Beery_A_MalwareDetection.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Beery_A_MalwareDetection.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "3SgoQShs6JVF",
        "iJ35u3SZ5_BO",
        "3jTBmap73RIR"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aidan-B1409/AutomaticMalwareDetection/blob/main/Beery_A_MalwareDetection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2SoVnzS8UHDs"
      },
      "source": [
        "# Programming Lab 5 - Deep Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9fnVvt7UK9x"
      },
      "source": [
        "***\n",
        "##### CS 434 - Data Mining and Machine Learning\n",
        "##### Oregon State University-Cascades\n",
        "***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkzIVhj6FGe0"
      },
      "source": [
        "name = 'Aidan Beery'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hu2HDJf6UObF"
      },
      "source": [
        "***\n",
        "# Load packages \n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LZTf_JwKUQp1"
      },
      "source": [
        "Add the packages you need here (not below!)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yk4gw-zCxuBa"
      },
      "source": [
        "# packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from os import path\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.base import BaseEstimator\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras import regularizers\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PEcFIbtRT2v"
      },
      "source": [
        "assert name != '<replace with your name>'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euBP3zbtxwzp"
      },
      "source": [
        "***\n",
        "# Objective\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OePCST_ex431"
      },
      "source": [
        "This is an open-ended lab in which you will explore TensorFlow on a problem of your choosing.\n",
        "\n",
        "1.  Pick a dataset\n",
        "2.  Build a tensorflow model\n",
        "3.  Train and test model\n",
        "4.  Analyze your results\n",
        "5.  Write a report\n",
        "\n",
        "**Submission**: run the entire notebook before submission.  I will **not** re-run it to grade."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjOFDAXoUVCv"
      },
      "source": [
        "***\n",
        "# Data\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMu6gEJ3dbdc"
      },
      "source": [
        "You will find and choose a dataset$^1$ suitable for deep-learning.  Explore the [TensorFlow datasets](https://www.tensorflow.org/datasets/catalog/overview). \n",
        "\n",
        "If you wish, you may choose a dataset from a different source (for ideas, start [here](https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research), [here](http://deeplearning.net/datasets/), or [here](https://gym.openai.com/)).\n",
        "\n",
        "$^1$ You may **not** use MNIST, CelebA, or MIT datasets that we used in the Activities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SgoQShs6JVF"
      },
      "source": [
        "##### Protips"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EsiRAOLLEdeV"
      },
      "source": [
        "A good strategy is to read several related articles on a topic (e.g., image classification with CNN) and **interpolate** an approach that combines aspects from your \"inspired by\" (cited) sources, but on an entirely **different dataset** not used in the articles. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yKMaD0DN6LXO"
      },
      "source": [
        "> * Choose something that interests you.  It makes the lab more fun.\n",
        ">\n",
        "> * Determine your problem type \n",
        ">   * e.g., classification, regression, auto-regression, reinforcement\n",
        ">\n",
        "> * Split to train/validation/test and use each appropriately.\n",
        ">\n",
        "> * Some datasets are very large. You do not have to use ALL of it (i.e. subsample).\n",
        ">   * size is problem dependent, but look to Activies 18-20 for guidance\n",
        ">\n",
        ">\n",
        "> * Develop your approach on a (very?) small subset of data (for speed) and scale up after you finish your empirical design. \n",
        "> \n",
        "> * Look at our Activities and [Towards Data Science](https://towardsdatascience.com/) articles for ideas and inspiration."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pSn4CcZb7LyW"
      },
      "source": [
        "### Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qfnyhh6dNZmK",
        "outputId": "f5b4b4b3-0221-4312-f997-b8b842ad0174"
      },
      "source": [
        "# Get zip file\n",
        "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00541/datasets_DetectingMalwareUsingAHybridApproach.zip'\n",
        "zip_file = 'datasets_DetectingMalwareUsingAHybridApproach.zip'\n",
        "\n",
        "if(not path.exists(zip_file)):\n",
        "  !wget $url\n",
        "  !7za e $zip_file\n",
        "else:\n",
        "  print('data has already been downloaded')\n",
        "\n",
        "# TODO - more descriptive names\n",
        "benign_file = 'staDynBenignLab.csv'\n",
        "vxheaven_file = 'staDynVxHeaven2698Lab.csv'\n",
        "vtotal_file = 'staDynVt2955Lab.csv'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data has already been downloaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rG1uwiKUSvS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e2f570e3-6c32-4996-af20-1a9a1aa44983"
      },
      "source": [
        "# load your datasets\n",
        "benign_df = pd.read_csv(benign_file)\n",
        "vxheaven_df = pd.read_csv(vxheaven_file)\n",
        "vtotal_df = pd.read_csv(vtotal_file)\n",
        "\n",
        "display(benign_df)\n",
        "display(vxheaven_df)\n",
        "display(vtotal_df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>16777216.0</td>\n",
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              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>03877b98c8be5f43a6ab6e0e7dad493082897aceb80256...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>765</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>10752</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1024</td>\n",
              "      <td>3</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>19525</td>\n",
              "      <td>1024</td>\n",
              "      <td>72145</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>16777216.0</td>\n",
              "      <td>40960</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>04bc9c75612c5489ad943b31e4bf55827b47c25cd2583d...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>266</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>7168</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>9788</td>\n",
              "      <td>1024</td>\n",
              "      <td>44894</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>32768</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </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",
              "      <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",
              "      <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",
              "      <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>590</th>\n",
              "      <td>590</td>\n",
              "      <td>fd02187327b607950b9d1f812f8946903e1ccd7faa7ccf...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>78848</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5164</td>\n",
              "      <td>1024</td>\n",
              "      <td>94067</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>98304</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>591</td>\n",
              "      <td>fec10e7e80849f849fa90fe28a82195b47f69e03549f11...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1798</td>\n",
              "      <td>129</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>131584</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1024</td>\n",
              "      <td>3</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>33690</td>\n",
              "      <td>1024</td>\n",
              "      <td>203208</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>176128</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>113.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>592</td>\n",
              "      <td>fed63df0389061c5eb6d8cf9c203bbe95fa2165d4f1124...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>418</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>10240</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>11841</td>\n",
              "      <td>1024</td>\n",
              "      <td>69700</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>32768.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>40960</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>116.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>593</td>\n",
              "      <td>fee023a4d0f49785ae79016419f5d9a96c82f7d5977264...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8546</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>71363</td>\n",
              "      <td>26507</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>4194304.0</td>\n",
              "      <td>4259840</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>155.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>594</td>\n",
              "      <td>ff779b220a6783e758bf842fe5004b1c67aedf6af0e92e...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>2315</td>\n",
              "      <td>282</td>\n",
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              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>33088</td>\n",
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              "      <td>1024</td>\n",
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              "      <td>8192.0</td>\n",
