Build_Hioerarchy_Ward.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_Hioerarchy_Ward.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": "220cd837-6cf9-4c22-b164-294c0731e9e1"
      },
      "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": "83b13e5d-3c4d-46ca-c352-3e5ade529f74"
      },
      "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 15.3 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: 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: 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: 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: 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: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.12->lime) (2.6.3)\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: 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: 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: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->lime) (3.1.0)\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",
            "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=de4dec06f174cd98075da1d268b55172f817a7615b7cd779f6b40fbbc31ecb96\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": "09c4e368-2ef8-4fc1-b76f-b0ff81df53db"
      },
      "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",
              "  <tbody>\n",
              "    <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",
              "      <td>6.0</td>\n",
              "      <td>0.00000</td>\n",
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              "      <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>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-a5a0cd30-7738-470a-bc10-bd968f94b6e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \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",
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              "\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",
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              "\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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              "          document.querySelector('#df-a5a0cd30-7738-470a-bc10-bd968f94b6e8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-a5a0cd30-7738-470a-bc10-bd968f94b6e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "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": "b427d9a9-d7c7-4ce3-c25f-00cfd12adba9"
      },
      "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": "1d1fc5d2-f0a5-4330-8823-ebcddc1177fa"
      },
      "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",
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              "\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",
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              "...                 ...             ...  ...                 ...   \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",
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              "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",
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              "...                          ...                ...                    ...   \n",
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              "692701                  0.000000           0.000023                    1.0   \n",
              "692702                  0.000000           0.000014                    1.0   \n",
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              "        Active Mean   Active Std   Active Max  \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": "00b16c74-e418-4eb9-bee3-f996bddd06a9"
      },
      "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": "beeba411-298e-4560-d25c-d99c15b694a8"
      },
      "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.         0.         ... 0.         0.         0.        ]\n",
            " [0.02064516 0.00807964 0.         ... 0.         0.         0.        ]\n",
            " [0.01978495 0.00774299 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": "ce9aca1e-90aa-456b-c6a4-fe3dee75e223"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
            "0                     0.000000                 0.000000   \n",
            "1                     0.020645                 0.008080   \n",
            "2                     0.019785                 0.007743   \n",
            "3                     0.016774                 0.006565   \n",
            "4                     0.020645                 0.008080   \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                          0.0               0.000307                0.002072   \n",
            "1                          0.0               0.002458                0.016575   \n",
            "2                          0.0               0.004659                0.031423   \n",
            "3                          0.0               0.002816                0.018992   \n",
            "4                          0.0               0.002458                0.016575   \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                      0.001034                     0.0      0.111921   \n",
            "1                      0.008275                     0.0      0.111922   \n",
            "2                      0.015688                     0.0      0.111922   \n",
            "3                      0.009482                     0.0      0.112428   \n",
            "4                      0.008275                     0.0      0.111921   \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    2.471488e-03  ...              0.000010   \n",
            "1              0.333339    4.940583e-04  ...              0.000003   \n",
            "2              0.333340    3.997500e-04  ...              0.000003   \n",
            "3              0.337526    5.499999e-07  ...              0.000007   \n",
            "4              0.333333    1.858334e-01  ...              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             2.746192e-08                0.125015                  0.000015   \n",
            "1             7.323179e-08                0.000000                  0.000000   \n",
            "2             1.388353e-07                0.000000                  0.000000   \n",
            "3             1.678229e-07                0.000000                  0.000000   \n",
            "4             1.464636e-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                0.000000                    1.0     0.000000          0.0   \n",
            "1                0.000000                    1.0     0.000000          0.0   \n",
            "2                0.000000                    1.0     0.000000          0.0   \n",
            "3                0.000005                    1.0     0.000000          0.0   \n",
            "4                0.000005                    1.0     0.000448          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          0.000000  BENIGN  \n",
            "1          0.000000  BENIGN  \n",
            "2          0.000000  BENIGN  \n",
            "3          0.000000  BENIGN  \n",
            "4          0.000448  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": "fbad6fe2-c04d-4ca2-967c-fae09eeb8030"
      },
      "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 20.9 MB/s \n",
            "\u001b[?25hCollecting 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 62.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.08) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox==1.08) (4.64.0)\n",
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            "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",
            "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 50.2 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 1.4 MB/s \n",
            "\u001b[?25hRequirement 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",
            "Requirement 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",
            "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": "ea74cc2b-9a0a-4426-f326-b4012c496809"
      },
      "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": "6772d732-e498-405a-e244-8ba5c64d0a07"
      },
      "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 [==============================] - 0s 2ms/sample - loss: 2.6763 - accuracy: 0.1221 - val_loss: 2.6113 - val_accuracy: 0.1327 - lr: 0.0010\n",
            "Epoch 2/1600\n",
            "262/262 [==============================] - 0s 175us/sample - loss: 2.5780 - accuracy: 0.1832 - val_loss: 2.5317 - val_accuracy: 0.1947 - lr: 0.0010\n",
            "Epoch 3/1600\n",
            "262/262 [==============================] - 0s 176us/sample - loss: 2.4965 - accuracy: 0.2443 - val_loss: 2.4516 - val_accuracy: 0.2389 - lr: 0.0010\n",
            "Epoch 4/1600\n"
          ]
        },
        {
          "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": [
            "262/262 [==============================] - 0s 195us/sample - loss: 2.4126 - accuracy: 0.2824 - val_loss: 2.3677 - val_accuracy: 0.2389 - lr: 0.0010\n",
            "Epoch 5/1600\n",
            "262/262 [==============================] - 0s 243us/sample - loss: 2.3273 - accuracy: 0.3130 - val_loss: 2.2872 - val_accuracy: 0.2832 - lr: 0.0010\n",
            "Epoch 6/1600\n",
            "262/262 [==============================] - 0s 255us/sample - loss: 2.2440 - accuracy: 0.3359 - val_loss: 2.2041 - val_accuracy: 0.3628 - lr: 0.0010\n",
            "Epoch 7/1600\n",
            "262/262 [==============================] - 0s 249us/sample - loss: 2.1609 - accuracy: 0.4046 - val_loss: 2.1198 - val_accuracy: 0.3982 - lr: 0.0010\n",
            "Epoch 8/1600\n",
            "262/262 [==============================] - 0s 245us/sample - loss: 2.0781 - accuracy: 0.4275 - val_loss: 2.0387 - val_accuracy: 0.4159 - lr: 0.0010\n",
            "Epoch 9/1600\n",
            "262/262 [==============================] - 0s 257us/sample - loss: 1.9945 - accuracy: 0.4504 - val_loss: 1.9520 - val_accuracy: 0.3805 - lr: 0.0010\n",
            "Epoch 10/1600\n",
            "262/262 [==============================] - 0s 183us/sample - loss: 1.9131 - accuracy: 0.4542 - val_loss: 1.8681 - val_accuracy: 0.4159 - lr: 0.0010\n",
            "Epoch 11/1600\n",
            "262/262 [==============================] - 0s 189us/sample - loss: 1.8358 - accuracy: 0.5038 - val_loss: 1.7906 - val_accuracy: 0.4690 - lr: 0.0010\n",
            "Epoch 12/1600\n",
            "262/262 [==============================] - 0s 169us/sample - loss: 1.7623 - accuracy: 0.5267 - val_loss: 1.7187 - val_accuracy: 0.4690 - lr: 0.0010\n",
            "Epoch 13/1600\n",
            "262/262 [==============================] - 0s 154us/sample - loss: 1.6923 - accuracy: 0.5305 - val_loss: 1.6540 - val_accuracy: 0.4867 - lr: 0.0010\n",
            "Epoch 14/1600\n",
            "262/262 [==============================] - 0s 191us/sample - loss: 1.6300 - accuracy: 0.5496 - val_loss: 1.6011 - val_accuracy: 0.4956 - lr: 0.0010\n",
            "Epoch 15/1600\n",
            "262/262 [==============================] - 0s 217us/sample - loss: 1.5714 - accuracy: 0.5573 - val_loss: 1.5496 - val_accuracy: 0.5133 - lr: 0.0010\n",
            "Epoch 16/1600\n",
            "262/262 [==============================] - 0s 168us/sample - loss: 1.5175 - accuracy: 0.5687 - val_loss: 1.5047 - val_accuracy: 0.5133 - lr: 0.0010\n",
            "Epoch 17/1600\n",
            "262/262 [==============================] - 0s 182us/sample - loss: 1.4669 - accuracy: 0.5687 - val_loss: 1.4618 - val_accuracy: 0.5221 - lr: 0.0010\n",
            "Epoch 18/1600\n",
            "262/262 [==============================] - 0s 171us/sample - loss: 1.4196 - accuracy: 0.5802 - val_loss: 1.4241 - val_accuracy: 0.5221 - lr: 0.0010\n",
            "Epoch 19/1600\n",
            "262/262 [==============================] - 0s 156us/sample - loss: 1.3754 - accuracy: 0.5802 - val_loss: 1.3919 - val_accuracy: 0.5221 - lr: 0.0010\n",
            "Epoch 20/1600\n",
            "262/262 [==============================] - 0s 192us/sample - loss: 1.3345 - accuracy: 0.5687 - val_loss: 1.3604 - val_accuracy: 0.5310 - lr: 0.0010\n",
            "Epoch 21/1600\n",
            "262/262 [==============================] - 0s 229us/sample - loss: 1.2956 - accuracy: 0.5763 - val_loss: 1.3341 - val_accuracy: 0.5310 - lr: 0.0010\n",
            "Epoch 22/1600\n",
            "262/262 [==============================] - 0s 182us/sample - loss: 1.2599 - accuracy: 0.5802 - val_loss: 1.3081 - val_accuracy: 0.5487 - lr: 0.0010\n",
            "Epoch 23/1600\n",
            "262/262 [==============================] - 0s 218us/sample - loss: 1.2256 - accuracy: 0.5687 - val_loss: 1.2858 - val_accuracy: 0.5575 - lr: 0.0010\n",
            "Epoch 24/1600\n",
            "262/262 [==============================] - 0s 202us/sample - loss: 1.1943 - accuracy: 0.5611 - val_loss: 1.2646 - val_accuracy: 0.5575 - lr: 0.0010\n",
            "Epoch 25/1600\n",
            "262/262 [==============================] - 0s 249us/sample - loss: 1.1644 - accuracy: 0.5687 - val_loss: 1.2448 - val_accuracy: 0.5575 - lr: 0.0010\n",
            "Epoch 26/1600\n",
            "262/262 [==============================] - 0s 195us/sample - loss: 1.1373 - accuracy: 0.5725 - val_loss: 1.2254 - val_accuracy: 0.5841 - lr: 0.0010\n",
            "Epoch 27/1600\n",
            "262/262 [==============================] - 0s 173us/sample - loss: 1.1116 - accuracy: 0.5611 - val_loss: 1.2112 - val_accuracy: 0.5841 - lr: 0.0010\n",
            "Epoch 28/1600\n",
            "262/262 [==============================] - 0s 179us/sample - loss: 1.0880 - accuracy: 0.5954 - val_loss: 1.1949 - val_accuracy: 0.6195 - lr: 0.0010\n",
            "Epoch 29/1600\n",
            "262/262 [==============================] - 0s 200us/sample - loss: 1.0653 - accuracy: 0.5916 - val_loss: 1.1807 - val_accuracy: 0.6283 - lr: 0.0010\n",
            "Epoch 30/1600\n",
            "262/262 [==============================] - 0s 199us/sample - loss: 1.0446 - accuracy: 0.5992 - val_loss: 1.1674 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 31/1600\n",
            "262/262 [==============================] - 0s 182us/sample - loss: 1.0249 - accuracy: 0.6336 - val_loss: 1.1541 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 32/1600\n",
            "262/262 [==============================] - 0s 189us/sample - loss: 1.0070 - accuracy: 0.6374 - val_loss: 1.1436 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 33/1600\n",
            "262/262 [==============================] - 0s 202us/sample - loss: 0.9892 - accuracy: 0.6489 - val_loss: 1.1331 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 34/1600\n",
            "262/262 [==============================] - 0s 164us/sample - loss: 0.9730 - accuracy: 0.6527 - val_loss: 1.1225 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 35/1600\n",
            "262/262 [==============================] - 0s 244us/sample - loss: 0.9571 - accuracy: 0.6527 - val_loss: 1.1135 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 36/1600\n",
            "262/262 [==============================] - 0s 405us/sample - loss: 0.9424 - accuracy: 0.6603 - val_loss: 1.1057 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 37/1600\n",
            "262/262 [==============================] - 0s 324us/sample - loss: 0.9283 - accuracy: 0.6603 - val_loss: 1.0968 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 38/1600\n",
            "262/262 [==============================] - 0s 237us/sample - loss: 0.9154 - accuracy: 0.6641 - val_loss: 1.0896 - val_accuracy: 0.6372 - lr: 0.0010\n",
            "Epoch 39/1600\n",
            "262/262 [==============================] - 0s 289us/sample - loss: 0.9018 - accuracy: 0.6641 - val_loss: 1.0817 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 40/1600\n",
            "262/262 [==============================] - 0s 246us/sample - loss: 0.8902 - accuracy: 0.6679 - val_loss: 1.0732 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 41/1600\n",
            "262/262 [==============================] - 0s 307us/sample - loss: 0.8773 - accuracy: 0.6718 - val_loss: 1.0655 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 42/1600\n",
            "262/262 [==============================] - 0s 243us/sample - loss: 0.8666 - accuracy: 0.6641 - val_loss: 1.0591 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 43/1600\n",
            "262/262 [==============================] - 0s 298us/sample - loss: 0.8557 - accuracy: 0.6718 - val_loss: 1.0505 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 44/1600\n",
            "262/262 [==============================] - 0s 234us/sample - loss: 0.8455 - accuracy: 0.6679 - val_loss: 1.0481 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 45/1600\n",
            "262/262 [==============================] - 0s 218us/sample - loss: 0.8343 - accuracy: 0.6718 - val_loss: 1.0400 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 46/1600\n",
            "262/262 [==============================] - 0s 236us/sample - loss: 0.8249 - accuracy: 0.6794 - val_loss: 1.0353 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 47/1600\n",
