005                    1.0   \n",
              "...                          ...                ...                    ...   \n",
              "692698                  0.000000           0.000014                    1.0   \n",
              "692699                  0.000000           0.000005                    1.0   \n",
              "692700                  0.000015           0.000000                    1.0   \n",
              "692701                  0.000000           0.000023                    1.0   \n",
              "692702                  0.000000           0.000014                    1.0   \n",
              "\n",
              "        Active Mean   Active Std   Active Max  \n",
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              "...             ...          ...          ...  \n",
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              "<p>2827876 rows × 58 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Import scikit learn\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# Creating X and Y from the dataset\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(df[' Label'])\n",
        "Y_attack = le.transform(df[' Label']) # multi-class \n",
        "Y_class = df.iloc[:,-1].values # binary\n",
        "\n",
        "# Extract only the numerical feature columns\n",
        "subset = df.iloc[:,7:65].astype(float)\n",
        "# Define the scaler\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "# Apply the scaler to each column of our dataframe\n",
        "df2 = pd.DataFrame(min_max_scaler.fit_transform(subset), columns=subset.columns, index=subset.index)\n",
        "df2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFpjJqAh5Ar2"
      },
      "source": [
        "### Separate data based on class\n",
        "\n",
        "We have scaled the entire dataset so that all data for each feature is scaled in a consistent manner. We now want to split our dataset based on the classes of data that exist. Here, we know we have benign and DDoS classes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xqd_s7d5Ar2",
        "outputId": "23d645b1-412c-49bb-9c6f-c5576154c135"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['BENIGN' 'DDoS' 'PortScan' 'Bot' 'Infiltration'\n",
            " 'Web Attack � Brute Force' 'Web Attack � XSS'\n",
            " 'Web Attack � Sql Injection' 'FTP-Patator' 'SSH-Patator' 'DoS slowloris'\n",
            " 'DoS Slowhttptest' 'DoS Hulk' 'DoS GoldenEye' 'Heartbleed']\n",
            "0         BENIGN\n",
            "1         BENIGN\n",
            "2         BENIGN\n",
            "3         BENIGN\n",
            "4         BENIGN\n",
            "           ...  \n",
            "692698    BENIGN\n",
            "692699    BENIGN\n",
            "692700    BENIGN\n",
            "692701    BENIGN\n",
            "692702    BENIGN\n",
            "Name:  Label, Length: 2827876, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# Output the classes\n",
        "outcome = df[' Label'].unique()\n",
        "print(outcome)\n",
        "\n",
        "Y_attack = df[' Label']\n",
        "print(Y_attack)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXAbfBVpQWIx"
      },
      "source": [
        "#Resample the dataset adjusting for bias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LgMFZLzjueV",
        "outputId": "e01bcd5c-cfd1-4481-e57f-37a298a4f02e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "imblearn:0.8.1\n",
            "0         BENIGN\n",
            "1         BENIGN\n",
            "2         BENIGN\n",
            "3         BENIGN\n",
            "4         BENIGN\n",
            "           ...  \n",
            "692698    BENIGN\n",
            "692699    BENIGN\n",
            "692700    BENIGN\n",
            "692701    BENIGN\n",
            "692702    BENIGN\n",
            "Name:  Label, Length: 2827876, dtype: object\n",
            "[0 0 0 ... 0 0 0]\n",
            "['BENIGN' 'DDoS' 'PortScan' 'Bot' 'Infiltration'\n",
            " 'Web Attack � Brute Force' 'Web Attack � XSS'\n",
            " 'Web Attack � Sql Injection' 'FTP-Patator' 'SSH-Patator' 'DoS slowloris'\n",
            " 'DoS Slowhttptest' 'DoS Hulk' 'DoS GoldenEye' 'Heartbleed']\n",
            "Oversampling...\n",
            "Finished oversampling...\n",
            "Undersampling...\n",
            "Finished undersampling...\n",
            "[[0.         0.00589141 0.00956825 ... 0.00689019 0.         0.00689019]\n",
            " [0.0172043  0.00673304 0.         ... 0.         0.         0.        ]\n",
            " [0.02150538 0.00841629 0.         ... 0.         0.         0.        ]\n",
            " ...\n",
            " [0.         0.         0.         ... 0.         0.         0.        ]\n",
            " [0.         0.         0.         ... 0.         0.         0.        ]\n",
            " [0.         0.         0.         ... 0.         0.         0.        ]]\n",
            "0                BENIGN\n",
            "1                BENIGN\n",
            "2                BENIGN\n",
            "3                BENIGN\n",
            "4                BENIGN\n",
            "             ...       \n",
            "370    Web Attack � XSS\n",
            "371    Web Attack � XSS\n",
            "372    Web Attack � XSS\n",
            "373    Web Attack � XSS\n",
            "374    Web Attack � XSS\n",
            "Name:  Label, Length: 375, dtype: object\n",
            "(225745, 59)\n",
            "[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1\n",
            "  1  1  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
            "  2  2  2  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3\n",
            "  3  3  3  3  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4\n",
            "  4  4  4  4  4  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5\n",
            "  5  5  5  5  5  5  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6\n",
            "  6  6  6  6  6  6  6  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7\n",
            "  7  7  7  7  7  7  7  7  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8\n",
            "  8  8  8  8  8  8  8  8  8  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9\n",
            "  9  9  9  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 10 10 10 10 10 10\n",
            " 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11\n",
            " 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 12 12\n",
            " 12 12 12 12 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13 13\n",
            " 13 13 13 13 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 14\n",
            " 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14]\n",
            "Index([' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
            "       ' Fwd Packet Length Std', 'Bwd Packet Length Max',\n",
            "       ' Bwd Packet Length Min', ' Bwd Packet Length Mean',\n",
            "       ' Bwd Packet Length Std', 'Flow Bytes/s', ' Flow Packets/s',\n",
            "       ' Flow IAT Mean', ' Flow IAT Std', ' Flow IAT Max', ' Flow IAT Min',\n",
            "       'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max',\n",
            "       ' Fwd IAT Min', 'Bwd IAT Total', ' Bwd IAT Mean', ' Bwd IAT Std',\n",
            "       ' Bwd IAT Max', ' Bwd IAT Min', 'Fwd PSH Flags', ' Fwd URG Flags',\n",
            "       ' Fwd Header Length', ' Bwd Header Length', 'Fwd Packets/s',\n",
            "       ' Bwd Packets/s', ' Min Packet Length', ' Max Packet Length',\n",
            "       ' Packet Length Mean', ' Packet Length Std', ' Packet Length Variance',\n",
            "       'FIN Flag Count', ' SYN Flag Count', ' RST Flag Count',\n",
            "       ' PSH Flag Count', ' ACK Flag Count', ' URG Flag Count',\n",
            "       ' CWE Flag Count', ' ECE Flag Count', ' Down/Up Ratio',\n",
            "       ' Average Packet Size', ' Avg Fwd Segment Size',\n",
            "       ' Avg Bwd Segment Size', ' Fwd Header Length.1', 'Subflow Fwd Packets',\n",
            "       ' Subflow Fwd Bytes', ' Subflow Bwd Packets', ' Subflow Bwd Bytes',\n",
            "       'Init_Win_bytes_forward', ' Init_Win_bytes_backward',\n",
            "       ' act_data_pkt_fwd', ' min_seg_size_forward', 'Active Mean',\n",
            "       ' Active Std', ' Active Max', ' Label'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Resample\n",
        "import imblearn\n",
        "print(\"imblearn:\" + imblearn.__version__)\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "\n",
        "# print(Y_class)\n",
        "print(Y_attack)\n",
        "x_scaled = np.array(df2)\n",
        "transformed_label = le.fit_transform(Y_attack)\n",
        "print(transformed_label)\n",
        "\n",
        "#define oversampling strategy\n",
        "print(outcome)\n",
        "# orig_sampling_strategy = {\n",
        "#     \"BENIGN\"                    : 2271320,\n",
        "#     \"DDoS\"                      :  128025,\n",
        "#     'PortScan'                  :  158804, \n",
        "#     'Bot'                       :    1956, \n",
        "#     'Infiltration'               :      36,\n",
        "#     'Web Attack � Brute Force'  :    1507, \n",
        "#     'Web Attack � XSS'          :     652,\n",
        "#     'Web Attack � Sql Injection':      21, \n",
        "#     'FTP-Patator'               :    7935, \n",
        "#     'SSH-Patator'               :    5897, \n",
        "#     'DoS slowloris'             :    5796,\n",
        "#     'DoS Slowhttptest'          :    5499, \n",
        "#     'DoS Hulk'                  :  231073, \n",
        "#     'DoS GoldenEye'             :   10293, \n",
        "#     'Heartbleed'                :      11\n",
        "# }\n",
        "\n",
        "over_sampling_strategy = { # Minimum 25\n",
        "    \"BENIGN\"                    : 2271320,\n",
        "    \"DDoS\"                      :  128025,\n",
        "    'PortScan'                  :  158804, \n",
        "    'Bot'                       :    1956, \n",
        "    'Infiltration'               :      36,\n",
        "    'Web Attack � Brute Force'  :    1507, \n",
        "    'Web Attack � XSS'          :     652,\n",
        "    'Web Attack � Sql Injection':      25, \n",
        "    'FTP-Patator'               :    7935, \n",
        "    'SSH-Patator'               :    5897, \n",
        "    'DoS slowloris'             :    5796,\n",
        "    'DoS Slowhttptest'          :    5499, \n",
        "    'DoS Hulk'                  :  231073, \n",
        "    'DoS GoldenEye'             :   10293, \n",
        "    'Heartbleed'                :      25\n",
        "}\n",
        "\n",
        "over = RandomOverSampler(sampling_strategy = over_sampling_strategy)\n",
        "print(\"Oversampling...\")\n",
        "x_over, y_over = over.fit_resample(x_scaled, Y_attack)\n",
        "x_scaled = x_over\n",
        "Y_attack = y_over\n",
        "\n",
        "print(\"Finished oversampling...\")\n",
        "\n",
        "#define undersampling strategy\n",
        "under = RandomUnderSampler(sampling_strategy = 'not minority')\n",
        "print(\"Undersampling...\")\n",
        "x_under, y_under = under.fit_resample(x_scaled, Y_attack)\n",
        "print(\"Finished undersampling...\")\n",
        "\n",
        "print(x_under)\n",
        "print(y_under)\n",
        "#from now on use undersampled\n",
        "x_scaled = x_under\n",
        "Y_class  = y_under\n",
        "\n",
        "tempdf2 = pd.DataFrame(x_scaled,columns=subset.columns)\n",
        "templabledf = pd.DataFrame(Y_class,columns=[' Label']) \n",
        "\n",
        "\n",
        "df2 = pd.concat([tempdf2, templabledf.reindex(df1.index)], axis=1)\n",
        "print(df2.shape)\n",
        "\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(df2[' Label'])\n",
        "transformed_label = le.transform(templabledf[' Label']) # multi-class \n",
        "print(transformed_label)\n",
        "print(df2.columns)\n",
        "\n",
        "orig_headers = df2.columns\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Smp3Cxw6vS3H",
        "outputId": "699332e1-cd4a-474f-e007-63fe19dea27b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
            "0                     0.000000                 0.005891   \n",
            "1                     0.017204                 0.006733   \n",
            "2                     0.021505                 0.008416   \n",
            "3                     0.024946                 0.009763   \n",
            "4                     0.014194                 0.005555   \n",
            "...                        ...                      ...   \n",
            "225740                     NaN                      NaN   \n",
            "225741                     NaN                      NaN   \n",
            "225742                     NaN                      NaN   \n",
            "225743                     NaN                      NaN   \n",
            "225744                     NaN                      NaN   \n",
            "\n",
            "         Fwd Packet Length Std  Bwd Packet Length Max   Bwd Packet Length Min  \\\n",
            "0                     0.009568               0.286329                0.000000   \n",
            "1                     0.000000               0.002867                0.019337   \n",
            "2                     0.000000               0.003379                0.022790   \n",
            "3                     0.000000               0.014439                0.097376   \n",
            "4                     0.000000               0.006605                0.044544   \n",
            "...                        ...                    ...                     ...   \n",
            "225740                     NaN                    NaN                     NaN   \n",
            "225741                     NaN                    NaN                     NaN   \n",
            "225742                     NaN                    NaN                     NaN   \n",
            "225743                     NaN                    NaN                     NaN   \n",
            "225744                     NaN                    NaN                     NaN   \n",
            "\n",
            "         Bwd Packet Length Mean   Bwd Packet Length Std  Flow Bytes/s  \\\n",
            "0                      0.216395                0.296668      0.111922   \n",
            "1                      0.009654                0.000000      0.112432   \n",
            "2                      0.011378                0.000000      0.111923   \n",
            "3                      0.048616                0.000000      0.111926   \n",
            "4                      0.022239                0.000000      0.112641   \n",
            "...                         ...                     ...           ...   \n",
            "225740                      NaN                     NaN           NaN   \n",
            "225741                      NaN                     NaN           NaN   \n",
            "225742                      NaN                     NaN           NaN   \n",
            "225743                      NaN                     NaN           NaN   \n",
            "225744                      NaN                     NaN           NaN   \n",
            "\n",
            "         Flow Packets/s   Flow IAT Mean  ...   Subflow Bwd Packets  \\\n",
            "0              0.333334    4.138520e-03  ...              0.000017   \n",
            "1              0.337474    5.555555e-07  ...              0.000007   \n",
            "2              0.333344    2.600333e-04  ...              0.000003   \n",
            "3              0.333344    2.551083e-04  ...              0.000003   \n",
            "4              0.336788    6.444444e-07  ...              0.000007   \n",
            "...                 ...             ...  ...                   ...   \n",
            "225740              NaN             NaN  ...                   NaN   \n",
            "225741              NaN             NaN  ...                   NaN   \n",
            "225742              NaN             NaN  ...                   NaN   \n",
            "225743              NaN             NaN  ...                   NaN   \n",
            "225744              NaN             NaN  ...                   NaN   \n",
            "\n",
            "         Subflow Bwd Bytes  Init_Win_bytes_forward   Init_Win_bytes_backward  \\\n",
            "0             9.575057e-06                0.445572                  0.009445   \n",
            "1             1.708742e-07                0.000000                  0.000000   \n",
            "2             1.006937e-07                0.000000                  0.000000   \n",
            "3             4.302368e-07                0.000000                  0.000000   \n",
            "4             3.936209e-07                0.000000                  0.000000   \n",
            "...                    ...                     ...                       ...   \n",
            "225740                 NaN                     NaN                       NaN   \n",
            "225741                 NaN                     NaN                       NaN   \n",
            "225742                 NaN                     NaN                       NaN   \n",
            "225743                 NaN                     NaN                       NaN   \n",
            "225744                 NaN                     NaN                       NaN   \n",
            "\n",
            "         act_data_pkt_fwd   min_seg_size_forward  Active Mean   Active Std  \\\n",
            "0                0.000014                    1.0      0.00689          0.0   \n",
            "1                0.000005                    1.0      0.00000          0.0   \n",
            "2                0.000000                    1.0      0.00000          0.0   \n",
            "3                0.000000                    1.0      0.00000          0.0   \n",
            "4                0.000005                    1.0      0.00000          0.0   \n",
            "...                   ...                    ...          ...          ...   \n",
            "225740                NaN                    NaN          NaN          NaN   \n",
            "225741                NaN                    NaN          NaN          NaN   \n",
            "225742                NaN                    NaN          NaN          NaN   \n",
            "225743                NaN                    NaN          NaN          NaN   \n",
            "225744                NaN                    NaN          NaN          NaN   \n",
            "\n",
            "         Active Max   Label  \n",
            "0           0.00689  BENIGN  \n",
            "1           0.00000  BENIGN  \n",
            "2           0.00000  BENIGN  \n",
            "3           0.00000  BENIGN  \n",
            "4           0.00000  BENIGN  \n",
            "...             ...     ...  \n",
            "225740          NaN     NaN  \n",
            "225741          NaN     NaN  \n",
            "225742          NaN     NaN  \n",
            "225743          NaN     NaN  \n",
            "225744          NaN     NaN  \n",
            "\n",
            "[225745 rows x 59 columns]\n"
          ]
        }
      ],
      "source": [
        "print(df2)\n",
        "df2.to_csv('mount/My Drive/Colab Notebooks/CICIDS2017/resampledforbalance.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gimgzLlC5Ar4"
      },
      "outputs": [],
      "source": [
        "# Split data based on identified classes\n",
        "# df2[' Label'] = df[' Label']\n",
        "benign = df2[df2[' Label'] == outcome[0]]\n",
        "ddos = df2[df2[' Label'] == outcome[1]]\n",
        "portscan = df2[df2[' Label'] == outcome[2]]\n",