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              "      <td>4194304.0</td>\n",
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              "      <td>4096</td>\n",
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              "</table>\n",
              "<p>595 rows × 1087 columns</p>\n",
              "</div>"
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            "text/plain": [
              "     Unnamed: 0  ... label\n",
              "0             0  ...     0\n",
              "1             1  ...     0\n",
              "2             2  ...     0\n",
              "3             3  ...     0\n",
              "4             4  ...     0\n",
              "..          ...  ...   ...\n",
              "590         590  ...     0\n",
              "591         591  ...     0\n",
              "592         592  ...     0\n",
              "593         593  ...     0\n",
              "594         594  ...     0\n",
              "\n",
              "[595 rows x 1087 columns]"
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          },
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            "tags": []
          }
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              "      <td>505359</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>475136</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>77.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>6144</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1423</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>664</td>\n",
              "      <td>1423</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>24576</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </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",
              "      <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",
              "      <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",
              "      <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>2693</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2560</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4096</td>\n",
              "      <td>1024</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>20480</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2694</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2225</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>8192</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>4096</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4204</td>\n",
              "      <td>4096</td>\n",
              "      <td>79602</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>57344</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2695</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1536</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4096</td>\n",
              "      <td>1024</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>20480</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2696</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>197</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>12288</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>4096</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6000</td>\n",
              "      <td>4096</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>28672</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2697</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3072</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "<p>2698 rows × 1087 columns</p>\n",
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              "      Virtual  Offset  loc  ...  count_file_opened  count_dll_loaded  label\n",
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              "...       ...     ...  ...  ...                ...               ...    ...\n",
              "2693        0       0    0  ...                  0                 0      1\n",
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              "2695        0       0    0  ...                  0                 0      1\n",
              "2696        0       0    0  ...                  0                 0      1\n",
              "2697        0       0    0  ...                  3                 0      1\n",
              "\n",
              "[2698 rows x 1087 columns]"
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              "      <td>2</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>51884</td>\n",
              "      <td>1024</td>\n",
              "      <td>231597</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>5.368709e+09</td>\n",
              "      <td>208896</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>182.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </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",
              "      <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",
              "      <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",
              "      <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>2950</th>\n",
              "      <td>27b8296b2027da4558a8413cb8292116d4ac53c4e16949...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3219</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>49548</td>\n",
              "      <td>15884</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>12288</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>4096</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4775936</td>\n",
              "      <td>4096</td>\n",
              "      <td>5877590</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4.194304e+06</td>\n",
              "      <td>4780032</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2951</th>\n",
              "      <td>27b8b3b6d7e012c00f22c5bcd0258bb333c7e5d32fa072...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6853</td>\n",
              "      <td>1817</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>94208</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1024</td>\n",
              "      <td>3</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>433803</td>\n",
              "      <td>1024</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4.194304e+06</td>\n",
              "      <td>544768</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>262.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2952</th>\n",
              "      <td>27bbc569dc83d9bbff39048ce3e0b1e3f2300063505f93...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>16933</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2048</td>\n",
              "      <td>0</td>\n",
              "      <td>8192</td>\n",
              "      <td>512</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>512</td>\n",
              "      <td>2</td>\n",
              "      <td>34112</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>86654</td>\n",
              "      <td>512</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4.194304e+06</td>\n",
              "      <td>106496</td>\n",
              "      <td>8192</td>\n",
              "      <td>16</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2953</th>\n",
              "      <td>27bc5524f4f6fe1c7375437c599041723dc3a5bb26f360...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>830</td>\n",
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              "      <td>148480</td>\n",
              "      <td>0</td>\n",
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              "      <td>512</td>\n",
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              "      <td>5</td>\n",
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              "      <td>75248</td>\n",
              "      <td>1024</td>\n",
              "      <td>0</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1.677722e+07</td>\n",
              "      <td>335872</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>161.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2954</th>\n",
              "      <td>27d8f64c55c5d0fa77475ef7abdf794cf0f614a3e0dd54...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>989</td>\n",
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              "      <td>...</td>\n",
              "      <td>31744</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>4</td>\n",
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              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
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              "      <td>152680</td>\n",
              "      <td>1024</td>\n",
              "      <td>242551</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>16384.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4.194304e+06</td>\n",
              "      <td>208896</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2955 rows × 1088 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               filename  ...  label\n",
              "0     0000fa4d5b6a06b20aebb76a2d46fa8c122d8e70566eda...  ...      1\n",
              "1     00050248396dab9852e60e8afaa265483a99ead18e6edc...  ...      1\n",
              "2     0005a6d64cba0a220f5621ad17afa52f7413cfe9b158bf...  ...      1\n",
              "3     0006253243ffb8901f1493540fd5b9fc8144a7d3d59fb8...  ...      1\n",
              "4     000d9c048778d192525053f87d35ba40b0b5f0e5acb50d...  ...      1\n",
              "...                                                 ...  ...    ...\n",
              "2950  27b8296b2027da4558a8413cb8292116d4ac53c4e16949...  ...      1\n",
              "2951  27b8b3b6d7e012c00f22c5bcd0258bb333c7e5d32fa072...  ...      1\n",
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              "2954  27d8f64c55c5d0fa77475ef7abdf794cf0f614a3e0dd54...  ...      1\n",
              "\n",
              "[2955 rows x 1088 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5WwbaWMhJjdH"
      },
      "source": [