            "262/262 [==============================] - 0s 242us/sample - loss: 0.8153 - accuracy: 0.6794 - val_loss: 1.0300 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 48/1600\n",
            "262/262 [==============================] - 0s 247us/sample - loss: 0.8056 - accuracy: 0.6870 - val_loss: 1.0253 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 49/1600\n",
            "262/262 [==============================] - 0s 236us/sample - loss: 0.7975 - accuracy: 0.6794 - val_loss: 1.0185 - val_accuracy: 0.6460 - lr: 0.0010\n",
            "Epoch 50/1600\n",
            "262/262 [==============================] - 0s 319us/sample - loss: 0.7877 - accuracy: 0.6832 - val_loss: 1.0160 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 51/1600\n",
            "262/262 [==============================] - 0s 351us/sample - loss: 0.7804 - accuracy: 0.6870 - val_loss: 1.0100 - val_accuracy: 0.6637 - lr: 0.0010\n",
            "Epoch 52/1600\n",
            "262/262 [==============================] - 0s 262us/sample - loss: 0.7715 - accuracy: 0.6908 - val_loss: 1.0054 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 53/1600\n",
            "262/262 [==============================] - 0s 307us/sample - loss: 0.7639 - accuracy: 0.7023 - val_loss: 1.0029 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 54/1600\n",
            "262/262 [==============================] - 0s 349us/sample - loss: 0.7559 - accuracy: 0.7061 - val_loss: 0.9968 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 55/1600\n",
            "262/262 [==============================] - 0s 274us/sample - loss: 0.7485 - accuracy: 0.7023 - val_loss: 0.9931 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 56/1600\n",
            "262/262 [==============================] - 0s 224us/sample - loss: 0.7409 - accuracy: 0.7023 - val_loss: 0.9887 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 57/1600\n",
            "262/262 [==============================] - 0s 247us/sample - loss: 0.7342 - accuracy: 0.7061 - val_loss: 0.9865 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 58/1600\n",
            "262/262 [==============================] - 0s 333us/sample - loss: 0.7272 - accuracy: 0.7023 - val_loss: 0.9796 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 59/1600\n",
            "262/262 [==============================] - 0s 261us/sample - loss: 0.7196 - accuracy: 0.7176 - val_loss: 0.9790 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 60/1600\n",
            "262/262 [==============================] - 0s 329us/sample - loss: 0.7137 - accuracy: 0.7176 - val_loss: 0.9744 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 61/1600\n",
            "262/262 [==============================] - 0s 329us/sample - loss: 0.7067 - accuracy: 0.7214 - val_loss: 0.9720 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 62/1600\n",
            "262/262 [==============================] - 0s 269us/sample - loss: 0.7008 - accuracy: 0.7176 - val_loss: 0.9675 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 63/1600\n",
            "262/262 [==============================] - 0s 266us/sample - loss: 0.6944 - accuracy: 0.7176 - val_loss: 0.9658 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 64/1600\n",
            "262/262 [==============================] - 0s 218us/sample - loss: 0.6885 - accuracy: 0.7214 - val_loss: 0.9609 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 65/1600\n",
            "262/262 [==============================] - 0s 474us/sample - loss: 0.6829 - accuracy: 0.7137 - val_loss: 0.9570 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 66/1600\n",
            "262/262 [==============================] - 0s 269us/sample - loss: 0.6765 - accuracy: 0.7214 - val_loss: 0.9541 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 67/1600\n",
            "262/262 [==============================] - 0s 441us/sample - loss: 0.6712 - accuracy: 0.7099 - val_loss: 0.9497 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 68/1600\n",
            "262/262 [==============================] - 0s 322us/sample - loss: 0.6653 - accuracy: 0.7214 - val_loss: 0.9478 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 69/1600\n",
            "262/262 [==============================] - 0s 279us/sample - loss: 0.6597 - accuracy: 0.7176 - val_loss: 0.9439 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 70/1600\n",
            "262/262 [==============================] - 0s 383us/sample - loss: 0.6547 - accuracy: 0.7214 - val_loss: 0.9422 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 71/1600\n",
            "262/262 [==============================] - 0s 249us/sample - loss: 0.6494 - accuracy: 0.7176 - val_loss: 0.9381 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 72/1600\n",
            "262/262 [==============================] - 0s 342us/sample - loss: 0.6444 - accuracy: 0.7214 - val_loss: 0.9365 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 73/1600\n",
            "262/262 [==============================] - 0s 293us/sample - loss: 0.6391 - accuracy: 0.7290 - val_loss: 0.9337 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 74/1600\n",
            "262/262 [==============================] - 0s 234us/sample - loss: 0.6340 - accuracy: 0.7405 - val_loss: 0.9312 - val_accuracy: 0.6903 - lr: 0.0010\n",
            "Epoch 75/1600\n",
            "262/262 [==============================] - 0s 236us/sample - loss: 0.6294 - accuracy: 0.7328 - val_loss: 0.9287 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 76/1600\n",
            "262/262 [==============================] - 0s 302us/sample - loss: 0.6245 - accuracy: 0.7405 - val_loss: 0.9253 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 77/1600\n",
            "262/262 [==============================] - 0s 238us/sample - loss: 0.6198 - accuracy: 0.7481 - val_loss: 0.9240 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 78/1600\n",
            "262/262 [==============================] - 0s 283us/sample - loss: 0.6153 - accuracy: 0.7519 - val_loss: 0.9236 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 79/1600\n",
            "262/262 [==============================] - 0s 238us/sample - loss: 0.6107 - accuracy: 0.7443 - val_loss: 0.9196 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 80/1600\n",
            "262/262 [==============================] - 0s 242us/sample - loss: 0.6070 - accuracy: 0.7519 - val_loss: 0.9177 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 81/1600\n",
            "262/262 [==============================] - 0s 308us/sample - loss: 0.6014 - accuracy: 0.7481 - val_loss: 0.9168 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 82/1600\n",
            "262/262 [==============================] - 0s 263us/sample - loss: 0.5985 - accuracy: 0.7481 - val_loss: 0.9150 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 83/1600\n",
            "262/262 [==============================] - 0s 256us/sample - loss: 0.5933 - accuracy: 0.7443 - val_loss: 0.9127 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 84/1600\n",
            "262/262 [==============================] - 0s 274us/sample - loss: 0.5897 - accuracy: 0.7557 - val_loss: 0.9106 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 85/1600\n",
            "262/262 [==============================] - 0s 278us/sample - loss: 0.5852 - accuracy: 0.7443 - val_loss: 0.9104 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 86/1600\n",
            "262/262 [==============================] - 0s 303us/sample - loss: 0.5818 - accuracy: 0.7405 - val_loss: 0.9082 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 87/1600\n",
            "262/262 [==============================] - 0s 308us/sample - loss: 0.5773 - accuracy: 0.7557 - val_loss: 0.9081 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 88/1600\n",
            "262/262 [==============================] - 0s 267us/sample - loss: 0.5737 - accuracy: 0.7443 - val_loss: 0.9063 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 89/1600\n",
            "262/262 [==============================] - 0s 294us/sample - loss: 0.5700 - accuracy: 0.7481 - val_loss: 0.9047 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 90/1600\n",
            "262/262 [==============================] - 0s 252us/sample - loss: 0.5661 - accuracy: 0.7481 - val_loss: 0.9042 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 91/1600\n",
            "262/262 [==============================] - 0s 307us/sample - loss: 0.5623 - accuracy: 0.7481 - val_loss: 0.9023 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 92/1600\n",
            "262/262 [==============================] - 0s 271us/sample - loss: 0.5590 - accuracy: 0.7519 - val_loss: 0.9011 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 93/1600\n",
            "262/262 [==============================] - 0s 283us/sample - loss: 0.5549 - accuracy: 0.7519 - val_loss: 0.9006 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 94/1600\n",
            "262/262 [==============================] - 0s 375us/sample - loss: 0.5518 - accuracy: 0.7519 - val_loss: 0.8998 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 95/1600\n",
            "262/262 [==============================] - 0s 328us/sample - loss: 0.5480 - accuracy: 0.7481 - val_loss: 0.8989 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 96/1600\n",
            "262/262 [==============================] - 0s 277us/sample - loss: 0.5451 - accuracy: 0.7481 - val_loss: 0.8967 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 97/1600\n",
            "262/262 [==============================] - 0s 142us/sample - loss: 0.5410 - accuracy: 0.7519 - val_loss: 0.8999 - val_accuracy: 0.6991 - lr: 0.0010\n",
            "Epoch 98/1600\n",
            "262/262 [==============================] - 0s 144us/sample - loss: 0.5385 - accuracy: 0.7519 - val_loss: 0.8969 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 99/1600\n",
            "262/262 [==============================] - 0s 144us/sample - loss: 0.5343 - accuracy: 0.7443 - val_loss: 0.8994 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 100/1600\n",
            "262/262 [==============================] - 0s 362us/sample - loss: 0.5318 - accuracy: 0.7519 - val_loss: 0.8964 - val_accuracy: 0.7080 - lr: 0.0010\n",
            "Epoch 101/1600\n",
            "262/262 [==============================] - 0s 518us/sample - loss: 0.5278 - accuracy: 0.7557 - val_loss: 0.8957 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 102/1600\n",
            "262/262 [==============================] - 0s 141us/sample - loss: 0.5251 - accuracy: 0.7557 - val_loss: 0.8967 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 103/1600\n",
            "262/262 [==============================] - 0s 182us/sample - loss: 0.5216 - accuracy: 0.7557 - val_loss: 0.8961 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 104/1600\n",
            "262/262 [==============================] - 0s 140us/sample - loss: 0.5185 - accuracy: 0.7710 - val_loss: 0.8959 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 105/1600\n",
            "262/262 [==============================] - 0s 382us/sample - loss: 0.5160 - accuracy: 0.7595 - val_loss: 0.8951 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 106/1600\n",
            "262/262 [==============================] - 0s 217us/sample - loss: 0.5124 - accuracy: 0.7672 - val_loss: 0.8952 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 107/1600\n",
            "262/262 [==============================] - 0s 324us/sample - loss: 0.5096 - accuracy: 0.7710 - val_loss: 0.8929 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 108/1600\n",
            "262/262 [==============================] - 0s 141us/sample - loss: 0.5066 - accuracy: 0.7710 - val_loss: 0.8938 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 109/1600\n",
            "262/262 [==============================] - 0s 222us/sample - loss: 0.5032 - accuracy: 0.7710 - val_loss: 0.8946 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 110/1600\n",
            "262/262 [==============================] - 0s 215us/sample - loss: 0.5007 - accuracy: 0.7748 - val_loss: 0.8966 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 111/1600\n",
            "262/262 [==============================] - 0s 269us/sample - loss: 0.4987 - accuracy: 0.7824 - val_loss: 0.8926 - val_accuracy: 0.7168 - lr: 0.0010\n",
            "Epoch 112/1600\n",
            "262/262 [==============================] - 0s 167us/sample - loss: 0.4944 - accuracy: 0.7710 - val_loss: 0.8972 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 113/1600\n",
            "262/262 [==============================] - 0s 207us/sample - loss: 0.4927 - accuracy: 0.7824 - val_loss: 0.8956 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 114/1600\n",
            "262/262 [==============================] - 0s 212us/sample - loss: 0.4890 - accuracy: 0.7824 - val_loss: 0.8979 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 115/1600\n",
            "262/262 [==============================] - 0s 154us/sample - loss: 0.4874 - accuracy: 0.7824 - val_loss: 0.8948 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 116/1600\n",
            "262/262 [==============================] - 0s 206us/sample - loss: 0.4835 - accuracy: 0.7863 - val_loss: 0.8989 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 117/1600\n",
            "262/262 [==============================] - 0s 147us/sample - loss: 0.4822 - accuracy: 0.7824 - val_loss: 0.8949 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 118/1600\n",
            "262/262 [==============================] - 0s 154us/sample - loss: 0.4782 - accuracy: 0.7824 - val_loss: 0.8989 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 119/1600\n",
            "262/262 [==============================] - 0s 215us/sample - loss: 0.4769 - accuracy: 0.7939 - val_loss: 0.8961 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 120/1600\n",
            "262/262 [==============================] - 0s 189us/sample - loss: 0.4736 - accuracy: 0.7901 - val_loss: 0.8990 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 121/1600\n",
            "262/262 [==============================] - 0s 159us/sample - loss: 0.4716 - accuracy: 0.7939 - val_loss: 0.8952 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 122/1600\n",
            "262/262 [==============================] - 0s 151us/sample - loss: 0.4678 - accuracy: 0.7901 - val_loss: 0.9001 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 123/1600\n",
            "262/262 [==============================] - 0s 177us/sample - loss: 0.4674 - accuracy: 0.7977 - val_loss: 0.8983 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 124/1600\n",
            "262/262 [==============================] - 0s 188us/sample - loss: 0.4629 - accuracy: 0.8053 - val_loss: 0.9013 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 125/1600\n",
            "262/262 [==============================] - 0s 225us/sample - loss: 0.4621 - accuracy: 0.7977 - val_loss: 0.8990 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 126/1600\n",
            "262/262 [==============================] - 0s 215us/sample - loss: 0.4585 - accuracy: 0.7901 - val_loss: 0.9012 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 127/1600\n",
            "262/262 [==============================] - 0s 161us/sample - loss: 0.4569 - accuracy: 0.7939 - val_loss: 0.9014 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 128/1600\n",
            "262/262 [==============================] - 0s 163us/sample - loss: 0.4539 - accuracy: 0.8015 - val_loss: 0.9030 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 129/1600\n",
            "262/262 [==============================] - 0s 198us/sample - loss: 0.4522 - accuracy: 0.7977 - val_loss: 0.9032 - val_accuracy: 0.7434 - lr: 0.0010\n",
            "Epoch 130/1600\n",
            "262/262 [==============================] - 0s 296us/sample - loss: 0.4496 - accuracy: 0.7901 - val_loss: 0.9019 - val_accuracy: 0.7345 - lr: 0.0010\n",
            "Epoch 131/1600\n",
            "262/262 [==============================] - 0s 311us/sample - loss: 0.4473 - accuracy: 0.8053 - val_loss: 0.9062 - val_accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 132/1600\n",
            "262/262 [==============================] - 0s 143us/sample - loss: 0.4398 - accuracy: 0.8130 - val_loss: 0.9005 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 133/1600\n",
            "262/262 [==============================] - 0s 251us/sample - loss: 0.4380 - accuracy: 0.8244 - val_loss: 0.8997 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
            "Epoch 134/1600\n",
            "262/262 [==============================] - 0s 160us/sample - loss: 0.4358 - accuracy: 0.8244 - val_loss: 0.9023 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 135/1600\n",
            "262/262 [==============================] - 0s 194us/sample - loss: 0.4350 - accuracy: 0.8092 - val_loss: 0.9029 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 136/1600\n",
            "262/262 [==============================] - 0s 168us/sample - loss: 0.4340 - accuracy: 0.8206 - val_loss: 0.9033 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 137/1600\n",
