DL_Malware_Notebook.ipynb

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      "name": "DL_Malware_Notebook.ipynb",
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      "include_colab_link": true
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kamkali/Malware_Discovery_ML/blob/master/DL_Malware_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rp4LelB4jM7J",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# Welcome to the Jupyter Notebook with Google Colab! Here we will post our research on discovering malware using machine learning.\n",
        "\n",
        "*authors: Jakub Burghardt, Kamil Kaliś, Michał Szczepaniak-Krupowski*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjnVomalm7iN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def upload_files():\n",
        "    \"\"\"Function for uploading files to the project\"\"\"\n",
        "    from google.colab import files\n",
        "    uploaded = files.upload()\n",
        "    for k, v in uploaded.items():\n",
        "        open(k, 'wb').write(v)\n",
        "    return list(uploaded.keys())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4jwWlmDkolB",
        "colab_type": "text"
      },
      "source": [
        "-----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXzB8IW4wh_3",
        "colab_type": "text"
      },
      "source": [
        "# Part I: planning\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4W_Rct2gYbN",
        "colab_type": "text"
      },
      "source": [
        "## Main tasks:\n",
        "- get acquaint with malware topic:\n",
        "    - types of malware\n",
        "    - frequency of appearance on different operating systems\n",
        "    - detection methods: static and dynamic\n",
        "    - trends\n",
        "- familiarity with machine learning topic: \n",
        "    - selection of different algorithms\n",
        "- most significant features pick\n",
        "- dataset pick:\n",
        "    - should be up to date (as current as possible)\n",
        "    - should contain proper features selected earlier\n",
        "- analysis of efficiency of chosen algorithms:\n",
        "    - statistical comparison of accuracy, true positives/false negatives/etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGONaqPalQHq",
        "colab_type": "text"
      },
      "source": [
        "## Assignment of the task:\n",
        "### Michał Szczepaniak-Krupowski:\n",
        "A deeper look at malware – types and families, comparison between static and dynamic methods.\n",
        "\n",
        "The popularity of malware on different OS – why Windows users are the most threatened on the malicious software attack?\n",
        "\n",
        "### Kamil Kaliś:\n",
        "What is Machine Learning and why is it crucial in Cybersecurity – the need for accelerating the usage of ML in modern detecting malware.\n",
        "\n",
        "Comparison of different supervised learning algorithms – pros and cons in malware detection.\n",
        "\n",
        "### Jakub Burghardt:\n",
        "Malware detection methods – more detailed reasoning about static detection methods.\n",
        "\n",
        "A brief look at PE format (Portable Executable) – how can they be helpful in detecting malware with static methods?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCLfUg4anHAm",
        "colab_type": "text"
      },
      "source": [
        "-----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2u92j9RanK5C",
        "colab_type": "text"
      },
      "source": [
        "# Mid-term presentation\n",
        "### link to presentation:\n",
        "https://drive.google.com/open?id=1x1DcDueWrDM8XHEd-sKMWykt5G3wMW9X-8NbKFgRhmk"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ns6hf_nkb4xH",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ki4t_5tBMm4x",
        "colab_type": "text"
      },
      "source": [
        "# Project implementation:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-ELzWs2Jy6U",
        "colab_type": "text"
      },
      "source": [
        "># *'Before'* section:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thgj7PYGJ8kK",
        "colab_type": "text"
      },
      "source": [
        "Code snippet to upload MalwareData.csv if not already in colab directory:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NG3cOt_JJ7BF",
        "colab_type": "code",
        "outputId": "67c583d2-564f-4c68-e89f-b75fb04277f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "import os\n",
        "if not os.path.exists('Malware_Discovery_ML'):\n",
        "    !git clone https://github.com/kamkali/Malware_Discovery_ML\n",
        "    !unzip Malware_Discovery_ML/MalwareData.csv.zip\n",
        "    # !unzip Malware_Discovery_ML/KNN_logs.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Malware_Discovery_ML'...\n",
            "remote: Enumerating objects: 77, done.\u001b[K\n",
            "remote: Counting objects:   1% (1/77)\u001b[K\rremote: Counting objects:   2% (2/77)\u001b[K\rremote: Counting objects:   3% (3/77)\u001b[K\rremote: Counting objects:   5% (4/77)\u001b[K\rremote: Counting objects:   6% (5/77)\u001b[K\rremote: Counting objects:   7% (6/77)\u001b[K\rremote: Counting objects:   9% (7/77)\u001b[K\rremote: Counting objects:  10% (8/77)\u001b[K\rremote: Counting objects:  11% (9/77)\u001b[K\rremote: Counting objects:  12% (10/77)\u001b[K\rremote: Counting objects:  14% (11/77)\u001b[K\rremote: Counting objects:  15% (12/77)\u001b[K\rremote: Counting objects:  16% (13/77)\u001b[K\rremote: Counting objects:  18% (14/77)\u001b[K\rremote: Counting objects:  19% (15/77)\u001b[K\rremote: Counting objects:  20% (16/77)\u001b[K\rremote: Counting objects:  22% (17/77)\u001b[K\rremote: Counting objects:  23% (18/77)\u001b[K\rremote: Counting objects:  24% (19/77)\u001b[K\rremote: Counting objects:  25% (20/77)\u001b[K\rremote: Counting objects:  27% (21/77)\u001b[K\rremote: Counting objects:  28% (22/77)\u001b[K\rremote: Counting objects:  29% (23/77)\u001b[K\rremote: Counting objects:  31% (24/77)\u001b[K\rremote: Counting objects:  32% (25/77)\u001b[K\rremote: Counting objects:  33% (26/77)\u001b[K\rremote: Counting objects:  