        "### Pre-process dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRhxLDvtUS_l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "659b404c-e0a0-46ac-d384-e302e0af7c0c"
      },
      "source": [
        "# Drop unnecesscary, unaligned columns.\n",
        "print(benign_df.columns)\n",
        "benign_df.drop(['Unnamed: 0', 'filename'], axis=1, inplace=True)\n",
        "vtotal_df.drop('filename', axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Unnamed: 0', 'filename', 'Virtual', 'Offset', 'loc', 'Import',\n",
            "       'Imports', 'var', 'Forwarder', 'UINT',\n",
            "       ...\n",
            "       'count_file_written', 'count_file_exists', 'count_file_deleted',\n",
            "       'count_file_copied', 'count_file_renamed', 'count_regkey_written',\n",
            "       'count_regkey_deleted', 'count_file_opened', 'count_dll_loaded',\n",
            "       'label'],\n",
            "      dtype='object', length=1087)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdahaYaCJl3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "4a9f18f3-51d2-411d-8f5f-188e4207651d"
      },
      "source": [
        "# process your dataset \n",
        "# Merge all of the above files together\n",
        "# We want our dataset to have both benign samples and malicious ones so that our network can differentiate between the two classes.\n",
        "data = pd.concat([benign_df, vxheaven_df, vtotal_df])\n",
        "display(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Virtual</th>\n",
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              "      <th>nullsub</th>\n",
              "      <th>DllEntryPoint</th>\n",
              "      <th>System32</th>\n",
              "      <th>dll</th>\n",
              "      <th>...</th>\n",
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              "      <th>file_alignment</th>\n",
              "      <th>major_operating_system_version</th>\n",
              "      <th>minor_operating_system_version</th>\n",
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              "      <th>minor_image_version</th>\n",
              "      <th>major_subsystem_version</th>\n",
              "      <th>minor_subsystem_version</th>\n",
              "      <th>size_of_headers</th>\n",
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              "      <td>5</td>\n",
              "      <td>9788</td>\n",
              "      <td>1024</td>\n",
              "      <td>44894</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>32768</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <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",
              "      <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",
              "      <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>2950</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3219</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>49548</td>\n",
              "      <td>15884</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>233</td>\n",
              "      <td>...</td>\n",
              "      <td>4096</td>\n",
              "      <td>4096</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4096</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4775936</td>\n",
              "      <td>4096</td>\n",
              "      <td>5877590</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>4780032</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2951</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6853</td>\n",
              "      <td>1817</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1024</td>\n",
              "      <td>3</td>\n",
              "      <td>33088</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>433803</td>\n",
              "      <td>1024</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>544768</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>262.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2952</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16933</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>8192</td>\n",
              "      <td>512</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>512</td>\n",
              "      <td>2</td>\n",
              "      <td>34112</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>86654</td>\n",
              "      <td>512</td>\n",
              "      <td>0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>106496</td>\n",
              "      <td>8192</td>\n",
              "      <td>16</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2953</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10483</td>\n",
              "      <td>830</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1024</td>\n",
              "      <td>3</td>\n",
              "      <td>32768</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>75248</td>\n",
              "      <td>1024</td>\n",
              "      <td>0</td>\n",
              "      <td>262144.0</td>\n",
              "      <td>8192.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>16777216.0</td>\n",
              "      <td>335872</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>161.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2954</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3164</td>\n",
              "      <td>989</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>4096</td>\n",
              "      <td>512</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1024</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>152680</td>\n",
              "      <td>1024</td>\n",
              "      <td>242551</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>16384.0</td>\n",
              "      <td>1048576.0</td>\n",
              "      <td>4096.0</td>\n",
              "      <td>4194304.0</td>\n",
              "      <td>208896</td>\n",
              "      <td>4096</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6248 rows × 1087 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Virtual  Offset   loc  ...  label  __vbaVarIndexLoad  SafeArrayPtrOfIndex\n",
              "0           0       0     1  ...      0                NaN                  NaN\n",
              "1           0       0     0  ...      0                NaN                  NaN\n",
              "2           0       0     1  ...      0                NaN                  NaN\n",
              "3           0       0     1  ...      0                NaN                  NaN\n",
              "4           0       0     0  ...      0                NaN                  NaN\n",
              "...       ...     ...   ...  ...    ...                ...                  ...\n",
              "2950        0       0  3219  ...      1                0.0                  0.0\n",
              "2951        0       0    14  ...      1                0.0                  0.0\n",
              "2952        0       0     0  ...      1                0.0                  0.0\n",
              "2953        0       0    41  ...      1                0.0                  0.0\n",
              "2954        0       0    12  ...      1                0.0                  0.0\n",
              "\n",
              "[6248 rows x 1087 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_Ohqvf7U5lM",
        "outputId": "4382c06a-0392-4e83-bd60-d493f3db29ac"
      },
      "source": [
        "# Count nans\n",
        "nans = data.isna().sum()\n",
        "print(nans[nans > 0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__vbaVarIndexLoad      595\n",
            "SafeArrayPtrOfIndex    595\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ceoAUpZ-XfG_"
      },
      "source": [
        "# Drop NAN rows\n",
        "data.drop(['__vbaVarIndexLoad', 'SafeArrayPtrOfIndex'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Qw20Dm-eYpHg",
        "outputId": "faa1cf5c-6f84-4b5f-e66a-a79a8de4b3b9"
      },
      "source": [
        "# Get dataset\n",
        "data_tensor = tf.convert_to_tensor(data)\n",
        "dataset = tf.data.Dataset.from_tensor_slices(data_tensor)\n",
        "display(dataset)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<TensorSliceDataset shapes: (1085,), types: tf.float64>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTD0Ng6QbHQM"
      },
      "source": [
        "# TODO - Handling counting features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VPs0aPJy0Gk6"
      },
      "source": [
        "***\n",
        "# Workspace\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TKxNvuNpFTO"
      },
      "source": [
        "## Part 1 - Traditional ML approach with Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmgitpDk5sDm"
      },
      "source": [
        "class SkDataset():\n",
        "  def __init__(self, data: pd.DataFrame, test_size: int) -> None:\n",
        "    self.data = data\n",
        "    self.test_size = test_size\n",
        "    self.X, self.y = SkDataset._split_x_y(data)\n",
        "    self.X_train, self.X_test, \\\n",
        "    self.y_train, self.y_test = train_test_split(self.X, self.y, test_size=test_size, stratify=self.y, random_state=1)\n",
        "\n",
        "  def _split_x_y(df: pd.DataFrame): \n",
        "    X, y = df.drop('label', axis=1).values, df['label'].values\n",
        "    return X, y\n",
        "\n",
        "  # def update_train_test(self, X, y):\n",
        "  #   self.X_train, self.X_test, \\\n",
        "  #   self.y_train, self.y_test = train_test_split(X, y, test_size=self.test_size, stratify=y, random_state=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRYMS2rZ4bLZ"
      },
      "source": [
        "class RFExperiment():\n",
        "  def __init__(self, data: pd.DataFrame, **kwargs):\n",
        "    self.df = data\n",
        "    self.test_size = kwargs.get('test_size', 0.3)\n",
        "    self.pca_percent = kwargs.get('pca_percent', 0.95)\n",
        "    self.param_grid = kwargs.get('param_grid', None)\n",