        "bot = df2[df2[' Label'] == outcome[3]]\n",
        "infiltration = df2[df2[' Label'] == outcome[4]]\n",
        "webattackbruteforce = df2[df2[' Label'] == outcome[5]]\n",
        "webattackxxs = df2[df2[' Label'] == outcome[6]]\n",
        "webattacksqlinjection = df2[df2[' Label'] == outcome[7]]\n",
        "ftppatator = df2[df2[' Label'] == outcome[8]]\n",
        "sshpatator = df2[df2[' Label'] == outcome[9]]\n",
        "dosslowloris = df2[df2[' Label'] == outcome[10]]\n",
        "dosslowhttptest = df2[df2[' Label'] == outcome[11]]\n",
        "doshulk = df2[df2[' Label'] == outcome[12]]\n",
        "dosgoldeneye = df2[df2[' Label'] == outcome[13]]\n",
        "heartbleed = df2[df2[' Label'] == outcome[14]]\n",
        "\n",
        "traffictypes = [benign, ddos, portscan ,bot, infiltration,\n",
        "webattackbruteforce, webattackxxs, webattacksqlinjection,\n",
        "ftppatator, sshpatator, dosslowloris, dosslowhttptest, doshulk , dosgoldeneye, heartbleed ]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Do Keras"
      ],
      "metadata": {
        "id": "JeSzAR5ub8Hk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "tf.compat.v1.disable_eager_execution()\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "from tensorflow.keras.losses import sparse_categorical_crossentropy\n",
        "from tensorflow.keras.losses import binary_crossentropy\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import numpy as np\n",
        "print(\"Numpy:\" + np.__version__)\n",
        "\n",
        "!pip install adversarial-robustness-toolbox==1.08\n",
        "from art.attacks.evasion import FastGradientMethod\n",
        "from art.attacks.evasion import SaliencyMapMethod\n",
        "from art.estimators.classification import KerasClassifier\n",
        "from art.estimators.classification import SklearnClassifier\n",
        "from art.estimators.classification import EnsembleClassifier\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "RVJgbNCfb5tC",
        "outputId": "34d18c87-733e-40ca-8c32-b77a1b4d8bfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numpy:1.21.6\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting adversarial-robustness-toolbox==1.08\n",
            "  Downloading adversarial_robustness_toolbox-1.8.0-py3-none-any.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.08) (4.64.0)\n",
            "Collecting scikit-learn<0.24.3,>=0.22.2\n",
            "  Downloading scikit_learn-0.24.2-cp37-cp37m-manylinux2010_x86_64.whl (22.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 22.3 MB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.08) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.08) (1.21.6)\n",
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            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.08) (1.5.4)\n",
            "Collecting numba~=0.53.1\n",
            "  Downloading numba-0.53.1-cp37-cp37m-manylinux2014_x86_64.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 34.2 MB/s \n",
            "\u001b[?25hCollecting llvmlite<0.37,>=0.36.0rc1\n",
            "  Downloading llvmlite-0.36.0-cp37-cp37m-manylinux2010_x86_64.whl (25.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 25.3 MB 90.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<0.24.3,>=0.22.2->adversarial-robustness-toolbox==1.08) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<0.24.3,>=0.22.2->adversarial-robustness-toolbox==1.08) (1.1.0)\n",
            "Installing collected packages: llvmlite, scikit-learn, numba, adversarial-robustness-toolbox\n",
            "  Attempting uninstall: llvmlite\n",
            "    Found existing installation: llvmlite 0.34.0\n",
            "    Uninstalling llvmlite-0.34.0:\n",
            "      Successfully uninstalled llvmlite-0.34.0\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "  Attempting uninstall: numba\n",
            "    Found existing installation: numba 0.51.2\n",
            "    Uninstalling numba-0.51.2:\n",
            "      Successfully uninstalled numba-0.51.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.24.2 which is incompatible.\u001b[0m\n",
            "Successfully installed adversarial-robustness-toolbox-1.8.0 llvmlite-0.36.0 numba-0.53.1 scikit-learn-0.24.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "sklearn"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(df2[' Label'])\n",
        "Y_class = transformed_label # multi-class \n",
        "\n",
        "#Train test split\n",
        "\n",
        "testSize = 0.3\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_scaled, Y_class, test_size = testSize, shuffle=True, random_state = 42)\n",
        "\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "\n",
        "num_classes = len(np.unique(y_train))\n",
        "print(\"There are \"+ str(num_classes) +\" distinct classes\")\n",
        "print(np.unique(y_train))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3y8ACBwZb6TJ",
        "outputId": "1c608e23-5d87-4926-e9a4-322a32fc75da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(262, 58)\n",
            "(113, 58)\n",
            "There are 15 distinct classes\n",
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CLASS_TO_TRAIN =\"multiclass\"\n",
        "epochs=1600\n",
        "    \n",
        "model = tf.keras.Sequential([\n",
        "        tf.keras.layers.Dense(128, activation='relu', input_shape=[x_train.shape[1]]),\n",
        "        tf.keras.layers.Dense(64, activation='relu'),\n",
        "        tf.keras.layers.Dense(len(np.unique(Y_class))),\n",
        "        tf.keras.layers.Activation(tf.nn.softmax)\n",
        "])\n",
        "\n",
        "print(model.summary())\n",
        "\n",
        "print(\"There are \" +str(len(x_train)) +\" lines in x_train\")\n",
        "  \n",
        "basedir = \"/mount/Notebooks/Classifier/ModelH5s/\"\n",
        "best_model_filename = basedir + CLASS_TO_TRAIN + \"-instance_best_model\" + \".h5\"\n",
        "    \n",
        "callbacks = [\n",
        "  keras.callbacks.ModelCheckpoint(\n",
        "    best_model_filename, save_best_only=True, monitor=\"val_loss\"\n",
        "  ),\n",
        "  keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor=\"val_loss\", factor=0.5, patience=20, min_lr=0.0001\n",
        "  ),\n",
        "  keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1),\n",
        "]\n",
        " \n",
        "model.compile(optimizer='adam',\n",
        "            #loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "            loss= 'sparse_categorical_crossentropy',\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(\n",
        "        x_train, y_train, epochs=epochs,\n",
        "        callbacks=callbacks,\n",
        "        shuffle=False,\n",
        "        validation_data=(x_test, y_test),\n",
        "        verbose=1\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYh9gWZnb605",
        "outputId": "9c19f050-d035-4ba1-b202-a5cbb7365cba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 128)               7552      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 15)                975       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 15)                0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 16,783\n",
            "Trainable params: 16,783\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "There are 262 lines in x_train\n",
            "Train on 262 samples, validate on 113 samples\n",
            "Epoch 1/1600\n",
            "262/262 [==============================] - 1s 2ms/sample - loss: 2.7098 - accuracy: 0.0954 - val_loss: 2.6611 - val_accuracy: 0.1504 - lr: 0.0010\n",
            "Epoch 2/1600\n",
            " 32/262 [==>...........................] - ETA: 0s - loss: 2.6827 - accuracy: 0.1250"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py:2057: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  updates = self.state_updates\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r262/262 [==============================] - 0s 236us/sample - loss: 2.6219 - accuracy: 0.2366 - val_loss: 2.5988 - val_accuracy: 0.2566 - lr: 0.0010\n",
            "Epoch 3/1600\n",
            "262/262 [==============================] - 0s 195us/sample - loss: 2.5504 - accuracy: 0.2290 - val_loss: 2.5298 - val_accuracy: 0.2301 - lr: 0.0010\n",
            "Epoch 4/1600\n",
            "262/262 [==============================] - 0s 251us/sample - loss: 2.4718 - accuracy: 0.2672 - val_loss: 2.4470 - val_accuracy: 0.2301 - lr: 0.0010\n",
            "Epoch 5/1600\n",
            "262/262 [==============================] - 0s 197us/sample - loss: 2.3821 - accuracy: 0.3015 - val_loss: 2.3512 - val_accuracy: 0.2655 - lr: 0.0010\n",
            "Epoch 6/1600\n",
            "262/262 [==============================] - 0s 190us/sample - loss: 2.2802 - accuracy: 0.3473 - val_loss: 2.2501 - val_accuracy: 0.2832 - lr: 0.0010\n",
            "Epoch 7/1600\n",
            "262/262 [==============================] - 0s 192us/sample - loss: 2.1723 - accuracy: 0.3550 - val_loss: 2.1486 - val_accuracy: 0.3009 - lr: 0.0010\n",
            "Epoch 8/1600\n",
            "262/262 [==============================] - 0s 233us/sample - loss: 2.0640 - accuracy: 0.3969 - val_loss: 2.0521 - val_accuracy: 0.4071 - lr: 0.0010\n",
            "Epoch 9/1600\n",
            "262/262 [==============================] - 0s 209us/sample - loss: 1.9602 - accuracy: 0.4809 - val_loss: 1.9616 - val_accuracy: 0.4071 - lr: 0.0010\n",
            "Epoch 10/1600\n",
            "262/262 [==============================] - 0s 253us/sample - loss: 1.8633 - accuracy: 0.4809 - val_loss: 1.8784 - val_accuracy: 0.4159 - lr: 0.0010\n",
            "Epoch 11/1600\n",
            "262/262 [==============================] - 0s 303us/sample - loss: 1.7708 - accuracy: 0.5420 - val_loss: 1.8031 - val_accuracy: 0.4513 - lr: 0.0010\n",
            "Epoch 12/1600\n",
            "262/262 [==============================] - 0s 232us/sample - loss: 1.6881 - accuracy: 0.5687 - val_loss: 1.7360 - val_accuracy: 0.4779 - lr: 0.0010\n",
            "Epoch 13/1600\n",
            "262/262 [==============================] - 0s 224us/sample - loss: 1.6125 - accuracy: 0.5916 - val_loss: 1.6738 - val_accuracy: 0.4956 - lr: 0.0010\n",
            "Epoch 14/1600\n",
            "262/262 [==============================] - 0s 198us/sample - loss: 1.5414 - accuracy: 0.5878 - val_loss: 1.6187 - val_accuracy: 0.5044 - lr: 0.0010\n",
            "Epoch 15/1600\n",
            "262/262 [==============================] - 0s 196us/sample - loss: 1.4756 - accuracy: 0.5992 - val_loss: 1.5679 - val_accuracy: 0.5044 - lr: 0.0010\n",
            "Epoch 16/1600\n",
            "262/262 [==============================] - 0s 218us/sample - loss: 1.4140 - accuracy: 0.6107 - val_loss: 1.5217 - val_accuracy: 0.5133 - lr: 0.0010\n",
            "Epoch 17/1600\n",
            "262/262 [==============================] - 0s 464us/sample - loss: 1.3577 - accuracy: 0.5992 - val_loss: 1.4807 - val_accuracy: 0.5487 - lr: 0.0010\n",
            "Epoch 18/1600\n",
            "262/262 [==============================] - 0s 455us/sample - loss: 1.3059 - accuracy: 0.6107 - val_loss: 1.4427 - val_accuracy: 0.5487 - lr: 0.0010\n",
            "Epoch 19/1600\n",
            "262/262 [==============================] - 0s 304us/sample - loss: 1.2587 - accuracy: 0.6031 - val_loss: 1.4100 - val_accuracy: 0.5575 - lr: 0.0010\n",
            "Epoch 20/1600\n",
            "262/262 [==============================] - 0s 329us/sample - loss: 1.2164 - accuracy: 0.6183 - val_loss: 1.3798 - val_accuracy: 0.5575 - lr: 0.0010\n",
            "Epoch 21/1600\n",
            "262/262 [==============================] - 0s 292us/sample - loss: 1.1764 - accuracy: 0.6260 - val_loss: 1.3527 - val_accuracy: 0.5841 - lr: 0.0010\n",
            "Epoch 22/1600\n",
            "262/262 [==============================] - 0s 422us/sample - loss: 1.1406 - accuracy: 0.6221 - val_loss: 1.3311 - val_accuracy: 0.6195 - lr: 0.0010\n",
            "Epoch 23/1600\n",
            "262/262 [==============================] - 0s 385us/sample - loss: 1.1073 - accuracy: 0.6260 - val_loss: 1.3100 - val_accuracy: 0.6018 - lr: 0.0010\n",
            "Epoch 24/1600\n",
            "262/262 [==============================] - 0s 350us/sample - loss: 1.0771 - accuracy: 0.6183 - val_loss: 1.2917 - val_accuracy: 0.6018 - lr: 0.0010\n",
            "Epoch 25/1600\n",
            "262/262 [==============================] - 0s 354us/sample - loss: 1.0497 - accuracy: 0.6183 - val_loss: 1.2780 - val_accuracy: 0.5929 - lr: 0.0010\n",
            "Epoch 26/1600\n",
            "262/262 [==============================] - 0s 393us/sample - loss: 1.0241 - accuracy: 0.6145 - val_loss: 1.2620 - val_accuracy: 0.5929 - lr: 0.0010\n",
            "Epoch 27/1600\n",
            "262/262 [==============================] - 0s 300us/sample - loss: 1.0015 - accuracy: 0.6145 - val_loss: 1.2501 - val_accuracy: 0.5841 - lr: 0.0010\n",
            "Epoch 28/1600\n",
            "262/262 [==============================] - 0s 435us/sample - loss: 0.9793 - accuracy: 0.6183 - val_loss: 1.2384 - val_accuracy: 0.5841 - lr: 0.0010\n",
            "Epoch 29/1600\n",
            "262/262 [==============================] - 0s 416us/sample - loss: 0.9606 - accuracy: 0.6450 - val_loss: 1.2294 - val_accuracy: 0.5929 - lr: 0.0010\n",
            "Epoch 30/1600\n",
            "262/262 [==============================] - 0s 318us/sample - loss: 0.9415 - accuracy: 0.6260 - val_loss: 1.2187 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 31/1600\n",
            "262/262 [==============================] - 0s 333us/sample - loss: 0.9253 - accuracy: 0.6489 - val_loss: 1.2112 - val_accuracy: 0.6283 - lr: 0.0010\n",
            "Epoch 32/1600\n",
            "262/262 [==============================] - 0s 483us/sample - loss: 0.9090 - accuracy: 0.6412 - val_loss: 1.2038 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 33/1600\n",
            "262/262 [==============================] - 0s 355us/sample - loss: 0.8943 - accuracy: 0.6679 - val_loss: 1.1958 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 34/1600\n",
            "262/262 [==============================] - 0s 282us/sample - loss: 0.8806 - accuracy: 0.6832 - val_loss: 1.1895 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 35/1600\n",
            "262/262 [==============================] - 0s 407us/sample - loss: 0.8675 - accuracy: 0.6908 - val_loss: 1.1829 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 36/1600\n",
            "262/262 [==============================] - 0s 401us/sample - loss: 0.8553 - accuracy: 0.7023 - val_loss: 1.1786 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 37/1600\n",
            "262/262 [==============================] - 0s 316us/sample - loss: 0.8442 - accuracy: 0.7099 - val_loss: 1.1733 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 38/1600\n",
            "262/262 [==============================] - 0s 403us/sample - loss: 0.8339 - accuracy: 0.7099 - val_loss: 1.1686 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 39/1600\n",
            "262/262 [==============================] - 0s 317us/sample - loss: 0.8230 - accuracy: 0.7137 - val_loss: 1.1653 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 40/1600\n",
            "262/262 [==============================] - 0s 369us/sample - loss: 0.8138 - accuracy: 0.7137 - val_loss: 1.1625 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 41/1600\n",
            "262/262 [==============================] - 0s 522us/sample - loss: 0.8044 - accuracy: 0.7137 - val_loss: 1.1581 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 42/1600\n",
            "262/262 [==============================] - 0s 615us/sample - loss: 0.7958 - accuracy: 0.7137 - val_loss: 1.1569 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 43/1600\n",
            "262/262 [==============================] - 0s 375us/sample - loss: 0.7870 - accuracy: 0.7137 - val_loss: 1.1541 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 44/1600\n",
            "262/262 [==============================] - 0s 369us/sample - loss: 0.7791 - accuracy: 0.7137 - val_loss: 1.1516 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 45/1600\n",
            "262/262 [==============================] - 0s 324us/sample - loss: 0.7713 - accuracy: 0.7137 - val_loss: 1.1481 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 46/1600\n",
            "262/262 [==============================] - 0s 307us/sample - loss: 0.7639 - accuracy: 0.7137 - val_loss: 1.1463 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 47/1600\n",
            "262/262 [==============================] - 0s 338us/sample - loss: 0.7568 - accuracy: 0.7137 - val_loss: 1.1452 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 48/1600\n",
            "262/262 [==============================] - 0s 303us/sample - loss: 0.7503 - accuracy: 0.7137 - val_loss: 1.1425 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 49/1600\n",
            "262/262 [==============================] - 0s 306us/sample - loss: 0.7433 - accuracy: 0.7137 - val_loss: 1.1412 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 50/1600\n",
            "262/262 [==============================] - 0s 331us/sample - loss: 0.7373 - accuracy: 0.7137 - val_loss: 1.1386 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 51/1600\n",
            "262/262 [==============================] - 0s 373us/sample - loss: 0.7311 - accuracy: 0.7137 - val_loss: 1.1380 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 52/1600\n",
            "262/262 [==============================] - 0s 313us/sample - loss: 0.7252 - accuracy: 0.7137 - val_loss: 1.1364 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 53/1600\n",
            "262/262 [==============================] - 0s 297us/sample - loss: 0.7187 - accuracy: 0.7137 - val_loss: 1.1344 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 54/1600\n",
            "262/262 [==============================] - 0s 312us/sample - loss: 0.7138 - accuracy: 0.7137 - val_loss: 1.1329 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 55/1600\n",
            "262/262 [==============================] - 0s 289us/sample - loss: 0.7075 - accuracy: 0.7137 - val_loss: 1.1322 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 56/1600\n",
            "262/262 [==============================] - 0s 303us/sample - loss: 0.7022 - accuracy: 0.7137 - val_loss: 1.1298 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 57/1600\n",
            "262/262 [==============================] - 0s 286us/sample - loss: 0.6971 - accuracy: 0.7137 - val_loss: 1.1275 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 58/1600\n",
            "262/262 [==============================] - 0s 289us/sample - loss: 0.6919 - accuracy: 0.7137 - val_loss: 1.1268 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 59/1600\n",
            "262/262 [==============================] - 0s 290us/sample - loss: 0.6866 - accuracy: 0.7137 - val_loss: 1.1262 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 60/1600\n",
            "262/262 [==============================] - 0s 286us/sample - loss: 0.6814 - accuracy: 0.7176 - val_loss: 1.1243 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 61/1600\n",
            "262/262 [==============================] - 0s 348us/sample - loss: 0.6762 - accuracy: 0.7176 - val_loss: 1.1216 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 62/1600\n",
            "262/262 [==============================] - 0s 284us/sample - loss: 0.6714 - accuracy: 0.7176 - val_loss: 1.1211 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 63/1600\n",
            "262/262 [==============================] - 0s 362us/sample - loss: 0.6668 - accuracy: 0.7176 - val_loss: 1.1200 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 64/1600\n",
            "262/262 [==============================] - 0s 447us/sample - loss: 0.6618 - accuracy: 0.7176 - val_loss: 1.1189 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 65/1600\n",