35% (27/77)\u001b[K\rremote: Counting objects:  36% (28/77)\u001b[K\rremote: Counting objects:  37% (29/77)\u001b[K\rremote: Counting objects:  38% (30/77)\u001b[K\rremote: Counting objects:  40% (31/77)\u001b[K\rremote: Counting objects:  41% (32/77)\u001b[K\rremote: Counting objects:  42% (33/77)\u001b[K\rremote: Counting objects:  44% (34/77)\u001b[K\rremote: Counting objects:  45% (35/77)\u001b[K\rremote: Counting objects:  46% (36/77)\u001b[K\rremote: Counting objects:  48% (37/77)\u001b[K\rremote: Counting objects:  49% (38/77)\u001b[K\rremote: Counting objects:  50% (39/77)\u001b[K\rremote: Counting objects:  51% (40/77)\u001b[K\rremote: Counting objects:  53% (41/77)\u001b[K\rremote: Counting objects:  54% (42/77)\u001b[K\rremote: Counting objects:  55% (43/77)\u001b[K\rremote: Counting objects:  57% (44/77)\u001b[K\rremote: Counting objects:  58% (45/77)\u001b[K\rremote: Counting objects:  59% (46/77)\u001b[K\rremote: Counting objects:  61% (47/77)\u001b[K\rremote: Counting objects:  62% (48/77)\u001b[K\rremote: Counting objects:  63% (49/77)\u001b[K\rremote: Counting objects:  64% (50/77)\u001b[K\rremote: Counting objects:  66% (51/77)\u001b[K\rremote: Counting objects:  67% (52/77)\u001b[K\rremote: Counting objects:  68% (53/77)\u001b[K\rremote: Counting objects:  70% (54/77)\u001b[K\rremote: Counting objects:  71% (55/77)\u001b[K\rremote: Counting objects:  72% (56/77)\u001b[K\rremote: Counting objects:  74% (57/77)\u001b[K\rremote: Counting objects:  75% (58/77)\u001b[K\rremote: Counting objects:  76% (59/77)\u001b[K\rremote: Counting objects:  77% (60/77)\u001b[K\rremote: Counting objects:  79% (61/77)\u001b[K\rremote: Counting objects:  80% (62/77)\u001b[K\rremote: Counting objects:  81% (63/77)\u001b[K\rremote: Counting objects:  83% (64/77)\u001b[K\rremote: Counting objects:  84% (65/77)\u001b[K\rremote: Counting objects:  85% (66/77)\u001b[K\rremote: Counting objects:  87% (67/77)\u001b[K\rremote: Counting objects:  88% (68/77)\u001b[K\rremote: Counting objects:  89% (69/77)\u001b[K\rremote: Counting objects:  90% (70/77)\u001b[K\rremote: Counting objects:  92% (71/77)\u001b[K\rremote: Counting objects:  93% (72/77)\u001b[K\rremote: Counting objects:  94% (73/77)\u001b[K\rremote: Counting objects:  96% (74/77)\u001b[K\rremote: Counting objects:  97% (75/77)\u001b[K\rremote: Counting objects:  98% (76/77)\u001b[K\rremote: Counting objects: 100% (77/77)\u001b[K\rremote: Counting objects: 100% (77/77), done.\u001b[K\n",
            "remote: Compressing objects: 100% (67/67), done.\u001b[K\n",
            "remote: Total 204 (delta 25), reused 34 (delta 7), pack-reused 127\u001b[K\n",
            "Receiving objects: 100% (204/204), 23.91 MiB | 15.42 MiB/s, done.\n",
            "Resolving deltas: 100% (71/71), done.\n",
            "Archive:  Malware_Discovery_ML/MalwareData.csv.zip\n",
            "  inflating: MalwareData.csv         \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqUwz-vIKEyX",
        "colab_type": "text"
      },
      "source": [
        "Logger module to save outputs from functions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8RVUXrVKMj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import logging\n",
        "import functools\n",
        "import time\n",
        "\n",
        "\"\"\"Logger module created by Kamil Kaliś\"\"\"\n",
        "\n",
        "\n",
        "def logger_setup(logger_file=\"results.log\"):\n",
        "    \"\"\"Sets up the logger.\n",
        "    Usage:\n",
        "        1. With wrapper @log_to_file(logger_file=<loggername.log>)\n",
        "        2. Set logger within module as:\n",
        "            2.1 log = get_logger(logger_file-<loggername.log>)\n",
        "            2.2 use 'log' variable to use logger and write them to file\"\"\"\n",
        "\n",
        "    logformat = \"[%(asctime)s %(levelname)s] %(message)s\"\n",
        "    dateformat = \"%d-%m-%y %H:%M:%S\"\n",
        "    logger = logging.getLogger(logger_file)\n",
        "    formatter = logging.Formatter(logformat)\n",
        "    formatter.datefmt = dateformat\n",
        "    fh = logging.FileHandler(logger_file, mode=\"a\")\n",
        "    fh.setFormatter(formatter)\n",
        "    sh = logging.StreamHandler()\n",
        "    sh.setFormatter(formatter)\n",
        "    logger.setLevel(logging.INFO)\n",
        "    logger.addHandler(fh)\n",
        "    logger.addHandler(sh)\n",
        "    logger.propagate = False\n",
        "\n",
        "\n",
        "def log_to_file(func=None, logger_file='results.log'):\n",
        "    def log_exec_time(original_func):\n",
        "        \"\"\"Wrapper for logging execution time of marked function.\n",
        "        Usage:\n",
        "        Add adnotation above function: '@log_to_file(<logger_file.log>)'\"\"\"\n",
        "\n",
        "        @functools.wraps(original_func)\n",
        "        def wrapper(*args, **kwargs):\n",
        "            log = logging.getLogger(logger_file)\n",
        "            log.info(f\"Running {original_func.__name__}...\")\n",
        "            start_time = time.time()\n",
        "            result = original_func(*args, **kwargs)\n",
        "            exec_time = time.time() - start_time\n",
        "            log.info(f\"Function {original_func.__name__} finished in {exec_time}s\")\n",
        "            return result\n",
        "\n",
        "        return wrapper\n",
        "\n",
        "    return log_exec_time if func is None else log_exec_time(func)\n",
        "\n",
        "\n",
        "def get_logger(logger_file='results.log'):\n",
        "    return logging.getLogger(logger_file)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XeXnUrh2nXPA",
        "colab_type": "text"
      },
      "source": [
        "--------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApZikBoQnYky",
        "colab_type": "text"
      },
      "source": [
        "> # K-Nearest Neighbors:\n",
        "*author: Kamil Kaliś*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBm7luWAlrdI",
        "colab_type": "text"
      },
      "source": [