            "262/262 [==============================] - 0s 151us/sample - loss: 0.4327 - accuracy: 0.8092 - val_loss: 0.9049 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 138/1600\n",
            "262/262 [==============================] - 0s 168us/sample - loss: 0.4317 - accuracy: 0.8130 - val_loss: 0.9046 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 139/1600\n",
            "262/262 [==============================] - 0s 122us/sample - loss: 0.4308 - accuracy: 0.8244 - val_loss: 0.9046 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 140/1600\n",
            "262/262 [==============================] - 0s 155us/sample - loss: 0.4296 - accuracy: 0.8168 - val_loss: 0.9063 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 141/1600\n",
            "262/262 [==============================] - 0s 122us/sample - loss: 0.4286 - accuracy: 0.8092 - val_loss: 0.9060 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 142/1600\n",
            "262/262 [==============================] - 0s 126us/sample - loss: 0.4275 - accuracy: 0.8244 - val_loss: 0.9068 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 143/1600\n",
            "262/262 [==============================] - 0s 135us/sample - loss: 0.4266 - accuracy: 0.8168 - val_loss: 0.9078 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 144/1600\n",
            "262/262 [==============================] - 0s 137us/sample - loss: 0.4255 - accuracy: 0.8130 - val_loss: 0.9091 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 145/1600\n",
            "262/262 [==============================] - 0s 134us/sample - loss: 0.4245 - accuracy: 0.8130 - val_loss: 0.9089 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 146/1600\n",
            "262/262 [==============================] - 0s 140us/sample - loss: 0.4232 - accuracy: 0.8244 - val_loss: 0.9094 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 147/1600\n",
            "262/262 [==============================] - 0s 135us/sample - loss: 0.4224 - accuracy: 0.8206 - val_loss: 0.9101 - val_accuracy: 0.7522 - lr: 5.0000e-04\n",
            "Epoch 148/1600\n",
            "262/262 [==============================] - 0s 146us/sample - loss: 0.4213 - accuracy: 0.8244 - val_loss: 0.9109 - val_accuracy: 0.7699 - lr: 5.0000e-04\n",
            "Epoch 149/1600\n",
            "262/262 [==============================] - 0s 179us/sample - loss: 0.4202 - accuracy: 0.8168 - val_loss: 0.9114 - val_accuracy: 0.7699 - lr: 5.0000e-04\n",
            "Epoch 150/1600\n",
            "262/262 [==============================] - 0s 163us/sample - loss: 0.4193 - accuracy: 0.8282 - val_loss: 0.9117 - val_accuracy: 0.7699 - lr: 5.0000e-04\n",
            "Epoch 151/1600\n",
            "262/262 [==============================] - 0s 226us/sample - loss: 0.4185 - accuracy: 0.8244 - val_loss: 0.9121 - val_accuracy: 0.7699 - lr: 5.0000e-04\n",
            "Epoch 152/1600\n",
            "262/262 [==============================] - 0s 177us/sample - loss: 0.4147 - accuracy: 0.8435 - val_loss: 0.9117 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 153/1600\n",
            "262/262 [==============================] - 0s 133us/sample - loss: 0.4143 - accuracy: 0.8473 - val_loss: 0.9110 - val_accuracy: 0.7699 - lr: 2.5000e-04\n",
            "Epoch 154/1600\n",
            "262/262 [==============================] - 0s 126us/sample - loss: 0.4137 - accuracy: 0.8473 - val_loss: 0.9110 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 155/1600\n",
            "262/262 [==============================] - 0s 127us/sample - loss: 0.4131 - accuracy: 0.8473 - val_loss: 0.9111 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 156/1600\n",
            "262/262 [==============================] - 0s 191us/sample - loss: 0.4126 - accuracy: 0.8473 - val_loss: 0.9115 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 157/1600\n",
            "262/262 [==============================] - 0s 207us/sample - loss: 0.4122 - accuracy: 0.8473 - val_loss: 0.9112 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 158/1600\n",
            "262/262 [==============================] - 0s 169us/sample - loss: 0.4116 - accuracy: 0.8473 - val_loss: 0.9111 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 159/1600\n",
            "262/262 [==============================] - 0s 196us/sample - loss: 0.4111 - accuracy: 0.8473 - val_loss: 0.9117 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 160/1600\n",
            "262/262 [==============================] - 0s 164us/sample - loss: 0.4105 - accuracy: 0.8473 - val_loss: 0.9119 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 161/1600\n",
            "262/262 [==============================] - 0s 218us/sample - loss: 0.4102 - accuracy: 0.8473 - val_loss: 0.9119 - val_accuracy: 0.7611 - lr: 2.5000e-04\n",
            "Epoch 161: 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": [
            "e2b6b7ccedf944798732d2a9b7c4d5a6",
            "88a41d55c7ba46c39b9a37019a41f1bb",
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            "15928bc1d3334dc4bdaf7313911be53f",
            "0aaf4a9e24024cb8808c89e692a51dd7",
            "ecbd0bc998de46638993798c3ffefbe1",
            "990f4e284f92476a80b4278fed131231",
            "1fb2473de3354e51b006257f06285681",
            "aae4d56c27ee455e825a463e3afa6b54",
            "1e1f5d5be6394aabac7ceb581b2ace1e",
            "fcb35df3e72248c09cf70ecad98ca220",
            "2dcda0d0df8448ea8aa517d42d20f7ab",
            "097e737adf8043f9847dab6b3831ebb1",
            "9e51713d124b4cacbf40e468a6d5d13e",
            "60b1482d442e4943a7171272fbced3bc"
          ]
        },
        "id": "BY9JegJ_eKOO",
        "outputId": "bc60f6b7-b87a-452f-c714-2cde3798ced1"
      },
      "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": "e2b6b7ccedf944798732d2a9b7c4d5a6"
            }
          },
          "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": "0aaf4a9e24024cb8808c89e692a51dd7"
            }
          },
          "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": "273987bf-32ea-4785-9fba-13fb81f1e98e"
      },
      "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>['61', '71', '57', '54', '56', '73', '60', '64', '67', '50', '51', '85', '87', '109', '116', '82', '90', '77', '99', '63', '70', '69', '111', '124', '105', '75', '88', '97', '98', '78', '83', '101', '44', '125', '129', '127', '141', '143', '135', '144', '145', '146', '133', '134', '145', '16', '246', '128', '164', '230', '248', '235', '141', '307', '368', '314', '306', '371', '313', '300', '310', '303', '308', '364', '352', '358', '317', '322', '367', '370', '356', '321', '372', '324', '369', '318', '319', '304', '357', '361', '301', '323', '354', '350', '374', '309', '355', '351', '353', '366', '373', '312', '365', '362', '363', '156', '23', '262', '271', '270', '274', '266', '265', '269', '258', '261', '264', '268', '32', '34', '40', '28', '43', '47', '30', '49', '31', '36', '48', '0', '249', '15', '17', '20', '151', '162', '161', '170', '153', '158', '267', '272', '251', '259', '263', '250', '252', '255', '253', '256', '260', '257', '254', '250', '273', '281', '298', '287', '297', '276', '275', '283', '288', '286', '290', '299', '186', '190', '182', '175', '194', '198', '196', '185', '184', '199', '178', '197', '176', '180', '187', '181', '183', '335', '344', '11', '19', '302', '320', '333', '343', '332', '348', '327', '329', '330', '334', '345', '359', '7', '18', '84', '92', '337', '346', '79', '80', '89', '81', '342', '305']</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>['126', '132', '140', '131', '167', '169', '193', '195', '179', '191', '177', '188', '159', '163', '12', '192', '189', '241', '244', '155', '160', '227', '238', '243', '247', '226', '239', '232', '245', '225', '240', '136', '148', '33', '35', '45', '27', '242', '29', '39', '9', '291', '295', '315', '328', '347', '349', '277', '(2)', '339', '279', '280', '(2)', '(2)', '289', '325', '(2)', '326', '278', '293', '282', '341', '340', '316', '331', '296', '25', '38', '26', '41', '37', '311', '42', '46', '62', '121', '117', '110', '119', '53', '66', '104', '86', '91', '93', '95', '228', '231', '236', '233', '234', '100', '114', '112', '115', '120', '113', '103', '118', '14', '22', '229', '10', '360', '76', '52', '55', '74', '96', '237', '59', '65', '58', '72', '68', '94', '130', '106', '123', '5', '21', '2', '8', '1', '3', '6', '2', '13']</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>['200', '215', '218', '210', '211', '209', '223', '207', '212', '214', '222', '202', '221', '224', '203', '204', '213', '217', '208', '216', '220', '206', '219', '205', '201', '108', '122', '107', '102', '142', '147', '149', '139', '137', '138', '168', '24', '165', '166', '172', '157', '154', '173', '174', '150', '171', '152', '4']</code></td></tr></table>\n",
            "Class0\n",
            "======\n",
            "[12 13 11  7 15  3  0  0  0  8  0 14  3 11  1]\n",
            "count of zeros: 12\n",
            "Class1\n",
            "======\n",
            "[10 12 14 18  6 12  8 17  0  5 25 11 22 14 24]\n",
            "Class2\n",
            "======\n",
            "[ 2  0  0  0  4  6 11]\n",
            "Class3\n",
            "======\n",
            "[ 0  0  0  0  0  0  0  0 25]\n",
            "Class4\n",
            "======\n",
            "[ 1  0  0  0  0  4  6  8  0 12]\n",
            "0 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "1 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "2 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "3 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "4 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 2\n",
            "5 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "6 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "7 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "8 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "9 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "10 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "11 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "12 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 4\n",
            "13 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "14 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "15 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "16 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "17 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "18 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "19 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "20 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "21 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "22 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 0\n",
            "23 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 1\n",
            "24 : ORIG_LABEL : 0 - BENIGN; CLUSTER_LABEL: 2\n",
            "25 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "26 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "27 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "28 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "29 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "30 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "31 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "32 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "33 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "34 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "35 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "36 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "37 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "38 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "39 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "40 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "41 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "42 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "43 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "44 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "45 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "46 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 0\n",
            "47 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "48 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "49 : ORIG_LABEL : 1 - DDoS; CLUSTER_LABEL: 1\n",
            "50 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "51 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "52 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "53 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "54 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "55 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\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: 0\n",
            "59 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "60 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "61 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "62 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "63 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "64 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "65 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "66 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "67 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "68 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "69 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "70 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "71 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "72 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "73 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 1\n",
            "74 : ORIG_LABEL : 2 - PortScan; CLUSTER_LABEL: 0\n",
            "75 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "76 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "77 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "78 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "79 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "80 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "81 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "82 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "83 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "84 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "85 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "86 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "87 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "88 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "89 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "90 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "91 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "92 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "93 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "94 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "95 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "96 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 0\n",
            "97 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "98 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "99 : ORIG_LABEL : 3 - Bot; CLUSTER_LABEL: 1\n",
            "100 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "101 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "102 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 2\n",
            "103 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "104 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "105 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "106 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "107 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 2\n",
            "108 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 2\n",
            "109 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "110 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "111 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "112 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "113 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "114 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "115 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "116 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "117 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "118 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "119 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "120 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "121 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "122 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 2\n",
            "123 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 0\n",
            "124 : ORIG_LABEL : 4 - Infiltration; CLUSTER_LABEL: 1\n",
            "125 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "126 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "127 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "128 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "129 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "130 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 0\n",
            "131 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "132 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "133 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "134 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "135 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "136 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 0\n",