            "262/262 [==============================] - 0s 388us/sample - loss: 0.6572 - accuracy: 0.7176 - val_loss: 1.1173 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 66/1600\n",
            "262/262 [==============================] - 0s 307us/sample - loss: 0.6528 - accuracy: 0.7023 - val_loss: 1.1162 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 67/1600\n",
            "262/262 [==============================] - 0s 453us/sample - loss: 0.6481 - accuracy: 0.7176 - val_loss: 1.1156 - val_accuracy: 0.6549 - lr: 0.0010\n",
            "Epoch 68/1600\n",
            "262/262 [==============================] - 0s 331us/sample - loss: 0.6436 - accuracy: 0.7061 - val_loss: 1.1124 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 69/1600\n",
            "262/262 [==============================] - 0s 321us/sample - loss: 0.6388 - accuracy: 0.7328 - val_loss: 1.1115 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 70/1600\n",
            "262/262 [==============================] - 0s 290us/sample - loss: 0.6345 - accuracy: 0.7366 - val_loss: 1.1111 - val_accuracy: 0.6814 - lr: 0.0010\n",
            "Epoch 71/1600\n",
            "262/262 [==============================] - 0s 288us/sample - loss: 0.6304 - accuracy: 0.7137 - val_loss: 1.1092 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 72/1600\n",
            "262/262 [==============================] - 0s 288us/sample - loss: 0.6256 - accuracy: 0.7443 - val_loss: 1.1076 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 73/1600\n",
            "262/262 [==============================] - 0s 620us/sample - loss: 0.6218 - accuracy: 0.7595 - val_loss: 1.1066 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 74/1600\n",
            "262/262 [==============================] - 0s 351us/sample - loss: 0.6173 - accuracy: 0.7557 - val_loss: 1.1034 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 75/1600\n",
            "262/262 [==============================] - 0s 408us/sample - loss: 0.6140 - accuracy: 0.7405 - val_loss: 1.1031 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 76/1600\n",
            "262/262 [==============================] - 0s 350us/sample - loss: 0.6089 - accuracy: 0.7748 - val_loss: 1.1020 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 77/1600\n",
            "262/262 [==============================] - 0s 330us/sample - loss: 0.6059 - accuracy: 0.7443 - val_loss: 1.1006 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 78/1600\n",
            "262/262 [==============================] - 0s 345us/sample - loss: 0.6015 - accuracy: 0.7748 - val_loss: 1.0993 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 79/1600\n",
            "262/262 [==============================] - 0s 336us/sample - loss: 0.5985 - accuracy: 0.7557 - val_loss: 1.0973 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 80/1600\n",
            "262/262 [==============================] - 0s 387us/sample - loss: 0.5934 - accuracy: 0.7863 - val_loss: 1.0967 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 81/1600\n",
            "262/262 [==============================] - 0s 344us/sample - loss: 0.5912 - accuracy: 0.7634 - val_loss: 1.0966 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 82/1600\n",
            "262/262 [==============================] - 0s 454us/sample - loss: 0.5869 - accuracy: 0.7672 - val_loss: 1.0951 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 83/1600\n",
            "262/262 [==============================] - 0s 491us/sample - loss: 0.5836 - accuracy: 0.7634 - val_loss: 1.0947 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 84/1600\n",
            "262/262 [==============================] - 0s 413us/sample - loss: 0.5793 - accuracy: 0.7824 - val_loss: 1.0941 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 85/1600\n",
            "262/262 [==============================] - 0s 422us/sample - loss: 0.5763 - accuracy: 0.7672 - val_loss: 1.0921 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 86/1600\n",
            "262/262 [==============================] - 0s 359us/sample - loss: 0.5731 - accuracy: 0.7710 - val_loss: 1.0924 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 87/1600\n",
            "262/262 [==============================] - 0s 564us/sample - loss: 0.5695 - accuracy: 0.7634 - val_loss: 1.0907 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 88/1600\n",
            "262/262 [==============================] - 0s 573us/sample - loss: 0.5661 - accuracy: 0.7901 - val_loss: 1.0903 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 89/1600\n",
            "262/262 [==============================] - 0s 234us/sample - loss: 0.5624 - accuracy: 0.7710 - val_loss: 1.0906 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 90/1600\n",
            "262/262 [==============================] - 0s 408us/sample - loss: 0.5597 - accuracy: 0.7710 - val_loss: 1.0898 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 91/1600\n",
            "262/262 [==============================] - 0s 603us/sample - loss: 0.5565 - accuracy: 0.7672 - val_loss: 1.0868 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 92/1600\n",
            "262/262 [==============================] - 0s 200us/sample - loss: 0.5537 - accuracy: 0.7710 - val_loss: 1.0869 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 93/1600\n",
            "262/262 [==============================] - 0s 167us/sample - loss: 0.5504 - accuracy: 0.7672 - val_loss: 1.0872 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 94/1600\n",
            "262/262 [==============================] - 0s 346us/sample - loss: 0.5467 - accuracy: 0.7710 - val_loss: 1.0867 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 95/1600\n",
            "262/262 [==============================] - 0s 200us/sample - loss: 0.5442 - accuracy: 0.7672 - val_loss: 1.0880 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 96/1600\n",
            "262/262 [==============================] - 0s 452us/sample - loss: 0.5408 - accuracy: 0.7710 - val_loss: 1.0864 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 97/1600\n",
            "262/262 [==============================] - 0s 342us/sample - loss: 0.5387 - accuracy: 0.7672 - val_loss: 1.0861 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 98/1600\n",
            "262/262 [==============================] - 0s 632us/sample - loss: 0.5352 - accuracy: 0.7710 - val_loss: 1.0833 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 99/1600\n",
            "262/262 [==============================] - 0s 572us/sample - loss: 0.5327 - accuracy: 0.7672 - val_loss: 1.0808 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 100/1600\n",
            "262/262 [==============================] - 0s 281us/sample - loss: 0.5291 - accuracy: 0.7710 - val_loss: 1.0775 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 101/1600\n",
            "262/262 [==============================] - 0s 306us/sample - loss: 0.5275 - accuracy: 0.7672 - val_loss: 1.0761 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 102/1600\n",
            "262/262 [==============================] - 0s 303us/sample - loss: 0.5227 - accuracy: 0.7710 - val_loss: 1.0734 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 103/1600\n",
            "262/262 [==============================] - 0s 272us/sample - loss: 0.5210 - accuracy: 0.7672 - val_loss: 1.0745 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 104/1600\n",
            "262/262 [==============================] - 0s 469us/sample - loss: 0.5173 - accuracy: 0.7710 - val_loss: 1.0705 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 105/1600\n",
            "262/262 [==============================] - 0s 163us/sample - loss: 0.5161 - accuracy: 0.7672 - val_loss: 1.0710 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 106/1600\n",
            "262/262 [==============================] - 0s 288us/sample - loss: 0.5121 - accuracy: 0.7710 - val_loss: 1.0675 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 107/1600\n",
            "262/262 [==============================] - 0s 433us/sample - loss: 0.5109 - accuracy: 0.7672 - val_loss: 1.0672 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 108/1600\n",
            "262/262 [==============================] - 0s 383us/sample - loss: 0.5065 - accuracy: 0.7863 - val_loss: 1.0641 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 109/1600\n",
            "262/262 [==============================] - 0s 211us/sample - loss: 0.5052 - accuracy: 0.7710 - val_loss: 1.0659 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 110/1600\n",
            "262/262 [==============================] - 0s 335us/sample - loss: 0.5021 - accuracy: 0.7748 - val_loss: 1.0623 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 111/1600\n",
            "262/262 [==============================] - 0s 311us/sample - loss: 0.4998 - accuracy: 0.7748 - val_loss: 1.0613 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 112/1600\n",
            "262/262 [==============================] - 0s 215us/sample - loss: 0.4970 - accuracy: 0.7786 - val_loss: 1.0616 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 113/1600\n",
            "262/262 [==============================] - 0s 302us/sample - loss: 0.4950 - accuracy: 0.7710 - val_loss: 1.0607 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 114/1600\n",
            "262/262 [==============================] - 0s 542us/sample - loss: 0.4920 - accuracy: 0.7824 - val_loss: 1.0592 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 115/1600\n",
            "262/262 [==============================] - 0s 200us/sample - loss: 0.4900 - accuracy: 0.7824 - val_loss: 1.0599 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 116/1600\n",
            "262/262 [==============================] - 0s 457us/sample - loss: 0.4875 - accuracy: 0.7824 - val_loss: 1.0583 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 117/1600\n",
            "262/262 [==============================] - 0s 510us/sample - loss: 0.4857 - accuracy: 0.7786 - val_loss: 1.0583 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 118/1600\n",
            "262/262 [==============================] - 0s 417us/sample - loss: 0.4827 - accuracy: 0.7824 - val_loss: 1.0573 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 119/1600\n",
            "262/262 [==============================] - 0s 165us/sample - loss: 0.4812 - accuracy: 0.7863 - val_loss: 1.0574 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 120/1600\n",
            "262/262 [==============================] - 0s 321us/sample - loss: 0.4779 - accuracy: 0.7863 - val_loss: 1.0546 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 121/1600\n",
            "262/262 [==============================] - 0s 187us/sample - loss: 0.4761 - accuracy: 0.7824 - val_loss: 1.0566 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 122/1600\n",
            "262/262 [==============================] - 0s 209us/sample - loss: 0.4739 - accuracy: 0.7863 - val_loss: 1.0556 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 123/1600\n",
            "262/262 [==============================] - 0s 226us/sample - loss: 0.4721 - accuracy: 0.7863 - val_loss: 1.0553 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 124/1600\n",
            "262/262 [==============================] - 0s 355us/sample - loss: 0.4691 - accuracy: 0.7863 - val_loss: 1.0539 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 125/1600\n",
            "262/262 [==============================] - 0s 401us/sample - loss: 0.4681 - accuracy: 0.7863 - val_loss: 1.0528 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 126/1600\n",
            "262/262 [==============================] - 0s 171us/sample - loss: 0.4649 - accuracy: 0.7863 - val_loss: 1.0541 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 127/1600\n",
            "262/262 [==============================] - 0s 188us/sample - loss: 0.4632 - accuracy: 0.7824 - val_loss: 1.0537 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 128/1600\n",
            "262/262 [==============================] - 0s 303us/sample - loss: 0.4614 - accuracy: 0.7863 - val_loss: 1.0526 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 129/1600\n",
            "262/262 [==============================] - 0s 536us/sample - loss: 0.4591 - accuracy: 0.7824 - val_loss: 1.0505 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 130/1600\n",
            "262/262 [==============================] - 0s 200us/sample - loss: 0.4571 - accuracy: 0.7863 - val_loss: 1.0532 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 131/1600\n",
            "262/262 [==============================] - 0s 163us/sample - loss: 0.4555 - accuracy: 0.7863 - val_loss: 1.0512 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 132/1600\n",
            "262/262 [==============================] - 0s 371us/sample - loss: 0.4528 - accuracy: 0.7939 - val_loss: 1.0491 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 133/1600\n",
            "262/262 [==============================] - 0s 191us/sample - loss: 0.4516 - accuracy: 0.7863 - val_loss: 1.0517 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 134/1600\n",
            "262/262 [==============================] - 0s 294us/sample - loss: 0.4494 - accuracy: 0.7824 - val_loss: 1.0486 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 135/1600\n",
            "262/262 [==============================] - 0s 263us/sample - loss: 0.4473 - accuracy: 0.7824 - val_loss: 1.0488 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 136/1600\n",
            "262/262 [==============================] - 0s 267us/sample - loss: 0.4454 - accuracy: 0.7863 - val_loss: 1.0494 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 137/1600\n",
            "262/262 [==============================] - 0s 360us/sample - loss: 0.4440 - accuracy: 0.7863 - val_loss: 1.0480 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 138/1600\n",
            "262/262 [==============================] - 0s 293us/sample - loss: 0.4415 - accuracy: 0.7824 - val_loss: 1.0476 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 139/1600\n",
            "262/262 [==============================] - 0s 210us/sample - loss: 0.4403 - accuracy: 0.7824 - val_loss: 1.0495 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 140/1600\n",
            "262/262 [==============================] - 0s 246us/sample - loss: 0.4378 - accuracy: 0.7863 - val_loss: 1.0485 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 141/1600\n",
            "262/262 [==============================] - 0s 436us/sample - loss: 0.4366 - accuracy: 0.7824 - val_loss: 1.0493 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 142/1600\n",
            "262/262 [==============================] - 0s 322us/sample - loss: 0.4345 - accuracy: 0.7786 - val_loss: 1.0479 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 143/1600\n",
            "262/262 [==============================] - 0s 399us/sample - loss: 0.4331 - accuracy: 0.7786 - val_loss: 1.0469 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 144/1600\n",
            "262/262 [==============================] - 0s 241us/sample - loss: 0.4309 - accuracy: 0.7863 - val_loss: 1.0493 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 145/1600\n",
            "262/262 [==============================] - 0s 213us/sample - loss: 0.4300 - accuracy: 0.7824 - val_loss: 1.0503 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 146/1600\n",
            "262/262 [==============================] - 0s 249us/sample - loss: 0.4273 - accuracy: 0.7863 - val_loss: 1.0498 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 147/1600\n",
            "262/262 [==============================] - 0s 259us/sample - loss: 0.4264 - accuracy: 0.7863 - val_loss: 1.0510 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 148/1600\n",
            "262/262 [==============================] - 0s 273us/sample - loss: 0.4245 - accuracy: 0.7863 - val_loss: 1.0499 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 149/1600\n",
            "262/262 [==============================] - 0s 175us/sample - loss: 0.4233 - accuracy: 0.7863 - val_loss: 1.0489 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 150/1600\n",
            "262/262 [==============================] - 0s 175us/sample - loss: 0.4207 - accuracy: 0.7824 - val_loss: 1.0484 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 151/1600\n",
            "262/262 [==============================] - 0s 262us/sample - loss: 0.4201 - accuracy: 0.7863 - val_loss: 1.0499 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 152/1600\n",
            "262/262 [==============================] - 0s 167us/sample - loss: 0.4182 - accuracy: 0.7977 - val_loss: 1.0492 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 153/1600\n",
            "262/262 [==============================] - 0s 214us/sample - loss: 0.4165 - accuracy: 0.7863 - val_loss: 1.0491 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 154/1600\n",
            "262/262 [==============================] - 0s 229us/sample - loss: 0.4152 - accuracy: 0.7863 - val_loss: 1.0491 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 155/1600\n",
            "262/262 [==============================] - 0s 299us/sample - loss: 0.4140 - accuracy: 0.7901 - val_loss: 1.0505 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 156/1600\n",
            "262/262 [==============================] - 0s 284us/sample - loss: 0.4115 - accuracy: 0.7824 - val_loss: 1.0497 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 157/1600\n",
            "262/262 [==============================] - 0s 202us/sample - loss: 0.4110 - accuracy: 0.8015 - val_loss: 1.0529 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 158/1600\n",
            "262/262 [==============================] - 0s 273us/sample - loss: 0.4093 - accuracy: 0.7901 - val_loss: 1.0506 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 159/1600\n",
            "262/262 [==============================] - 0s 337us/sample - loss: 0.4078 - accuracy: 0.7939 - val_loss: 1.0522 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 160/1600\n",
            "262/262 [==============================] - 0s 199us/sample - loss: 0.4067 - accuracy: 0.7901 - val_loss: 1.0523 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 161/1600\n",
            "262/262 [==============================] - 0s 193us/sample - loss: 0.4046 - accuracy: 0.7824 - val_loss: 1.0514 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 162/1600\n",
            "262/262 [==============================] - 0s 318us/sample - loss: 0.4036 - accuracy: 0.7863 - val_loss: 1.0529 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 163/1600\n",
            "262/262 [==============================] - 0s 378us/sample - loss: 0.4024 - accuracy: 0.7901 - val_loss: 1.0516 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 164/1600\n",
            "262/262 [==============================] - 0s 205us/sample - loss: 0.3963 - accuracy: 0.8130 - val_loss: 1.0537 - val_accuracy: 0.7345 - lr: 5.0000e-04\n",
            "Epoch 165/1600\n",
            "262/262 [==============================] - 0s 188us/sample - loss: 0.3959 - accuracy: 0.8092 - val_loss: 1.0529 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 166/1600\n",
            "262/262 [==============================] - 0s 157us/sample - loss: 0.3952 - accuracy: 0.8130 - val_loss: 1.0539 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 167/1600\n",
            "262/262 [==============================] - 0s 282us/sample - loss: 0.3946 - accuracy: 0.8092 - val_loss: 1.0536 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 168/1600\n",
            "262/262 [==============================] - 0s 284us/sample - loss: 0.3938 - accuracy: 0.8168 - val_loss: 1.0534 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 169/1600\n",
            "262/262 [==============================] - 0s 356us/sample - loss: 0.3931 - accuracy: 0.8092 - val_loss: 1.0537 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 170/1600\n",