        "One simple way to solve malware detection problems is to use the K-Nearest Neighbors algorithm. KNN model from the third-party library (*sklearn*) will be used.\n",
        "We also need data to work on and in this case, it is collected in *MalwareData.csv* file. To process data, *pandas* library and sklearn modules are used. To visualize data and charts *plotly* library will be used."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0g2tZPcnnZR_",
        "colab_type": "text"
      },
      "source": [
        "As mentioned above, let's start with importing adequate libraries and modules:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6HcpRdbnX_v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "from plotly.offline import plot\n",
        "from sklearn import neighbors, preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBSeYTMm7EYo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "LOGGER_FILE = 'KNN_results_runtime.log'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXUfElNKnnfR",
        "colab_type": "text"
      },
      "source": [
        "Then, read data to work on:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSe3UA0voVYx",
        "colab_type": "code",
        "outputId": "a4593b63-5091-484d-bbeb-9ec4f79937e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "data = pd.read_csv('MalwareData.csv', sep='|')\n",
        "print(data.shape[0], data.shape[1])\n",
        "benign_files = data[data['legitimate'] == 1].count()\n",
        "malware_files = data[data['legitimate'] == 0].count()\n",
        "print(f\"Clean files count is {benign_files[1]} and malware files count is {malware_files[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "138047 57\n",
            "Clean files count is 41323 and malware files count is 96724\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfRHsxkdoEes",
        "colab_type": "text"
      },
      "source": [
        "MalwareData.csv contains 138 047 records as a whole, where 41 323 files are benign, clean files, and 96 724 files which are malicious. \n",
        "Data is reprezented with 56 features. Last 57'th feature represents a label *legitimate* classifies given sample:\n",
        "\n",
        "'1' – for clean file, '0' – for malware file.\n",
        "\n",
        "Knowing all this, we can start to process the data to more algorithm friendly form."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LyTpLt-a9Pfk",
        "colab_type": "text"
      },
      "source": [
        "## Function: malware_data_transform\n",
        "\n",
        "### **@description:** \n",
        "> Performs data transform as dropping insignificant columns and 'legitimate' column, which contains labels. It can normalize or standardize data.\n",
        "\n",
        "### **@params:**\n",
        "\n",
        ">*   optimize_data – enables data normalization or standardization: \n",
        "default is None\n",
        ">*   csv_data – cvs file to read and transform:\n",
        "default is MalwareData.csv\n",
        ">*   csv_sep – separator used in csv file:\n",
        "default is '|'\n",
        ">*   enable figures – enables plotly charts to show:\n",
        "default is False\n",
        "\n",
        "### **@returns:**\n",
        "\n",
        "\n",
        "> full_data.values, labels – tuple with values of features and labels for malicious and clean files\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxFEUrZMqZJw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@log_to_file(logger_file=LOGGER_FILE)\n",
        "def malware_data_transform(optimize_data=None, csv_data='MalwareData.csv', csv_sep='|', enable_figures=False):\n",
        "    log = get_logger(logger_file=LOGGER_FILE)\n",
        "    log.info(\"-Reading csv file\")\n",
        "    full_data = pd.read_csv(csv_data, sep=csv_sep)\n",
        "\n",
        "    pd.set_option(\"display.max_columns\", None)\n",
        "\n",
        "    labels = full_data['legitimate'].values\n",
        "    full_data: pd.DataFrame = full_data.drop(['Name', 'md5', 'legitimate'], axis=1)\n",
        "\n",
        "    if optimize_data == 'normalize':\n",
        "        log.info(\"--Data normalization processing...\")\n",
        "        full_data = pd.DataFrame(preprocessing.MinMaxScaler().fit_transform(full_data))\n",
        "    elif optimize_data == 'standardize':\n",
        "        log.info(\"--Data standardization processing...\")\n",
        "        full_data = pd.DataFrame(preprocessing.StandardScaler().fit_transform(full_data))\n",
        "\n",
        "    if enable_figures:\n",
        "        log.info(\"--Figures enabled\")\n",
        "        plot_bar_figures(full_data, optimize_data)\n",
        "\n",
        "    return full_data.values, labels\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pa7NfTXQBqyP",
        "colab_type": "text"
      },
      "source": [
        "When transforming data, it is good to see how it looks like. The next function provides a utility to show non-optimized data, normalized or standardized forms of data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyPQE8xcAiJU",
        "colab_type": "text"
      },
      "source": [
        "## Function: plot_bar_figures\n",
        "\n",
        "### **@description:** \n",
        "> Function to plot bar charts with features values\n",
        "### **@params:**\n",
        "\n",
        "\n",
        ">*   optimize_data – enables data normalization or standardization: \n",
        "default is None\n",
        ">*   full_data – transformed data\n",
        "\n",
        "### **@returns:**\n",
        "> Nothing\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2WwdyG2qrZF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_bar_figures(full_data, optimize_data):\n",
        "    cols = full_data.keys()\n",
        "    if optimize_data == 'normalize':\n",
        "        cols = full_data.keys()\n",
        "        full_data_normalized = pd.DataFrame(preprocessing.MinMaxScaler().fit_transform(full_data))\n",
        "        trace_norm_mean: pd.DataFrame = full_data_normalized.mean()\n",
        "        trace_norm_std: pd.DataFrame = full_data_normalized.std()\n",
        "\n",
        "        # -------------------------------------------------\n",