            "137 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "138 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "139 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "140 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 4\n",
            "141 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "142 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "143 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "144 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "145 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "146 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 1\n",
            "147 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "148 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 0\n",
            "149 : ORIG_LABEL : 5 - Web Attack � Brute Force; CLUSTER_LABEL: 2\n",
            "150 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "151 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "152 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\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: 4\n",
            "156 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "157 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "158 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "159 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 4\n",
            "160 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 4\n",
            "161 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "162 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "163 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 4\n",
            "164 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "165 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "166 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "167 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 4\n",
            "168 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "169 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 4\n",
            "170 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 1\n",
            "171 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "172 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "173 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "174 : ORIG_LABEL : 6 - Web Attack � XSS; CLUSTER_LABEL: 2\n",
            "175 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "176 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "177 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "178 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "179 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "180 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "181 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "182 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "183 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "184 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "185 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "186 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "187 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "188 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "189 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "190 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "191 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "192 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "193 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "194 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "195 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 4\n",
            "196 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "197 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "198 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "199 : ORIG_LABEL : 7 - Web Attack � Sql Injection; CLUSTER_LABEL: 1\n",
            "200 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "201 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "202 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "203 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "204 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "205 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "206 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "207 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "208 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "209 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "210 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "211 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "212 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "213 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "214 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "215 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "216 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "217 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "218 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "219 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "220 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "221 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "222 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "223 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "224 : ORIG_LABEL : 8 - FTP-Patator; CLUSTER_LABEL: 3\n",
            "225 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "226 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "227 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "228 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "229 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "230 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "231 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "232 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "233 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "234 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "235 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "236 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "237 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "238 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "239 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "240 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "241 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "242 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 0\n",
            "243 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "244 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "245 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "246 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "247 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 4\n",
            "248 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "249 : ORIG_LABEL : 9 - SSH-Patator; CLUSTER_LABEL: 1\n",
            "250 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "251 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "252 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "253 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "254 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "255 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "256 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "257 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "258 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "259 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "260 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "261 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "262 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "263 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "264 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "265 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "266 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "267 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "268 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "269 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "270 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "271 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "272 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "273 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "274 : ORIG_LABEL : 10 - DoS slowloris; CLUSTER_LABEL: 1\n",
            "275 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "276 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "277 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "278 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "279 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "280 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "281 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "282 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "283 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "284 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "285 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "286 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "287 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "288 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "289 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "290 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "291 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "292 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "293 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "294 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "295 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "296 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 0\n",
            "297 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "298 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "299 : ORIG_LABEL : 11 - DoS Slowhttptest; CLUSTER_LABEL: 1\n",
            "300 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "301 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "302 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "303 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "304 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "305 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "306 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "307 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "308 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "309 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "310 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "311 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 0\n",
            "312 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "313 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "314 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "315 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 0\n",
            "316 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 0\n",
            "317 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "318 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "319 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "320 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "321 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "322 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "323 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "324 : ORIG_LABEL : 12 - DoS Hulk; CLUSTER_LABEL: 1\n",
            "325 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "326 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "327 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "328 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "329 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "330 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "331 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "332 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "333 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "334 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "335 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "336 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "337 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "338 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "339 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "340 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "341 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "342 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "343 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "344 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "345 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "346 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "347 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "348 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 1\n",
            "349 : ORIG_LABEL : 13 - DoS GoldenEye; CLUSTER_LABEL: 0\n",
            "350 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "351 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "352 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "353 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "354 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "355 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "356 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "357 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "358 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "359 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "360 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 0\n",
            "361 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "362 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "363 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "364 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "365 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "366 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "367 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "368 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "369 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "370 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "371 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "372 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "373 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "374 : ORIG_LABEL : 14 - Heartbleed; CLUSTER_LABEL: 1\n",
            "=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=--=-=-=\n",
            "Class 0 - BENIGN: Cluster 3\n",
            "Class 1 - DDoS: Cluster 2\n",
            "Class 2 - PortScan: Cluster 2\n",
            "Class 3 - Bot: Cluster 2\n",
            "Class 4 - Infiltration: Cluster 3\n",
            "Class 5 - Web Attack � Brute Force: Cluster 3\n",
            "Class 6 - Web Attack � XSS: Cluster 0\n",
            "Class 7 - Web Attack � Sql Injection: Cluster 0\n",
            "Class 8 - FTP-Patator: Cluster 0\n",
            "Class 9 - SSH-Patator: Cluster 2\n",
            "Class 10 - DoS slowloris: Cluster 0\n",
            "Class 11 - DoS Slowhttptest: Cluster 2\n",
            "Class 12 - DoS Hulk: Cluster 2\n",
            "Class 13 - DoS GoldenEye: Cluster 2\n",
            "Class 14 - Heartbleed: Cluster 2\n",
            "    0   1   2   3   4                       Class\n",
            "0  12  10   2   0   1                      BENIGN\n",
            "0  13  12   0   0   0                        DDoS\n",
            "0  11  14   0   0   0                    PortScan\n",
            "0   7  18   0   0   0                         Bot\n",
            "0  15   6   4   0   0                Infiltration\n",
            "0   3  12   6   0   4    Web Attack � Brute Force\n",
            "0   0   8  11   0   6            Web Attack � XSS\n",
            "0   0  17   0   0   8  Web Attack � Sql Injection\n",
            "0   0   0   0  25   0                 FTP-Patator\n",
            "0   8   5   0   0  12                 SSH-Patator\n",
            "0   0  25   0   0   0               DoS slowloris\n",
            "0  14  11   0   0   0            DoS Slowhttptest\n",
            "0   3  22   0   0   0                    DoS Hulk\n",
            "0  11  14   0   0   0               DoS GoldenEye\n",
            "0   1  24   0   0   0                  Heartbleed\n",
            "            0     0         0    0             0                         0  \\\n",
            "0          12    13        11    7            15                         3   \n",
            "1          10    12        14   18             6                        12   \n",
            "2           2     0         0    0             4                         6   \n",
            "3           0     0         0    0             0                         0   \n",
            "4           1     0         0    0             0                         4   \n",
            "Class  BENIGN  DDoS  PortScan  Bot  Infiltration  Web Attack � Brute Force   \n",
            "\n",
            "                      0                           0            0            0  \\\n",
            "0                     0                           0            0            8   \n",
            "1                     8                          17            0            5   \n",
            "2                    11                           0            0            0   \n",
            "3                     0                           0           25            0   \n",
            "4                     6                           8            0           12   \n",