            "262/262 [==============================] - 0s 207us/sample - loss: 0.3927 - accuracy: 0.8130 - val_loss: 1.0550 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 171/1600\n",
            "262/262 [==============================] - 0s 196us/sample - loss: 0.3919 - accuracy: 0.8130 - val_loss: 1.0535 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 172/1600\n",
            "262/262 [==============================] - 0s 200us/sample - loss: 0.3912 - accuracy: 0.8092 - val_loss: 1.0538 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 173/1600\n",
            "262/262 [==============================] - 0s 263us/sample - loss: 0.3904 - accuracy: 0.8092 - val_loss: 1.0550 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 174/1600\n",
            "262/262 [==============================] - 0s 237us/sample - loss: 0.3900 - accuracy: 0.8168 - val_loss: 1.0549 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 175/1600\n",
            "262/262 [==============================] - 0s 279us/sample - loss: 0.3891 - accuracy: 0.8092 - val_loss: 1.0537 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 176/1600\n",
            "262/262 [==============================] - 0s 279us/sample - loss: 0.3886 - accuracy: 0.8206 - val_loss: 1.0542 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 177/1600\n",
            "262/262 [==============================] - 0s 283us/sample - loss: 0.3882 - accuracy: 0.8168 - val_loss: 1.0563 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 178/1600\n",
            "262/262 [==============================] - 0s 256us/sample - loss: 0.3872 - accuracy: 0.8168 - val_loss: 1.0552 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 179/1600\n",
            "262/262 [==============================] - 0s 218us/sample - loss: 0.3866 - accuracy: 0.8168 - val_loss: 1.0549 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 180/1600\n",
            "262/262 [==============================] - 0s 211us/sample - loss: 0.3863 - accuracy: 0.8168 - val_loss: 1.0556 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 181/1600\n",
            "262/262 [==============================] - 0s 215us/sample - loss: 0.3854 - accuracy: 0.8130 - val_loss: 1.0545 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 182/1600\n",
            "262/262 [==============================] - 0s 217us/sample - loss: 0.3849 - accuracy: 0.8130 - val_loss: 1.0557 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 183/1600\n",
            "262/262 [==============================] - 0s 183us/sample - loss: 0.3842 - accuracy: 0.8130 - val_loss: 1.0557 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 184/1600\n",
            "262/262 [==============================] - 0s 241us/sample - loss: 0.3816 - accuracy: 0.8168 - val_loss: 1.0565 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 185/1600\n",
            "262/262 [==============================] - 0s 222us/sample - loss: 0.3813 - accuracy: 0.8168 - val_loss: 1.0561 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 186/1600\n",
            "262/262 [==============================] - 0s 287us/sample - loss: 0.3811 - accuracy: 0.8168 - val_loss: 1.0562 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 187/1600\n",
            "262/262 [==============================] - 0s 223us/sample - loss: 0.3808 - accuracy: 0.8168 - val_loss: 1.0563 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 188/1600\n",
            "262/262 [==============================] - 0s 243us/sample - loss: 0.3804 - accuracy: 0.8168 - val_loss: 1.0561 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 189/1600\n",
            "262/262 [==============================] - 0s 259us/sample - loss: 0.3801 - accuracy: 0.8168 - val_loss: 1.0569 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 190/1600\n",
            "262/262 [==============================] - 0s 245us/sample - loss: 0.3798 - accuracy: 0.8168 - val_loss: 1.0568 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 191/1600\n",
            "262/262 [==============================] - 0s 181us/sample - loss: 0.3795 - accuracy: 0.8168 - val_loss: 1.0568 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 192/1600\n",
            "262/262 [==============================] - 0s 199us/sample - loss: 0.3792 - accuracy: 0.8168 - val_loss: 1.0568 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 193/1600\n",
            "262/262 [==============================] - 0s 194us/sample - loss: 0.3789 - accuracy: 0.8168 - val_loss: 1.0568 - val_accuracy: 0.7522 - lr: 2.5000e-04\n",
            "Epoch 193: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the ART Classifier\n",
        "art_classifier = KerasClassifier(model=model, use_logits=False)\n"
      ],
      "metadata": {
        "id": "lYHejM5LeILs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 0 - 14 labels =       [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
        "OHE_benign =            [1, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0,  0,  0,  0,  0]\n",
        "\n",
        "targets=[]\n",
        "\n",
        "for i in range(0,len(x_test)):\n",
        "  targets.append(OHE_benign)\n",
        "numpy_targets = np.array(targets)\n",
        "\n",
        "#Generate FGSM Adversarial Examples\n",
        "\n",
        "# attack = FastGradientMethod(estimator=art_classifier, eps=0.5)\n",
        "# print(\"Generating FGSM\")\n",
        "\n",
        "# x_test_fgsm = attack.generate(x=x_test)\n",
        "# print(x_test_fgsm)\n",
        "\n",
        "# Theta = perturbation to each feature\n",
        "# Gamma = float max fraction of features being perturbed\n",
        "for theta in ([0.05]):\n",
        "  for gamma in ([0.02]):\n",
        "\n",
        "    # attack = SaliencyMapMethod(classifier=art_classifier, theta=0.1, gamma=0.1, batch_size=1,verbose=True) # Theta = Small Perturbation , Gamma = 10% of features\n",
        "\n",
        "    attack = SaliencyMapMethod(classifier=art_classifier, theta=theta, gamma=gamma, batch_size=1,verbose=True) # Theta = Small Perturbation , Gamma = 10% of features\n",
        "    print(\"Starting to Generate untargeted JSMA\")\n",
        "    #x_test_adv = attack.generate(x=x_test, y=oh_target_labels)\n",
        "    x_test_jsma = attack.generate(x=x_test)\n",
        "    # print(x_test_jsma)\n",
        "\n",
        "    print(\"Starting to Generate Targeted JSMA\")\n",
        "\n",
        "    targeted_benign_x_test_jsma = attack.generate(x=x_test,y=numpy_targets)\n",
        "    print(f\"shape of adversarial examples: {targeted_benign_x_test_jsma.shape}\")\n",
        "\n",
        "    #Keep benign cases\n",
        "\n",
        "    for i in range(0,len(x_test)):\n",
        "      if(y_test[i] == 0):\n",
        "        # x_test_fgsm[i] = x_test[i]\n",
        "        x_test_jsma[i] = x_test[i]\n",
        "        targeted_benign_x_test_jsma[i] = x_test[i]\n",
        "\n",
        "\n",
        "    # Save x_test_jsma \n",
        "\n",
        "    # savename='x_test_jsma-' + 'theta-' + str(theta) + 'gamma-'+ str(gamma) +'.npy'\n",
        "    # basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "    # filename=basedir+savename\n",
        "    # print(f\"Writing file {filename}\")\n",
        "    # np.save(filename, x_test_jsma)\n",
        "\n",
        "    # savename='targeted_benign_jsma-'+ 'theta-' + str(theta) + 'gamma-'+ str(gamma) +'.npy'\n",
        "    # basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "    # filename=basedir+savename\n",
        "    # print(f\"Writing file {filename}\")\n",
        "    # np.save(filename, targeted_benign_x_test_jsma)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186,
          "referenced_widgets": [
            "af6512a8414441a4b0d062463778e1fd",
            "e9deb59dc4d04340a0db5020ec4fec3f",
            "777ca28670a54c6e94bcbc3931bf7042",
            "fbc5dbb071b24d9abd47d23f610a56e5",
            "c0fbf86b13c14172b2fae6edc4dc7941",
            "96aeec3d5ef5476c96ca48690f93ead9",
            "a80a410052004d61964a9b464bc464fb",
            "85970266b344431397eea49362038c6c",
            "3a6ad3306b8d400894ae1f4e7d9fdd4f",
            "a622e17e275d442aa78b6bbba98261d8",
            "c88079866c6f4c1eafe7a55eab3c7cdb",
            "6af08dacb5a7409fbe1e4a0dca90c015",
            "34d69952bd8044b79433ee1e3375eafb",
            "a621f0c98087421b846987d655cc12ab",
            "54ac9dcecc6e48bab8a35eac1c134886",
            "0d0f48d340834170a22d6f5fb2849805",
            "c350c0541b1841009980226b784c71d2",
            "80a912569316429bb3a638af058747f1",
            "2aaa78604ba64a16a7073aa6b3a32b9b",
            "52d994ce7b23400c9ae8dc8bf04cfa06",
            "aa3a49adefd94f3b893dff25ac5fa22a",
            "971429098549487abf1bf5f450774479"
          ]
        },
        "id": "BY9JegJ_eKOO",
        "outputId": "7896b4f9-2c1c-4855-8156-2e630828b610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting to Generate untargeted JSMA\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py:2079: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
            "  updates=self.state_updates,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "JSMA:   0%|          | 0/113 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af6512a8414441a4b0d062463778e1fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting to Generate Targeted JSMA\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "JSMA:   0%|          | 0/113 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6af08dacb5a7409fbe1e4a0dca90c015"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of adversarial examples: (113, 58)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rQIbyrAV5Ar6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1b5d97ee-86bf-4bef-da57-ebbb2f680bb7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0         BENIGN\n",
            "1         BENIGN\n",
            "2         BENIGN\n",
            "3         BENIGN\n",
            "4         BENIGN\n",
            "           ...  \n",
            "225740       NaN\n",
            "225741       NaN\n",
            "225742       NaN\n",
            "225743       NaN\n",
            "225744       NaN\n",
            "Name:  Label, Length: 225745, dtype: object\n",
            "[ 0  0  0 ... 15 15 15]\n",
            "(375, 58)\n",
            "(375, 58)\n",
            "Index([' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
            "       ' Fwd Packet Length Std', 'Bwd Packet Length Max',\n",
            "       ' Bwd Packet Length Min', ' Bwd Packet Length Mean',\n",
            "       ' Bwd Packet Length Std', 'Flow Bytes/s', ' Flow Packets/s',\n",
            "       ' Flow IAT Mean', ' Flow IAT Std', ' Flow IAT Max', ' Flow IAT Min',\n",
            "       'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max',\n",
            "       ' Fwd IAT Min', 'Bwd IAT Total', ' Bwd IAT Mean', ' Bwd IAT Std',\n",
            "       ' Bwd IAT Max', ' Bwd IAT Min', 'Fwd PSH Flags', ' Fwd URG Flags',\n",
            "       ' Fwd Header Length', ' Bwd Header Length', 'Fwd Packets/s',\n",
            "       ' Bwd Packets/s', ' Min Packet Length', ' Max Packet Length',\n",
            "       ' Packet Length Mean', ' Packet Length Std', ' Packet Length Variance',\n",
            "       'FIN Flag Count', ' SYN Flag Count', ' RST Flag Count',\n",
            "       ' PSH Flag Count', ' ACK Flag Count', ' URG Flag Count',\n",
            "       ' CWE Flag Count', ' ECE Flag Count', ' Down/Up Ratio',\n",
            "       ' Average Packet Size', ' Avg Fwd Segment Size',\n",
            "       ' Avg Bwd Segment Size', ' Fwd Header Length.1', 'Subflow Fwd Packets',\n",
            "       ' Subflow Fwd Bytes', ' Subflow Bwd Packets', ' Subflow Bwd Bytes',\n",
            "       'Init_Win_bytes_forward', ' Init_Win_bytes_backward',\n",
            "       ' act_data_pkt_fwd', ' min_seg_size_forward', 'Active Mean',\n",
            "       ' Active Std', ' Active Max'],\n",
            "      dtype='object')\n",
            "(375, 58)\n",
            "============\n",
            "<table style=\"border: 0;\"><tr style=\"border: 0;\"><td style=\"background-color: #ff7f0e; border: 0;\"><code style=\"background-color: #ff7f0e;\">C1</code></td><td style=\"border: 0\"><code>['127', '143', '126', '147', '149', '141']</code></td></tr><tr style=\"border: 0;\"><td style=\"background-color: #2ca02c; border: 0;\"><code style=\"background-color: #2ca02c;\">C2</code></td><td style=\"border: 0\"><code>['156', '163', '168', '162', '89', '91', '86', '241', '164']</code></td></tr><tr style=\"border: 0;\"><td style=\"background-color: #d62728; border: 0;\"><code style=\"background-color: #d62728;\">C3</code></td><td style=\"border: 0\"><code>['154', '169', '174', '177', '189', '184', '193', '175', '176', '179', '199', '231', '130', '145', '227', '244', '230', '237', '242', '232', '240', '246', '249', '225', '228', '234', '15']</code></td></tr><tr style=\"border: 0;\"><td style=\"background-color: #9467bd; border: 0;\"><code style=\"background-color: #9467bd;\">C4</code></td><td style=\"border: 0\"><code>['9', '39', '32', '37', '345', '346', '308', '340', '291', '294', '13', '293', '335', '280', '288', '348', '299', '297', '(4)', '327', '29', '47', '41', '299', '328', '30', '312', '320', '301', '310', '166', '138', '24', '133', '142', '131', '137', '129', '128', '140', '139', '148', '135', '144', '134', '125', '10', '151', '362', '374', '307', '311', '303', '355', '315', '361', '302', '309', '304', '319', '307', '316', '324', '313', '317', '322', '372', '360', '371', '359', '356', '(2)', '306', '363', '373', '(2)', '364', '365', '(2)', '352', '351', '353', '370', '357', '366', '358', '(3)', '136', '132', '256', '270', '263', '258', '(2)', '(2)', '(3)', '251', '265', '28', '40', '36', '42', '43', '45', '(3)', '(7)', '46', '236', '(2)', '(2)', '272', '253', '(2)', '268', '254', '274', '150', '157', '167', '172', '155', '165', '159', '167', '330', '341', '305', '194', '196', '(2)', '(4)', '(4)', '(4)', '186', '289', '298', '296', '(4)', '(4)', '(5)', '285', '226', '260', '342', '347', '332', '343', '349', '336', '344', '338', '337', '84', '326', '331', '334', '354', '94', '75', '79', '90', '97', '93', '80', '333', '85', '0', '62', '74', '72', '60', '64', '67', '78', '96', '88', '98', '109', '117', '83', '87', '81', '95', '77', '82', '95', '25', '1', '8', '2', '6', '19', '7', '12', '11', '22', '21', '5', '18', '4', '14', '17', '3', '116', '92', '99', '110', '123', '111', '58', '65', '55', '50', '71', '69', '70', '63', '68', '69', '108', '118', '(2)', '(2)', '23', '52', '229', '16', '146', '102']</code></td></tr><tr style=\"border: 0;\"><td style=\"background-color: #8c564b; border: 0;\"><code style=\"background-color: #8c564b;\">C5</code></td><td style=\"border: 0\"><code>['104', '119', '122', '103', '120', '107', '245', '247', '239', '248', '152', '171', '160', '153', '173', '161', '158', '170', '235', '243', '233', '53', '54', '57', '76', '200', '66', '73', '56', '112', '114', '105', '113', '124', '61', '101', '121', '51', '106', '59', '105', '210', '224', '204', '221', '222', '223', '208', '209', '217', '207', '211', '218', '219', '220', '203', '213', '214', '202', '216', '205', '212', '215', '206', '201']</code></td></tr></table>\n",
            "Class0\n",
            "======\n",
            "[0 0 0 3 0 0 5 0 0 1]\n",
            "count of zeros: 0\n",
            "Class1\n",
            "======\n",
            "[ 0  0  9  1 14  0  8  0 25  7]\n",
            "Class2\n",
            "======\n",
            "[ 1  0  0  0  0  2  3  8  0 13]\n",
            "Class3\n",
            "======\n",
            "[24 25 16 21 11 17  9 17  0  4 25 25 25 25 25]\n",
            "Class4\n",
            "======\n",
            "[0 0 0 0 0 6]\n",
            "0 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "1 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "2 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "3 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "4 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "5 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "6 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "7 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "8 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "9 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "10 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "11 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "12 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "13 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "14 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "15 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 2\n",
            "16 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "17 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "18 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "19 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "20 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "21 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "22 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "23 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "24 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 3\n",
            "25 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "26 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "27 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "28 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "29 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "30 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "31 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "32 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "33 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "34 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "35 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "36 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "37 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "38 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "39 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "40 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "41 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "42 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "43 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "44 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "45 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "46 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "47 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "48 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "49 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 3\n",
            "50 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "51 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "52 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "53 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "54 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "55 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "56 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "57 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "58 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "59 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "60 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "61 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "62 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "63 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "64 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "65 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "66 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "67 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "68 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "69 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "70 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "71 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "72 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "73 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "74 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 3\n",