        "        layout = go.Layout(title='Normalized data mean')\n",
        "        figure = go.Figure(go.Bar(y=trace_norm_mean,\n",
        "                                  x=cols\n",
        "                                  ), layout=layout)\n",
        "        figure.show()\n",
        "        # -------------------------------------------------\n",
        "        layout = go.Layout(title='Normalized data standard deviation')\n",
        "        figure = go.Figure(go.Bar(y=trace_norm_std,\n",
        "                                  x=cols\n",
        "                                  ), layout=layout)\n",
        "        figure.show()\n",
        "\n",
        "    elif optimize_data == 'standardize':\n",
        "        cols = full_data.keys()\n",
        "\n",
        "        full_data_standardized = pd.DataFrame(preprocessing.StandardScaler().fit_transform(full_data))\n",
        "        trace_stand_mean = full_data_standardized.mean()\n",
        "        trace_stand_std = full_data_standardized.std()\n",
        "\n",
        "        # -------------------------------------------------\n",
        "        layout = go.Layout(title='Standardized data mean')\n",
        "        figure = go.Figure(go.Bar(y=trace_stand_mean,\n",
        "                                  x=cols\n",
        "                                  ), layout=layout)\n",
        "        \n",
        "        figure.show()\n",
        "        # -------------------------------------------------\n",
        "        layout = go.Layout(title='Standardized data standard deviation')\n",
        "        figure = go.Figure(go.Bar(y=trace_stand_std,\n",
        "                                  x=cols\n",
        "                                  ), layout=layout)\n",
        "        figure.show()\n",
        "    else:\n",
        "        trace_mean = full_data.mean()\n",
        "        trace_std = full_data.std()\n",
        "\n",
        "        # -------------------------------------------------\n",
        "        layout = go.Layout(title='Data mean')\n",
        "        figure = go.Figure(go.Bar(y=trace_mean,\n",
        "                                  x=cols\n",
        "                                  ), layout=layout)\n",
        "        figure.show()\n",
        "        # -------------------------------------------------\n",
        "        layout = go.Layout(title='Data standard deviation')\n",
        "        figure = go.Figure(go.Bar(y=trace_std,\n",
        "                                  x=cols\n",
        "                                  ), layout=layout)\n",
        "        figure.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o3MhbAq22jAK",
        "colab_type": "text"
      },
      "source": [
        "The classifier is built using *sklearn* library. To get the best results, it needs to be parameterized and analyzed with different combinations of parameters, like:\n",
        "* Numbers of neighbors\n",
        "* Number of features and proper algorithm to choose them\n",
        "* Different metrics and weights in KNN algorithm\n",
        "* Non-optimized or normalized / standardized data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5fNfmY34kJR",
        "colab_type": "text"
      },
      "source": [
        "## Function: knn_classifier\n",
        "\n",
        "### **@description:** \n",
        "> Function to fit KNN classifier for different params and receive metrics, like accuracy, precision, recall percentage, false positives  and false negatives percentage.\n",
        "### **@params:**\n",
        "\n",
        "\n",
        ">*   input data – data applied to teach algorithm\n",
        ">*   labels – 0/1 label for each sample in input data\n",
        ">*   n_neighbors - upper limit of neighbors to classify with (descending order):\n",
        "default is 1\n",
        ">*   run_for_features – tuple of starting number of features to select, step, upper limit: default is (3, 30, 20)\n",
        "\n",
        "### **@returns:**\n",
        "> Nothing\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41iT-w-kq9ME",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@log_to_file(logger_file=LOGGER_FILE)\n",
        "def knn_classifier(input_data, labels, n_neighbors=1, run_for_features=(3, 30, 20)):\n",
        "    log = get_logger(logger_file=LOGGER_FILE)\n",
        "    for n in range(1, n_neighbors + 1):\n",
        "        log.info(f\"-Running KNN algorithm with n_neighbors={n_neighbors}\")\n",
        "        weights = ['distance', 'uniform']\n",
        "        metrics = ['chebyshev', 'euclidean', 'manhattan']\n",
        "        for metric in metrics:\n",
        "            for weight in weights:\n",
        "                classifier = neighbors.KNeighborsClassifier(n_neighbors, weights=weight, metric=metric)\n",
        "                for k in range(*run_for_features):\n",
        "                    start_time = time.time()\n",
        "                    log.info(f\"--Finding best {k} features\")\n",
        "                    best_features_data = SelectKBest(f_classif, k=k).fit_transform(input_data, labels)\n",
        "                    X_train, X_test, Y_train, Y_test = train_test_split(best_features_data, labels, test_size=0.3)\n",
        "\n",
        "                    log.info(f\"---Starting fitting for weight={weight} and metric={metric}\")\n",
        "                    classifier.fit(X_train, Y_train)\n",
        "\n",
        "                    score = classifier.score(X_test, Y_test)\n",
        "\n",
        "                    \"\"\" Confusion matrix \"\"\"\n",
        "                    log.info(\"----Measuring confusion matrix...\")\n",
        "                    result = classifier.predict(X_test)\n",
        "                    conf_matrix = confusion_matrix(Y_test, result)\n",
        "\n",
        "                    log.info(f\"-----KNN accuracy is: {score * 100}%\")\n",
        "\n",
        "                    precision = conf_matrix[0][0] / (conf_matrix[0][0] + conf_matrix[0][1]) * 100\n",
        "                    log.info(f\"-----Precision in percent: {precision}%\")\n",