        "    self.sk_dataset = SkDataset(self.df, self.test_size)\n",
        "\n",
        "  def run_experiment(self):\n",
        "    self._run_pca()\n",
        "    y_pred, score = self.train_rf_model()\n",
        "    RFExperiment.generate_report(y_pred, self.sk_dataset.y_test)\n",
        "\n",
        "  def train_rf_model(self):\n",
        "    model = RandomForestClassifier(random_state=1, n_jobs=-1, criterion='gini', )\n",
        "    best_estimator = self._train_hyperparams(model, 'accuracy')\n",
        "    self._crossval(best_estimator)\n",
        "    y_pred = best_estimator.predict(self.sk_dataset.X_test)\n",
        "    score = f1_score(y_pred, self.sk_dataset.y_test)\n",
        "    return y_pred, score\n",
        "\n",
        "\n",
        "  def generate_report(y_pred, y_actual):\n",
        "    print(f'F1 Score - {f1_score(y_pred, y_actual)}')\n",
        "    print(f'Accuracy - {accuracy_score(y_pred, y_actual)}')\n",
        "    conf_mat = confusion_matrix(y_actual, y_pred)\n",
        "    sns.heatmap(conf_mat, annot=True)\n",
        "\n",
        "   \n",
        "  def _run_pca(self):\n",
        "    pipe_decomp = make_pipeline(StandardScaler(),\n",
        "                                PCA(self.pca_percent, random_state=1))\n",
        "    # Fit on training elements\n",
        "    self.sk_dataset.X_train = pipe_decomp.fit_transform(self.sk_dataset.X_train)\n",
        "    # Transform the test data\n",
        "    self.sk_dataset.X_test = pipe_decomp.transform(self.sk_dataset.X_test)\n",
        "\n",
        "    pca = pipe_decomp['pca']\n",
        "    print(f'PCA n - components: {pca.n_components_}')\n",
        "\n",
        "  def _crossval(self, estimator):\n",
        "    kfold = StratifiedKFold(n_splits=10).split(self.sk_dataset.X_train, self.sk_dataset.y_train)\n",
        "\n",
        "    scores = []\n",
        "    for k, (train, val) in enumerate(kfold):\n",
        "      estimator.fit(self.sk_dataset.X_train[train], self.sk_dataset.y_train[train])\n",
        "      score = estimator.score(self.sk_dataset.X_train[val], self.sk_dataset.y_train[val])\n",
        "      scores.append(score)\n",
        "      print(f\"Fold: {k}\\t Class dist.: {np.bincount(self.sk_dataset.y_train[train])}\\t Acc.: {score}\")\n",
        "\n",
        "    print(f'\\nCV Accuracy: {np.mean(scores)} +/- {np.std(scores)}')\n",
        "\n",
        "  def _train_hyperparams(self, estimator: Pipeline, scoring: str) -> Pipeline:\n",
        "    gs = GridSearchCV(estimator=estimator, \n",
        "                      param_grid=self.param_grid,\n",
        "                      scoring=scoring,\n",
        "                      refit=True,\n",
        "                      cv=10,\n",
        "                      n_jobs=-1)\n",
        "    \n",
        "    gs = gs.fit(self.sk_dataset.X_train, self.sk_dataset.y_train)\n",
        "    print(gs.best_score_)\n",
        "    print(gs.best_params_)\n",
        "    return gs.best_estimator_\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMbOOvoe3aSN"
      },
      "source": [
        "def feat_importance(X_train, y_train):\n",
        "  feat_labels = data.columns\n",
        "\n",
        "  forest = RandomForestClassifier(n_estimators=100,\n",
        "                                  max_depth=10,\n",
        "                                  random_state=1)\n",
        "\n",
        "  forest.fit(X_train, y_train)\n",
        "  importances = forest.feature_importances_\n",
        "  n_features = 25\n",
        "\n",
        "  indices = np.argsort(importances)[::-1]\n",
        "\n",
        "  for f in range(n_features):\n",
        "      print(\"%2d) %-*s %f\" % (f + 1, 30, \n",
        "                              feat_labels[indices[f]], \n",
        "                              importances[indices[f]]))\n",
        "\n",
        "  plt.title('Feature Importance')\n",
        "  plt.bar(range(n_features), \n",
        "          importances[indices[:n_features]],\n",
        "          align='center')\n",
        "\n",
        "  plt.xticks(range(n_features), \n",
        "            feat_labels[indices[:n_features]], rotation=90)\n",
        "  plt.xlim([-1, n_features])\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ga51-l37TPd9"
      },
      "source": [
        "## Part 2 - Deep Learning Approach  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_t_JVI3HQ-d"
      },
      "source": [
        "# LSP violation I can't be fucked to fix rn. \n",
        "batch_size = 3\n",
        "test_size = 0.3\n",
        "class TfDataset(SkDataset):\n",
        "  def __init__(self, df: pd.DataFrame, test_size: int):\n",
        "    super().__init__(df, test_size)\n",
        "    stdsc = StandardScaler()\n",
        "    self.X_train = stdsc.fit_transform(self.X_train)\n",
        "    self.X_test = stdsc.transform(self.X_test)\n",
        "    self.train_size = self.X_train.shape[0]\n",
        "    self.test_size = self.X_test.shape[0]\n",
        "    self.train_ds = tf.data.Dataset.from_tensor_slices((self.X_train, self.y_train))\n",
        "    self.test_ds = tf.data.Dataset.from_tensor_slices((self.X_test, self.y_test))\n",
        "\n",
        "  def shuffle_and_batch(ds, ds_len, batch_size):\n",
        "    return ds.shuffle(buffer_size=ds_len).batch(batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQLWIu1OTVTA",
        "outputId": "c9f9184e-4c0e-442a-eb02-172a45197605"
      },
      "source": [
        "# Convert the Pandas dataframe to a Tensor\n",
        "ds = TfDataset(data, test_size)\n",
        "ds.train_ds = TfDataset.shuffle_and_batch(ds.train_ds, ds.train_size, batch_size)\n",
        "ds.test_ds = TfDataset.shuffle_and_batch(ds.test_ds, ds.test_size, batch_size)\n",
        "\n",
        "\n",
        "for feat, targ in ds.train_ds.take(5):\n",
        "  print(f'Features: {feat}\\tTarget: {targ}')\n",
        "\n",
        "\n",
        "\n",
        "model = Sequential([\n",
        "  Dense(50, activation='relu'),\n",
        "  Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "adam_optimizer = tf.keras.optimizers.Adam()\n",
        "\n",
        "neg, pos = np.bincount(data['label'])\n",
        "negative_weight = (1 / neg) * (len(data) / 2.0)\n",
        "positive_weight = (1 / pos) * (len(data) / 2.0)\n",
        "\n",
        "print(f'Positive weights: {positive_weight} --- Negative Weights: {negative_weight}')\n",
        "\n",
        "weights = {0: negative_weight, 1: positive_weight}\n",
        "\n",
        "# https://medium.datadriveninvestor.com/building-neural-network-using-keras-for-classification-3a3656c726c1\n",
        "model.compile(optimizer=adam_optimizer,\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=[tf.keras.metrics.BinaryAccuracy(name='accuracy'),\n",
        "                       tf.keras.metrics.FalseNegatives(),\n",
        "                       tf.keras.metrics.FalsePositives()])\n",
        "\n",
        "model.fit(ds.train_ds, epochs=100)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Features: [[ 0.          0.         -0.08436338 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]\n",
            " [ 0.          0.         -0.0632664  ... -0.04884699 -0.10896247\n",
            "  -0.43592812]\n",
            " [ 0.          0.         -0.08436338 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]]\tTarget: [0 1 1]\n",
            "Features: [[ 0.          0.         -0.08172626 ... -0.04884699 -0.03719609\n",
            "   0.02497944]\n",
            " [ 0.          0.         -0.08398665 ... -0.04884699 -0.07307928\n",
            "  -0.28229227]\n",
            " [ 0.          0.         -0.04292288 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]]\tTarget: [1 1 1]\n",
            "Features: [[ 0.          0.         -0.08436338 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]\n",
            " [ 0.          0.         -0.08360991 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]\n",
            " [ 0.          0.         -0.08398665 ... -0.04884699 -0.07307928\n",
            "   1.25406628]]\tTarget: [1 1 1]\n",
            "Features: [[ 0.          0.         -0.08436338 ... -0.04884699 -0.10896247\n",
            "  -0.12865641]\n",
            " [ 0.          0.          0.25431854 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]\n",
            " [ 0.          0.         -0.08436338 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]]\tTarget: [1 1 1]\n",
            "Features: [[ 0.          0.         -0.08398665 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]\n",
            " [ 0.          0.         -0.08436338 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]\n",
            " [ 0.          0.         -0.08360991 ... -0.04884699 -0.10896247\n",
            "  -0.43592812]]\tTarget: [1 1 1]\n",
            "Positive weights: 0.5526269237572969 --- Negative Weights: 5.250420168067227\n",
            "Epoch 1/100\n",
            "1458/1458 [==============================] - 3s 1ms/step - loss: 0.2723 - accuracy: 0.9522 - false_negatives_1: 91.0000 - false_positives_1: 118.0000\n",
            "Epoch 2/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0972 - accuracy: 0.9732 - false_negatives_1: 61.0000 - false_positives_1: 56.0000\n",
            "Epoch 3/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0638 - accuracy: 0.9792 - false_negatives_1: 52.0000 - false_positives_1: 39.0000\n",
            "Epoch 4/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0555 - accuracy: 0.9808 - false_negatives_1: 48.0000 - false_positives_1: 36.0000\n",
            "Epoch 5/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0481 - accuracy: 0.9842 - false_negatives_1: 39.0000 - false_positives_1: 30.0000\n",
            "Epoch 6/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0408 - accuracy: 0.9865 - false_negatives_1: 32.0000 - false_positives_1: 27.0000\n",