            "75 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "76 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "77 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "78 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "79 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "80 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "81 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "82 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "83 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "84 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "85 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "86 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "87 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "88 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "89 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "90 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "91 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "92 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "93 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "94 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "95 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "96 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "97 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "98 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "99 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 3\n",
            "100 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "101 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "102 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "103 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "104 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "105 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "106 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "107 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "108 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "109 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "110 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "111 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "112 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "113 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "114 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "115 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "116 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "117 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "118 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "119 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "120 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "121 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "122 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "123 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 3\n",
            "124 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "125 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "126 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "127 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "128 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "129 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "130 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "131 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "132 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "133 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "134 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "135 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "136 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "137 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "138 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "139 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "140 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "141 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "142 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "143 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "144 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "145 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "146 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "147 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "148 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 3\n",
            "149 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "150 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "151 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "152 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "153 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "154 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "155 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "156 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 0\n",
            "157 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "158 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "159 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "160 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "161 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "162 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 0\n",
            "163 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 0\n",
            "164 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 0\n",
            "165 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "166 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "167 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "168 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 0\n",
            "169 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "170 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "171 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "172 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 3\n",
            "173 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "174 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "175 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "176 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "177 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "178 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "179 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "180 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "181 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "182 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "183 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "184 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "185 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "186 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "187 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "188 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "189 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "190 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "191 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "192 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "193 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "194 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "195 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "196 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "197 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "198 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 3\n",
            "199 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 2\n",
            "200 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "201 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "202 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "203 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "204 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "205 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "206 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "207 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "208 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "209 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "210 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "211 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "212 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "213 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "214 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "215 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "216 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "217 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "218 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "219 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "220 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "221 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "222 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "223 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "224 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 1\n",
            "225 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "226 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 3\n",
            "227 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "228 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "229 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 3\n",
            "230 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "231 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "232 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "233 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "234 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "235 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "236 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 3\n",
            "237 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "238 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 3\n",
            "239 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "240 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "241 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "242 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "243 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "244 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "245 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "246 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "247 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "248 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "249 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 2\n",
            "250 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "251 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "252 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "253 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "254 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "255 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "256 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "257 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "258 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "259 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "260 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "261 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "262 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "263 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "264 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "265 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "266 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "267 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "268 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "269 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "270 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "271 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "272 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "273 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "274 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 3\n",
            "275 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "276 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "277 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "278 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "279 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "280 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "281 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "282 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "283 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "284 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "285 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "286 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "287 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "288 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "289 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "290 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "291 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "292 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "293 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "294 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "295 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "296 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "297 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "298 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "299 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 3\n",
            "300 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "301 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "302 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "303 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "304 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "305 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "306 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "307 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "308 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "309 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "310 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "311 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "312 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "313 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "314 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "315 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "316 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "317 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "318 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "319 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "320 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "321 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "322 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "323 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "324 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 3\n",
            "325 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "326 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "327 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "328 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "329 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "330 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "331 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "332 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "333 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "334 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "335 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "336 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "337 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "338 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "339 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "340 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "341 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "342 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "343 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "344 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "345 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "346 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "347 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "348 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "349 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 3\n",
            "350 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "351 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "352 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "353 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "354 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "355 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "356 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "357 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "358 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "359 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "360 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "361 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "362 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "363 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "364 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "365 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "366 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "367 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "368 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "369 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "370 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "371 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "372 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "373 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "374 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 3\n",
            "=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=--=-=-=\n",
            "Class 0 - BENIGN: Cluster 0\n",
            "Class 1 - DDoS: Cluster 0\n",
            "Class 2 - PortScan: Cluster 0\n",
            "Class 3 - Bot: Cluster 2\n",
            "Class 4 - Infiltration: Cluster 0\n",
            "Class 5 - Web Attack � Brute Force: Cluster 0\n",
            "Class 6 - Web Attack � XSS: Cluster 4\n",
            "Class 7 - Web Attack � Sql Injection: Cluster 0\n",
            "Class 8 - FTP-Patator: Cluster 0\n",
            "Class 9 - SSH-Patator: Cluster 4\n",
            "Class 10 - DoS slowloris: Cluster 0\n",
            "Class 11 - DoS Slowhttptest: Cluster 0\n",
            "Class 12 - DoS Hulk: Cluster 0\n",
            "Class 13 - DoS GoldenEye: Cluster 0\n",
            "Class 14 - Heartbleed: Cluster 0\n",
            "   0   1   2   3  4                       Class\n",
            "0  0   0   1  24  0                      BENIGN\n",
            "0  0   0   0  25  0                        DDoS\n",
            "0  0   9   0  16  0                    PortScan\n",
            "0  3   1   0  21  0                         Bot\n",
            "0  0  14   0  11  0                Infiltration\n",
            "0  0   0   2  17  6    Web Attack � Brute Force\n",
            "0  5   8   3   9  0            Web Attack � XSS\n",
            "0  0   0   8  17  0  Web Attack � Sql Injection\n",
            "0  0  25   0   0  0                 FTP-Patator\n",
            "0  1   7  13   4  0                 SSH-Patator\n",
            "0  0   0   0  25  0               DoS slowloris\n",
            "0  0   0   0  25  0            DoS Slowhttptest\n",
            "0  0   0   0  25  0                    DoS Hulk\n",
            "0  0   0   0  25  0               DoS GoldenEye\n",
            "0  0   0   0  25  0                  Heartbleed\n",
            "            0     0         0    0             0                         0  \\\n",
            "0           0     0         0    3             0                         0   \n",