        "\n",
        "                    recall = conf_matrix[0][0] / (conf_matrix[0][0] + conf_matrix[1][0]) * 100\n",
        "                    log.info(f\"-----Recall in percent: {recall}%\")\n",
        "\n",
        "                    false_positives = conf_matrix[0][1] / sum(conf_matrix[0]) * 100\n",
        "                    log.info(f\"-----False positives in percent: {false_positives}%\")\n",
        "\n",
        "                    false_negatives = conf_matrix[1][0] / sum(conf_matrix[1]) * 100\n",
        "                    log.info(f\"-----False negatives in percent: {false_negatives}%\")\n",
        "                    log.info(f\"Exec time for {k} features: {time.time() - start_time}s\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RysrRygr60LH",
        "colab_type": "text"
      },
      "source": [
        "The output of functions is collected by the logger module. Output file with results is named *KNN_results_runtime.log*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrJBHPH07otM",
        "colab_type": "text"
      },
      "source": [
        "Below is the section, which evokes the above functions. It is *main* part of the program."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIJ7vXmoK1MU",
        "colab_type": "code",
        "outputId": "3e244f88-dfab-4a28-8882-ccd69d3da460",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "logger_setup(logger_file=LOGGER_FILE)\n",
        "get_logger(logger_file=LOGGER_FILE).info(\"---------------Starting---------------\")\n",
        "get_logger(logger_file=LOGGER_FILE).info(\"--------------------------------------\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20-01-20 19:07:31 INFO] ---------------Starting---------------\n",
            "[20-01-20 19:07:31 INFO] --------------------------------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJJ7HnLMrara",
        "colab_type": "code",
        "outputId": "973b27aa-47a4-4f7f-e544-c28f40e0d29d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "input_data, labels = malware_data_transform(enable_figures=True)\n",
        "knn_classifier(input_data, labels, 1, (3, 24, 20))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20-01-20 19:07:31 INFO] Running malware_data_transform...\n",
            "[20-01-20 19:07:31 INFO] -Reading csv file\n",
            "[20-01-20 19:07:32 INFO] --Figures enabled\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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            "[20-01-20 19:07:33 INFO] Function malware_data_transform finished in 1.7889420986175537s\n",
            "[20-01-20 19:07:33 INFO] Running knn_classifier...\n",
            "[20-01-20 19:07:33 INFO] -Running KNN algorithm with n_neighbors=1\n",
            "[20-01-20 19:07:33 INFO] --Finding best 3 features\n",
            "[20-01-20 19:07:33 INFO] ---Starting fitting for weight=distance and metric=chebyshev\n",
            "[20-01-20 19:07:39 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:07:41 INFO] -----KNN accuracy is: 96.4336593021852%\n",
            "[20-01-20 19:07:41 INFO] -----Precision in percent: 97.39534883720931%\n",
            "[20-01-20 19:07:41 INFO] -----Recall in percent: 97.51293549499827%\n",
            "[20-01-20 19:07:41 INFO] -----False positives in percent: 2.604651162790698%\n",
            "[20-01-20 19:07:41 INFO] -----False negatives in percent: 5.8192090395480225%\n",
            "[20-01-20 19:07:41 INFO] Exec time for 3 features: 7.847141981124878s\n",
            "[20-01-20 19:07:41 INFO] --Finding best 23 features\n",
            "[20-01-20 19:07:41 INFO] ---Starting fitting for weight=distance and metric=chebyshev\n",
            "[20-01-20 19:07:55 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:08:05 INFO] -----KNN accuracy is: 97.4912471326814%\n",
            "[20-01-20 19:08:05 INFO] -----Precision in percent: 98.30066256994748%\n",
            "[20-01-20 19:08:05 INFO] -----Recall in percent: 98.13558160257728%\n",
            "[20-01-20 19:08:05 INFO] -----False positives in percent: 1.699337430052525%\n",
            "[20-01-20 19:08:05 INFO] -----False negatives in percent: 4.427804004558034%\n",
            "[20-01-20 19:08:05 INFO] Exec time for 23 features: 24.22573494911194s\n",
            "[20-01-20 19:08:05 INFO] --Finding best 3 features\n",
            "[20-01-20 19:08:05 INFO] ---Starting fitting for weight=uniform and metric=chebyshev\n",
            "[20-01-20 19:08:12 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:08:14 INFO] -----KNN accuracy is: 96.593021851986%\n",
            "[20-01-20 19:08:14 INFO] -----Precision in percent: 97.6614045390169%\n",
            "[20-01-20 19:08:14 INFO] -----Recall in percent: 97.46948907122665%\n",
            "[20-01-20 19:08:14 INFO] -----False positives in percent: 2.3385954609831083%\n",
            "[20-01-20 19:08:14 INFO] -----False negatives in percent: 5.888015401893149%\n",
            "[20-01-20 19:08:14 INFO] Exec time for 3 features: 9.399003744125366s\n",
            "[20-01-20 19:08:14 INFO] --Finding best 23 features\n",
            "[20-01-20 19:08:14 INFO] ---Starting fitting for weight=uniform and metric=chebyshev\n",
            "[20-01-20 19:08:29 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:08:38 INFO] -----KNN accuracy is: 97.6747555233611%\n",
            "[20-01-20 19:08:38 INFO] -----Precision in percent: 98.46619437375335%\n",
            "[20-01-20 19:08:38 INFO] -----Recall in percent: 98.22635424885931%\n",
            "[20-01-20 19:08:38 INFO] -----False positives in percent: 1.533805626246647%\n",
            "[20-01-20 19:08:38 INFO] -----False negatives in percent: 4.19064602415498%\n",
            "[20-01-20 19:08:38 INFO] Exec time for 23 features: 23.24976944923401s\n",
            "[20-01-20 19:08:38 INFO] --Finding best 3 features\n",
            "[20-01-20 19:08:38 INFO] ---Starting fitting for weight=distance and metric=euclidean\n",
            "[20-01-20 19:08:44 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:08:45 INFO] -----KNN accuracy is: 96.5374864179645%\n",
            "[20-01-20 19:08:45 INFO] -----Precision in percent: 97.55853650125866%\n",