            "Class  Web Attack � XSS  Web Attack � Sql Injection  FTP-Patator  SSH-Patator   \n",
            "\n",
            "                   0                 0         0              0           0  \n",
            "0                  0                14         3             11           1  \n",
            "1                 25                11        22             14          24  \n",
            "2                  0                 0         0              0           0  \n",
            "3                  0                 0         0              0           0  \n",
            "4                  0                 0         0              0           0  \n",
            "Class  DoS slowloris  DoS Slowhttptest  DoS Hulk  DoS GoldenEye  Heartbleed  \n",
            "=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=--=-=-=\n",
            "[['1', '6'], ['0', '7'], ['0', '7'], ['0', '7'], ['2', '4'], ['0', '7'], ['0', '7'], ['1', '6'], ['0', '7'], ['0', '11'], ['0', '12'], ['1', '6'], ['4', '3'], ['0', '7'], ['0', '12'], ['1', '6'], ['1', '5'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['0', '7'], ['0', '12'], ['1', '14'], ['2', '4'], ['0', '11'], ['0', '11'], ['0', '11'], ['1', '6'], ['0', '11'], ['1', '6'], ['1', '6'], ['1', '6'], ['0', '11'], ['1', '6'], ['0', '11'], ['1', '6'], ['0', '11'], ['0', '11'], ['0', '11'], ['1', '6'], ['0', '11'], ['0', '11'], ['1', '6'], ['1', '2'], ['0', '11'], ['0', '11'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '2'], ['1', '2'], ['0', '12'], ['0', '13'], ['1', '2'], ['0', '12'], ['1', '2'], ['1', '2'], ['0', '12'], ['0', '12'], ['1', '2'], ['1', '2'], ['0', '13'], ['1', '2'], ['1', '2'], ['0', '12'], ['0', '13'], ['1', '2'], ['0', '12'], ['1', '2'], ['1', '2'], ['1', '2'], ['0', '12'], ['1', '2'], ['0', '12'], ['1', '2'], ['0', '12'], ['1', '2'], ['1', '2'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '2'], ['1', '2'], ['1', '6'], ['1', '2'], ['0', '8'], ['1', '2'], ['1', '2'], ['1', '6'], ['1', '2'], ['0', '8'], ['1', '6'], ['0', '8'], ['0', '12'], ['0', '8'], ['0', '12'], ['1', '2'], ['1', '2'], ['1', '2'], ['0', '12'], ['1', '2'], ['2', '9'], ['0', '12'], ['0', '13'], ['1', '2'], ['0', '7'], ['2', '9'], ['2', '9'], ['1', '2'], ['0', '13'], ['1', '2'], ['0', '12'], ['0', '12'], ['0', '12'], ['0', '12'], ['1', '2'], ['0', '13'], ['0', '12'], ['0', '13'], ['0', '12'], ['0', '13'], ['2', '9'], ['0', '7'], ['1', '2'], ['1', '5'], ['4', '3'], ['1', '5'], ['1', '5'], ['1', '5'], ['0', '12'], ['4', '3'], ['4', '3'], ['1', '5'], ['1', '5'], ['1', '5'], ['0', '11'], ['2', '4'], ['2', '4'], ['2', '4'], ['4', '3'], ['1', '5'], ['2', '4'], ['1', '5'], ['1', '5'], ['1', '5'], ['1', '5'], ['2', '4'], ['0', '11'], ['2', '4'], ['2', '4'], ['1', '6'], ['2', '4'], ['1', '6'], ['2', '4'], ['4', '0'], ['1', '14'], ['2', '4'], ['1', '6'], ['4', '3'], ['4', '0'], ['1', '6'], ['1', '6'], ['4', '3'], ['1', '5'], ['2', '4'], ['2', '4'], ['4', '3'], ['2', '4'], ['4', '3'], ['1', '6'], ['2', '4'], ['2', '4'], ['2', '4'], ['2', '4'], ['1', '6'], ['1', '6'], ['4', '3'], ['1', '6'], ['4', '3'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['4', '3'], ['4', '3'], ['1', '6'], ['4', '3'], ['4', '3'], ['4', '3'], ['1', '6'], ['4', '3'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['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', '1'], ['3', '1'], ['4', '0'], ['4', '0'], ['4', '0'], ['0', '10'], ['0', '12'], ['1', '5'], ['0', '10'], ['4', '0'], ['0', '10'], ['0', '10'], ['1', '5'], ['0', '10'], ['0', '12'], ['4', '0'], ['4', '0'], ['4', '0'], ['4', '3'], ['0', '11'], ['4', '0'], ['4', '3'], ['4', '0'], ['1', '5'], ['4', '0'], ['1', '5'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['0', '11'], ['0', '11'], ['0', '11'], ['0', '11'], ['1', '6'], ['0', '11'], ['1', '6'], ['0', '11'], ['0', '11'], ['1', '6'], ['1', '6'], ['1', '6'], ['0', '11'], ['1', '6'], ['0', '11'], ['0', '11'], ['0', '11'], ['0', '11'], ['0', '11'], ['0', '11'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '14'], ['1', '14'], ['1', '6'], ['1', '14'], ['1', '14'], ['1', '6'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['0', '11'], ['1', '14'], ['1', '14'], ['1', '14'], ['0', '11'], ['0', '11'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '6'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['0', '11'], ['0', '11'], ['1', '6'], ['0', '11'], ['1', '6'], ['1', '6'], ['0', '11'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['0', '11'], ['1', '6'], ['0', '11'], ['0', '11'], ['0', '11'], ['0', '11'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '6'], ['0', '11'], ['1', '6'], ['0', '11'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '6'], ['0', '12'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14'], ['1', '14']]\n",
            "=============================\n",
            "The Hierarchy Looks like this\n",
            "=============================\n",
            "['0', '10']\n",
            "['0', '11']\n",
            "['0', '12']\n",
            "['0', '13']\n",
            "['0', '7']\n",
            "['0', '8']\n",
            "['1', '14']\n",
            "['1', '2']\n",
            "['1', '5']\n",
            "['1', '6']\n",
            "['2', '4']\n",
            "['2', '9']\n",
            "['3', '1']\n",
            "['4', '0']\n",
            "['4', '3']\n",
            "=============================\n",
            "375\n",
            "375\n",
            "0\n",
            "parent of 10 is 0\n",
            "class 0 is mostly 0 - BENIGN\n",
            "relabel to align wioth expert defined labels\n",
            "class 1 is mostly 0 - BENIGN\n",
            "relabel to align wioth expert defined labels\n",
            "class 2 is mostly 1 - DDoS\n",
            "relabel to align wioth expert defined labels\n",
            "class 3 is mostly 1 - DDoS\n",
            "relabel to align wioth expert defined labels\n",
            "class 4 is mostly 0 - BENIGN\n",
            "relabel to align wioth expert defined labels\n",
            "['(BENIGN)', '(BENIGN)', '(DDoS)', '(DDoS)', '(BENIGN)']\n",
            "class 0 is mostly 0 - BENIGN\n",
            "relabel to align with expert defined labels\n",
            "class 1 is mostly 0 - BENIGN\n",
            "relabel to align with expert defined labels\n",
            "class 2 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 3 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 4 is mostly 0 - BENIGN\n",
            "relabel to align with expert defined labels\n",
            "class 5 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 6 is mostly 2 - PortScan\n",
            "relabel to align with expert defined labels\n",
            "class 7 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 8 is mostly 3 - Bot\n",
            "relabel to align with expert defined labels\n",
            "class 9 is mostly 4 - Infiltration\n",
            "relabel to align with expert defined labels\n",
            "class 10 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 11 is mostly 0 - BENIGN\n",
            "relabel to align with expert defined labels\n",
            "class 12 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 13 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "class 14 is mostly 1 - DDoS\n",
            "relabel to align with expert defined labels\n",
            "['BENIGN - [4] [0]', 'BENIGN - [3] [1]', 'DDoS - [1] [2]', 'DDoS - [4] [3]', 'BENIGN - [2] [4]', 'DDoS - [1] [5]', 'PortScan - [1] [6]', 'DDoS - [0] [7]', 'Bot - [0] [8]', 'Infiltration - [2] [9]', 'DDoS - [0] [10]', 'BENIGN - [0] [11]', 'DDoS - [0] [12]', 'DDoS - [0] [13]', 'DDoS - [1] [14]']\n",
            " Label\n",
            "6\n",
            "6\n",
            "3\n",
            "3\n",
            "6\n",
            "3\n",
            "3\n",
            "3\n",
            "3\n",
            "6\n",
            "6\n",
            "6\n",
            "11\n",
            "3\n",
            "6\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='ward')\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='ward'),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-Ward-Coarse.png')\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.title(\"Cluster 8 Classes Dendograms\")\n",
        "dend = shc.dendrogram(shc.linkage(X, method='ward'),leaf_rotation=90, truncate_mode=\"level\", p=2,\n",
        "                              leaf_font_size=12, orientation='top')\n",
        "plt.savefig('Dendogram-Ward-Fine.png')\n",
        "  \n",
        "cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')\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": "c7f79817-e8f7-4aa2-843c-cbcced3842c5"
      },
      "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": "4663762b-f339-4772-96d5-d5f4cd7f31bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.27132251e-03 2.49128195e-04 1.95633235e-08 ... 2.06216165e-03\n",
            "  6.74116401e-04 1.31394587e-10]\n",
            " [2.31627544e-09 2.76454507e-12 1.01720372e-08 ... 2.74555764e-04\n",
            "  1.68598789e-05 3.19662246e-04]\n",
            " [2.04765926e-05 2.31452265e-09 2.65526360e-05 ... 1.32232840e-07\n",
            "  9.66963472e-09 3.26962971e-04]\n",
            " ...\n",
            " [3.68258293e-06 1.45547091e-10 1.22641230e-05 ... 4.03980363e-08\n",
            "  8.45653467e-10 3.65092222e-04]\n",
            " [8.72545996e-09 7.39498712e-06 4.08593399e-10 ... 3.23255299e-14\n",
            "  3.80795682e-07 3.11840127e-12]\n",
            " [1.04949506e-07 1.89245442e-10 4.16861485e-04 ... 7.50983773e-09\n",
            "  1.81488800e-08 9.97203383e-01]]\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 1]]\n",
            "-------\n",
            "[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, 0, 1, 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, 0, 1, 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, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]\n",
            "[3, 11, 6, 11, 2, 11, 12, 13, 11, 3, 10, 11, 6, 12, 11, 6, 6, 0, 6, 2, 11, 6, 12, 0, 6, 4, 14, 4, 4, 14, 11, 9, 11, 14, 4, 12, 2, 12, 6, 3, 6, 6, 6, 1, 2, 2, 2, 2, 7, 11, 0, 5, 6, 1, 1, 6, 0, 7, 3, 2, 3, 11, 2, 11, 11, 2, 0, 0, 1, 2, 12, 6, 14, 14, 11, 3, 2, 6, 14, 5, 13, 11, 6, 12, 2, 11, 11, 6, 1, 13, 11, 2, 6, 6, 1, 11, 5, 6, 6, 4, 1, 6, 14, 1, 3, 14, 11, 14, 6, 3, 6, 4, 14]\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": "13332024-f48e-4b59-8f21-f47fdad2a0e2"
      },
      "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",
            "           0       1.00      0.83      0.91         6\n",
            "           1       1.00      1.00      1.00         8\n",
            "           2       1.00      1.00      1.00        14\n",
            "           3       1.00      1.00      1.00         8\n",
            "           4       1.00      1.00      1.00         6\n",
            "           5       1.00      1.00      1.00         3\n",
            "           6       1.00      1.00      1.00        24\n",
            "           7       1.00      1.00      1.00         2\n",
            "           8       0.00      0.00      0.00         0\n",
            "           9       1.00      1.00      1.00         1\n",
            "          10       1.00      1.00      1.00         1\n",
            "          11       1.00      1.00      1.00        20\n",
            "          12       1.00      1.00      1.00         7\n",
            "          13       1.00      1.00      1.00         3\n",
            "          14       1.00      1.00      1.00        10\n",
            "\n",
            "    accuracy                           0.99       113\n",
            "   macro avg       0.93      0.92      0.93       113\n",
            "weighted avg       1.00      0.99      1.00       113\n",
            "\n",
            "[ 3 11  6 11  2 11 12 13 11  3 10 11  6 12 11  6  6  0  6  2 11  6 12  0\n",
            "  6  4 14  4  4 14 11  9 11 14  4 12  2 12  6  3  6  6  6  1  2  2  2  2\n",
            "  7 11  0  5  6  1  1  6  8  7  3  2  3 11  2 11 11  2  0  0  1  2 12  6\n",
            " 14 14 11  3  2  6 14  5 13 11  6 12  2 11 11  6  1 13 11  2  6  6  1 11\n",
            "  5  6  6  4  1  6 14  1  3 14 11 14  6  3  6  4 14]\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 5\n",
            "1 8\n",
            "2 14\n",
            "3 8\n",
            "4 6\n",
            "5 3\n",
            "6 24\n",
            "7 2\n",
            "8 1\n",
            "9 1\n",
            "10 1\n",
            "11 20\n",
            "12 7\n",
            "13 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \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": "0568d3e0-aa4c-4f9f-acb6-71d690fb5322"
      },
      "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 0:00:36\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\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\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- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \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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\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\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",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - hiclass=3.1.6\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    aiohttp-3.7.4.post0        |   py37h5e8e339_0         625 KB  conda-forge\n",
            "    aiohttp-cors-0.7.0         |             py_0          22 KB  conda-forge\n",
            "    aioredis-2.0.1             |     pyhd8ed1ab_0          60 KB  conda-forge\n",
            "    async-timeout-3.0.1        |          py_1000          11 KB  conda-forge\n",
            "    attrs-21.4.0               |     pyhd8ed1ab_0          49 KB  conda-forge\n",
            "    blessings-1.7              |py37h89c1867_1006          28 KB  conda-forge\n",
            "    ca-certificates-2022.6.15  |       ha878542_0         149 KB  conda-forge\n",
            "    cachetools-5.0.0           |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    certifi-2022.6.15          |   py37h89c1867_0         155 KB  conda-forge\n",
            "    click-8.1.3                |   py37h89c1867_0         145 KB  conda-forge\n",
            "    colorama-0.4.5             |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    colorful-0.5.4             |     pyhd8ed1ab_0         162 KB  conda-forge\n",
            "    conda-4.12.0               |   py37h89c1867_0         1.0 MB  conda-forge\n",
            "    cudatoolkit-11.1.1         |       h6406543_8        1.20 GB  conda-forge\n",
            "    decorator-5.1.1            |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    deprecated-1.2.13          |     pyh6c4a22f_0          12 KB  conda-forge\n",
            "    filelock-3.7.1             |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    google-api-core-2.8.2      |     pyhd8ed1ab_0          70 KB  conda-forge\n",
            "    google-auth-2.9.1          |     pyh6c4a22f_0          90 KB  conda-forge\n",
            "    googleapis-common-protos-1.56.4|   py37h89c1867_0         123 KB  conda-forge\n",
            "    gpustat-0.6.0              |     pyhd8ed1ab_1          16 KB  conda-forge\n",
            "    grpcio-1.38.1              |   py37hb27c1af_0         2.2 MB  conda-forge\n",
            "    hiclass-3.1.6              |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    importlib-metadata-4.11.4  |   py37h89c1867_0          33 KB  conda-forge\n",
            "    importlib_resources-5.8.0  |     pyhd8ed1ab_0          22 KB  conda-forge\n",
            "    joblib-1.1.0               |     pyhd8ed1ab_0         210 KB  conda-forge\n",
            "    jsonschema-4.7.2           |     pyhd8ed1ab_0          63 KB  conda-forge\n",
            "    libblas-3.9.0              |11_linux64_openblas          12 KB  conda-forge\n",
            "    libcblas-3.9.0             |11_linux64_openblas          11 KB  conda-forge\n",
            "    libgfortran-ng-12.1.0      |      h69a702a_16          23 KB  conda-forge\n",
            "    libgfortran5-12.1.0        |      hdcd56e2_16         1.8 MB  conda-forge\n",
            "    liblapack-3.9.0            |11_linux64_openblas          11 KB  conda-forge\n",
            "    libopenblas-0.3.17         |pthreads_h8fe5266_1         9.2 MB  conda-forge\n",
            "    libprotobuf-3.17.2         |       h780b84a_1         2.5 MB  conda-forge\n",
            "    msgpack-python-1.0.2       |   py37h2527ec5_1          91 KB  conda-forge\n",
            "    multidict-5.1.0            |   py37h5e8e339_1          67 KB  conda-forge\n",
            "    networkx-2.5               |             py_0         1.2 MB  conda-forge\n",
            "    numpy-1.21.1               |   py37h038b26d_0         6.1 MB  conda-forge\n",
            "    nvidia-ml-7.352.0          |             py_0          19 KB  conda-forge\n",
            "    opencensus-0.10.0          |     pyhd8ed1ab_1          67 KB  conda-forge\n",
            "    opencensus-context-0.1.2   |   py37h89c1867_6          14 KB  conda-forge\n",
            "    openssl-1.1.1k             |       h7f98852_0         2.1 MB  conda-forge\n",