            "Epoch 7/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0358 - accuracy: 0.9879 - false_negatives_1: 25.0000 - false_positives_1: 28.0000\n",
            "Epoch 8/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0352 - accuracy: 0.9877 - false_negatives_1: 29.0000 - false_positives_1: 25.0000\n",
            "Epoch 9/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0317 - accuracy: 0.9872 - false_negatives_1: 27.0000 - false_positives_1: 29.0000\n",
            "Epoch 10/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0284 - accuracy: 0.9888 - false_negatives_1: 24.0000 - false_positives_1: 25.0000\n",
            "Epoch 11/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0261 - accuracy: 0.9904 - false_negatives_1: 20.0000 - false_positives_1: 22.0000\n",
            "Epoch 12/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0260 - accuracy: 0.9897 - false_negatives_1: 24.0000 - false_positives_1: 21.0000\n",
            "Epoch 13/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0237 - accuracy: 0.9909 - false_negatives_1: 18.0000 - false_positives_1: 22.0000\n",
            "Epoch 14/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0218 - accuracy: 0.9931 - false_negatives_1: 16.0000 - false_positives_1: 14.0000\n",
            "Epoch 15/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0243 - accuracy: 0.9920 - false_negatives_1: 15.0000 - false_positives_1: 20.0000\n",
            "Epoch 16/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0210 - accuracy: 0.9925 - false_negatives_1: 17.0000 - false_positives_1: 16.0000\n",
            "Epoch 17/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0176 - accuracy: 0.9936 - false_negatives_1: 14.0000 - false_positives_1: 14.0000\n",
            "Epoch 18/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0174 - accuracy: 0.9931 - false_negatives_1: 16.0000 - false_positives_1: 14.0000\n",
            "Epoch 19/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0229 - accuracy: 0.9920 - false_negatives_1: 17.0000 - false_positives_1: 18.0000\n",
            "Epoch 20/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0295 - accuracy: 0.9927 - false_negatives_1: 17.0000 - false_positives_1: 15.0000\n",
            "Epoch 21/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0158 - accuracy: 0.9943 - false_negatives_1: 14.0000 - false_positives_1: 11.0000\n",
            "Epoch 22/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0151 - accuracy: 0.9941 - false_negatives_1: 11.0000 - false_positives_1: 15.0000\n",
            "Epoch 23/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0167 - accuracy: 0.9943 - false_negatives_1: 12.0000 - false_positives_1: 13.0000\n",
            "Epoch 24/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0142 - accuracy: 0.9945 - false_negatives_1: 13.0000 - false_positives_1: 11.0000\n",
            "Epoch 25/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0140 - accuracy: 0.9931 - false_negatives_1: 14.0000 - false_positives_1: 16.0000\n",
            "Epoch 26/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0127 - accuracy: 0.9950 - false_negatives_1: 9.0000 - false_positives_1: 13.0000\n",
            "Epoch 27/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0128 - accuracy: 0.9954 - false_negatives_1: 7.0000 - false_positives_1: 13.0000\n",
            "Epoch 28/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0121 - accuracy: 0.9957 - false_negatives_1: 7.0000 - false_positives_1: 12.0000\n",
            "Epoch 29/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0114 - accuracy: 0.9957 - false_negatives_1: 8.0000 - false_positives_1: 11.0000\n",
            "Epoch 30/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0111 - accuracy: 0.9957 - false_negatives_1: 9.0000 - false_positives_1: 10.0000\n",
            "Epoch 31/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0164 - accuracy: 0.9957 - false_negatives_1: 8.0000 - false_positives_1: 11.0000\n",
            "Epoch 32/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0106 - accuracy: 0.9959 - false_negatives_1: 9.0000 - false_positives_1: 9.0000\n",
            "Epoch 33/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0113 - accuracy: 0.9947 - false_negatives_1: 10.0000 - false_positives_1: 13.0000\n",
            "Epoch 34/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0098 - accuracy: 0.9961 - false_negatives_1: 8.0000 - false_positives_1: 9.0000\n",
            "Epoch 35/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0107 - accuracy: 0.9959 - false_negatives_1: 8.0000 - false_positives_1: 10.0000\n",
            "Epoch 36/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0097 - accuracy: 0.9966 - false_negatives_1: 8.0000 - false_positives_1: 7.0000\n",
            "Epoch 37/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0095 - accuracy: 0.9968 - false_negatives_1: 6.0000 - false_positives_1: 8.0000\n",
            "Epoch 38/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0102 - accuracy: 0.9966 - false_negatives_1: 5.0000 - false_positives_1: 10.0000\n",
            "Epoch 39/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0073 - accuracy: 0.9977 - false_negatives_1: 3.0000 - false_positives_1: 7.0000\n",
            "Epoch 40/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0082 - accuracy: 0.9970 - false_negatives_1: 7.0000 - false_positives_1: 6.0000\n",
            "Epoch 41/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0087 - accuracy: 0.9961 - false_negatives_1: 6.0000 - false_positives_1: 11.0000\n",
            "Epoch 42/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0089 - accuracy: 0.9966 - false_negatives_1: 7.0000 - false_positives_1: 8.0000\n",
            "Epoch 43/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0061 - accuracy: 0.9970 - false_negatives_1: 6.0000 - false_positives_1: 7.0000\n",
            "Epoch 44/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0072 - accuracy: 0.9979 - false_negatives_1: 4.0000 - false_positives_1: 5.0000\n",
            "Epoch 45/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0084 - accuracy: 0.9963 - false_negatives_1: 7.0000 - false_positives_1: 9.0000\n",
            "Epoch 46/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0077 - accuracy: 0.9970 - false_negatives_1: 6.0000 - false_positives_1: 7.0000\n",
            "Epoch 47/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0090 - accuracy: 0.9959 - false_negatives_1: 9.0000 - false_positives_1: 9.0000\n",
            "Epoch 48/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0076 - accuracy: 0.9975 - false_negatives_1: 6.0000 - false_positives_1: 5.0000\n",
            "Epoch 49/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0146 - accuracy: 0.9966 - false_negatives_1: 7.0000 - false_positives_1: 8.0000\n",
            "Epoch 50/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0117 - accuracy: 0.9979 - false_negatives_1: 5.0000 - false_positives_1: 4.0000\n",
            "Epoch 51/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0061 - accuracy: 0.9975 - false_negatives_1: 6.0000 - false_positives_1: 5.0000\n",
            "Epoch 52/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0052 - accuracy: 0.9984 - false_negatives_1: 3.0000 - false_positives_1: 4.0000\n",
            "Epoch 53/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0066 - accuracy: 0.9970 - false_negatives_1: 6.0000 - false_positives_1: 7.0000\n",
            "Epoch 54/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0062 - accuracy: 0.9975 - false_negatives_1: 6.0000 - false_positives_1: 5.0000\n",
            "Epoch 55/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0045 - accuracy: 0.9982 - false_negatives_1: 3.0000 - false_positives_1: 5.0000\n",
            "Epoch 56/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0057 - accuracy: 0.9977 - false_negatives_1: 5.0000 - false_positives_1: 5.0000\n",
            "Epoch 57/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0073 - accuracy: 0.9970 - false_negatives_1: 5.0000 - false_positives_1: 8.0000\n",
            "Epoch 58/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0153 - accuracy: 0.9979 - false_negatives_1: 6.0000 - false_positives_1: 3.0000\n",
            "Epoch 59/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0076 - accuracy: 0.9982 - false_negatives_1: 5.0000 - false_positives_1: 3.0000\n",
            "Epoch 60/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0064 - accuracy: 0.9979 - false_negatives_1: 4.0000 - false_positives_1: 5.0000\n",
            "Epoch 61/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0045 - accuracy: 0.9984 - false_negatives_1: 3.0000 - false_positives_1: 4.0000\n",
            "Epoch 62/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0062 - accuracy: 0.9973 - false_negatives_1: 8.0000 - false_positives_1: 4.0000\n",
            "Epoch 63/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0046 - accuracy: 0.9984 - false_negatives_1: 2.0000 - false_positives_1: 5.0000\n",
            "Epoch 64/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0041 - accuracy: 0.9989 - false_negatives_1: 2.0000 - false_positives_1: 3.0000\n",
            "Epoch 65/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0051 - accuracy: 0.9975 - false_negatives_1: 4.0000 - false_positives_1: 7.0000\n",
            "Epoch 66/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0039 - accuracy: 0.9982 - false_negatives_1: 4.0000 - false_positives_1: 4.0000\n",
            "Epoch 67/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0067 - accuracy: 0.9970 - false_negatives_1: 6.0000 - false_positives_1: 7.0000\n",
            "Epoch 68/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0041 - accuracy: 0.9986 - false_negatives_1: 3.0000 - false_positives_1: 3.0000\n",
            "Epoch 69/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0049 - accuracy: 0.9982 - false_negatives_1: 4.0000 - false_positives_1: 4.0000\n",