            "[20-01-20 19:08:45 INFO] -----Recall in percent: 97.49801840300513%\n",
            "[20-01-20 19:08:45 INFO] -----False positives in percent: 2.441463498741336%\n",
            "[20-01-20 19:08:45 INFO] -----False negatives in percent: 5.84729381443299%\n",
            "[20-01-20 19:08:45 INFO] Exec time for 3 features: 7.346361398696899s\n",
            "[20-01-20 19:08:45 INFO] --Finding best 23 features\n",
            "[20-01-20 19:08:45 INFO] ---Starting fitting for weight=distance and metric=euclidean\n",
            "[20-01-20 19:09:00 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:09:09 INFO] -----KNN accuracy is: 97.69407219606423%\n",
            "[20-01-20 19:09:09 INFO] -----Precision in percent: 98.4726493659076%\n",
            "[20-01-20 19:09:09 INFO] -----Recall in percent: 98.2316442605998%\n",
            "[20-01-20 19:09:09 INFO] -----False positives in percent: 1.5273506340924012%\n",
            "[20-01-20 19:09:09 INFO] -----False negatives in percent: 4.111894838089132%\n",
            "[20-01-20 19:09:09 INFO] Exec time for 23 features: 23.8876850605011s\n",
            "[20-01-20 19:09:09 INFO] --Finding best 3 features\n",
            "[20-01-20 19:09:09 INFO] ---Starting fitting for weight=uniform and metric=euclidean\n",
            "[20-01-20 19:09:16 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:09:18 INFO] -----KNN accuracy is: 96.52782808161294%\n",
            "[20-01-20 19:09:18 INFO] -----Precision in percent: 97.55229040622298%\n",
            "[20-01-20 19:09:18 INFO] -----Recall in percent: 97.47814972190555%\n",
            "[20-01-20 19:09:18 INFO] -----False positives in percent: 2.4477095937770095%\n",
            "[20-01-20 19:09:18 INFO] -----False negatives in percent: 5.844675740592474%\n",
            "[20-01-20 19:09:18 INFO] Exec time for 3 features: 9.410200119018555s\n",
            "[20-01-20 19:09:18 INFO] --Finding best 23 features\n",
            "[20-01-20 19:09:18 INFO] ---Starting fitting for weight=uniform and metric=euclidean\n",
            "[20-01-20 19:09:31 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:09:41 INFO] -----KNN accuracy is: 97.6506096824822%\n",
            "[20-01-20 19:09:41 INFO] -----Precision in percent: 98.50220872446162%\n",
            "[20-01-20 19:09:41 INFO] -----Recall in percent: 98.14655617069565%\n",
            "[20-01-20 19:09:41 INFO] -----False positives in percent: 1.4977912755383764%\n",
            "[20-01-20 19:09:41 INFO] -----False negatives in percent: 4.333145751266179%\n",
            "[20-01-20 19:09:41 INFO] Exec time for 23 features: 22.98038911819458s\n",
            "[20-01-20 19:09:41 INFO] --Finding best 3 features\n",
            "[20-01-20 19:09:41 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[20-01-20 19:09:48 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:09:50 INFO] -----KNN accuracy is: 96.6944343836774%\n",
            "[20-01-20 19:09:50 INFO] -----Precision in percent: 97.68413059984815%\n",
            "[20-01-20 19:09:50 INFO] -----Recall in percent: 97.59318644184684%\n",
            "[20-01-20 19:09:50 INFO] -----False positives in percent: 2.3158694001518603%\n",
            "[20-01-20 19:09:50 INFO] -----False negatives in percent: 5.610481472550438%\n",
            "[20-01-20 19:09:50 INFO] Exec time for 3 features: 8.474524974822998s\n",
            "[20-01-20 19:09:50 INFO] --Finding best 23 features\n",
            "[20-01-20 19:09:50 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[20-01-20 19:10:03 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:10:12 INFO] -----KNN accuracy is: 97.98382228661113%\n",
            "[20-01-20 19:10:12 INFO] -----Precision in percent: 98.52880374862183%\n",
            "[20-01-20 19:10:12 INFO] -----Recall in percent: 98.59334597483192%\n",
            "[20-01-20 19:10:12 INFO] -----False positives in percent: 1.4711962513781698%\n",
            "[20-01-20 19:10:12 INFO] -----False negatives in percent: 3.2927124525865548%\n",
            "[20-01-20 19:10:12 INFO] Exec time for 23 features: 22.042607069015503s\n",
            "[20-01-20 19:10:12 INFO] --Finding best 3 features\n",
            "[20-01-20 19:10:12 INFO] ---Starting fitting for weight=uniform and metric=manhattan\n",
            "[20-01-20 19:10:19 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:10:21 INFO] -----KNN accuracy is: 96.66304479053484%\n",
            "[20-01-20 19:10:21 INFO] -----Precision in percent: 97.64920492875335%\n",
            "[20-01-20 19:10:21 INFO] -----Recall in percent: 97.5954592363261%\n",
            "[20-01-20 19:10:21 INFO] -----False positives in percent: 2.3507950712466443%\n",
            "[20-01-20 19:10:21 INFO] -----False negatives in percent: 5.654882291076774%\n",
            "[20-01-20 19:10:21 INFO] Exec time for 3 features: 9.685320377349854s\n",
            "[20-01-20 19:10:21 INFO] --Finding best 23 features\n",
            "[20-01-20 19:10:22 INFO] ---Starting fitting for weight=uniform and metric=manhattan\n",
            "[20-01-20 19:10:35 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:10:45 INFO] -----KNN accuracy is: 97.84377640951347%\n",
            "[20-01-20 19:10:45 INFO] -----Precision in percent: 98.38204413232813%\n",
            "[20-01-20 19:10:45 INFO] -----Recall in percent: 98.54147989793807%\n",
            "[20-01-20 19:10:45 INFO] -----False positives in percent: 1.6179558676718646%\n",
            "[20-01-20 19:10:45 INFO] -----False negatives in percent: 3.420669577874818%\n",
            "[20-01-20 19:10:45 INFO] Exec time for 23 features: 23.489351987838745s\n",
            "[20-01-20 19:10:45 INFO] Function knn_classifier finished in 192.06835961341858s\n"
          ],
          "name": "stderr"
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    {
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      "source": [
        "When data is a raw input without optimization, charts show that the value of one feature dominates another one. The function executes quickly, because it may enter this dominant value and pretend based on its value. However, results, in this case, may not be the most accurate."