            "    packaging-21.3             |     pyhd8ed1ab_0          36 KB  conda-forge\n",
            "    pickle5-0.0.11             |   py37h5e8e339_0         173 KB  conda-forge\n",
            "    prometheus_client-0.14.1   |     pyhd8ed1ab_0          49 KB  conda-forge\n",
            "    protobuf-3.17.2            |   py37hcd2ae1e_0         347 KB  conda-forge\n",
            "    psutil-5.8.0               |   py37h5e8e339_1         342 KB  conda-forge\n",
            "    pyasn1-0.4.8               |             py_0          53 KB  conda-forge\n",
            "    pyasn1-modules-0.2.7       |             py_0          60 KB  conda-forge\n",
            "    pyparsing-3.0.9            |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    pyrsistent-0.17.3          |   py37h5e8e339_2          89 KB  conda-forge\n",
            "    python_abi-3.7             |          2_cp37m           4 KB  conda-forge\n",
            "    pyu2f-0.1.5                |     pyhd8ed1ab_0          31 KB  conda-forge\n",
            "    pyyaml-5.4.1               |   py37h5e8e339_0         189 KB  conda-forge\n",
            "    ray-core-1.3.0             |   py37hf931bba_0        23.6 MB  conda-forge\n",
            "    redis-py-4.1.4             |     pyhd8ed1ab_1         132 KB  conda-forge\n",
            "    rsa-4.8                    |     pyhd8ed1ab_0          31 KB  conda-forge\n",
            "    scikit-learn-0.24.2        |   py37h18a542f_0         7.5 MB  conda-forge\n",
            "    scipy-1.7.0                |   py37h29e03ee_1        21.7 MB  conda-forge\n",
            "    setproctitle-1.1.10        |py37h5e8e339_1004          15 KB  conda-forge\n",
            "    threadpoolctl-3.1.0        |     pyh8a188c0_0          18 KB  conda-forge\n",
            "    typing-extensions-4.3.0    |       hd8ed1ab_0           8 KB  conda-forge\n",
            "    typing_extensions-4.3.0    |     pyha770c72_0          28 KB  conda-forge\n",
            "    wrapt-1.12.1               |   py37h5e8e339_3          47 KB  conda-forge\n",
            "    yarl-1.6.3                 |   py37h5e8e339_2         142 KB  conda-forge\n",
            "    zipp-3.8.0                 |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        1.28 GB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  aiohttp            conda-forge/linux-64::aiohttp-3.7.4.post0-py37h5e8e339_0\n",
            "  aiohttp-cors       conda-forge/noarch::aiohttp-cors-0.7.0-py_0\n",
            "  aioredis           conda-forge/noarch::aioredis-2.0.1-pyhd8ed1ab_0\n",
            "  async-timeout      conda-forge/noarch::async-timeout-3.0.1-py_1000\n",
            "  attrs              conda-forge/noarch::attrs-21.4.0-pyhd8ed1ab_0\n",
            "  blessings          conda-forge/linux-64::blessings-1.7-py37h89c1867_1006\n",
            "  cachetools         conda-forge/noarch::cachetools-5.0.0-pyhd8ed1ab_0\n",
            "  click              conda-forge/linux-64::click-8.1.3-py37h89c1867_0\n",
            "  colorama           conda-forge/noarch::colorama-0.4.5-pyhd8ed1ab_0\n",
            "  colorful           conda-forge/noarch::colorful-0.5.4-pyhd8ed1ab_0\n",
            "  cudatoolkit        conda-forge/linux-64::cudatoolkit-11.1.1-h6406543_8\n",
            "  decorator          conda-forge/noarch::decorator-5.1.1-pyhd8ed1ab_0\n",
            "  deprecated         conda-forge/noarch::deprecated-1.2.13-pyh6c4a22f_0\n",
            "  filelock           conda-forge/noarch::filelock-3.7.1-pyhd8ed1ab_0\n",
            "  google-api-core    conda-forge/noarch::google-api-core-2.8.2-pyhd8ed1ab_0\n",
            "  google-auth        conda-forge/noarch::google-auth-2.9.1-pyh6c4a22f_0\n",
            "  googleapis-common~ conda-forge/linux-64::googleapis-common-protos-1.56.4-py37h89c1867_0\n",
            "  gpustat            conda-forge/noarch::gpustat-0.6.0-pyhd8ed1ab_1\n",
            "  grpcio             conda-forge/linux-64::grpcio-1.38.1-py37hb27c1af_0\n",
            "  hiclass            conda-forge/noarch::hiclass-3.1.6-pyhd8ed1ab_0\n",
            "  importlib-metadata conda-forge/linux-64::importlib-metadata-4.11.4-py37h89c1867_0\n",
            "  importlib_resourc~ conda-forge/noarch::importlib_resources-5.8.0-pyhd8ed1ab_0\n",
            "  joblib             conda-forge/noarch::joblib-1.1.0-pyhd8ed1ab_0\n",
            "  jsonschema         conda-forge/noarch::jsonschema-4.7.2-pyhd8ed1ab_0\n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-11_linux64_openblas\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-11_linux64_openblas\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-12.1.0-h69a702a_16\n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-12.1.0-hdcd56e2_16\n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-11_linux64_openblas\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.17-pthreads_h8fe5266_1\n",
            "  libprotobuf        conda-forge/linux-64::libprotobuf-3.17.2-h780b84a_1\n",
            "  msgpack-python     conda-forge/linux-64::msgpack-python-1.0.2-py37h2527ec5_1\n",
            "  multidict          conda-forge/linux-64::multidict-5.1.0-py37h5e8e339_1\n",
            "  networkx           conda-forge/noarch::networkx-2.5-py_0\n",
            "  numpy              conda-forge/linux-64::numpy-1.21.1-py37h038b26d_0\n",
            "  nvidia-ml          conda-forge/noarch::nvidia-ml-7.352.0-py_0\n",
            "  opencensus         conda-forge/noarch::opencensus-0.10.0-pyhd8ed1ab_1\n",
            "  opencensus-context conda-forge/linux-64::opencensus-context-0.1.2-py37h89c1867_6\n",
            "  packaging          conda-forge/noarch::packaging-21.3-pyhd8ed1ab_0\n",
            "  pickle5            conda-forge/linux-64::pickle5-0.0.11-py37h5e8e339_0\n",
            "  prometheus_client  conda-forge/noarch::prometheus_client-0.14.1-pyhd8ed1ab_0\n",
            "  protobuf           conda-forge/linux-64::protobuf-3.17.2-py37hcd2ae1e_0\n",
            "  psutil             conda-forge/linux-64::psutil-5.8.0-py37h5e8e339_1\n",
            "  pyasn1             conda-forge/noarch::pyasn1-0.4.8-py_0\n",
            "  pyasn1-modules     conda-forge/noarch::pyasn1-modules-0.2.7-py_0\n",
            "  pyparsing          conda-forge/noarch::pyparsing-3.0.9-pyhd8ed1ab_0\n",
            "  pyrsistent         conda-forge/linux-64::pyrsistent-0.17.3-py37h5e8e339_2\n",
            "  pyu2f              conda-forge/noarch::pyu2f-0.1.5-pyhd8ed1ab_0\n",
            "  pyyaml             conda-forge/linux-64::pyyaml-5.4.1-py37h5e8e339_0\n",
            "  ray-core           conda-forge/linux-64::ray-core-1.3.0-py37hf931bba_0\n",
            "  redis-py           conda-forge/noarch::redis-py-4.1.4-pyhd8ed1ab_1\n",
            "  rsa                conda-forge/noarch::rsa-4.8-pyhd8ed1ab_0\n",
            "  scikit-learn       conda-forge/linux-64::scikit-learn-0.24.2-py37h18a542f_0\n",
            "  scipy              conda-forge/linux-64::scipy-1.7.0-py37h29e03ee_1\n",
            "  setproctitle       conda-forge/linux-64::setproctitle-1.1.10-py37h5e8e339_1004\n",
            "  threadpoolctl      conda-forge/noarch::threadpoolctl-3.1.0-pyh8a188c0_0\n",
            "  typing-extensions  conda-forge/noarch::typing-extensions-4.3.0-hd8ed1ab_0\n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-4.3.0-pyha770c72_0\n",
            "  wrapt              conda-forge/linux-64::wrapt-1.12.1-py37h5e8e339_3\n",
            "  yarl               conda-forge/linux-64::yarl-1.6.3-py37h5e8e339_2\n",
            "  zipp               conda-forge/noarch::zipp-3.8.0-pyhd8ed1ab_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2020.12.5-ha878542_0 --> 2022.6.15-ha878542_0\n",
            "  certifi                          2020.12.5-py37h89c1867_1 --> 2022.6.15-py37h89c1867_0\n",
            "  conda                                4.9.2-py37h89c1867_0 --> 4.12.0-py37h89c1867_0\n",
            "  openssl                                 1.1.1j-h7f98852_0 --> 1.1.1k-h7f98852_0\n",
            "  python_abi                                    3.7-1_cp37m --> 3.7-2_cp37m\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "blessings-1.7        | 28 KB     | : 100% 1.0/1 [00:00<00:00,  6.93it/s]              \n",
            "setproctitle-1.1.10  | 15 KB     | : 100% 1.0/1 [00:00<00:00, 14.88it/s]\n",
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            "cachetools-5.0.0     | 12 KB     | : 100% 1.0/1 [00:00<00:00, 25.37it/s]\n",
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            "pyyaml-5.4.1         | 189 KB    | : 100% 1.0/1 [00:00<00:00, 12.92it/s]\n",
            "click-8.1.3          | 145 KB    | : 100% 1.0/1 [00:00<00:00, 14.74it/s]\n",
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            "packaging-21.3       | 36 KB     | : 100% 1.0/1 [00:00<00:00, 25.68it/s]\n",
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            "pickle5-0.0.11       | 173 KB    | : 100% 1.0/1 [00:00<00:00, 10.59it/s]\n",
            "colorama-0.4.5       | 18 KB     | : 100% 1.0/1 [00:00<00:00, 20.91it/s]\n",
            "openssl-1.1.1k       | 2.1 MB    | : 100% 1.0/1 [00:00<00:00,  2.33it/s]\n",
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            "aiohttp-cors-0.7.0   | 22 KB     | : 100% 1.0/1 [00:00<00:00, 23.19it/s]\n",
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            "colorful-0.5.4       | 162 KB    | : 100% 1.0/1 [00:00<00:00, 11.40it/s]\n",
            "aioredis-2.0.1       | 60 KB     | : 100% 1.0/1 [00:00<00:00, 20.86it/s]\n",
            "filelock-3.7.1       | 12 KB     | : 100% 1.0/1 [00:00<00:00, 28.31it/s]\n",
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            "numpy-1.21.1         | 6.1 MB    | : 100% 1.0/1 [00:01<00:00,  1.55s/it]\n",
            "scipy-1.7.0          | 21.7 MB   | : 100% 1.0/1 [00:04<00:00,  4.32s/it]              \n",
            "certifi-2022.6.15    | 155 KB    | : 100% 1.0/1 [00:00<00:00, 18.34it/s]\n",
            "wrapt-1.12.1         | 47 KB     | : 100% 1.0/1 [00:00<00:00, 15.33it/s]\n",
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            "importlib-metadata-4 | 33 KB     | : 100% 1.0/1 [00:00<00:00, 22.83it/s]\n",
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            "pyasn1-0.4.8         | 53 KB     | : 100% 1.0/1 [00:00<00:00, 16.36it/s]\n",
            "threadpoolctl-3.1.0  | 18 KB     | : 100% 1.0/1 [00:00<00:00, 12.55it/s]\n",
            "ca-certificates-2022 | 149 KB    | : 100% 1.0/1 [00:00<00:00, 18.24it/s]\n",
            "rsa-4.8              | 31 KB     | : 100% 1.0/1 [00:00<00:00, 15.75it/s]\n",
            "networkx-2.5         | 1.2 MB    | : 100% 1.0/1 [00:00<00:00,  2.64it/s]\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Verifying transaction: | \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",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html\n",
            "\n",
            "\b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\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"
          ]
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wq8TF0naGjTQ",
        "outputId": "94628f73-8eed-4afb-d9c7-c4c73f43ee2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading file\n",
            "(113, 58)\n",
            "(375, 58)\n"
          ]
        }
      ]
    },
    {
      "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-Ward-Plot.png'\n",
        "filename=basedir+savename\n",
        "plt.savefig(filename,dpi=300,bbox_inches='tight')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6LimQ2nWDwyD",
        "outputId": "46e74684-5e43-4d6b-bd70-ab6e0b449c47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 3 11  6 11  2 11 12 13 11  3 10 11  6 12 11  6  6  0  6  2 11  6 12  0\n",
            "  6  4 14  4  4 14 11  9 11 14  4 12  2 12  6  3  6  6  6  1  2  2  2  2\n",
            "  7 11  0  5  6  1  1  6 11  7  3  2  3 11  2 11 11  2  0  0  1  2 12  6\n",
            " 14 14 11  3  2  6 14  5 13 11  6 12  2 11 11  6  1 13 11  2  6  6  1 11\n",
            "  5  6  6  4  1  6 14  1  3 14 11 14  6  3  6  4 14]\n",
            "[[ 5  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  8  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0 14  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  8  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  6  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  3  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 24  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  2  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  1  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  0  0  0  0  1  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0 20  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  7  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  3  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 10]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x6000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 3 11  6 11  2 11 12 13 11  3 10 11  6 12 11  6  6  0  6  2 11  6 12  0\n",
            "  6  4 14  4  4 14 11  9 11 14  4 12  2 12  6  3  6  6  6  1  2  2  2  2\n",
            "  7 11  0  5  6  1  1  6 11  7  3  2  3 11  2 11 11  2  0  0  1  2 12  6\n",
            " 14 14 11  3  2  6 14  5 13 11  6 12  2 11 11  6  1 13 11  2  6  6  1 11\n",
            "  5  6  6  4  1  6 14  1  3 14 11 14  6  3  6  4 14]\n",
            "[ 3 11  6 11  2 11 12 13 11  3 10 11  6 12 11  6  6  0  6  2 11  6 12  0\n",
            "  6  4 14  4  4 14 11  9 11 14  4 12  2 12  6  3  6  6  6  1  2  2  2  2\n",
            "  7 11  0  5  6  1  1  6  8  7  3  2  3 11  2 11 11  2  0  0  1  2 12  6\n",
            " 14 14 11  3  2  6 14  5 13 11  6 12  2 11 11  6  1 13 11  2  6  6  1 11\n",
            "  5  6  6  4  1  6 14  1  3 14 11 14  6  3  6  4 14]\n",
            "                           precision    recall  f1-score   support\n",
            "\n",
            "                   BENIGN       1.00      1.00      1.00         5\n",
            "                     DDoS       1.00      1.00      1.00         8\n",
            "                 PortScan       1.00      1.00      1.00        14\n",
            "                      Bot       1.00      1.00      1.00         8\n",
            "             Infiltration       1.00      1.00      1.00         6\n",
            "  Web Attack  Brute Force       1.00      1.00      1.00         3\n",
            "          Web Attack  XSS       1.00      1.00      1.00        24\n",
            "Web Attack  Sql Injection       1.00      1.00      1.00         2\n",
            "              FTP-Patator       0.00      0.00      0.00         0\n",
            "              SSH-Patator       1.00      1.00      1.00         1\n",
            "            DoS slowloris       1.00      1.00      1.00         1\n",
            "         DoS Slowhttptest       1.00      0.95      0.98        21\n",
            "                 DoS Hulk       1.00      1.00      1.00         7\n",
            "            DoS GoldenEye       1.00      1.00      1.00         3\n",
            "               Heartbleed       1.00      1.00      1.00        10\n",
            "\n",
            "                 accuracy                           0.99       113\n",
            "                macro avg       0.93      0.93      0.93       113\n",
            "             weighted avg       1.00      0.99      1.00       113\n",
            "\n",
            "{'BENIGN': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 5}, 'DDoS': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 8}, 'PortScan': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 14}, 'Bot': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 8}, 'Infiltration': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6}, 'Web Attack  Brute Force': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3}, 'Web Attack  XSS': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 24}, 'Web Attack  Sql Injection': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}, 'FTP-Patator': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 0}, 'SSH-Patator': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, 'DoS slowloris': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, 'DoS Slowhttptest': {'precision': 1.0, 'recall': 0.9523809523809523, 'f1-score': 0.975609756097561, 'support': 21}, 'DoS Hulk': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 7}, 'DoS GoldenEye': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3}, 'Heartbleed': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 10}, 'accuracy': 0.9911504424778761, 'macro avg': {'precision': 0.9333333333333333, 'recall': 0.9301587301587302, 'f1-score': 0.9317073170731708, 'support': 113}, 'weighted avg': {'precision': 1.0, 'recall': 0.9911504424778761, 'f1-score': 0.9954672998057414, 'support': 113}}\n",
            "0.9317073170731708\n",
            "\\begin{table}\n",
            "\\centering\n",
            "\\caption{MLPClassification Report (Original)}\n",
            "\\begin{tabular}{lrrrr}\n",
            "\\toprule\n",
            "{} &  precision &  recall &  f1-score &  support \\\\\n",
            "\\midrule\n",
            "BENIGN                    &       1.00 &    1.00 &      1.00 &     5.00 \\\\\n",