            "1           0     0         9    1            14                         0   \n",
            "2           1     0         0    0             0                         2   \n",
            "3          24    25        16   21            11                        17   \n",
            "4           0     0         0    0             0                         6   \n",
            "Class  BENIGN  DDoS  PortScan  Bot  Infiltration  Web Attack � Brute Force   \n",
            "\n",
            "                      0                           0            0            0  \\\n",
            "0                     5                           0            0            1   \n",
            "1                     8                           0           25            7   \n",
            "2                     3                           8            0           13   \n",
            "3                     9                          17            0            4   \n",
            "4                     0                           0            0            0   \n",
            "Class  Web Attack � XSS  Web Attack � Sql Injection  FTP-Patator  SSH-Patator   \n",
            "\n",
            "                   0                 0         0              0           0  \n",
            "0                  0                 0         0              0           0  \n",
            "1                  0                 0         0              0           0  \n",
            "2                  0                 0         0              0           0  \n",
            "3                 25                25        25             25          25  \n",
            "4                  0                 0         0              0           0  \n",
            "Class  DoS slowloris  DoS Slowhttptest  DoS Hulk  DoS GoldenEye  Heartbleed  \n",
            "=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=--=-=-=\n",
            "[['3', '5'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '10'], ['3', '5'], ['3', '2'], ['3', '2'], ['3', '10'], ['3', '2'], ['2', '6'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '2'], ['1', '12'], ['3', '2'], ['1', '7'], ['1', '7'], ['3', '2'], ['1', '12'], ['1', '7'], ['3', '2'], ['1', '12'], ['3', '5'], ['1', '12'], ['3', '5'], ['3', '2'], ['3', '5'], ['3', '2'], ['1', '12'], ['3', '5'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '2'], ['3', '5'], ['1', '12'], ['3', '5'], ['3', '5'], ['1', '7'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['0', '0'], ['3', '5'], ['3', '5'], ['0', '0'], ['3', '5'], ['0', '0'], ['3', '2'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '2'], ['3', '2'], ['1', '12'], ['3', '2'], ['1', '4'], ['1', '4'], ['1', '12'], ['1', '12'], ['1', '4'], ['3', '2'], ['3', '5'], ['3', '2'], ['3', '2'], ['1', '12'], ['1', '12'], ['1', '12'], ['3', '2'], ['3', '2'], ['3', '5'], ['3', '2'], ['1', '4'], ['1', '4'], ['1', '12'], ['1', '4'], ['3', '2'], ['1', '12'], ['3', '5'], ['4', '9'], ['4', '9'], ['3', '5'], ['3', '5'], ['2', '1'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['4', '9'], ['3', '5'], ['4', '9'], ['3', '5'], ['2', '1'], ['3', '2'], ['4', '9'], ['3', '5'], ['4', '9'], ['3', '5'], ['3', '5'], ['1', '8'], ['1', '8'], ['2', '1'], ['3', '5'], ['0', '11'], ['3', '5'], ['1', '8'], ['3', '5'], ['1', '8'], ['1', '8'], ['0', '11'], ['0', '11'], ['0', '0'], ['3', '5'], ['3', '10'], ['3', '5'], ['0', '11'], ['2', '1'], ['1', '8'], ['1', '8'], ['3', '5'], ['1', '8'], ['2', '1'], ['2', '1'], ['2', '1'], ['2', '1'], ['3', '5'], ['2', '1'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['2', '1'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['2', '1'], ['3', '5'], ['3', '5'], ['3', '5'], ['2', '1'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['2', '1'], ['1', '14'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['1', '3'], ['2', '6'], ['3', '5'], ['2', '6'], ['2', '6'], ['3', '2'], ['2', '6'], ['2', '1'], ['2', '6'], ['1', '8'], ['2', '6'], ['1', '8'], ['3', '5'], ['2', '6'], ['3', '2'], ['1', '13'], ['2', '6'], ['0', '0'], ['2', '6'], ['1', '8'], ['2', '6'], ['1', '13'], ['2', '6'], ['1', '13'], ['1', '13'], ['2', '6'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '10'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '10'], ['3', '10'], ['3', '5'], ['3', '10'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5'], ['3', '5']]\n",
            "=============================\n",
            "The Hierarchy Looks like this\n",
            "=============================\n",
            "['0', '0']\n",
            "['0', '11']\n",
            "['1', '12']\n",
            "['1', '13']\n",
            "['1', '14']\n",
            "['1', '3']\n",
            "['1', '4']\n",
            "['1', '7']\n",
            "['1', '8']\n",
            "['2', '1']\n",
            "['2', '6']\n",
            "['3', '10']\n",
            "['3', '2']\n",
            "['3', '5']\n",
            "['4', '9']\n",
            "=============================\n",
            "375\n",
            "375\n",
            "0\n",
            "parent of 10 is 3\n",
            "class 0 is mostly 3 - Bot\n",
            "relabel to align wioth expert defined labels\n",
            "class 1 is mostly 3 - Bot\n",
            "relabel to align wioth expert defined labels\n",
            "class 2 is mostly 3 - Bot\n",
            "relabel to align wioth expert defined labels\n",
            "class 3 is mostly 3 - Bot\n",
            "relabel to align wioth expert defined labels\n",
            "class 4 is mostly 1 - DDoS\n",
            "relabel to align wioth expert defined labels\n",
            "['(Bot)', '(Bot)', '(Bot)', '(Bot)', '(DDoS)']\n",
            "class 0 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 1 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 2 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 3 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 4 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 5 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 6 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 7 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 8 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 9 is mostly 2 - PortScan\n",
            "relabel to align with expert defined labels\n",
            "class 10 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 11 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 12 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 13 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 14 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "['Bot - [0] [0]', 'Bot - [2] [1]', 'Bot - [3] [2]', 'Bot - [1] [3]', 'DDoS - [1] [4]', 'Bot - [3] [5]', 'Bot - [2] [6]', 'Bot - [1] [7]', 'DDoS - [1] [8]', 'PortScan - [4] [9]', 'Bot - [3] [10]', 'Bot - [0] [11]', 'Bot - [1] [12]', 'Bot - [1] [13]', 'Bot - [1] [14]']\n",
            " Label\n",
            "1\n",
            "5\n",
            "1\n",
            "5\n",
            "5\n",
            "1\n",
            "5\n",
            "1\n",
            "5\n",
            "5\n",
            "5\n",
            "10\n",
            "5\n",
            "5\n",
            "2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x6480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "benign\n",
        "\n",
        "# First define the leaf label function.\n",
        "def llf(id):\n",
        "    if id < 2:\n",
        "        return str(transformed_label[id])\n",
        "    else:\n",
        "        return str(id)\n",
        "\n",
        "# The text for the leaf nodes is going to be big so force\n",
        "# a rotation of 90 degrees.\n",
        "# dendrogram(Z, leaf_label_func=llf, leaf_rotation=90)\n",
        "\n",
        "# leaf_label_func can also be used together with ``truncate_mode`` parameter,\n",
        "# in which case you will get your leaves labeled after truncation:\n",
        "# dendrogram(Z, leaf_label_func=llf, leaf_rotation=90,\n",
        "          #  truncate_mode='level', p=2)\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from scipy.cluster.hierarchy import dendrogram\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "\n",
        "def plot_dendrogram(model, **kwargs):\n",
        "    # Create linkage matrix and then plot the dendrogram\n",
        "\n",
        "    # create the counts of samples under each node\n",
        "    counts = np.zeros(model.children_.shape[0])\n",
        "    n_samples = len(model.labels_)\n",
        "    for i, merge in enumerate(model.children_):\n",
        "        current_count = 0\n",
        "        for child_idx in merge:\n",
        "            if child_idx < n_samples:\n",
        "                current_count += 1  # leaf node\n",
        "            else:\n",
        "                current_count += counts[child_idx - n_samples]\n",
        "        counts[i] = current_count\n",
        "\n",
        "    linkage_matrix = np.column_stack(\n",
        "        [\n",
        "        model.children_, \n",
        "        model.distances_, \n",
        "        counts]\n",
        "    ).astype(float)\n",
        "\n",
        "    # Plot the corresponding dendrogram\n",
        "    dendrogram(linkage_matrix, **kwargs)\n",
        "\n",
        "\n",
        "# iris = load_iris()\n",
        "# X = iris.data\n",
        "\n",
        "\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(df2[' Label'])\n",
        "templabledf= df2[' Label']\n",
        "print(templabledf)\n",
        "transformed_label = le.transform(templabledf) # multi-class \n",
        "print(transformed_label)\n",
        "df2 = df2.drop(columns=[' Label'])\n",
        "\n",
        "# df2[' Label'] = transformed_label\n",
        "\n",
        "# Remove NaN and Inf\n",
        "df2 = df2[~df2.isin([np.nan, np.inf, -np.inf]).any(1)]\n",
        "print(df2.shape)\n",
        "# Remove columns with all zero values\n",
        "# df2 = df2.loc[:, (df2 != 0).any(axis=0)]\n",
        "print(df2.shape)\n",
        "print(df2.columns)\n",
        "cluster_columns = df2.columns\n",
        "# Output table\n",
        "# print(df2)\n",
        "X = df2.values\n",
        "print(X.shape)\n",
        "\n",
        "# setting distance_threshold=0 ensures we compute the full tree.\n",
        "# model = AgglomerativeClustering(distance_threshold=0, n_clusters=None) # None)\n",
        "\n",
        "metrics = [ \"manhattan\", \"euclidean\", \"minkowski\"]\n",
        "\n",
        "# for metric in metrics:\n",
        "\n",
        "#   model = AgglomerativeClustering(distance_threshold=0,\n",
        "#                                   n_clusters=None,\n",
        "#                                   affinity = metric,\n",
        "#                                   linkage = \"single\",\n",
        "#                                 )\n",
        "\n",
        "#   model = model.fit(X)\n",
        "#   # model = model.fit_predict(X)\n",
        "#   plt.title(f\"Hierarchical Clustering Dendrogram\\nusing {metric} distance metric\")\n",
        "#   # plot the top three levels of the dendrogram\n",
        "#   # \n",
        "#   plot_dendrogram(model,  leaf_rotation=90, truncate_mode=\"level\", p=3,\n",
        "#                               leaf_font_size=12, orientation='top')\n",
        "#   plt.xlabel(\"Number of points in node (or index of point if no parenthesis).\")\n",
        "#   plt.show()\n",
        "#   # print(model.labels_)\n",
        "#   # print(model.children_)\n",
        "#   # print(len(model.labels_))\n",
        "#   # print(len(X))\n",
        "\n",
        "#   # print(model)\n",
        "\n",
        "#   # for i in range (0,len(model.labels_)):\n",
        "#   #   print(transformed_label[model.labels_])\n",
        "\n",
        "\n",
        "print('============')\n",
        "\n",
        "cluster = AgglomerativeClustering(n_clusters=15, affinity='euclidean', linkage='complete')\n",
        "cluster.fit_predict(X)\n",
        "\n",
        "class0 = []\n",
        "class1 = []\n",
        "class2 = []\n",
        "class3 = []\n",
        "class4 = []\n",
        "class5 = []\n",
        "class6 = []\n",
        "class7 = []\n",
        "class8 = []\n",
        "class9 = []\n",
        "class10 = []\n",
        "class11 = []\n",
        "class12 = []\n",
        "class13 = []\n",
        "class14 = []\n",
        "\n",
        "from collections import defaultdict\n",
        "  \n",
        "bottom_layer_labels = (cluster.labels_)\n",
        "\n",
        "for i in range(0,len(cluster.labels_)):\n",
        "  # print(f'{i} : ORIG_LABEL : {transformed_label[i]} - {outcome[transformed_label[i]]}; CLUSTER_LABEL: {cluster.labels_[i]}')\n",
        "  # print(cluster.labels_[i])\n",
        "  if(int(cluster.labels_[i]) == 0):\n",
        "    class0.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 1):\n",
        "    class1.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 2):\n",
        "    class2.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 3):\n",
        "    class3.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 4):\n",
        "    class4.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 5):\n",
        "    class5.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 6):\n",
        "    class6.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 7):\n",
        "    class7.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 8):\n",
        "    class8.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 9):\n",
        "    class9.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 10):\n",
        "    class10.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 11):\n",
        "    class11.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 12):\n",
        "    class12.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 13):\n",
        "    class13.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 14):\n",
        "    class14.append(transformed_label[i])\n",
        "       \n",
        "  pass\n",
        "\n",
        "\n",
        "    # print(class0)\n",
        "    # print(class1)\n",
        "    # print(class2)\n",
        "\n",
        "\n",
        "import scipy.cluster.hierarchy as shc\n",
        "from matplotlib.colors import rgb2hex, colorConverter\n",
        "\n",
        "plt.figure(figsize=(30, 90))\n",
        "plt.title(\"Cluster 16 Classes Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='complete'),leaf_rotation=0, truncate_mode=\"level\", p=16,\n",
        "                              leaf_font_size=12, orientation='left')\n",
        "\n",
        "\n",
        "cluster_idxs = defaultdict(list)\n",
        "for c, pi in zip(dend['color_list'], dend['icoord']):\n",
        "    for leg in pi[1:3]:\n",
        "        i = (leg - 5.0) / 10.0\n",
        "        if abs(i - int(i)) < 1e-5:\n",
        "            cluster_idxs[c].append(int(i))\n",
        "\n",
        "\n",
        "cluster_idxs\n",
        "\n",
        "class Clusters(dict):\n",
        "    def _repr_html_(self):\n",
        "        html = '<table style=\"border: 0;\">'\n",
        "        for c in self:\n",
        "            hx = rgb2hex(colorConverter.to_rgb(c))\n",
        "            html += '<tr style=\"border: 0;\">' \\\n",
        "            '<td style=\"background-color: {0}; ' \\\n",
        "                       'border: 0;\">' \\\n",
        "            '<code style=\"background-color: {0};\">'.format(hx)\n",
        "            html += c + '</code></td>'\n",
        "            html += '<td style=\"border: 0\"><code>' \n",
        "            html += repr(self[c]) + '</code>'\n",
        "            html += '</td></tr>'\n",
        "        \n",
        "        html += '</table>'\n",
        "        \n",
        "        return html\n",
        "\n",
        "cluster_classes = Clusters()\n",
        "for c, l in cluster_idxs.items():\n",
        "    i_l = [dend['ivl'][i] for i in l]\n",
        "    cluster_classes[c] = i_l\n",
        "\n",
        "cluster_classes\n",
        "\n",
        "print(cluster_classes._repr_html_())\n",
        "\n",
        "plt.savefig('Dendogram-complete-Coarse.png')\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.title(\"Cluster 8 Classes Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='complete'),leaf_rotation=90, truncate_mode=\"level\", p=2,\n",
        "                              leaf_font_size=12, orientation='top')\n",
        "plt.savefig('Dendogram-complete-Fine.png')\n",
        "  \n",
        "cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='complete')\n",
        "cluster.fit_predict(X)\n",
        "\n",
        "\n",
        "class0 = []\n",
        "class1 = []\n",
        "class2 = []\n",
        "class3 = []\n",
        "class4 = []\n",
        "\n",
        "\n",
        "top_layer_labels = (cluster.labels_)\n",
        "# print(top_layer_labels)\n",
        "for i in range(0,len(cluster.labels_)):\n",
        "  # print(f'{i} : ORIG_LABEL : {transformed_label[i]} - {outcome[transformed_label[i]]}; CLUSTER_LABEL: {cluster.labels_[i]}')\n",
        "  if(int(cluster.labels_[i]) == 0):\n",
        "    class0.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 1):\n",
        "    class1.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 2):\n",
        "    class2.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 3):\n",
        "    class3.append(transformed_label[i])\n",
        "  elif(cluster.labels_[i] == 4):\n",
        "    class4.append(transformed_label[i])\n",
        "  \n",
        "  pass\n",
        "\n",
        "print(\"Class0\")\n",
        "print(\"======\")\n",
        "count = 0\n",
        "class0bin = np.bincount(np.array(class0))\n",
        "print(class0bin)\n",
        "for i in range(0,len (class0 )) :\n",
        "  if class0[i] == 0:\n",
        "    count = count +1\n",
        "print(f\"count of zeros: {count}\")\n",
        "\n",
        "\n",
        "\n",
        "print(\"Class1\")\n",
        "print(\"======\")\n",
        "print(np.bincount(np.array(class1)))\n",
        "\n",
        "print(\"Class2\")\n",
        "print(\"======\")\n",
        "print(np.bincount(np.array(class2)))\n",
        "\n",
        "print(\"Class3\")\n",
        "print(\"======\")\n",
        "print(np.bincount(np.array(class3)))\n",
        "\n",
        "print(\"Class4\")\n",
        "print(\"======\")\n",
        "print(np.bincount(np.array(class4)))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# =====\n",
        "\n",
        "class0 = []\n",
        "class1 = []\n",
        "class2 = []\n",
        "class3 = []\n",
        "class4 = []\n",
        "class5 = []\n",
        "class6 = []\n",
        "class7 = []\n",
        "class8 = []\n",
        "class9 = []\n",
        "class10 = []\n",
        "class11 = []\n",
        "class12 = []\n",
        "class13 = []\n",
        "class14 = []\n",
        "\n",
        "for i in range(0,len(cluster.labels_)):\n",
        "  print(f'{i} : ORIG_LABEL : {transformed_label[i]} - {outcome[transformed_label[i]]}; CLUSTER_LABEL: {cluster.labels_[i]}')\n",
        "  if(int(transformed_label[i]) == 0):\n",
        "    class0.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 1):\n",
        "    class1.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 2):\n",
        "    class2.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 3):\n",
        "    class3.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 4):\n",
        "    class4.append(cluster.labels_[i])\n",
        "\n",
        "  elif(int(transformed_label[i]) == 5):\n",
        "    class5.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 6):\n",
        "    class6.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 7):\n",
        "    class7.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 8):\n",
        "    class8.append(cluster.labels_[i])\n",
        "\n",
        "  elif(int(transformed_label[i]) == 9):\n",
        "    class9.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 10):\n",