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    },
    {
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      "source": [
        "get_logger(logger_file=LOGGER_FILE).info(\"--------------------------------------\")\n",
        "input_data, labels = malware_data_transform(optimize_data='normalize', enable_figures=True)\n",
        "knn_classifier(input_data, labels, 1, (3, 24, 20))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20-01-20 19:10:45 INFO] --------------------------------------\n",
            "[20-01-20 19:10:45 INFO] Running malware_data_transform...\n",
            "[20-01-20 19:10:45 INFO] -Reading csv file\n",
            "[20-01-20 19:10:46 INFO] --Data normalization processing...\n",
            "[20-01-20 19:10:46 INFO] --Figures enabled\n"
          ],
          "name": "stderr"
        },
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          "text": [
            "[20-01-20 19:10:46 INFO] Function malware_data_transform finished in 1.5447800159454346s\n",
            "[20-01-20 19:10:46 INFO] Running knn_classifier...\n",
            "[20-01-20 19:10:46 INFO] -Running KNN algorithm with n_neighbors=1\n",
            "[20-01-20 19:10:46 INFO] --Finding best 3 features\n",
            "[20-01-20 19:10:47 INFO] ---Starting fitting for weight=distance and metric=chebyshev\n",
            "[20-01-20 19:10:51 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:10:53 INFO] -----KNN accuracy is: 96.50368224073404%\n",
            "[20-01-20 19:10:53 INFO] -----Precision in percent: 97.46206896551725%\n",
            "[20-01-20 19:10:53 INFO] -----Recall in percent: 97.54279403644396%\n",
            "[20-01-20 19:10:53 INFO] -----False positives in percent: 2.5379310344827584%\n",
            "[20-01-20 19:10:53 INFO] -----False negatives in percent: 5.734997986306887%\n",
            "[20-01-20 19:10:53 INFO] Exec time for 3 features: 6.6847357749938965s\n",
            "[20-01-20 19:10:53 INFO] --Finding best 23 features\n",
            "[20-01-20 19:10:53 INFO] ---Starting fitting for weight=distance and metric=chebyshev\n",
            "[20-01-20 19:11:20 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:11:35 INFO] -----KNN accuracy is: 99.04623928528311%\n",
            "[20-01-20 19:11:35 INFO] -----Precision in percent: 99.20779802293941%\n",
            "[20-01-20 19:11:35 INFO] -----Recall in percent: 99.43040596520298%\n",
            "[20-01-20 19:11:35 INFO] -----False positives in percent: 0.7922019770605863%\n",
            "[20-01-20 19:11:35 INFO] -----False negatives in percent: 1.3325795509610725%\n",
            "[20-01-20 19:11:35 INFO] Exec time for 23 features: 41.85010480880737s\n",
            "[20-01-20 19:11:35 INFO] --Finding best 3 features\n",
            "[20-01-20 19:11:35 INFO] ---Starting fitting for weight=uniform and metric=chebyshev\n",
            "[20-01-20 19:11:40 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:11:42 INFO] -----KNN accuracy is: 96.42400096583363%\n",
            "[20-01-20 19:11:42 INFO] -----Precision in percent: 97.47632983301773%\n",
            "[20-01-20 19:11:42 INFO] -----Recall in percent: 97.4260151410874%\n",
            "[20-01-20 19:11:42 INFO] -----False positives in percent: 2.523670166982269%\n",
            "[20-01-20 19:11:42 INFO] -----False negatives in percent: 6.046887631366209%\n",
            "[20-01-20 19:11:42 INFO] Exec time for 3 features: 7.48384690284729s\n",
            "[20-01-20 19:11:42 INFO] --Finding best 23 features\n",
            "[20-01-20 19:11:43 INFO] ---Starting fitting for weight=uniform and metric=chebyshev\n",
            "[20-01-20 19:12:21 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:12:41 INFO] -----KNN accuracy is: 99.04623928528311%\n",
            "[20-01-20 19:12:41 INFO] -----Precision in percent: 99.23132604874013%\n",
            "[20-01-20 19:12:41 INFO] -----Recall in percent: 99.4060773480663%\n",
            "[20-01-20 19:12:41 INFO] -----False positives in percent: 0.768673951259867%\n",
            "[20-01-20 19:12:41 INFO] -----False negatives in percent: 1.3866494679135764%\n",
            "[20-01-20 19:12:41 INFO] Exec time for 23 features: 58.90753412246704s\n",
            "[20-01-20 19:12:41 INFO] --Finding best 3 features\n",
            "[20-01-20 19:12:41 INFO] ---Starting fitting for weight=distance and metric=euclidean\n",
            "[20-01-20 19:12:46 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:12:47 INFO] -----KNN accuracy is: 96.32258843414222%\n",
            "[20-01-20 19:12:47 INFO] -----Precision in percent: 97.54800386793757%\n",
            "[20-01-20 19:12:47 INFO] -----Recall in percent: 97.20224371107058%\n",
            "[20-01-20 19:12:47 INFO] -----False positives in percent: 2.4519961320624395%\n",
            "[20-01-20 19:12:47 INFO] -----False negatives in percent: 6.525403322899109%\n",
            "[20-01-20 19:12:47 INFO] Exec time for 3 features: 5.622771739959717s\n",
            "[20-01-20 19:12:47 INFO] --Finding best 23 features\n",
            "[20-01-20 19:12:47 INFO] ---Starting fitting for weight=distance and metric=euclidean\n",
            "[20-01-20 19:13:32 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:13:56 INFO] -----KNN accuracy is: 99.11626222383194%\n",
            "[20-01-20 19:13:56 INFO] -----Precision in percent: 99.2878625505583%\n",
            "[20-01-20 19:13:56 INFO] -----Recall in percent: 99.44600256223815%\n",