            "DDoS                      &       1.00 &    1.00 &      1.00 &     8.00 \\\\\n",
            "PortScan                  &       1.00 &    1.00 &      1.00 &    14.00 \\\\\n",
            "Bot                       &       1.00 &    1.00 &      1.00 &     8.00 \\\\\n",
            "Infiltration              &       1.00 &    1.00 &      1.00 &     6.00 \\\\\n",
            "Web Attack  Brute Force   &       1.00 &    1.00 &      1.00 &     3.00 \\\\\n",
            "Web Attack  XSS           &       1.00 &    1.00 &      1.00 &    24.00 \\\\\n",
            "Web Attack  Sql Injection &       1.00 &    1.00 &      1.00 &     2.00 \\\\\n",
            "FTP-Patator               &       0.00 &    0.00 &      0.00 &     0.00 \\\\\n",
            "SSH-Patator               &       1.00 &    1.00 &      1.00 &     1.00 \\\\\n",
            "DoS slowloris             &       1.00 &    1.00 &      1.00 &     1.00 \\\\\n",
            "DoS Slowhttptest          &       1.00 &    0.95 &      0.98 &    21.00 \\\\\n",
            "DoS Hulk                  &       1.00 &    1.00 &      1.00 &     7.00 \\\\\n",
            "DoS GoldenEye             &       1.00 &    1.00 &      1.00 &     3.00 \\\\\n",
            "Heartbleed                &       1.00 &    1.00 &      1.00 &    10.00 \\\\\n",
            "accuracy                  &       0.99 &    0.99 &      0.99 &     0.99 \\\\\n",
            "macro avg                 &       0.93 &    0.93 &      0.93 &   113.00 \\\\\n",
            "weighted avg              &       1.00 &    0.99 &      1.00 &   113.00 \\\\\n",
            "\\bottomrule\n",
            "\\end{tabular}\n",
            "\\end{table}\n",
            "\n",
            "[[1 0 0 1 0 0 1 0 0 0 0 1 0 1 0]\n",
            " [0 8 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 9 0 1 0 1 0 0 0 0 0 2 1 0]\n",
            " [0 0 0 3 1 3 0 0 0 0 0 0 0 0 1]\n",
            " [0 0 0 1 3 0 0 0 0 0 0 0 1 0 1]\n",
            " [0 0 0 0 0 2 0 0 0 0 0 0 1 0 0]\n",
            " [0 0 1 2 1 1 8 1 0 0 0 3 0 0 7]\n",
            " [0 0 0 0 0 0 0 1 0 0 0 0 1 0 0]\n",
            " [0 0 1 0 0 0 0 0 0 0 0 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 0 0 0 0 1 0 0 0 0]\n",
            " [0 0 0 0 0 0 6 1 0 0 1 9 0 0 3]\n",
            " [0 0 5 0 0 1 0 0 0 0 0 0 1 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 1 0 0 0 2 0]\n",
            " [0 0 0 0 0 0 1 0 0 0 0 0 0 0 9]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x6000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14 11 11 14  2 11  2 13 14  5 10  6  6  2 11 14  6  6 14  4  7 14  2 11\n",
            "  6  3 14  4  4 14 11  9  6 14 14  2  2  5  3  3  6  6 11  1  2  2 12 12\n",
            "  7  6  0  5 14  1  1 14  2 12  3  2  4 11  2  6 11  2 13  3  1  6  2 14\n",
            " 14  6 11  3  2  2 14 12 13 14  5 12 13  6 11 11  1  9 11  2  4  6  1 10\n",
            "  5  7  3 12  1  6 14  1  5 14  6 14 14  5  6  4 14]\n",
            "[ 3 11  6 11  2 11 12 13 11  3 10 11  6 12 11  6  6  0  6  2 11  6 12  0\n",
            "  6  4 14  4  4 14 11  9 11 14  4 12  2 12  6  3  6  6  6  1  2  2  2  2\n",
            "  7 11  0  5  6  1  1  6  8  7  3  2  3 11  2 11 11  2  0  0  1  2 12  6\n",
            " 14 14 11  3  2  6 14  5 13 11  6 12  2 11 11  6  1 13 11  2  6  6  1 11\n",
            "  5  6  6  4  1  6 14  1  3 14 11 14  6  3  6  4 14]\n",
            "                           precision    recall  f1-score   support\n",
            "\n",
            "                   BENIGN       0.20      1.00      0.33         1\n",
            "                     DDoS       1.00      1.00      1.00         8\n",
            "                 PortScan       0.64      0.56      0.60        16\n",
            "                      Bot       0.38      0.43      0.40         7\n",
            "             Infiltration       0.50      0.50      0.50         6\n",
            "  Web Attack  Brute Force       0.67      0.29      0.40         7\n",
            "          Web Attack  XSS       0.33      0.47      0.39        17\n",
            "Web Attack  Sql Injection       0.50      0.33      0.40         3\n",
            "              FTP-Patator       0.00      0.00      0.00         0\n",
            "              SSH-Patator       1.00      0.50      0.67         2\n",
            "            DoS slowloris       1.00      0.50      0.67         2\n",
            "         DoS Slowhttptest       0.45      0.69      0.55        13\n",
            "                 DoS Hulk       0.14      0.17      0.15         6\n",
            "            DoS GoldenEye       0.67      0.50      0.57         4\n",
            "               Heartbleed       0.90      0.43      0.58        21\n",
            "\n",
            "                 accuracy                           0.51       113\n",
            "                macro avg       0.56      0.49      0.48       113\n",
            "             weighted avg       0.60      0.51      0.53       113\n",
            "\n",
            "{'BENIGN': {'precision': 0.2, 'recall': 1.0, 'f1-score': 0.33333333333333337, 'support': 1}, 'DDoS': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 8}, 'PortScan': {'precision': 0.6428571428571429, 'recall': 0.5625, 'f1-score': 0.6000000000000001, 'support': 16}, 'Bot': {'precision': 0.375, 'recall': 0.42857142857142855, 'f1-score': 0.39999999999999997, 'support': 7}, 'Infiltration': {'precision': 0.5, 'recall': 0.5, 'f1-score': 0.5, 'support': 6}, 'Web Attack  Brute Force': {'precision': 0.6666666666666666, 'recall': 0.2857142857142857, 'f1-score': 0.4, 'support': 7}, 'Web Attack  XSS': {'precision': 0.3333333333333333, 'recall': 0.47058823529411764, 'f1-score': 0.39024390243902435, 'support': 17}, 'Web Attack  Sql Injection': {'precision': 0.5, 'recall': 0.3333333333333333, 'f1-score': 0.4, 'support': 3}, 'FTP-Patator': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 0}, 'SSH-Patator': {'precision': 1.0, 'recall': 0.5, 'f1-score': 0.6666666666666666, 'support': 2}, 'DoS slowloris': {'precision': 1.0, 'recall': 0.5, 'f1-score': 0.6666666666666666, 'support': 2}, 'DoS Slowhttptest': {'precision': 0.45, 'recall': 0.6923076923076923, 'f1-score': 0.5454545454545455, 'support': 13}, 'DoS Hulk': {'precision': 0.14285714285714285, 'recall': 0.16666666666666666, 'f1-score': 0.15384615384615383, 'support': 6}, 'DoS GoldenEye': {'precision': 0.6666666666666666, 'recall': 0.5, 'f1-score': 0.5714285714285715, 'support': 4}, 'Heartbleed': {'precision': 0.9, 'recall': 0.42857142857142855, 'f1-score': 0.5806451612903225, 'support': 21}, 'accuracy': 0.5132743362831859, 'macro avg': {'precision': 0.5584920634920636, 'recall': 0.4912168713639302, 'f1-score': 0.4805523334083523, 'support': 113}, 'weighted avg': {'precision': 0.6036978508217447, 'recall': 0.5132743362831859, 'f1-score': 0.5267911064447831, 'support': 113}}\n",
            "0.4805523334083523\n",
            "\\begin{table}\n",
            "\\centering\n",
            "\\caption{MLPClassification Report (JSMA)}\n",
            "\\begin{tabular}{lrrrr}\n",
            "\\toprule\n",
            "{} &  precision &  recall &  f1-score &  support \\\\\n",
            "\\midrule\n",
            "BENIGN                    &       0.20 &    1.00 &      0.33 &     1.00 \\\\\n",
            "DDoS                      &       1.00 &    1.00 &      1.00 &     8.00 \\\\\n",
            "PortScan                  &       0.64 &    0.56 &      0.60 &    16.00 \\\\\n",
            "Bot                       &       0.38 &    0.43 &      0.40 &     7.00 \\\\\n",
            "Infiltration              &       0.50 &    0.50 &      0.50 &     6.00 \\\\\n",
            "Web Attack  Brute Force   &       0.67 &    0.29 &      0.40 &     7.00 \\\\\n",
            "Web Attack  XSS           &       0.33 &    0.47 &      0.39 &    17.00 \\\\\n",
            "Web Attack  Sql Injection &       0.50 &    0.33 &      0.40 &     3.00 \\\\\n",
            "FTP-Patator               &       0.00 &    0.00 &      0.00 &     0.00 \\\\\n",
            "SSH-Patator               &       1.00 &    0.50 &      0.67 &     2.00 \\\\\n",
            "DoS slowloris             &       1.00 &    0.50 &      0.67 &     2.00 \\\\\n",
            "DoS Slowhttptest          &       0.45 &    0.69 &      0.55 &    13.00 \\\\\n",
            "DoS Hulk                  &       0.14 &    0.17 &      0.15 &     6.00 \\\\\n",
            "DoS GoldenEye             &       0.67 &    0.50 &      0.57 &     4.00 \\\\\n",
            "Heartbleed                &       0.90 &    0.43 &      0.58 &    21.00 \\\\\n",
            "accuracy                  &       0.51 &    0.51 &      0.51 &     0.51 \\\\\n",
            "macro avg                 &       0.56 &    0.49 &      0.48 &   113.00 \\\\\n",
            "weighted avg              &       0.60 &    0.51 &      0.53 &   113.00 \\\\\n",
            "\\bottomrule\n",
            "\\end{tabular}\n",
            "\\end{table}\n",
            "\n",
            "Name, original f1_macro, JSMA f1 macro\n",
            "MLP 0.9317073170731708 0.4805523334083523\n",
            "  Model Name  Original F1-Score Macro Avg  Adversarial F1-Score Macro Avg\n",
            "0        MLP                         0.93                            0.48\n",
            "  Model Name  Original F1-Score Macro Avg  Adversarial F1-Score Macro Avg\n",
            "0        MLP                         0.93                            0.48\n",
            "\\begin{table}\n",
            "\\centering\n",
            "\\caption{Model Types sorted by Adversarial F1-Score Macro Avg}\n",
            "\\begin{tabular}{llrr}\n",
            "\\toprule\n",
            "{} & Model Name &  Original F1-Score Macro Avg &  Adversarial F1-Score Macro Avg \\\\\n",
            "\\midrule\n",
            "0 &        MLP &                         0.93 &                            0.48 \\\\\n",
            "\\bottomrule\n",
            "\\end{tabular}\n",
            "\\end{table}\n",
            "\n",
            "  Model Name  F1-Score   Conditions\n",
            "0        MLP  0.931707     Original\n",
            "0        MLP  0.480552  Adversarial\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Ctm3bIiMjA5aWlmVuIzMzs8Qu048++kihSw8AatSoUezURXp6Ou7evVvqFboZGRllv8lX+Pv7Y/HixZDJZHB1dZUHw+s2bdqEOXPmYMiQIfDy8oKJiQkEQUDv3r3lF/dkZWWhsLAQVlZWb9xuSe+/PPvldUUfXgICApCVlSWf3rFjR2zevBmJiYmoV68ebG1t0aJFC0RERKBXr17IysrCkSNHFC4CLfrHVbNmTYVtvP66NPn5+W8MfgDw8PCAt7c3Vq5cWeKHrrL2R9H8V5XWDf/qhykA8lMbpqamxaa/eqHWoUOHMGHCBHTv3h1jx45FjRo1IBKJMHz48Le+IK179+6YMGECTp06hY8//hiRkZFwdXWFg4ODvE2dOnWwdu1ahIaGYvLkycjPz4erqysmTZoET0/Pcm0vPz8fMTExaNq0KapXry7/3fj8888RExODvXv3yu/ikUql8n37qtenVa1aFT4+PoiKisL48eNRWFiI6OhodOrUSX76qygkBw8eXGJdr+/7kn52I0aMQJUqVRAVFYXVq1ejWrVq6NSpE7777juYm5vj+fPnGDp0KAwNDTFx4kTY29tDX18f27Ztw65du4qt703/lwCgsLAQo0aNQmpqKgIDA1G/fn0YGBjg4MGDWL16tVIX8hkaGvKCv0rkgwh/VZg5c6bCfapl/cEYGBhg2LBhiIyMxK1bt9C2bVvUqFEDN2/eLHMbpqamJd7y9Pjx42L/FEq67c7MzAzm5uaYNm1aiesvOj+pjM6dO2Pu3LnYuXMnfvjhh1Lb7dmzB61bt1a4r/f1c7AmJibQ0dEp8SLA15X0vsqzX15XdHQfGhqK0NDQEudPmDABwMsj5+nTp+PBgweIi4uDVCqVHxUC//3Mnzx5gmrVqsmnK3sEamZmpvABpCwTJkxAz549sW3btmLzTE1NSzyCK9pHyvyuvIs9e/agTp06mD9/vnyaVCp94wexsrRv3x7GxsaIiIiAmZkZbty4geDg4GLtWrVqhVatWiE/Px9///03fv75Z4wYMQKxsbHyc+rKOHz4MDIyMnD+/Hm0aNGi2PywsDD07t0bNWrUgL6+fqm/f68f/UskEoSFheHvv/9GXl4e0tLSFD7AmZmZAQDmz58vP1h41et3NZT0s9PX18fw4cMxfPhwpKWl4ciRI/jxxx+Rm5uLpUuX4uLFi3jw4AG2bNkCDw8P+XKbN29+w14p3b179xAfH4+FCxcqvJ/Dhw8rvY6MjAz5+6cPn9aEf2lHvqmpqSV+ECjq/iz65P7JJ59gz549OHToENq1a1fiulq0aIFjx44hOztbftFPdnY2Dh8+rNSRjbe3NzZv3gwbGxulj0ZLY2JiguHDh+PatWvo0qVLqe3y8vKK3Q70+lXvVatWRfPmzeUXESrTxfiqt90v+fn5iI6OhpubGyZOnFhs/o8//ojIyEiMHz8eIpEInTt3xuzZsxEVFYVjx47Bw8ND4Z97kyZNIBKJsG/fPoWBaV49VVCW+vXrQyqV4tGjR2/sBXF2dkbHjh2xZs0aWFhYKHTPe3p6Yt++ffj7778VLh6Mjo5GzZo1SwwVVcrLyyvW6xQREaHUVd+lMTAwQJcuXRAVFQUDAwPo6+uXesEjAFSpUgWtW7dGTk4ORo8ejaSkpHKFf1hYGKpVq4ZVq1YVuysnPDwcu3fvxr1792Bvbw8XFxfs378fgYGB8rb//PMPHjx4UCz8W7ZsCSsrK0RERCAvLw+2trYKAdysWTNUr14dd+/eVbiL4W1ZWFigV69eOHr0qPzgIjc3F8B/PTnAy96gku5mUVZeXl6xdUqlUkRFRSm9jqSkpHIdgND7rVKE/5kzZ5Ceni7/dB8fHy8/snv9/O7rgoODkZ2djY4dO6Jhw4YoLCzE5cuXsXbtWtjb28vPt3br1g07d+7ExIkTMXz4cLi5ueH58+c4fvw4Bg0aBAcHB4wePRpHjhzB4MGDERAQAJFIhNDQUOTm5sqvvC/L4MGDERMTg/79+2Pw4MGoV68ecnNz8e+//+LcuXP43//+V679UtpANK/y9vZGaGgoVq9eDVdXV5w6dQr79+8v1m7y5MkYOHAg+vTpgyFDhsDKygr3799HQkICpk+fXuY23na/HD16FBkZGQgKCirxToU+ffogJCQEp0+fRqtWrWBkZIT27dtjy5YtSEtLw+zZsxXaOzg4wNfXF8uWLYMgCHB2dsapU6fkRz9vumW0KAQuXbqk1CmQb775Bn5+fnj8+LHCh5zu3btj48aNCAwMxIQJE1CrVi1ERUXhxIkTmDVrlsruZCmNt7c3Dh48iHnz5qFt27a4fPmyfLyLd+Hv74/t27djx44d+Pzzz4sdJW7btg3nzp3Dp59+Cmtrazx9+hS//PILLC0tIRaLAbz8Wx48eDDmzZtX6oh9ReM8dOvWDa1bty42/6OPPsLu3bsRHh6OcePGYdy4cRg6dChGjx6Nvn37Ij09HcuXLy+xS15HRwd+fn7Yvn07CgoKMGjQIIWjdyMjI0yePBmzZs1Ceno6Pv30UxgbGyMlJQVnz56Fp6fnG6+GHzVqFBo1agRnZ2eYmJjg6tWrOH78uPwuj2bNmsHIyAgzZ87EuHHjkJOTg//973+oUaNGiVfxK6N+/fqwtbXFkiVLoKOjAz09vXKNYyIIAi5fvlzsTgP6cFWK8F++fLnC+fwtW7Zgy5YtAPDGEb++/PJLREdHY8uWLUhNTYVUKoWVlRW6deuG0aNHy7vx9PX1sW7dOqxYsQI7duzAypUrYWZmhmbNmsn/yTVq1AibNm3CkiVLEBQUBEEQ4Obmhs2bN7/xdjYAMDY2xu+//46VK1ciNDQUqampMDY2Rr169dCxY8e33T1lGjNmDLKysvDrr7/ixYsX8PT0xNq1a9GhQweFdq6urti2bRt+/vlnzJkzB/n5+bCxsUGPHj3euI233S9hYWGoXr16qR/gfH19MX/+fISHh8tvJ5NIJIiJiYGBgUGJy82ePRvVq1fH2rVrIZVK0bJlSwQHB2PEiBFvHBDFzs4Orq6uOHz4sFI/DwcHB0gkkmI9KdWqVcOmTZvw008/YdGiRXj+/Dnq1atXrEtWXXr37o3k5GTs2rUL27dvR5MmTbB69WqlPiyWpVmzZqhbty7u3LlT4vto1KgRjh07hsWLF+PJkyfyv59FixbJe5MEQYBMJpNfOFiSqKgoFBQUoGfPniXOd3BwgLu7O8LDwxEYGIiPP/4YixYtwvLlyzF27FjUqVMHU6dOLXX4bolEIj/FVNL76Nu3L6ytrbF27VpER0dDJpOhVq1aaN68ebGLXUvSokUL7Nu3D1u3bkVubi6sra3x9ddfY+TIkQBeXli3YsUKLFiwAOPGjYOlpSW++uorZGZmvvWYCFWqVMHKlSsxa9YsTJkyBaampujZsydsbGzKPC1Y5Pz588jMzCyzN4c+LCJBeMNoJESV3Lp16/DTTz/h0KFDsLGxKbPt7t27MXfuXMTFxb3xzgeiymLGjBm4efOmfNAu+vBViiN/ImUdPnwYN2/eRKNGjaCjo4Nz585h/fr16Ny58xuDH3h5+ic0NBRbt27FsGHDNFAxUcVKS0tDeHg41q5dW9GlkAox/EmrVK9eHQcPHsSaNWuQm5uLWrVqYeDAgRg3bpxSy+vp6eHHH3/ElStX1Fwp0fvhwYMHmDJlSol3VdCHi93+REREWuaDGOGPiIiIVKfSdPvn5eUhPj4eFhYWar9VioiospDJZEhLS4OLi0u5x/CgD1elCf/4+Hj5U8uIiKh8Xh9RsLykUimSkpLkAwpRxdLV1YWZmRk++uijEscwqTThXzRgx5YtW5QagIWIiIBHjx7hyy+/LPejnF+XlJQEY2Nj1K1bV+VDUlP5CIIAqVSKlJQUJCUlwd7evlibShP+RV39VlZWsLOzq+BqiIg+LO96ujQvL4/B/54QiUSoUqUKbG1tSx3ojhf8ERGRSjD43y9lDVnO8CciIpWT5kk/qPVqm0rT7U9ERO8PfUN9TKgxQeXrXfJ0idJtMzMz4e3tjd69e5f6DIOBAwdi6NChaNu2rapKVIlly5ahYcOGZT6VFXj7+nnkT0RElVLRI8H37NmD/Px8jWzzXR6N/eo6vvnmmzcG/7vgkT8REVVKu3btwnfffYdffvkFsbGx6Ny5M27duoXvv/8eOTk5EIvFePHiBQDg3LlzmDNnDsLDw+XL9+jRA0FBQfD09ERYWBi2bt0KmUwGIyMjhISEoH79+ti9ezciIyNRvXp13L17Fz/99BNiY2MRHR0NAwMDiEQibNy4ESYmJpg4cSISExMhlUphb2+PefPmwdTUFKdPn8acOXPg4uKCq1evYvz48di/fz9cXFwwYMAAnDx5EkuXLsWLFy8gk8kwcuTId37CIsOfiIgqnYSEBGRkZKBVq1ZIS0vDrl270LlzZ0yePBkDBw5E9+7dcfHiRfTr1w8A4OHhgZycHCQkJKBRo0a4fv06srKy0KJFC5w7dw579+7Fli1bUKVKFRw9ehRTp07F77//DgD4559/EBERAXt7e2RkZODXX39FXFwcDA0NkZ2dLR88adq0aTA3NwcALFmyBKGhoZg0aRIA4NatW5g1axbc3d0BAPv375e/l8aNG2Pr1q3Q1dXF48eP0aNHD3h5ecHU1PSt9w/Dn4iIKp0//vgDEokEIpEIHTt2xJw5c/DgwQPcuHEDEokEANC0aVOIxWL5Mv7+/ggLC8P333+PsLAw+Pv7QyQS4dChQ0hISECvXr0AvLyPPisrS75cs2bN5PfSGxsbw97eHpMnT4aXlxfatGkDIyMjAEBERASioqIglUqRk5ODunXrytdRp04defC/Lj09HVOnTsXdu3ehq6uLzMxMJCYmomnTpm+9fxj+RBqQXyBDFT0OO02K+HuhHvn5+YiOjkaVKlUQEREB4OUIhGFhYWUu5+/vj969e+Pbb79FdHQ0tm/fDuBl2Pfs2RPffPNNictVr15d/r2uri527NiB8+fP49SpU+jRowfWrl2L7OxsbNu2Db///jvMzc0RFRWFHTt2yJerVq1aqXWFhISgXbt2WLFiBUQiEXx8fOSnK94Ww59IA6ro6aLXrG0VXQa9Z3YG96voEiql2NhY1KtXD9u2/fc3d+HCBUyZMgVisRhRUVGQSCS4dOkSbty4IW9jY2ODBg0aYM6cOWjQoAFsbW0BAO3atcOUKVPQp08fWFlZQSaT4dq1a3BxcSm27ezsbOTk5MDT0xOenp64ePEibt68ierVq8PIyAhmZmbIz8/Hrl27lH4/z549g62tLUQiEU6cOIG7d+++w955ieFPREQqJ82Tluu2vPKsV99Qv8w2u3btgp+fn8I0d3d3FBYWIigoCD/99BNCQ0MhFovRpEkThXbdu3fH5MmTsXDhQvm0Fi1aYPz48Rg1ahRkMhmkUik6depUavgHBgYiLy8PgiCgcePG6NixI3R0dBAZGQkfHx/UqFEDHh4euHz5slLveeLEiZg5cyaWL1+OJk2awNHRUanlyiISBEF457W8B5KSktC+fXvExsZyeF96L/HIn173Phz5q+p/57Vr1+Dk5KTCykgVSvu58D5/IiIiLcPwJyIi0jIMfyIiIi3D8CciItIyDH8iIiItw/AnIiLSMgx/IiJSufyCd3+63busNz8/H/Pnz0eHDh3QqVMn+Pv74+DBg6W2nzZtGs6dO/fG9S5btgwxMTFK11uSgQMH4vDhw++0jnfFQX6IiEjl1DWqpbJjI4SEhCAnJwd79uyBgYEBbty4ga+//hqmpqZo0aKFQluZTIa5c+cqtd7Shvj90DD8iYioUnnw4AH27t2Lw4cPw8DAAAAgFosxcuRIrFixAhKJpNhjeOfNm4ehQ4eibdu2SElJweTJk/H48WPUrl0bAODl5YUBAwYgKChI/qjd5cuXIzExEc+ePcP9+/dhb2+PZcuWoWrVqmp5DK8qMfyJiKhSuXHjBuzt7WFmZqYwvWnTpli2bBkkEonCY3hfN2fOHLRs2RKjR4/GgwcP4OfnBy8vrxK3FR8fjz/++APGxsYYNmwYoqKi0Lt3b7U8hleVGP5ERFSpKDNq/auP4X3d6dOn8cMPPwAAbG1t0bp161LX4+XlBbxiEqMAAB7tSURBVBMTEwCAq6sr7t27B0A9j+FVJV7wR0RElYpYLMa9e/eQkZGhMP3ixYvyh+K8+hjed1F0WgF4+ThfmezlBYkhISHw9PREVFQUIiIiYGVl9c6P4VUlhj8REVUqdnZ26NSpE0JCQuSBe+PGDaxevRpjx4594/Kenp4ICwsDACQnJ+PUqVPlrkEdj+FVJXb7ExFRpTNjxgwsXrwYXbp0gb6+PgwMDDBt2jR4enoiKSmpzGWnTZuGyZMnIyoqCnZ2dnB1dYWRkVG5tq+Ox/CqEh/pS6QhfKQvva4yP9I3v0CGKnq6qihRgbrW+6q8vDzo6elBT08Pqamp+OKLL/Drr7+ifv36at2uOpT2SF8e+RMRkcqpK6DVHfwAcOfOHUyZMgWCIKCgoABjx479IIO/LAx/IiKiVzRq1AgREREVXYZa8YI/IiIiLcPwJyIilagkl5BVGoWFhaXOY/gTEdE7MzQ0xJMnT/gB4D0gCALy8/Px4MGDUscz4Dl/IiJ6Z3Z2dkhKSkJaWlpFl0IA9PT0YGpqio8++qjk+Rquh4iIKiF9fX3Uq1evossgJWks/BMTExEUFISMjAyYmZlhwYIFqFu3rkKbJ0+e4Pvvv0dycjIKCgrQsmVL/PDDD9DT42cUIiIiVdHYOf8ZM2agf//+2L9/P/r374/g4OBibVavXg0HBwdERUUhMjISV65cwYEDBzRVIhERkVbQSPg/efIEV69eha+vLwDA19cXV69eRXp6ukI7kUiE58+fo7CwEPn5+ZBKpahVq5YmSiQiItIaGgn/5ORk1KpVC7q6L0dm0tXVhaWlJZKTkxXajR49GomJifDy8pJ/NW/eXBMlEhERaY336la/ffv2wdHREXFxcTh27BjOnTuHffv2VXRZRERElYpGwt/a2hopKSny5xzLZDKkpqbC2tpaod3mzZvRrVs36OjowNjYGO3atcPp06c1USIREZHW0Ej416xZE05OToiOjgYAREdHw8nJCebm5