        "    class10.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 11):\n",
        "    class11.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 12):\n",
        "    class12.append(cluster.labels_[i])\n",
        "\n",
        "  elif(int(transformed_label[i]) == 13):\n",
        "    class13.append(cluster.labels_[i])\n",
        "  elif(int(transformed_label[i]) == 14):\n",
        "    class14.append(cluster.labels_[i])\n",
        "  \n",
        "  \n",
        "  pass\n",
        "\n",
        "all_classes = [class0, class1, class2, class3, class4, class5, class6, class7, class8, class9, class10, class11, class12, class13, class14]\n",
        "print(\"=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=--=-=-=\")\n",
        "resultdf = pd.DataFrame(index=range(0))\n",
        "\n",
        "class_count = 0 \n",
        "for class_name in all_classes:\n",
        "  tempbinnp = np.bincount(np.array(class_name), minlength=5)\n",
        "  print(f\"Class {class_count} - {outcome[class_count]}: Cluster {np.argmin(tempbinnp)}\")\n",
        "  class_count = class_count + 1\n",
        "  tempbindf = pd.DataFrame(tempbinnp).transpose()\n",
        "  frames = [resultdf, tempbindf]\n",
        "  resultdf = pd.concat(frames)\n",
        "\n",
        "\n",
        "# resultdf['class'] = outcome\n",
        "# resultdf['cluster'] = ['Cluster0', 'Cluster1', 'Cluster2', 'Cluster3', 'Cluster4',]\n",
        "# print(resultdf.columns)\n",
        "resultdf['Class']=outcome\n",
        "# resultdf.iloc[:, [5,0,1,2,3,4]]\n",
        "print(resultdf)\n",
        "print(resultdf.transpose())\n",
        "\n",
        "print(\"=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=--=-=-=\")\n",
        "# resultdf.plot.bar\n",
        "#set seaborn plotting aesthetics\n",
        "sns.set(style='white')\n",
        "sns.set_context(\"paper\")\n",
        "sns.color_palette(\"dark:salmon_r\", as_cmap=True)\n",
        "\n",
        "\n",
        "\n",
        "hier_labels = []\n",
        "parent_dict = {}\n",
        "for i in range(0,len(top_layer_labels)):\n",
        "  hier_labels.append([str(top_layer_labels[i]),str(bottom_layer_labels[i])])\n",
        "  parent_dict[bottom_layer_labels[i]] = top_layer_labels[i]\n",
        "\n",
        "print(hier_labels)\n",
        "\n",
        "from operator import itemgetter\n",
        "sorted = hier_labels\n",
        "sorted.sort(key=itemgetter(0,1))\n",
        "# print(sorted)\n",
        "# print(hier_labels)\n",
        "\n",
        "res = set() \n",
        "for i in range(0,len(sorted)):\n",
        "  # print(sorted[i])\n",
        "  # Extract unique tuples from list(Order Irrespective)\n",
        "  # using list comprehension + set()\n",
        "\n",
        "  res.add(str(sorted[i]))\n",
        "\n",
        "res =list(res)\n",
        "res.sort()\n",
        "print(\"=============================\")\n",
        "print(\"The Hierarchy Looks like this\")\n",
        "print(\"=============================\")\n",
        "\n",
        "for i in range(0,len(res)):\n",
        "  print(res[i])\n",
        "print(\"=============================\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  # print(f'{i} : TL: {top_layer_labels[i]} BL: {bottom_layer_labels[i]}')\n",
        "  # pass\n",
        "\n",
        "# # Define Hierarchy\n",
        "# hier_labels = []\n",
        "# for i in range (0,len(Y_class)):\n",
        "#   # print(str(Y_class[i]))\n",
        "#   # print(outcome)\n",
        "#   if(int(Y_class[i]) == 0): # outcome[0]):\n",
        "#     hier_labels.append(['Other', 'BENIGN'])\n",
        "\n",
        "#   elif(int(Y_class[i]) == 1):\n",
        "#     hier_labels.append(['DoS','DDoS'])\n",
        "\n",
        "#   elif(int(Y_class[i]) == 2):\n",
        "#       hier_labels.append(['Penetration','PortScan'])\n",
        "\n",
        "#   elif(int(Y_class[i]) == 3):\n",
        "#       hier_labels.append(['Penetration','Bot'])\n",
        "  \n",
        "#   elif(int(Y_class[i]) == 4):\n",
        "#       hier_labels.append(['Penetration','Infiltration'])\n",
        "  \n",
        "#   elif(int(Y_class[i]) == 5):\n",
        "#       hier_labels.append(['BruteForce','WebAttack Brute Force'])\n",
        "  \n",
        "#   elif(int(Y_class[i]) == 6):\n",
        "#       hier_labels.append(['Exploit','XXS'])\n",
        "#   elif(int(Y_class[i]) == 7):\n",
        "#       hier_labels.append(['Exploit','SQL Injection'])\n",
        "#   elif(int(Y_class[i]) == 8):\n",
        "#       hier_labels.append(['BruteForce','FTP-Patator'])\n",
        "#   elif(int(Y_class[i]) == 9):\n",
        "#       hier_labels.append(['BruteForce','SSH-Patator'])\n",
        "#   elif(int(Y_class[i]) == 10):\n",
        "#       hier_labels.append(['DoS','DoS slowloris'])\n",
        "#   elif(int(Y_class[i]) == 11):\n",
        "#       hier_labels.append(['DoS','DoS Slowhttptest'])\n",
        "#   elif(int(Y_class[i]) == 12):\n",
        "#       hier_labels.append(['DoS','DoS Hulk'])\n",
        "#   elif(int(Y_class[i]) == 13):\n",
        "#       hier_labels.append(['DoS','Dos GoldenEye'])\n",
        "#   elif(int(Y_class[i]) == 14):\n",
        "#       hier_labels.append(['Exploit','Heartbleed'])\n",
        "print(len(Y_class))\n",
        "print(len(hier_labels))\n",
        "print(len(Y_class)-(len(hier_labels)))\n",
        "\n",
        "\n",
        "\n",
        "print(f\"parent of 10 is {parent_dict[10]}\")\n",
        "\n",
        "\n",
        "\n",
        "i = -1\n",
        "roughcoarse_labels = []\n",
        "for class_list in [class0, class1, class2, class3, class4]:\n",
        "  i = i + 1\n",
        "  myarray = np.array(class_list)\n",
        "  # print (myarray)\n",
        "  mycount = np.bincount(myarray)\n",
        "  # print(mycount)\n",
        "  \n",
        "  print(f'class {i} is mostly {np.argmax(mycount)} - {outcome[np.argmax(mycount)]}')\n",
        "  print('relabel to align wioth expert defined labels')\n",
        "  roughcoarse_labels.append('(' + str(outcome[np.argmax(mycount)]) + ')')\n",
        "\n",
        "print(roughcoarse_labels)\n",
        "\n",
        "i = -1\n",
        "roughfine_labels = []\n",
        "for class_list in [class0, class1, class2, class3, class4, class5, class6, class7, class8, class9, class10, class11, class12, class13, class14]:\n",
        "  i = i + 1\n",
        "  myarray = np.array(class_list)\n",
        "  # print (myarray)\n",
        "  mycount = np.bincount(myarray)\n",
        "  # print(mycount)\n",
        "    \n",
        "  print(f'class {i} is mostly {np.argmax(mycount)} - {outcome[np.argmax(mycount)]}')\n",
        "  print('relabel to align with expert defined labels')\n",
        "  roughfine_labels.append(outcome[np.argmax(mycount)] + ' - [' + str(parent_dict[i]) + '] [' + str(i) +']' )\n",
        "\n",
        "print(roughfine_labels)\n",
        "\n",
        "\n",
        "for i in range(0,len(top_layer_labels)):\n",
        "  # print(f'{i} : TL: {top_layer_labels[i]} BL: {bottom_layer_labels[i]}')\n",
        "\n",
        "  pass\n",
        "\n",
        "for i in range(0,len(orig_headers)):\n",
        "  if(orig_headers[i] in cluster_columns):\n",
        "    pass\n",
        "  else:\n",
        "    print(orig_headers[i])\n",
        "\n",
        "for i in [184, 180, 189, 191, 183, 179, 188, 177, 195, 197, 194, 345, 338, 131, 17]:\n",
        "  print(bottom_layer_labels[i])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_scaled = np.array(df2)\n",
        "\n",
        "Y_class = bottom_layer_labels # multi-class \n",
        "\n",
        "#Train test split\n",
        "\n",
        "testSize = 0.3\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_scaled, Y_class, test_size = testSize, shuffle=True, random_state = 42)\n",
        "\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "\n",
        "num_classes = len(np.unique(y_train))\n",
        "print(\"There are \"+ str(num_classes) +\" distinct classes\")\n",
        "print(np.unique(y_train))\n",
        "\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "skMLP = MLPClassifier(hidden_layer_sizes=(128,64,15),activation ='relu',solver='adam', max_iter=300, shuffle=False,random_state=1, verbose=False) # Converges around 1600 iters / Achieves similar accuracy to keras at 300 iters\n",
        "\n",
        "\n",
        "\n",
        "history = skMLP.fit(\n",
        "        x_train, y_train\n",
        ")        \n",
        "\n",
        "result = skMLP.score(x_test, y_test)\n",
        "print(result)\n",
        "print(\"Accuracy: %.2f%%\" % (result*100.0))\n"
      ],
      "metadata": {
        "id": "DEQHNgPXrxGp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdb3825c-e07b-4bb3-a1a9-c08a7261983a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(262, 58)\n",
            "(113, 58)\n",
            "There are 15 distinct classes\n",
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
            "0.9911504424778761\n",
            "Accuracy: 99.12%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = skMLP.predict_proba(x_test)\n",
        "print(predictions)\n",
        "rounded_predictions = predictions.round().astype(int)\n",
        "rounded_prediction = predictions * 100\n",
        "print(rounded_predictions)\n",
        "integer_predictions = []\n",
        "\n",
        "print(\"-------\")\n",
        "for i in range(0,len(rounded_predictions)):\n",
        "  arr = []\n",
        "  for j in range(0,num_classes):\n",
        "\n",
        "    arr.append(rounded_predictions[i][j]) \n",
        "    \n",
        "  print(arr)\n",
        "  # print(arr)\n",
        "  HighestProbabilityClass = arr.index(max(arr))\n",
        "  # print(HighestProbabilityClass)\n",
        "  integer_predictions.append(HighestProbabilityClass)\n",
        "\n",
        "print(integer_predictions)\n",
        "# print(len(integer_predictions))\n",
        "\n",
        "\n",
        "# results = model.evaluate(x_test, y_test)\n",
        "\n",
        "# # print(y_test)\n",
        "# print(len(y_test))\n",
        "\n",
        "# print(\"----- NORMAL test loss, test acc:\", results)\n",
        "# print(predictions)\n",
        "# OneHotEncoded_y_test = to_categorical(y_test)\n",
        "# print(OneHotEncoded_y_test)\n",
        "# print(len(OneHotEncoded_y_test))\n",
        "# rounded_predictions = predictions.round().astype(int)\n",
        "# integer_predictions = []\n",
        "# for i in range(len(rounded_predictions)):\n",
        "#   print(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EqD1gVDwKzB",
        "outputId": "56080e15-fdd5-4967-abcb-9328614b137f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4.77281017e-08 1.65616508e-06 2.82324650e-05 ... 5.09094577e-13\n",
            "  1.54540544e-07 1.94639844e-08]\n",
            " [1.04455383e-07 6.66472617e-06 3.14002767e-04 ... 2.54226519e-12\n",
            "  4.96874748e-07 1.26976531e-07]\n",
            " [2.47214907e-04 3.16092425e-04 1.14122165e-08 ... 3.35174427e-03\n",
            "  1.99720152e-02 3.86769384e-06]\n",
            " ...\n",
            " [4.70334580e-08 1.83282574e-06 3.84488163e-05 ... 4.66924163e-13\n",
            "  1.51530406e-07 2.15724509e-08]\n",
            " [1.51856377e-10 7.42762647e-07 1.08409306e-08 ... 1.29000660e-09\n",
            "  2.07807251e-04 1.30385539e-07]\n",
            " [3.31386230e-08 6.84441786e-07 4.81921608e-06 ... 2.78498935e-13\n",
            "  7.56680905e-08 6.04167429e-09]]\n",
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n",
            "-------\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]\n",
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            "[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
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            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n",
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            "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[5, 5, 6, 5, 5, 10, 7, 4, 10, 9, 6, 10, 5, 2, 5, 10, 5, 6, 5, 12, 10, 5, 5, 2, 5, 5, 5, 5, 8, 5, 5, 2, 5, 5, 5, 12, 2, 2, 8, 1, 5, 5, 10, 3, 5, 12, 5, 12, 2, 5, 13, 1, 5, 3, 3, 10, 5, 2, 5, 5, 5, 10, 5, 5, 5, 12, 13, 5, 3, 5, 5, 5, 5, 10, 5, 1, 5, 5, 5, 2, 12, 5, 2, 12, 2, 5, 10, 5, 3, 2, 5, 5, 8, 5, 3, 6, 9, 2, 5, 10, 3, 10, 5, 3, 5, 5, 5, 5, 5, 6, 5, 8, 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "targets = []\n",
        "classes = transformed_label\n",
        "#.unique gives the order of appearance and therefore does not sort.\n",
        "print(classes)\n",
        "new_order_classes = np.unique(Y_class)\n",
        "print(new_order_classes)\n",
        "\n",
        "\n",
        "print(classification_report(\n",
        "    integer_predictions,\n",
        "    y_test #,\n",
        "    # target_names=outcome,\n",
        "    # labels=range(0,(len(outcome))))\n",
        "))\n",
        "\n",
        "print(y_test)\n",
        "print(outcome)\n",
        "# print(len(outcome))\n",
        "count_arr = np.bincount(y_test)\n",
        "for i in range(0,len(outcome)-1):\n",
        "  print(i, count_arr[i])\n",
        "\n",
        "\n",
        "# print_classification_report(integer_predictions,y_test, \"SK-MLPOriginal.tex\",\"Multiclass Original Classification Report\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zli1OI6AwSN9",
        "outputId": "2519526a-bdf9-4d7c-eb94-bc7e95421d02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0  0  0 ... 15 15 15]\n",
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      1.00      1.00         3\n",
            "           2       1.00      1.00      1.00        12\n",
            "           3       1.00      1.00      1.00         8\n",
            "           4       1.00      1.00      1.00         1\n",
            "           5       1.00      0.98      0.99        56\n",
            "           6       1.00      1.00      1.00         5\n",
            "           7       0.50      1.00      0.67         1\n",
            "           8       1.00      1.00      1.00         4\n",
            "           9       1.00      1.00      1.00         2\n",
            "          10       1.00      1.00      1.00        12\n",
            "          12       1.00      1.00      1.00         7\n",
            "          13       1.00      1.00      1.00         2\n",
            "\n",
            "    accuracy                           0.99       113\n",
            "   macro avg       0.96      1.00      0.97       113\n",
            "weighted avg       1.00      0.99      0.99       113\n",
            "\n",
            "[ 5  5  6  5  7 10  7  4 10  9  6 10  5  2  5 10  5  6  5 12 10  5  5  2\n",
            "  5  5  5  5  8  5  5  2  5  5  5 12  2  2  8  1  5  5 10  3  5 12  5 12\n",
            "  2  5 13  1  5  3  3 10  5  2  5  5  5 10  5  5  5 12 13  5  3  5  5  5\n",
            "  5 10  5  1  5  5  5  2 12  5  2 12  2  5 10  5  3  2  5  5  8  5  3  6\n",
            "  9  2  5 10  3 10  5  3  5  5  5  5  5  6  5  8  5]\n",
            "['BENIGN' 'DDoS' 'PortScan' 'Bot' 'Infiltration'\n",
            " 'Web Attack � Brute Force' 'Web Attack � XSS'\n",
            " 'Web Attack � Sql Injection' 'FTP-Patator' 'SSH-Patator' 'DoS slowloris'\n",
            " 'DoS Slowhttptest' 'DoS Hulk' 'DoS GoldenEye' 'Heartbleed']\n",
            "0 0\n",
            "1 3\n",
            "2 12\n",
            "3 8\n",
            "4 1\n",
            "5 55\n",
            "6 5\n",
            "7 2\n",
            "8 4\n",
            "9 2\n",
            "10 12\n",
            "11 0\n",
            "12 7\n",
            "13 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Do the Hierarchical \n",
        "\n",
        "# Install conda\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()\n",
        "\n",
        "!conda install -c conda-forge hiclass=3.1.6 --yes\n",
        "# !conda install -c conda-forge hiclass=4.0.6 --yes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNKPPo3k2GX2",
        "outputId": "3e488f3f-54bc-4f20-c28d-479f49a84edd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:41\n",
            "🔁 Restarting kernel...\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
            "Collecting package metadata (repodata.json): / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in the x_test_jsma numpy \n",
        "savename='x_test_jsma.npy'\n",
        "basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "filename=basedir+savename\n",
        "print(\"Reading file\")\n",
        "x_test_jsma = np.load(filename)\n",
        "print(x_test_jsma.shape)\n",
        "print(X.shape)"
      ],
      "metadata": {
        "id": "Wq8TF0naGjTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn import tree\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "from sklearn.experimental import enable_hist_gradient_boosting\n",
        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "#Importing MLPClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "# model = MLPClassifier(hidden_layer_sizes=(128,64),activation ='relu',solver='sgd',random_state=1)\n",
        "\n",
        "\n",
        "original_f1_macros = []\n",
        "jsma_f1_macros = []\n",
        "\n",
        "#prepare algorithms to test\n",
        "models = []\n",
        "# models.append(('Keras Model',keras_clf))\n",
        "models.append(('MLP',MLPClassifier(hidden_layer_sizes=(128,64,15),activation ='relu',solver='adam', max_iter=300, shuffle=False,random_state=1, verbose=False))) # Converges around 1600 iters / Achieves similar accuracy to keras at 300 iters\n",
        "# models.append(('NB', MultinomialNB())) # Naive Bayes\n",
        "# models.append(('KNN', KNeighborsClassifier(n_neighbors = 10))) # k Nearest Neighbour\n",
        "# models.append(('LR', LogisticRegression())) # Logistic Regression\n",
        "# models.append(('SVC', SVC(C=10, gamma=0.001, probability=True))) # Support Vector Classifier model is tuned using GridSearch (code in seperate file)\n",
        "# models.append(('DT', tree.DecisionTreeClassifier())) # Decision Tree\n",
        "# models.append(('RF', RandomForestClassifier(bootstrap=True, n_jobs=2, verbose=True, max_features=20, n_estimators=170, max_depth=None,min_samples_split=5, random_state=7))) # Random Forest\n",
        "# models.append(('AdaBoost', AdaBoostClassifier()))\n",
        "# models.append(('XGBoost',XGBClassifier()))\n",
        "# models.append(('QDA', QuadraticDiscriminantAnalysis())) # Quadratic Discrimination Analysis\n",
        "# models.append(('HBBC',HistGradientBoostingClassifier())) # Histogram Based Boosting Classifier\n",
        "# models.append(('LGBM',LGBMClassifier())) # Light Gradient Boosting Machine\n",
        "    \n",
        "\n",
        "\n",
        "\n",
        "#evaluate each algorithm\n",
        "results = []\n",
        "names = []\n",
        "for name, current_model in models:\n",
        "  names.append(name)\n",
        "  current_model.fit(x_train, y_train)\n",
        "  orig_predictions = current_model.predict(x_test)\n",
        "  print(orig_predictions)\n",
        "  # loss, accuracy = current_model.score(x_test,y_test)\n",
        "  # print(loss, accuracy)\n",
        "  if(name=='Kerasy Model'):\n",
        "    predictions = current_model.predict(x_test)\n",
        "    print(predictions)\n",
        "    # rounded_predictions = predictions.round().astype(int)\n",
        "    # print(rounded_predictions)\n",
        "    # integer_predictions = []\n",
        "\n",
        "    # print(\"-------\")\n",
        "    # for i in range(len(rounded_predictions)):\n",
        "\n",
        "    #   arr = [rounded_predictions[i][0], rounded_predictions[i][1], rounded_predictions[i][2], \n",
        "    #         rounded_predictions[i][3], rounded_predictions[i][4], rounded_predictions[i][5], \n",
        "    #         rounded_predictions[i][6], rounded_predictions[i][7], rounded_predictions[i][8],\n",