            "Epoch 70/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0059 - accuracy: 0.9982 - false_negatives_1: 4.0000 - false_positives_1: 4.0000\n",
            "Epoch 71/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0049 - accuracy: 0.9986 - false_negatives_1: 4.0000 - false_positives_1: 2.0000\n",
            "Epoch 72/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0063 - accuracy: 0.9963 - false_negatives_1: 7.0000 - false_positives_1: 9.0000\n",
            "Epoch 73/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0043 - accuracy: 0.9986 - false_negatives_1: 3.0000 - false_positives_1: 3.0000\n",
            "Epoch 74/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0046 - accuracy: 0.9991 - false_negatives_1: 1.0000 - false_positives_1: 3.0000\n",
            "Epoch 75/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0036 - accuracy: 0.9989 - false_negatives_1: 2.0000 - false_positives_1: 3.0000\n",
            "Epoch 76/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0103 - accuracy: 0.9970 - false_negatives_1: 6.0000 - false_positives_1: 7.0000\n",
            "Epoch 77/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0040 - accuracy: 0.9986 - false_negatives_1: 3.0000 - false_positives_1: 3.0000\n",
            "Epoch 78/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0042 - accuracy: 0.9977 - false_negatives_1: 5.0000 - false_positives_1: 5.0000\n",
            "Epoch 79/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0046 - accuracy: 0.9982 - false_negatives_1: 4.0000 - false_positives_1: 4.0000\n",
            "Epoch 80/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0059 - accuracy: 0.9977 - false_negatives_1: 4.0000 - false_positives_1: 6.0000\n",
            "Epoch 81/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0024 - accuracy: 0.9993 - false_negatives_1: 0.0000e+00 - false_positives_1: 3.0000\n",
            "Epoch 82/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0065 - accuracy: 0.9982 - false_negatives_1: 4.0000 - false_positives_1: 4.0000\n",
            "Epoch 83/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0052 - accuracy: 0.9986 - false_negatives_1: 4.0000 - false_positives_1: 2.0000\n",
            "Epoch 84/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0037 - accuracy: 0.9989 - false_negatives_1: 2.0000 - false_positives_1: 3.0000\n",
            "Epoch 85/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0049 - accuracy: 0.9977 - false_negatives_1: 6.0000 - false_positives_1: 4.0000\n",
            "Epoch 86/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0065 - accuracy: 0.9973 - false_negatives_1: 5.0000 - false_positives_1: 7.0000\n",
            "Epoch 87/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0044 - accuracy: 0.9984 - false_negatives_1: 5.0000 - false_positives_1: 2.0000\n",
            "Epoch 88/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0026 - accuracy: 0.9989 - false_negatives_1: 2.0000 - false_positives_1: 3.0000\n",
            "Epoch 89/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0069 - accuracy: 0.9970 - false_negatives_1: 6.0000 - false_positives_1: 7.0000\n",
            "Epoch 90/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0023 - accuracy: 0.9993 - false_negatives_1: 2.0000 - false_positives_1: 1.0000\n",
            "Epoch 91/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0050 - accuracy: 0.9979 - false_negatives_1: 5.0000 - false_positives_1: 4.0000\n",
            "Epoch 92/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0048 - accuracy: 0.9982 - false_negatives_1: 5.0000 - false_positives_1: 3.0000\n",
            "Epoch 93/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0036 - accuracy: 0.9986 - false_negatives_1: 2.0000 - false_positives_1: 4.0000\n",
            "Epoch 94/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0090 - accuracy: 0.9984 - false_negatives_1: 3.0000 - false_positives_1: 4.0000\n",
            "Epoch 95/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0053 - accuracy: 0.9982 - false_negatives_1: 4.0000 - false_positives_1: 4.0000\n",
            "Epoch 96/100\n",
            "1458/1458 [==============================] - 2s 1ms/step - loss: 0.0029 - accuracy: 0.9991 - false_negatives_1: 2.0000 - false_positives_1: 2.0000\n",
            "Epoch 97/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0070 - accuracy: 0.9977 - false_negatives_1: 5.0000 - false_positives_1: 5.0000\n",
            "Epoch 98/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0023 - accuracy: 0.9993 - false_negatives_1: 1.0000 - false_positives_1: 2.0000\n",
            "Epoch 99/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0036 - accuracy: 0.9989 - false_negatives_1: 3.0000 - false_positives_1: 2.0000\n",
            "Epoch 100/100\n",
            "1458/1458 [==============================] - 2s 2ms/step - loss: 0.0021 - accuracy: 0.9995 - false_negatives_1: 2.0000 - false_positives_1: 0.0000e+00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f54a88ddb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ah4YpXiQcFoY"
      },
      "source": [
        "y_pred = model(ds.X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "3lBL4et3enbF",
        "outputId": "297ea5dc-4afb-4472-9afe-6cbb89f79cdc"
      },
      "source": [
        "display(np.bincount(y_pred.numpy().flatten().astype(int)))\n",
        "display(np.bincount(ds.y_test))\n",
        "RFExperiment.generate_report(y_pred.numpy().flatten().astype(int), ds.y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([ 270, 1605])"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([ 179, 1696])"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "F1 Score - 0.972432596182975\n",
            "Accuracy - 0.9514666666666667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJ35u3SZ5_BO"
      },
      "source": [
        "##### Protips"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VlXeHSA5Ct88"
      },
      "source": [
        "Organization:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rv552V2b6EV5"
      },
      "source": [
        "\n",
        "\n",
        "> * Use section subheaders (i.e. `##`, `###`, `####`, etc.) to organize\n",
        "> * Organize your work using different text and code blocks\n",
        "> * Wrap your code in functions for reuse in report\n",
        "> * Document your work: either with code comments or text blocks\n",
        "> * Make sure your work is organized and \"readable\" to someone else\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPWHv6dCCrjk"
      },
      "source": [
        "Model design:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9mPtCl7CqaX"
      },
      "source": [
        "\n",
        "> * Different data problems require different architectures. For example,\n",
        ">    * images/videos use CNNs\n",
        ">    * sequential/temporal data uses RNNs\n",
        ">    * game/action data uses DQNs\n",
        ">\n",
        "> * Use at least 2 hidden layers (for deep-learning)\n",
        ">    * *how many to use?* is data/problem dependent question\n",
        ">\n",
        "> * If appropriate, consider an empirical comparison to a baseline/simple model\n",
        ">   * e.g., `sklearn` classification/regression or MLP model\n",
        ">   * note: this doesn't make sense for some data (e.g., images)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IO9-GGiNCnGY"
      },
      "source": [
        "Ethics:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9dKGNMbHCyDD"
      },
      "source": [
        "> * **DO** use web articles and APIs for ideas, tips, and guidance\n",
        "> * **DO** read code examples from sources including TowardsDataScience, Stackoverflow, github \n",
        "> * **DO** adapt ideas from your \"inspired by\" sources\n",
        "> * **DO** cite any articles you used for inspiration or for technical help\n",
        ">\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJMuw5CBDqLh"
      },
      "source": [
        "> * **DON'T** need to cite common APIs (e.g., tensorflow, pandas)\n",
        "> * **DON'T** just blindly recreate some web tutorial\n",
        "> * **DON'T** cut and paste from the web\n",
        "> * **DON'T** plagiarize (rather you should adapt and cite)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AG5NCz1b0Jie"
      },
      "source": [
        "## Experiment(s)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gv3aav4UCUi"
      },
      "source": [
        "### Experiment 1 - Random Forest Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ewgxku3XUF4h"
      },
      "source": [
        "def run_rf_experiment():\n",
        "  param_grid = [{\n",
        "    'n_estimators': [100, 200]\n",
        "  }]\n",
        "  pca_percent = 0.95\n",
        "  test_size = 0.3\n",
        "  RFExperiment(data, test_size=test_size, pca_percent=pca_percent, param_grid=param_grid).run_experiment()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "1217i2iSUW8B",
        "outputId": "ca9eebad-1afe-4a50-a1a6-eafb18b6fce0"
      },
      "source": [
        "run_rf_experiment()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PCA n - components: 62\n",
            "0.9748487508228582\n",
            "{'n_estimators': 200}\n",
            "Fold: 0\t Class dist.: [ 374 3561]\t Acc.: 0.9680365296803652\n",
            "Fold: 1\t Class dist.: [ 374 3561]\t Acc.: 0.9748858447488584\n",
            "Fold: 2\t Class dist.: [ 374 3561]\t Acc.: 0.9680365296803652\n",
            "Fold: 3\t Class dist.: [ 375 3561]\t Acc.: 0.9748283752860412\n",
            "Fold: 4\t Class dist.: [ 375 3561]\t Acc.: 0.9748283752860412\n",
            "Fold: 5\t Class dist.: [ 375 3561]\t Acc.: 0.977116704805492\n",
            "Fold: 6\t Class dist.: [ 375 3561]\t Acc.: 0.977116704805492\n",
            "Fold: 7\t Class dist.: [ 374 3562]\t Acc.: 0.9725400457665904\n",