grt7OzscOzYMQBAfn4+Tp48iYYNG2qiRCIiIq2hsW7/kJAQbN68GT4+Pti8eTNmzpwJAAgICMDly5cBAFOnTsXff/8NPz8/+Pv7o27duujdu7emSiQiItIKGruB3sHBATt37iw2PTQ0VP69vb09NmzYoKmSiIiItNJ7dcEfERERqR/Dn4iISMsw/ImIiLQMw5+IiEjLMPyJiIi0DMOfiIhIyyh1q9/JkydLnF6lShVYWVnB1tZWpUURERGR+igV/tOmTUNqaioAwMzMDBkZGQBejtz3+PFjODo6YvHixahbt67aCiUiIiLVUKrb/4svvsDAgQNx7tw5xMXF4dy5cxg0aBD69u2Ls2fPwsXFRT5iHxEREb3flAr/jRs3YuLEiTA0NAQAGBoaYvz48fjtt99QrVo1BAUFIT4+Xq2FEhERkWooFf7VqlWTj79f5MqVK6haterLlejwukEiIqIPhVLn/MeNG4ehQ4eiXbt2sLa2xqNHj3D48GFMnz4dwMsLAn18fNRaKBEREamGUuHv7+8PFxcX7N+/H6mpqahbty62b9+OBg0aAADatm2Ltm3bqrVQIiIiUg2lwv/atWtwcnKShz0RERF9uJQK/6FDh8Lc3Bxdu3aFn58fateure66iIiISE2UCv+4uDgcP34c0dHRkEgkaNiwIXx9fdGlSxfUrFlT3TUSERGRCikV/rq6umjTpg3atGmDvLw8xMbGYtu2bViwYAFv8SMiIvrAlOsevRcvXuDw4cOIiYlBfHw8PDw81FUXERERqYlSR/5Hjx5FVFQUDh06hAYNGqBLly4ICQmBhYWFuusjIiIiFVMq/BcsWICuXbsiPDwc9vb28ulZWVkwMTFRW3FERESkekqFf0xMjPx7mUyGo0ePIjw8HEeOHMGlS5fUVhwRERGpnlLhDwBXr15FeHg4oqKikJGRga5du2Lz5s3qrI2IiIjUoMwL/h4/foz169fDz88PvXr1wr///ospU6bA1NQU33//PVxdXTVVJxEREalImUf+n332GYyNjTFmzBiFe/oXLVqkkeKIiIhI9co88vfz80N+fj7Wr1+PX3/9FdevX9dUXURERKQmZYb//PnzceLECXzzzTe4fPky/P394efnh+zsbDx9+lRTNRIREZEKvXGQn6pVq8Lf3x+//vorYmNj0aVLF1hZWcHf3x/ffPONJmokIiIiFSrXCH82NjYYNWoU9u3bh02bNsHMzExddREREZGaKH2r3+vc3d3h7u6uylqIiIhIA8p15E9EREQfPoY/ERGRlmH4ExERaRmlz/nfuXMH0dHRSE1NhaWlJbp27Yp69eqpszYiIiJSA6WO/A8dOoQePXogMTERpqamSExMxBdffIHY2Fh110dEREQqptSR/5IlS7Bq1Sq0atVKPu306dOYPXs22rdvr7biiIiISPWUOvJ/9OgRPDw8FKY1b94cjx49UnpDiYmJ6NOnD3x8fNCnTx/cuXOnxHYxMTHw8/ODr68v/Pz88PjxY6W3QURERG+mVPg3atQI69evV5i2YcMGODk5Kb2hGTNmoH///ti/fz/69++P4ODgYm0uX76MFStWYP369YiOjsbWrVthbGys9DaIiIjozZTq9g8JCcGoUaOwceNGWFtbIzk5GVWrVsXq1auV2siTJ09w9epVbNiwAQDg6+uL2bNnIz09Hebm5vJ2v/76K4YOHQoLCwsAYPATERGpwRvDv7CwEKmpqQgLC8O1a9fkV/u7ublBX19fqY0kJyejVq1a0NXVBQDo6urC0tISycnJCuF/+/Zt2NnZ4csvv0ROTg4+//xzjBo1CiKR6C3fHhEREb3ujeGvo6OD0aNH48KFC8XO+6uaTCbD9evXsWHDBuTn5+Prr7+GjY0N/P391bpdIiIibaLUOf8WLVrg4sWLb70Ra2trpKSkQCaTAXgZ8qmpqbC2tlZoZ2Njg06dOqFKlSowMjJC+/btcenSpbfeLhERERWn1Dl/GxsbBAQEoH379rCyslLohlfmsb41a9aEk5MToqOjIZFIEB0dDScnJ4Uuf+DltQBHjx6FRCJBQUEBTp06BR8fn3K+JSIiIiqLUuH/4sULdOjQAQCQkpLyVhsKCQlBUFAQVq1aBRMTEyxYsAAAEBAQgHHjxqFJkybo2rUr4uPj0aVLF+jo6MDLywtffPHFW22PiIiISiYSBEGo6CJUISkpCe3bt0dsbCzs7OwquhyiYnrN2lbRJdB7Zmdwv4ougf87tZRS5/zDw8ORkJCgMC0hIQHh4eFqKYqIiIjUR6nwX7ZsWbGL86ysrLBs2TK1FEVERETqo1T4Z2dnw8jISGGasbExsrKy1FIUERERqY9S4e/g4ID9+/crTPvzzz/h4OCglqKIiIhIfZS62n/SpEkYPnw49u7di9q1a+PevXs4efIk1qxZo+76iIiISMWUOvL38PBAVFQUmjRpgtzcXLi6uiI6OhrNmzdXd31ERESkYkod+QOAra0thg8frs5aiIiISAOUDv/Y2FicPXsWT58+xatDAyxcuFAthREREZF6KNXtv2LFCsyYMQOFhYXYt28fzMzMEBcXBxMTE3XXR0RERCqmVPjv2rUL69evx9SpU6Gvr4+pU6di9erVSEpKUnd9REREpGJKhX9WVhbEYjEAQF9fH1KpFK6urjh79qxaiyMiIiLVU+qcv729PW7evImGDRuiYcOG2LZtG0xMTGBqaqru+oiIiEjFlAr/8ePHIyMjAwAwceJETJo0CTk5OZgxY4ZaiyMiIiLVUyr8P/vsM/n3bm5u+PPPP9VWEBEREalXmeH/8OHDN67AxsZGZcUQERGR+pUZ/u3atYNIJAIAhXv7i4hEIly7dk09lREREZFalBn+jRo1Ql5eHrp3745u3brB0tJSU3URERGRmpQZ/uHh4bhx4wbCwsLQr18/ODg4QCKRoGPHjjA0NNRUjURERKRCb7zPXywWY8qUKTh06BAGDx6MI0eOwMvLC1euXNFEfURERKRiSg3yAwB37tzB2bNncfHiRTg5OXFoXyIiog9Umd3+GRkZ2LNnD8LCwvD8+XNIJBJs3ryZV/gTERF9wMoMf29vb9jZ2UEikcDNzQ0AcPfuXdy9e1fepnXr1uqt8AMkzZNC31C/ossgIiIqUZnhb2FhgRcvXmDHjh3YsWNHsfkikQixsbFqK+5DpW+ojwk1JlR0GfQeWfJ0SUWXQEQkV2b4Hzp0SFN1EBERkYYofcEfERERVQ4MfyIiIi3D8CciItIyDH8iIiIto3T4P336FOHh4QgNDQUApKSk4NGjR2orjIiIiNRDqfA/c+YMOnXqhKioKKxatQrAy/v9Q0JC1FkbERERqYFS4T9v3jwsXboU69atg57ey7sD3dzccOnSJbUWR0RERKqnVPg/ePBAPpKfSCQCAOjr60Mmk6mvMiIiIlILpcLfwcEBx48fV5j2119/QSwWq6UoIiIiUp8yR/grEhQUhBEjRqBNmzbIy8tDcHAwDh06JD//T0RERB8OpY78XV1dERkZiQYNGqBnz56ws7PDH3/8AVdXV3XXR0RERCr2xiN/mUwGd3d3nDt3DgEBAZqoiYiIiNTojUf+urq6qFu3Lp4+ffpOG0pMTESfPn3g4+ODPn364M6dO6W2/ffff+Hm5oYFCxa80zaJiIioOKXO+fv5+WHkyJH46quvYGVlpTCv6C6AN5kxYwb69+8PiUSCiIgIBAcHY+PGjcXayWQyzJgxAx06dFBqvURERFQ+SoX/tm3bAADLly9XmC4SiRAbG/vG5Z88eYKrV69iw4YNAABfX1/Mnj0b6enpMDc3V2i7Zs0atGnTBjk5OcjJyVHqTRAREZHylAr/Q4cOvdNGkpOTUatWLejq6gJ4eSrB0tISycnJCuGfkJCAuLg4bNy4kXcSEBERqYlS4Q8ABQUFuHDhAlJSUmBlZYWmTZvKR/tTBalUiunTp+PHH3+Uf0ggIiIi1VMqvW/fvo1Ro0YhLy8P1tbWSE5OhoGBAVavXg0HB4c3Lm9tbY2UlBTIZDLo6upCJpMhNTUV1tbW8jZpaWm4d+8ehg8fDgDIysqCIAjIzs7G7Nmz3/LtERER0euUCv+ZM2eid+/eGDZsmHx433Xr1iEkJASbNm164/I1a9aEk5MToqOjIZFIEB0dDScnJ4UufxsbG5w+fVr+evny5cjJycGUKVPK+56IiIioDEoN8pOQkIAhQ4bIgx8ABg0ahISEBKU3FBISgs2bN8PHxwebN2/GzJkzAQABAQG4fPlyOcsmIiKit6XUkb+lpSXOnDmjcFvfuXPnYGlpqfSGHBwcsHPnzmLTQ0NDS2wfGBio9LqJiIhIeUqF/4QJEzB69Gi0adMGNjY2ePjwIY4cOYKffvpJ3fURERGRiinV7d++fXvs3r0bDRs2xPPnz9GwYUPs3r2bA/EQERF9gJQ68s/Pz4ednR1Gjx4tnyaVSpGfn48qVaqorTgiIiJSPaWO/IcMGYIrV64oTLty5QqGDRumlqKIiIhIfZQK/xs3bsDNzU1hmqura7mu9iciIqL3g1Lhb2xsjMePHytMe/z4MapWraqWooiIiEh9lAr/jh07YuLEibhx4wZyc3Nx/fp1TJkyBZ07d1Z3fURERKRiSoX/hAkT4ODggF69eqFZs2bo3bs36tWrh4kTJ6q7PiIiIlIxpa72NzAwwIwZMxAcHIynT5+iRo0aCqP9ERER0YejzPDPyckBAFSrVk0+7eDBg7hx4wbc3d3RtWtX9VZHREREKldmt/+ECRNw4MAB+esFCxbg//7v/5Camoo5c+Zg/fr1ai+QiIiIVKvM8L9y5QratWsH4OVAPzt27MCyZcvw888/45dffsGOHTs0UiQRERGpTpnhn5ubCxMTEwBAfHw89PT00KpVKwAv7/NPS0tTf4VERESkUmWGv6WlpXwgnxMnTqB58+byeVlZWRzal4iI6ANU5gV/Q4cOxbBhw+Du7o64uDgsX75cPi8uLg6Ojo5qL5CIiIhUq8zw79WrF+rUqYP4+HgMHjwYHh4e8nkGBgYYO3as2gskIiIi1Xrjff6enp7w9PSUv3706BGsrKzQvn17tRZGRERE6qHUCH+v6tKlizrqICIiIg0pd/gTERHRh63c4S8IgjrqICIiIg0pd/hfuHBBHXUQERGRhrx1t78gCDh79qwqayEiIiINeOvwl0ql+Oqrr1RZCxEREWlAmbf6hYeHlzpPKpWqvBgiIiJSvzLD//vvv4ezs3OJw/jywj8iIqIPU5nhX6dOHUyaNEn+MJ9XvXjxAm5ubmorjIiIiNSjzHP+np6e+Pfff0teUEcHLVq0UEtRREREpD5lHvnPmjWr1Hn6+vrYtGmTygsiIiIi9SrzyD8tLU1TdRAREZGGlBn+Pj4+Cq/5FD8iIqIPX5nh//oV/WfOnFFrMURERKR+ZYa/SCTSVB1ERESkIWVe8CeTyXDq1Cl5D0BBQYHCawBo3bq1eiskIiIilSoz/GvWrImpU6fKX5uZmSm8FolEiI2NVV91REREpHJlhv+hQ4c0VQcRERFpSJnhr0qJiYkICgpCRkYGzMzMsGDBAtStW1ehzcqVKxETEwMdHR3o6+tjwoQJ8Pb21lSJREREWkFj4T9jxgz0798fEokEERERCA4OxsaNGxXauLq6YujQoahatSoSEhIwYMAAxMXFwdDQUFNlEhERVXpv/Ujf8njy5AmuXr0KX19fAICvry+uXr2K9PR0hXbe3t6oWrUqAMDR0RGCICAjI0MTJRIREWkNjYR/cnIyatWqBV1dXQCArq4uLC0tkZycXOoy4eHhsLe3h5WVlSZKJCIi0hoa6/YvjzNnzmDZsmVYv359RZdCRERU6WjkyN/a2hopKSmQyWQAXo4fkJqaCmtr62JtL1y4gO+++w4rV65E/fr1NVEeERGRVtFI+NesWRNOTk6Ijo4GAERHR8PJyQnm5uYK7S5duoQJEybg559/hrOzsyZKIyIi0joaCX8ACAkJwebNm+Hj44PNmzdj5syZAICAgABcvnwZADBz5kzk5eUhODgYEokEEokE169f11SJREREWkFj5/wdHBywc+fOYtNDQ0Pl3+/atUtT5RAREWktjR35ExER0fuB4U9ERKRlGP5ERERahuFPRESkZRj+REREWobhT0REpGUY/kRERFqG4U9ERKRlGP5ERERahuFPRESkZRj+REREWobhT0REpGUY/kRERFqG4U9ERKRlGP5ERERahuFPRESkZRj+REREWobhT0REpGUY/kRERFqG4U9ERKRlGP5ERERahuFPRESkZRj+REREWobhT0REpGUY/kRERFqG4U9ERKRlGP5ERERahuFPRESkZRj+REREWobhT0REpGUY/kRERFqG4U9ERKRlGP5ERERahuFPRESkZRj+REREWobhT0REpGU0Fv6JiYno06cPfHx80KdPH9y5c6dYG5lMhpkzZ6JDhw74/PPPsXPnTk2VR0REpDU0Fv4zZsxA//79sX//fvTv3x/BwcHF2kRFReHevXs4cOAAtm/fjuXLlyMpKUlTJRIREWkFPU1s5MmTJ7h69So2bNgAAPD19cXs2bORnp4Oc3NzebuYmBj06tULOjo6MDc3R4cOHbBv3z58/fXXb9yGTCYDADx69Eg9b6KccnRyKroEeo8kJSUhP/tpRZdB75n34eCm6H9m0f9Q0g4aCf/k5GTUqlULurq6AABdXV1YWloiOTlZIfyTk5NhY2Mjf21tba10mKelpQEAvvzySxVW/g4+qugC6H1ypP2Rii6B3kPtI5ZVdAlyaWlpqFOnTkWXQRqikfDXBBcXF2zZsgUWFhbyDxlERFQ2mUyGtLQ0uLi4VHQppEEaCX9ra2ukpKRAJpNBV1cXMpkMqampsLa2Ltbu4cOHcHV1BVC8J6AshoaG8PDwUHntRESVHY/4tY9GLvirWbMmnJycEB0dDQCIjo6Gk5OTQpc/AHTq1Ak7d+5EYWEh0tPTcfDgQfj4+GiiRCIiIq0hEgRB0MSGbt++jaCgIGRlZcHExAQLFixA/fr1ERAQgHHjxqFJkyaQyWSYNWsWTpw4AQAICAhAnz59NFEeERGR1tBY+BMREdH7gSP8ERERaRmGPxERkZZh+BMREWkZhj8REZGWYfgTERFpGYY/ERGRlmH4ExERaRmGP5EaPHz4EIGBgfDz88PkyZORnp5e0SUREclxkB8iNRg2bBgaNmyI1q1b48CBAygoKMCCBQsquiwiIgCV6Kl+RO+T1NRUrFu3DgDg7e2NHj16VHBFRET/Ybc/kRro6f33uVpHRwcikagCqyEiUsRufyI1aNq0KRo0aCB/fevWLYXXf/zxR0WURUQEgN3+RGqxZs2aii6BiKhUDH8iNfD09Cx13s8//1zmfCIideM5fyIN2717d0WXQERajuFPpGG8zIaIKhrDn0jDeOU/EVU0nvMnUoNx48aVGPKCICAzM7MCKiIi+g/Dn0gN2rZt+1bziIg0geFPpAY5OTkVXQIRUakY/kRqMHv2bDg7O0MsFld0KURExXCEPyI12L17N8LCwpCbm4vu3bvD19cXpqamFV0WEREAhj+RWt2/fx/h4eGIiYmBWCzGqFGj0KhRo4oui4i0HG/1I1Kj2rVrY/Dgwfjqq69w5swZXL58uaJLIiLikT+ROgiCgOPHj2P37t24efMmOnfuDIlEgtq1a1d0aUREDH8idfD29oalpSV69OgBT0/PYvf8v/qEPyIiTWP4E6lBu3bt5N+LRCKFIX1FIhFiY2MroiwiIgAMfyIiIq3DC/6IiIi0DMOfiIhIyzD8iZSUlJQER0dHFBQUvLHt7t270a9fPw1URURUfgx/qpTatWsHFxcXpKenK0z39/eHo6MjkpKSKqiy/z5EBAQEKEyfNGkSli9fXkFVEZE2YfhTpWVra4s9e/bIX1+/fh25ubkVWJGiS5cu4fz58xVdBhFpIYY/VVoSiQTh4eHy1+Hh4fD391do8+zZM0yePBmtWrVC27ZtsWrVKhQWFgIAZDIZFixYgJYtW6J9+/Y4evRosWWnTp0KLy8veHt7Y8mSJZDJZErXN2zYMCxZsqTEeZmZmRgxYgRatWqFFi1aYMSIEXj06JF8/sCBA7FkyRL07dsX7u7uGDlyJJ4+fYqJEyeiWbNm6Nmzp0Lvxu3btzFkyBB4enrCx8cHMTExStdJRJUPw58qraZNmyI7Oxu3b9+GTCbDnj170K1bN4U2s2fPxrNnz3Dw4EFs2rQJERER2LVrFwBgx44dOHz4MMLDw7Fr1y7s27dPYdmgoCDo6enhwIEDCA8Px4kTJ7Bz506l6+vfvz/u3LmDv/76q9i8wsJC9OjRA4cPH8bhw4dhYGCAWbNmKbSJiYnBwoULcezYMdy7dw99+/ZFz549cebMGTg4OGDlypUAXj5eeOjQofD19cVff/2FJUuWYObMmbh165bStRJR5cLwp0qt6Oj/xIkTcHBwQK1ateTzZDIZYmJiMHHiRBgZGcHOzg5DhgxBZGQkAGDv3r0YNGgQrK2tYWZmhhEjRsiXffz4MY4ePYqpU6eiWrVqqFmzJgYPHqxwmuFNDA0NMXLkSCxdurTYvBo1asDHxwdVq1aFkZERRo0ahbNnzyq06dGjB+zt7WFsbIxPP/0UtWvXxscffww9PT106tQJV69eBQAcOXIEtra26NmzJ/T09NC4cWP4+PgU+zBDRNpDr6ILIFIniUSCAQMGICkpCRKJRGHe06dPIZVKYWNjI59mY2ODlJQUAEBqaiqsra0V5hV5+PAhCgoK4OXlJZ9WWFio0F4ZvXr1wrp163Do0CGF6bm5ufjxxx9x/PhxZGZmAgCeP38OmUwGXV1dAMBHH30kb29gYKDw2tDQEDk5OQCABw8e4NKlS/Dw8JDPl8lkxXpBiEh7MPypUrO1tYWdnR2OHj2KuXPnKsyrUaMG9PX18fDhQ/lY+8nJyfLeAQsLCyQnJ8vbv/q9lZUVqlSpglOnTkFP7+3/jKpUqYKxY8di2bJlCuP9r1+/HomJidixYwcsLCxw7do1+Pv7420G5LS2tkaLFi2wYcOGt66TiCoXdvtTpTd37lz89ttvqFatmsJ0XV1ddOrUCUuWLEF2djYePHiADRs2yI+IO3fujE2bNuHRo0fIzMzEmjVr5MtaWlrik08+wfz585GdnY3CwkLcu3cPZ86cKXd9EokEL168QFxcnHza8+fPYWBgABMTE2RkZGDFihVv+e6BNm3a4M6dOwgPD4dUKoVUKsWlS5dw+/btt14nEX3YGP5U6dnb26NJkyYlzps+fTqqVq2KDh06oH///vD19UXPnj0BAL1794aXlxckEgm6d++Ojh07Kiy7cOFCSKVSdOnSBS1atMC4ceOQlpZW7vp0dXUxbtw4ZGRkyKcNGjQIL168QKtWrdCnTx94e3uXe71FjIyMsG7dOsTExMDb2xteXl5YtGgR8vPz33qdRPRh44N9iIiItAyP/ImIiLQMw5+IiEjLMPyJiIi0DMOfiIhIyzD8iYiItAzDn4iISMsw/ImIiLQMw5+IiEjLMPyJiIi0zP8DvWddAKsqKaUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b3bd5fd5-85a4-464b-d61a-80738b15b6ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "[11, 11, 11, 12, 13, 11, 10, 11, 12, 11, 11, 12, 11, 11, 12, 12, 7, 11, 0, 7, 11, 11, 11, 12, 11, 13, 11, 12, 11, 11, 13, 11, 11, 11]\n",
            "--------------------\n",
            "[11, 11, 11, 12, 13, 11, 10, 11, 12, 11, 11, 12, 11, 11, 12, 12, 7, 11, 8, 7, 11, 11, 11, 12, 11, 13, 11, 12, 11, 11, 13, 11, 11, 11]\n",
            "--------------------\n",
            "[[ 0  0  1  0  0  0  0]\n",
            " [ 0  2  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0]\n",
            " [ 0  0  0  1  0  0  0]\n",
            " [ 0  0  0  0 20  0  0]\n",
            " [ 0  0  0  0  0  7  0]\n",
            " [ 0  0  0  0  0  0  3]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x6000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "[6, 2, 6, 6, 6, 6, 2, 6, 6, 14, 14, 14, 2, 6, 6, 6, 6, 2, 2, 2, 2, 5, 6, 6, 2, 2, 2, 2, 6, 14, 14, 2, 6, 14, 5, 6, 2, 6, 2, 6, 6, 5, 6, 6, 6, 14, 14, 14, 6, 6, 14]\n",
            "--------------------\n",
            "[6, 2, 6, 6, 6, 6, 2, 6, 6, 14, 14, 14, 2, 6, 6, 6, 6, 2, 2, 2, 2, 5, 6, 6, 2, 2, 2, 2, 6, 14, 14, 2, 6, 14, 5, 6, 2, 6, 2, 6, 6, 5, 6, 6, 6, 14, 14, 14, 6, 6, 14]\n",
            "--------------------\n",
            "[[14  0  0  0]\n",
            " [ 0  3  0  0]\n",
            " [ 0  0 24  0]\n",
            " [ 0  0  0 10]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x6000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "[4, 4, 4, 9, 4, 4, 4]\n",
            "--------------------\n",
            "[4, 4, 4, 9, 4, 4, 4]\n",
            "--------------------\n",
            "[[6 0]\n",
            " [0 1]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x6000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "[1, 1, 1, 1, 1, 1, 1, 1]\n",
            "--------------------\n",
            "[1, 1, 1, 1, 1, 1, 1, 1]\n",
            "--------------------\n",
            "[[8]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x6000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "[3, 3, 0, 0, 3, 0, 3, 3, 0, 0, 3, 3, 3]\n",
            "--------------------\n",
            "[3, 3, 0, 0, 3, 0, 3, 3, 0, 0, 3, 3, 3]\n",
            "--------------------\n",
            "[[5 0]\n",
            " [0 8]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x6000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyQXb0iu7BtV",
        "outputId": "075d87d7-2228-43ce-86ab-28305e4649cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "375\n",
            "375\n",
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            "0\n",
            "11\n",
            "[['1', '2'], ['0', '11'], ['0', '11'], ['2', '9'], ['0', '12'], ['1', '6'], ['0', '13'], ['1', '14'], ['1', '6'], ['1', '14'], ['1', '6'], ['0', '11'], ['1', '6'], ['0', '12'], ['1', '6'], ['2', '4'], ['0', '10'], ['1', '6'], ['3', '1'], ['1', '14'], ['0', '11'], ['1', '5'], ['0', '12'], ['1', '6'], ['1', '6'], ['1', '14'], ['3', '1'], ['1', '2'], ['1', '2'], ['4', '3'], ['0', '11'], ['1', '14'], ['0', '11'], ['4', '3'], ['1', '2'], ['1', '14'], ['0', '12'], ['0', '11'], ['1', '2'], ['1', '5'], ['1', '6'], ['1', '6'], ['0', '11'], ['1', '6'], ['1', '14'], ['1', '14'], ['0', '7'], ['0', '12'], ['0', '11'], ['0', '11'], ['1', '6'], ['1', '2'], ['1', '6'], ['1', '6'], ['1', '6'], ['4', '0'], ['0', '7'], ['0', '10'], ['1', '5'], ['0', '13'], ['1', '14'], ['1', '6'], ['0', '13'], ['0', '11'], ['3', '1'], ['0', '12'], ['1', '6'], ['1', '2'], ['1', '6'], ['0', '13'], ['0', '8'], ['1', '6'], ['4', '0'], ['2', '4'], ['4', '0'], ['1', '6'], ['0', '12'], ['0', '7'], ['4', '3'], ['0', '11'], ['0', '12'], ['1', '6'], ['0', '11'], ['1', '14'], ['1', '14'], ['3', '1'], ['3', '1'], ['1', '6'], ['1', '6'], ['1', '14'], ['1', '6'], ['0', '12'], ['1', '2'], ['1', '6'], ['1', '6'], ['1', '6'], ['1', '14'], ['0', '11'], ['1', '6'], ['0', '11'], ['1', '6'], ['3', '1'], ['4', '3'], ['1', '6'], ['1', '14'], ['2', '4'], ['4', '0'], ['4', '3'], ['2', '4'], ['1', '6'], ['1', '6'], ['1', '2'], ['4', '0']]\n",
            "0\n",
            "0\n",
            "11\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "hje4vUav7XlZ",
        "outputId": "7e8979e9-1f63-4fa3-ca76-feca14d67eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-37876f0dff53>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_extraction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCountVectorizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTfidfTransformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mhiclass\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLocalClassifierPerNode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLocalClassifierPerParentNode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLocalClassifierPerLevel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipeline\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/hiclass/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mLocalClassifierPerNode\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLocalClassifierPerNode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mLocalClassifierPerParentNode\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLocalClassifierPerParentNode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mLocalClassifierPerLevel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLocalClassifierPerLevel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/hiclass/LocalClassifierPerNode.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnetworkx\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBaseEstimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/ray/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mctypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mctypes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCDLL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0mCDLL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mso_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRTLD_GLOBAL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raylet\u001b[0m  \u001b[0;31m# noqa: E402\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/ctypes/__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, handle, use_errno, use_last_error)\u001b[0m\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 364\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_dlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    365\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.26' not found (required by /usr/local/lib/python3.7/site-packages/ray/_raylet.so)"
          ]
        }
      ]
    },
    {
      "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-Ward-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-Ward-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], average=\"macro\")\n",
        "    f1_micro = f1_score(y_test_df[col], predictions_df[col], average=\"micro\")\n",
        "    f1_weighted = f1_score(y_test_df[col], predictions_df[col], average=\"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": []
    },
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      "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": []
    },
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      "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-Ward-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-Ward-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], average=\"macro\")\n",
        "    f1_micro = f1_score(y_test_df[col], predictions_df[col], average=\"micro\")\n",
        "    f1_weighted = f1_score(y_test_df[col], predictions_df[col], average=\"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": []
    },
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