        "    #         rounded_predictions[i][9], rounded_predictions[i][10], rounded_predictions[i][11],\n",
        "    #         rounded_predictions[i][12], rounded_predictions[i][13], rounded_predictions[i][14],\n",
        "    #         ]\n",
        "\n",
        "    #   # print(arr)\n",
        "    #   HighestProbabilityClass = arr.index(max(arr))\n",
        "    #   # print(HighestProbabilityClass)\n",
        "    #   integer_predictions.append(HighestProbabilityClass)\n",
        "\n",
        "    print(predictions)\n",
        "    cm = confusion_matrix(y_test,predictions)\n",
        "    plot_cm(cm,str(name) + ' (Original)',str(name) + '-Original.png')\n",
        "\n",
        "    jsma_predictions = current_model.predict(x_test_jsma)\n",
        "    print(jsma_predictions)\n",
        "    print(jsma_predictions)\n",
        "    cm = confusion_matrix(y_test,jsma_predictions)\n",
        "    plot_cm(cm,str(name) + ' (JSMA)',str(name) + '-JSMA.png')\n",
        "\n",
        "  else:\n",
        "    cm = confusion_matrix(y_test,orig_predictions)\n",
        "    plot_cm(cm,str(name) + ' (Original)',str(name) + '-Original.png')\n",
        "    DICT = classification_report(\n",
        "        orig_predictions,y_test,\n",
        "        # target_names=l_col_list,\n",
        "        labels=range(0,(len(outcome))),output_dict=True\n",
        "    )\n",
        "    original_f1_macros.append(DICT['macro avg']['f1-score'])\n",
        "    print_classification_report(orig_predictions, y_test, 'class_report_' + str(name) +'-Original.tex', str(name) + 'Classification Report (Original)')\n",
        "\n",
        "    jsma_predictions = current_model.predict(x_test_jsma)\n",
        "    cm = confusion_matrix(y_test,jsma_predictions)\n",
        "    plot_cm(cm,str(name) + ' (JSMA)',str(name) + '-JSMA.png')\n",
        "    DICT = classification_report(\n",
        "        jsma_predictions,y_test,\n",
        "        # target_names=l_col_list,\n",
        "        labels=range(0,(len(outcome))),output_dict=True\n",
        "    )\n",
        "    jsma_f1_macros.append(DICT['macro avg']['f1-score'])\n",
        "    print_classification_report(jsma_predictions, y_test, 'class_report_' + str(name) +'-JSMA.tex', str(name) + 'Classification Report (JSMA)')\n",
        "\n",
        "print(\"Name, original f1_macro, JSMA f1 macro\")\n",
        "for i in range(0,len(names)):\n",
        "  print(names[i],original_f1_macros[i],jsma_f1_macros[i])\n",
        "\n",
        "robustness_df = pd.DataFrame(np.column_stack([names, original_f1_macros, jsma_f1_macros]), \n",
        "                               columns=['Model Name', 'Original F1-Score Macro Avg', 'Adversarial F1-Score Macro Avg'])\n",
        "\n",
        "robustness_df[[\"Original F1-Score Macro Avg\", \"Adversarial F1-Score Macro Avg\"]] = robustness_df[[\"Original F1-Score Macro Avg\", \"Adversarial F1-Score Macro Avg\"]].apply(pd.to_numeric)\n",
        "rounded_decimals = robustness_df.round(decimals=2)\n",
        "print(rounded_decimals)\n",
        "print(rounded_decimals.sort_values('Adversarial F1-Score Macro Avg'))\n",
        "sorted_df = rounded_decimals.sort_values('Adversarial F1-Score Macro Avg')\n",
        "\n",
        "print(sorted_df.to_latex(caption='Model Types sorted by Adversarial F1-Score Macro Avg'))\n",
        "with open('modeltype_f1_score_amacro_avg.tex', 'w') as f:\n",
        "  f.write(sorted_df.to_latex(caption='Model Types sorted by Adversarial F1-Score Macro Avg'))\n",
        "\n",
        "conditions = []\n",
        "for i in range (0,len(names)):\n",
        "  conditions.append('Original')\n",
        "for i in range (0,len(names)):\n",
        "  conditions.append('Adversarial')\n",
        "\n",
        "\n",
        "orig_df = pd.DataFrame(np.column_stack([names, original_f1_macros]), \n",
        "                               columns=['Model Name', 'F1-Score'])\n",
        "adversarial_df = pd.DataFrame(np.column_stack([names, jsma_f1_macros]), \n",
        "                               columns=['Model Name', 'F1-Score'])\n",
        "\n",
        "group_frames = [orig_df,adversarial_df]\n",
        "group_df = pd.concat(group_frames)\n",
        "group_df['Conditions'] = conditions\n",
        "group_df[['F1-Score']] = group_df[['F1-Score']].apply(pd.to_numeric)\n",
        "print(group_df)\n",
        "group_df = group_df.round(decimals=2)\n",
        "group_df = group_df.sort_values('F1-Score')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#set seaborn plotting aesthetics\n",
        "sns.set(style='white')\n",
        "\n",
        "#create grouped bar chart\n",
        "sns.barplot(x='Model Name', y='F1-Score', hue='Conditions', hue_order={'Original','Adversarial'}, data=group_df,\n",
        "            palette=['purple', 'steelblue'])\n",
        "\n",
        "#add overall title\n",
        "plt.title('F1-Score Macro Avg (Normal Vs. Adversarial)', fontsize=16)\n",
        "\n",
        "#add axis titles\n",
        "plt.xlabel('Model Name')\n",
        "plt.ylabel('F1-Score Macro Avg')\n",
        "\n",
        "#rotate x-axis labels\n",
        "plt.xticks(rotation=90)\n",
        "# Put the legend out of the figure\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
        "#save the plot\n",
        "basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "savename='F1-Score-complete-Plot.png'\n",
        "filename=basedir+savename\n",
        "plt.savefig(filename,dpi=300,bbox_inches='tight')\n"
      ],
      "metadata": {
        "id": "6LimQ2nWDwyD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parents = [ 0, 1, 2, 3, 4]\n",
        "\n",
        "\n",
        "for parent in parents:\n",
        "  layer2_preds =[]\n",
        "  ground_truth = []\n",
        "  for i in range(0,len(y_test)):\n",
        "    if (parent_dict[y_test[i]] == parent):\n",
        "      layer2_preds.append(integer_predictions[i])\n",
        "      ground_truth.append(y_test[i])\n",
        "    else:\n",
        "      pass\n",
        "  print(\"--------------------\")\n",
        "  print(layer2_preds)\n",
        "  print(\"--------------------\")\n",
        "  print(ground_truth)\n",
        "  print(\"--------------------\")\n",
        "  cm = confusion_matrix(layer2_preds,ground_truth)\n",
        "  plot_cm(cm, 'Layer2 Children of Parent' + str(parent) + ' (Original)',str(parent) + '-Original.png')\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "HLEFsuBQ2YlS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split training and test subsets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#Train test split\n",
        "\n",
        "testSize = 0.3\n",
        "\n",
        "print(len(x_scaled))\n",
        "print(len(hier_labels))\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_scaled, hier_labels, test_size = testSize, shuffle=True, random_state = 42)\n",
        "# X_train, X_test, y_train, y_test = train_test_split(\n",
        "#     X, y, test_size=0.3, random_state=42\n",
        "# )\n",
        "\n",
        "print(y_train)\n",
        "\n",
        "coarse_label = np.empty(len(x_train),dtype=object)\n",
        "finer_label = np.empty(len(x_train),dtype=object)\n",
        "\n",
        "for i in range(0,len(y_train)):\n",
        "\n",
        "  coarse_label[i] = y_train[i][0]\n",
        "  finer_label[i] = y_train[i][1]\n",
        "\n",
        "print(coarse_label[0])\n",
        "print(finer_label[0])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "print(y_test)\n",
        "\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(coarse_label)\n",
        "print(coarse_label[0])\n",
        "coarse_label= le.transform(coarse_label) # multi-class \n",
        "print(coarse_label[0])\n",
        "\n",
        "le.fit(finer_label)\n",
        "print(finer_label[0])\n",
        "finer_label= le.transform(finer_label) # multi-class \n",
        "print(finer_label[0])"
      ],
      "metadata": {
        "id": "wyQXb0iu7BtV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build pipeline\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from hiclass import LocalClassifierPerNode, LocalClassifierPerParentNode, LocalClassifierPerLevel\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# base_classifier = LogisticRegression(\n",
        "#     random_state=0,\n",
        "#     max_iter=10000,\n",
        "#     n_jobs=1,\n",
        "# )\n",
        "\n",
        "base_classifier = MLPClassifier(hidden_layer_sizes=(128,64,15),activation ='relu',solver='adam', max_iter=300, shuffle=False,random_state=1, verbose=False) # Converges around 1600 iters / Achieves similar accuracy to keras at 300 iters\n",
        "\n",
        "LCPPN = 0\n",
        "LCPN  = 0 \n",
        "LCPL  = 0\n",
        "\n",
        "lcpn = LocalClassifierPerParentNode(\n",
        "    local_classifier=base_classifier,\n",
        "    verbose=20,\n",
        "    n_jobs=1,\n",
        ")\n",
        "LCPPN = 1\n",
        "\n",
        "# lcpn = LocalClassifierPerNode(\n",
        "#     local_classifier=base_classifier,\n",
        "#     verbose=20,\n",
        "#     n_jobs=1,\n",
        "# )\n",
        "# LCPN = 1\n",
        "\n",
        "# lcpn = LocalClassifierPerLevel(\n",
        "#     local_classifier=base_classifier,\n",
        "#     verbose=20,\n",
        "#     n_jobs=1,\n",
        "# )\n",
        "# LCPL =1 \n",
        "\n",
        "\n",
        "pipeline = Pipeline([\n",
        "    # ('count', CountVectorizer()),\n",
        "    # ('tfidf', TfidfTransformer()),\n",
        "    ('model', lcpn),\n",
        "])\n",
        "\n",
        "\n",
        "\n",
        "# Train classifier\n",
        "pipeline.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "hje4vUav7XlZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict\n",
        "predictions = lcpn.predict(x_test)"
      ],
      "metadata": {
        "id": "mJPwH9zx7iXx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def unique(list1):\n",
        "    x = np.array(list1)\n",
        "    return (np.unique(x).tolist)\n",
        "\n",
        "# print(y_test)\n",
        "# print(predictions)\n",
        "coarse_ground_truth = []\n",
        "coarse_predictions = []\n",
        "\n",
        "fine_ground_truth = []\n",
        "fine_predictions = []\n",
        "\n",
        "for i in range(0,len(y_test)):\n",
        "  coarse_ground_truth.append(y_test[i][0])\n",
        "  coarse_predictions.append(predictions[i][0])\n",
        "  fine_ground_truth.append(y_test[i][1])\n",
        "  fine_predictions.append(predictions[i][1])\n",
        "print(coarse_ground_truth)\n",
        "cm = confusion_matrix(coarse_ground_truth,coarse_predictions)\n",
        "labels = unique(coarse_ground_truth)\n",
        "print(cm)\n",
        "\n",
        "parents = [ 0, 1, 2, 3, 4]\n",
        "\n",
        "\n",
        "for parent in parents:\n",
        "  layer2_preds =[]\n",
        "  ground_truth = []\n",
        "  for i in range(0,len(y_test)):\n",
        "    \n",
        "    if (parent_dict[int(y_test[i][1])] == parent):\n",
        "      layer2_preds.append(fine_predictions[i])\n",
        "      ground_truth.append(fine_ground_truth[i])\n",
        "    else:\n",
        "      pass\n",
        "  print(\"--------------------\")\n",
        "  print(layer2_preds)\n",
        "  print(\"--------------------\")\n",
        "  print(ground_truth)\n",
        "  print(\"--------------------\")\n",
        "  cm = confusion_matrix(layer2_preds,ground_truth)\n",
        "  plot_cm(cm, 'Layer2 Children of Parent' + str(parent) + ' (Original)',str(parent) + '-Original.png')\n",
        "\n",
        "\n",
        "if(LCPPN):\n",
        "  plot_cm_hier(cm,'Clustered Hierarchical MLP (LCPPN) \\n Ordinary Traffic \\n Coarse Labels\\n', [0,1,2,3,4], 'ConfusionMatrix-Clustered-complete-Hier-LCPPN-MLPCoarse.png')\n",
        "  # plot_cm_hier(cm,'Clustered Hierarchical MLP (LCPPN) \\n Ordinary Traffic \\n Coarse Labels\\n', [\"FTP/SSHPatator + DoSsloworis\",\"WebAttacks\", \"DoS + Heartbleed\",\"Portscan + Bot + Infiltration\",\"Benign + DDoS\"], 'ConfusionMatrix-Clustered-Hier-LCPPN-MLPCoarse.png')\n",
        "\n",
        "  cm = confusion_matrix(fine_ground_truth,fine_predictions)\n",
        "\n",
        "  \n",
        "\n",
        "  print(cm)\n",
        "  plot_cm(cm,'Clustered Hierarchical MLP (LCPPN) \\n Ordinary Traffic \\n Fine Labels\\n','ConfusionMatrix-Clustered-complete-Hier-LCPPN-MLPFine.png')\n",
        "# elif(LCPN):\n",
        "# #   plot_cm_thetahier(cm,'Hierarchical MLP (LCPN) \\n Targeted JSMA \\n Coarse Labels\\n', coarse_ground_truth, 'ConfusionMatrix-Hier-LCPN-MLPCoarse-TARGETED-JSMA.png')\n",
        "\n",
        "# #   cm = confusion_matrix(fine_ground_truth,fine_predictions)\n",
        "# #   print(cm)\n",
        "# #   plot_cm_theta(cm,'Hierarchical MLP (LCPN) \\n Targeted JSMA \\n Fine Labels\\n','ConfusionMatrix-Hier-LCPN-MLPFine-TARGETED-JSMA.png')\n",
        "# # elif(LCPL): \n",
        "#   plot_cm_thetahier(cm,'Hierarchical MLP (LCPL) \\n Targeted JSMA \\n Coarse Labels\\n', coarse_ground_truth, 'ConfusionMatrix-Hier-LCPL-MLPCoarse-TARGETED-JSMA.png')\n",
        "\n",
        "#   cm = confusion_matrix(fine_ground_truth,fine_predictions)\n",
        "#   print(cm)\n",
        "#   plot_cm_theta(cm,'Hierarchical MLP (LCPL) \\n Targeted JSMA \\n Fine Labels\\n','ConfusionMatrix-Hier-LCPL-MLPFine-TARGETED-JSMA.png')\n",
        "# Compute metrics\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from hiclass.metrics import f1\n",
        "\n",
        "# print(y_test.shape)\n",
        "\n",
        "# print(y_test[0])\n",
        "# print(y_test[1])\n",
        "\n",
        "y_test_df = pd.DataFrame(y_test)\n",
        "# print(y_test)\n",
        "predictions_df = pd.DataFrame(predictions)\n",
        "\n",
        "\n",
        "# print(y_test)\n",
        "# print(predictions[0][0])\n",
        "# # \n",
        "# # print(y_test[:, col])\n",
        "# print(predictions[:, col])\n",
        "# predictions = predictions.reshape(113,2)\n",
        "# print(predictions)\n",
        "\n",
        "# print(predictions_df[0])\n",
        "# print(y_test_df[0])\n",
        "# print(predictions_df[1])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "print(\"Level\\tf1_macro\\t\\tf1_micro\\t\\tf1_weighted\\t\\tf1_hierarchical\\n\")\n",
        "for col in range(2):\n",
        "    f1_macro = f1_score(y_test_df[col], predictions_df[col], complete=\"macro\")\n",
        "    f1_micro = f1_score(y_test_df[col], predictions_df[col], complete=\"micro\")\n",
        "    f1_weighted = f1_score(y_test_df[col], predictions_df[col], complete=\"weighted\")\n",
        "    f1_hierarchical = f1(y_test_df[col], predictions_df[col])\n",
        "    print(f\"{col + 1}\\t{f1_macro}\\t{f1_micro}\\t{f1_weighted}\\t{f1_hierarchical}\\n\")\n",
        "\n",
        "HierMLPNormalTrafficF1 = f1(y_test_df[0], predictions_df[0])\n",
        "print(HierMLPNormalTrafficF1)"
      ],
      "metadata": {
        "id": "vmmaBCDH78KW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Load in the x_test_jsma numpy \n",
        "# savename='x_test_jsma.npy'\n",
        "# basedir='mount/My Drive/Colab Notebooks/Figures/'\n",
        "# filename=basedir+savename\n",
        "# print(\"Reading file\")\n",
        "# x_test_jsma = np.load(filename)\n",
        "\n",
        "# # Predict\n",
        "predictions = lcpn.predict(x_test_jsma)"
      ],
      "metadata": {
        "id": "IC__JEHJWBPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print(y_test)\n",
        "# print(predictions)\n",
        "coarse_ground_truth = []\n",
        "coarse_predictions = []\n",
        "\n",
        "fine_ground_truth = []\n",
        "fine_predictions = []\n",
        "\n",
        "for i in range(0,len(y_test)):\n",
        "  coarse_ground_truth.append(y_test[i][0])\n",
        "  coarse_predictions.append(predictions[i][0])\n",
        "  fine_ground_truth.append(y_test[i][1])\n",
        "  fine_predictions.append(predictions[i][1])\n",
        "print(coarse_ground_truth)\n",
        "cm = confusion_matrix(coarse_ground_truth,coarse_predictions)\n",
        "labels = unique(coarse_ground_truth)\n",
        "print(cm)\n",
        "if(LCPPN):\n",
        "  plot_cm_thetahier(cm,'Clustered Hierarchical MLP (LCPPN) \\n JSMA Samples \\n Coarse Labels\\n', [0,1,2,3,4], 'ConfusionMatrix-Clustered-complete-Hier-LCPPN-JSMA-Coarse.png')\n",
        "  # [\"FTP/SSHPatator + DoSsloworis\",\"WebAttacks\", \"DoS + Heartbleed\",\"Portscan + Bot + Infiltration\",\"Benign + DDoS\"]\n",
        "  plot_cm_thetahier(cm,'Clustered Hierarchical MLP (LCPPN) \\n JSMA Samples \\n Coarse Labels\\n', [\"FTP/SSHPatator + DoSsloworis\",\"WebAttacks\", \"DoS + Heartbleed\",\"Portscan + Bot + Infiltration\",\"Benign + DDoS\"], 'ConfusionMatrix-Clustered-Hier-LCPPN-JSMA-Coarse.png')\n",
        "  cm = confusion_matrix(fine_ground_truth,fine_predictions)\n",
        "  print(cm)\n",
        "  plot_cm_theta(cm,'Clustered Hierarchical MLP (LCPPN) \\n JSMA Samples \\n Fine Labels\\n','ConfusionMatrix-Clustered-complete-Hier-LCPPN-JSMA-Fine.png')\n",
        "# elif(LCPN):\n",
        "# #   plot_cm_thetahier(cm,'Hierarchical MLP (LCPN) \\n Targeted JSMA \\n Coarse Labels\\n', coarse_ground_truth, 'ConfusionMatrix-Hier-LCPN-MLPCoarse-TARGETED-JSMA.png')\n",
        "\n",
        "# #   cm = confusion_matrix(fine_ground_truth,fine_predictions)\n",
        "# #   print(cm)\n",
        "# #   plot_cm_theta(cm,'Hierarchical MLP (LCPN) \\n Targeted JSMA \\n Fine Labels\\n','ConfusionMatrix-Hier-LCPN-MLPFine-TARGETED-JSMA.png')\n",
        "# # elif(LCPL): \n",
        "#   plot_cm_thetahier(cm,'Hierarchical MLP (LCPL) \\n Targeted JSMA \\n Coarse Labels\\n', coarse_ground_truth, 'ConfusionMatrix-Hier-LCPL-MLPCoarse-TARGETED-JSMA.png')\n",
        "\n",
        "#   cm = confusion_matrix(fine_ground_truth,fine_predictions)\n",
        "#   print(cm)\n",
        "#   plot_cm_theta(cm,'Hierarchical MLP (LCPL) \\n Targeted JSMA \\n Fine Labels\\n','ConfusionMatrix-Hier-LCPL-MLPFine-TARGETED-JSMA.png')\n",
        "# Compute metrics\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from hiclass.metrics import f1\n",
        "\n",
        "# print(y_test.shape)\n",
        "\n",
        "# print(y_test[0])\n",
        "# print(y_test[1])\n",
        "\n",
        "y_test_df = pd.DataFrame(y_test)\n",
        "# print(y_test)\n",
        "predictions_df = pd.DataFrame(predictions)\n",
        "\n",
        "\n",
        "# print(y_test)\n",
        "# print(predictions[0][0])\n",
        "# # \n",
        "# # print(y_test[:, col])\n",
        "# print(predictions[:, col])\n",
        "# predictions = predictions.reshape(113,2)\n",
        "# print(predictions)\n",
        "\n",
        "# print(predictions_df[0])\n",
        "# print(y_test_df[0])\n",
        "# print(predictions_df[1])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "print(\"Level\\tf1_macro\\t\\tf1_micro\\t\\tf1_weighted\\t\\tf1_hierarchical\\n\")\n",
        "for col in range(2):\n",
        "    f1_macro = f1_score(y_test_df[col], predictions_df[col], complete=\"macro\")\n",
        "    f1_micro = f1_score(y_test_df[col], predictions_df[col], complete=\"micro\")\n",
        "    f1_weighted = f1_score(y_test_df[col], predictions_df[col], complete=\"weighted\")\n",
        "    f1_hierarchical = f1(y_test_df[col], predictions_df[col])\n",
        "    print(f\"{col + 1}\\t{f1_macro}\\t{f1_micro}\\t{f1_weighted}\\t{f1_hierarchical}\\n\")\n",
        "\n",
        "HierMLPNormalTrafficF1 = f1(y_test_df[0], predictions_df[0])\n",
        "print(HierMLPNormalTrafficF1)"
      ],
      "metadata": {
        "id": "PbGHH28kaz9r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qZU-dx4fWhWh"
      },
      "execution_count": null,
      "outputs": []
    }
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