            "Fold: 8\t Class dist.: [ 374 3562]\t Acc.: 0.977116704805492\n",
            "Fold: 9\t Class dist.: [ 374 3562]\t Acc.: 0.9839816933638444\n",
            "\n",
            "CV Accuracy: 0.9748487508228582 +/- 0.004438316396660216\n",
            "F1 Score - 0.9880011706175007\n",
            "Accuracy - 0.9781333333333333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqmshHSaUOud"
      },
      "source": [
        "### Experiment 1.5 - Analyzing Feature Importance in Random Forest Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnJ05c0lUSzJ"
      },
      "source": [
        "def run_feat_experiment():\n",
        "  X_train, X_test, y_train, y_test = train_test_split(data.drop('label', axis=1).values, data['label'].values, stratify = data['label'].values, test_size = 0.3, random_state = 1)\n",
        "  feat_importance(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "3DsOlRU5UX9Q",
        "outputId": "844597d1-8c52-4863-cd40-99e947b24ebf"
      },
      "source": [
        "run_feat_experiment()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 1) minor_image_version            0.090336\n",
            " 2) major_operating_system_version 0.081562\n",
            " 3) major_image_version            0.053366\n",
            " 4) minor_operating_system_version 0.047913\n",
            " 5) dll_characteristics            0.034840\n",
            " 6) minor_subsystem_version        0.032803\n",
            " 7) major_linker_version           0.032008\n",
            " 8) major_subsystem_version        0.031365\n",
            " 9) characteristics                0.023699\n",
            "10) compile_date                   0.023631\n",
            "11) size_of_stack_reserve          0.022761\n",
            "12) CheckSum                       0.020825\n",
            "13) nop                            0.018334\n",
            "14) int                            0.016920\n",
            "15) number_of_IAT_entires.1        0.016428\n",
            "16) ent_max                        0.014621\n",
            "17) size_of_stack_commit           0.014517\n",
            "18) not                            0.012832\n",
            "19) minor_linker_version           0.012559\n",
            "20) SizeOfHeaders.1                0.011764\n",
            "21) size_of_headers                0.011080\n",
            "22) number_of_IAT_entires          0.010384\n",
            "23) number_of_sections.1           0.009546\n",
            "24) shr                            0.008017\n",
            "25) pushf                          0.007450\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32Lhls9YUZmr"
      },
      "source": [
        "## Experiment 2 - Deep Learning Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUT072VhUcAg",
        "outputId": "3927f857-1b78-4730-e009-9ccf826be242"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Features: [0. 0. 1. ... 0. 0. 0.]\tTarget: 0\n",
            "Features: [0. 0. 0. ... 0. 0. 0.]\tTarget: 0\n",
            "Features: [0. 0. 1. ... 0. 0. 0.]\tTarget: 0\n",
            "Features: [0. 0. 1. ... 0. 0. 0.]\tTarget: 0\n",
            "Features: [0. 0. 0. ... 0. 0. 0.]\tTarget: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svP2m5j-x0Z7"
      },
      "source": [
        "***\n",
        "# Report\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QUbZ1BC3U8u"
      },
      "source": [
        "### Expectations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LSKkzOVryvTX"
      },
      "source": [
        "Write a mini-research \"paper\" that summarizes your work.\n",
        "\n",
        "Your report should include the following sections.\n",
        "\n",
        "1. [Introduction](https://libguides.usc.edu/writingguide/introduction)\n",
        "2. Dataset and Pre-processing\n",
        "3. [Methods](https://libguides.usc.edu/writingguide/methodology) (Experiment)\n",
        "4. [Results and Analysis](https://libguides.usc.edu/writingguide/results)\n",
        "5. [Discussion](https://libguides.usc.edu/writingguide/discussion) and [Conclusion](https://libguides.usc.edu/writingguide/conclusion)\n",
        "6. [References](https://libguides.usc.edu/writingguide/citingsources)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jTBmap73RIR"
      },
      "source": [
        "##### Protips"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b48jOdrRyvRU"
      },
      "source": [
        "> * Explain well your problem statement, your data, your experimental design decisions, your results, and your analysis.\n",
        ">   * assume an audience that is completely unfamiliar with your problem, but understands deep-learning.  \n",
        ">\n",
        "> * Use subheaders for your sections.\n",
        ">\n",
        "> * Some sections will be shorter (e.g. Intro) while others will be much longer (e.g. Methods or Results).\n",
        ">\n",
        "> * Justify your design choices.\n",
        ">   * this could be shown empirically, or given as explanations of your intuitions\n",
        ">\n",
        "> * Integrate your programmatic results (results, graphs, tables, etc.) within your report to support your prose. \n",
        ">\n",
        "> * *Results* section explains the \"what\" of your findings; *Discussion* explains the \"why\".\n",
        ">\n",
        "> * You do not need to \"formally\" (e.g., APA) cite your references.\n",
        ">    * rather, provide a title, author (if given), and URL\n",
        ">    * e.g., Dumane, G. [Introduction to Convolutional Neural Network (CNN) using Tensorflow](https://towardsdatascience.com/introduction-to-convolutional-neural-network-cnn-de73f69c5b83)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRHnTMBc-FMs"
      },
      "source": [
        "### <MY TITLE HERE>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yn9l0JqG-Iho"
      },
      "source": [
        "## Prediction malicious software from static binary analysis and Cuckoo behavioral analysis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyFV97eT-RbB"
      },
      "source": [
        "Goal - Use deep learning techniques to identify malicious Windows software \n",
        "\n",
        "Binary classification problem\n",
        "\n",
        "Static analyzers\n",
        "\n",
        "Cuckoo dynamic malware analysis environment - Categorical features of behavior exhibited "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOPsBYkD9dbT"
      },
      "source": [
        "### Introduction and Background\n",
        "Identifying malicious software has been a concern since network-connected computers have existed. Traditional approaches rely on matching the signature of a binary with a list of known malicious signatures. For example, we could take the MD-5 hash of a binary and search a database of known dangerous MD-5 hashes [1]. However, this approach is a purely reactive one, and exploits can only be detected after a sample has been submitted to a lab and properly identified. This provides no protection against zero-day exploits. \n",
        "\n",
        "In order to improve zero-day exploit recognition, detection, and prevention, many anti-virus vendors have taken to using heuristic techniques to identify suspicious executables before they have the potential to execute their payload – e.g., successfully hooking into the operating system, encrypting local files, or stealing private user data [2]. One approach to this heuristic analysis is to use static and dynamic analysis of an executable to identify malicious behavior. \n",
        "\n",
        "Static analysis relies on decompilation techniques to analyze the instructions a program is designed to execute. Field experts can take these decompiled binaries and identify instructions which are meant to cause harm to a system, and flag that sample as malware. [3] Algorithms such as Autosig can generate signatures from known malicious patterns, further automating the task of exploit identification [1]. \n",
        "\n",
        "However, not all malware responds well to static analysis. Decompilation resistance techniques can prevent a static analyzer from viewing the instructions for a given program. Furthermore, metamorphic malware can alter its code when being executed on a new host, making identification of these samples even more difficult [4]. Dynamic analysis alleviates some of these problems by assessing the behavior of an executable, instead of its instructions. Dynamic analysis tools such as the Cuckoo virtual machine execute an unknown program in a sandboxed environment, and then record the changes the program attempted to make to the operating system [5]. \n",
        "\n",
        "However, both these techniques have historically been used to identify a malicious sample once it has already been sent to a lab, meaning real-world exploit detection still relied on signature-matching. Many anti-virus manufacturers have taken the principles of static and dynamic analysis and used them in a machine learning context to classify samples as benign or malicious. Heuristic analyzers are deployed on endpoint machines to flag suspicious executables before disaster strikes. Some services provide cloud analysis – limiting the overhead of an anti-virus program on an end-user’s machine while still providing real-time threat protection [2]. \n",
        "\n",
        "In this experiment, we evaluate the performance of traditional machine learning techniques and more novel deep learning approaches for the problem of classifying executable samples as malicious or benign. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vyq_oij2sNyS"
      },
      "source": [
        "### Dataset\n",
        "\n",
        "To perform our malware classification experiment, we use a dataset of executable samples, labeled as either malicious or benign. The benign samples were retrieved from the Program Files directory of clean Windows 7 and Windows 8 installations. Virus samples were retrieved from two sources, the first being VirusTotal, a popular commercial service for automatically analyzing programs and comparing the results of multiple antivirus engines to provide a risk score. The second source for malware was VxHeaven, a malicious software repository maintained for historical purposes and frequently used by the malware hobbyist community to acquire and test so-called vintage malware [6].  The features associated with these samples were generated through a hybrid approach. Static features were generated from decompiled assembly instructions and hex dumps from program execution, and dynamic features were extracted from the Cuckoo sandbox environment [7].\n",
        "\n",
        "We observe that this dataset has a severe class imbalance. For 6248 samples, 595 are benign, and 5653 are malicious. This could have implications for our model’s performance going forward. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clwJP6tIsUEy"
      },
      "source": [
        "### Experiments\n",
        "In the original experiments conducted on this dataset, a random forest classifier was used to identify safe and dangerous executables [7]. Here, we compare a similar random forest approach with a feed-forward neural network, designed using TensorFlow and Keras. We also use our random forest model to visualize which features best explain the variance of our dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "dqgptRtxsXfI",
        "outputId": "1b78f455-926a-4666-b107-07cc27da3707"
      },
      "source": [
        "run_rf_experiment()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PCA n - components: 62\n",
            "0.9748487508228582\n",
            "{'n_estimators': 200}\n",
            "Fold: 0\t Class dist.: [ 374 3561]\t Acc.: 0.9680365296803652\n",
            "Fold: 1\t Class dist.: [ 374 3561]\t Acc.: 0.9748858447488584\n",
            "Fold: 2\t Class dist.: [ 374 3561]\t Acc.: 0.9680365296803652\n",
            "Fold: 3\t Class dist.: [ 375 3561]\t Acc.: 0.9748283752860412\n",
            "Fold: 4\t Class dist.: [ 375 3561]\t Acc.: 0.9748283752860412\n",
            "Fold: 5\t Class dist.: [ 375 3561]\t Acc.: 0.977116704805492\n",
            "Fold: 6\t Class dist.: [ 375 3561]\t Acc.: 0.977116704805492\n",
            "Fold: 7\t Class dist.: [ 374 3562]\t Acc.: 0.9725400457665904\n",
            "Fold: 8\t Class dist.: [ 374 3562]\t Acc.: 0.977116704805492\n",
            "Fold: 9\t Class dist.: [ 374 3562]\t Acc.: 0.9839816933638444\n",
            "\n",
            "CV Accuracy: 0.9748487508228582 +/- 0.004438316396660216\n",
            "F1 Score - 0.9880011706175007\n",
            "Accuracy - 0.9781333333333333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNtuGrcCsa00"
      },
      "source": [
        "As we see above, the random forest model performs exceptionally well with this problem. In this pipeline, we standardize the data, and then use PCA to reduce the dimensionality of our feature space. This brings our dimensions down from 1084 features to only 62 components, while preserving 95% of the variance in the dataset.  \n",
        "\n",
        "Our cross-validation experiment yielded a 97.4% accuracy, demonstrating that a random forest model can effectively separate malicious files from benign one. We notice that it was more likely for a malicious sample to be classified as benign than it was for a benign sample to be classified as malicious. Our model predicted 8 false negatives and 33 false positives. This might be due in part to our class imbalance.  \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "MzZOpHtoseG4",
        "outputId": "8d2a9b81-7e53-491a-e9e9-97bfe7120687"
      },
      "source": [
        "run_feat_experiment()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 1) minor_image_version            0.090336\n",
            " 2) major_operating_system_version 0.081562\n",
            " 3) major_image_version            0.053366\n",
            " 4) minor_operating_system_version 0.047913\n",
            " 5) dll_characteristics            0.034840\n",
            " 6) minor_subsystem_version        0.032803\n",
            " 7) major_linker_version           0.032008\n",
            " 8) major_subsystem_version        0.031365\n",
            " 9) characteristics                0.023699\n",
            "10) compile_date                   0.023631\n",
            "11) size_of_stack_reserve          0.022761\n",
            "12) CheckSum                       0.020825\n",
            "13) nop                            0.018334\n",
            "14) int                            0.016920\n",
            "15) number_of_IAT_entires.1        0.016428\n",
            "16) ent_max                        0.014621\n",
            "17) size_of_stack_commit           0.014517\n",
            "18) not                            0.012832\n",
            "19) minor_linker_version           0.012559\n",
            "20) SizeOfHeaders.1                0.011764\n",
            "21) size_of_headers                0.011080\n",
            "22) number_of_IAT_entires          0.010384\n",
            "23) number_of_sections.1           0.009546\n",
            "24) shr                            0.008017\n",
            "25) pushf                          0.007450\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ayvXHyns6I6"
      },
      "source": [
        "Here, we look at which of our original 1084 features best represented our data. The majority of features explain very little variance, explaining in part why our PCA was able to reduce dimensionality to such a great degree. The features which do seem to explain the most variance seem to be related to operating system version and build numbers. This makes some sense, since it is likely that older operating system versions are likely vulnerable to exploits, and therefore there is a correlation between software designed for certain builds and versions of an operating system, and the malicious nature of said software. However, this could also be indicative of a bias in our dataset, since repositories like VxHeaven are more likely to host older malware, which would have been designed for older operating systems. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "Q4O3ABOKs__t",
        "outputId": "10da54f6-680e-4794-fb6a-87e51ff66800"
      },
      "source": [
        "RFExperiment.generate_report(y_pred.numpy().flatten().astype(int), ds.y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score - 0.972432596182975\n",
            "Accuracy - 0.9514666666666667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKpeUrUGtHwp"
      },
      "source": [
        "Our neural network also responds favorably to this problem. We achieve comparable performance to our random forest approach, with 95.1% accuracy and 97.2% f1 score, compared to the 97.8% accuracy and 98.8% f1 score of the random forest model. Strangely, there seem to be no false positives with this model, but 91 false negatives. This could be a result of the bias in our dataset. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zztXiUGtVhK"
      },
      "source": [
        "### Analysis and Discussion\n",
        "\n",
        "We see that this classification problem seems to be solved with relative ease by both traditional machine learning techniques and simple deep learning approaches. The tendency for our neural network to prioritize correctly identifying benign samples instead of prioritizing malicious ones could be concerning given the nature of our problem space. In the context of stopping zero-day exploits and cyberattacks, successfully preventing an attack is infinitely more important than avoiding false positives. \n",
        "\n",
        "It’s worth noting that our traditional machine learning approach ran much faster than training a neural network, because of our dimensionality reduction. The random forest model also had the better performance of the two techniques, if only marginally. This goes to show that, even though deep learning can successfully solve this problem, it may be worth using a more “old-school” approach.\n",
        "\n",
        "*“Just because you can [use deep learning], doesn’t mean you should.”.*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTGqONerFqvr"
      },
      "source": [
        "### References\n",
        "[1] K. Ask, \"Automatic Malware Signature Generation\", pp. 32-34; 40, 2006. [Accessed 8 June 2021].\n",
        "\n",
        "[2] *Microsoft*, \"Evolution of Malware Protection\", Windows 10 Security Whitepaper, 2021. [Online]. Available: http://info.microsoft.com/rs/157-GQE-382/images/Windows%2010%20Security%20Whitepaper.pdf. [Accessed: 08- Jun- 2021].\n",
        "\n",
        "[3] M. Al-Asli and T. A. Ghaleb, \"Review of Signature-based Techniques in Antivirus Products,\" 2019 International Conference on Computer and Information Sciences (ICCIS), 2019, pp. 1-6, doi: 10.1109/ICCISci.2019.8716381.\n",
        "\n",
        "[4] M. Brand, \"Forensic analysis avoidance techniques of malware\", Australian Digital Forensics Conference, 2007. Available: https://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1005&context=adf. [Accessed 8 June 2021].\n",
        "\n",
        "[5] Cuckoo Sandbox. https://cuckoosandbox.org/ [Accessed 8 June 2021]. \n",
        "\n",
        "[6] https://www.youtube.com/user/danooct1. \n",
        "> Side note: This youtube channel is how I first got interested in Computer Science. I've been following this content creator for almost 9 years. It demonstrates 90's and early 2000's malware payloads.\n",
        "\n",
        "[7] A. Redondo, Detecting Malware Using a Hybrid Approach. *Pending Publication*.\n",
        "> Side note: The paper assosciated with this dataset has yet to be published. I reached out to the author and he graciously provided me with some insight to their methodology and experiments. \n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}