            "[20-01-20 19:13:56 INFO] -----False positives in percent: 0.7121374494416981%\n",
            "[20-01-20 19:13:56 INFO] -----False negatives in percent: 1.2812299807815504%\n",
            "[20-01-20 19:13:56 INFO] Exec time for 23 features: 68.91065239906311s\n",
            "[20-01-20 19:13:56 INFO] --Finding best 3 features\n",
            "[20-01-20 19:13:56 INFO] ---Starting fitting for weight=uniform and metric=euclidean\n",
            "[20-01-20 19:14:02 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:14:04 INFO] -----KNN accuracy is: 96.46746347941567%\n",
            "[20-01-20 19:14:04 INFO] -----Precision in percent: 97.5535590877678%\n",
            "[20-01-20 19:14:04 INFO] -----Recall in percent: 97.39538413771692%\n",
            "[20-01-20 19:14:04 INFO] -----False positives in percent: 2.4464409122322044%\n",
            "[20-01-20 19:14:04 INFO] -----False negatives in percent: 6.052104208416834%\n",
            "[20-01-20 19:14:04 INFO] Exec time for 3 features: 7.930104732513428s\n",
            "[20-01-20 19:14:04 INFO] --Finding best 23 features\n",
            "[20-01-20 19:14:04 INFO] ---Starting fitting for weight=uniform and metric=euclidean\n",
            "[20-01-20 19:14:47 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:15:11 INFO] -----KNN accuracy is: 99.12109139200773%\n",
            "[20-01-20 19:15:11 INFO] -----Precision in percent: 99.33034173282707%\n",
            "[20-01-20 19:15:11 INFO] -----Recall in percent: 99.41269950943136%\n",
            "[20-01-20 19:15:11 INFO] -----False positives in percent: 0.6696582671729375%\n",
            "[20-01-20 19:15:11 INFO] -----False negatives in percent: 1.3660104459622338%\n",
            "[20-01-20 19:15:11 INFO] Exec time for 23 features: 67.4264440536499s\n",
            "[20-01-20 19:15:11 INFO] --Finding best 3 features\n",
            "[20-01-20 19:15:11 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[20-01-20 19:15:16 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:15:17 INFO] -----KNN accuracy is: 96.64131353374381%\n",
            "[20-01-20 19:15:17 INFO] -----Precision in percent: 97.7205222076397%\n",
            "[20-01-20 19:15:17 INFO] -----Recall in percent: 97.48148148148148%\n",
            "[20-01-20 19:15:17 INFO] -----False positives in percent: 2.2794777923602956%\n",
            "[20-01-20 19:15:17 INFO] -----False negatives in percent: 5.866302864938609%\n",
            "[20-01-20 19:15:17 INFO] Exec time for 3 features: 5.8360514640808105s\n",
            "[20-01-20 19:15:17 INFO] --Finding best 23 features\n",
            "[20-01-20 19:15:17 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[20-01-20 19:16:00 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:16:25 INFO] -----KNN accuracy is: 99.16213932150187%\n",
            "[20-01-20 19:16:25 INFO] -----Precision in percent: 99.34455636815234%\n",
            "[20-01-20 19:16:25 INFO] -----Recall in percent: 99.45777931272664%\n",
            "[20-01-20 19:16:25 INFO] -----False positives in percent: 0.6554436318476611%\n",
            "[20-01-20 19:16:25 INFO] -----False negatives in percent: 1.2633781282690915%\n",
            "[20-01-20 19:16:25 INFO] Exec time for 23 features: 68.39429306983948s\n",
            "[20-01-20 19:16:25 INFO] --Finding best 3 features\n",
            "[20-01-20 19:16:26 INFO] ---Starting fitting for weight=uniform and metric=manhattan\n",
            "[20-01-20 19:16:32 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:16:34 INFO] -----KNN accuracy is: 96.4312447180973%\n",
            "[20-01-20 19:16:34 INFO] -----Precision in percent: 97.65684051398337%\n",
            "[20-01-20 19:16:34 INFO] -----Recall in percent: 97.27583846680355%\n",
            "[20-01-20 19:16:34 INFO] -----False positives in percent: 2.3431594860166287%\n",
            "[20-01-20 19:16:34 INFO] -----False negatives in percent: 6.466812901129256%\n",
            "[20-01-20 19:16:34 INFO] Exec time for 3 features: 8.419345140457153s\n",
            "[20-01-20 19:16:34 INFO] --Finding best 23 features\n",
            "[20-01-20 19:16:34 INFO] ---Starting fitting for weight=uniform and metric=manhattan\n",
            "[20-01-20 19:17:20 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:17:48 INFO] -----KNN accuracy is: 99.18145599420501%\n",
            "[20-01-20 19:17:48 INFO] -----Precision in percent: 99.31851036001927%\n",
            "[20-01-20 19:17:48 INFO] -----Recall in percent: 99.51374280097941%\n",
            "[20-01-20 19:17:48 INFO] -----False positives in percent: 0.6814896399807255%\n",
            "[20-01-20 19:17:48 INFO] -----False negatives in percent: 1.1406844106463878%\n",
            "[20-01-20 19:17:48 INFO] Exec time for 23 features: 74.06178069114685s\n",
            "[20-01-20 19:17:48 INFO] Function knn_classifier finished in 421.5487856864929s\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdUlXd6Lps47",
        "colab_type": "text"
      },
      "source": [
        "After performing data normalization - a process of eliminating units of data, enabling to compare data more easily - mean value and std are looking better. It affects algorithm execution time, which doubles, but it performs better on data rescaled to values ​​between 0 and 1. The algorithm predicts classes more accurate and False positives/negatives results lower.\n",
        "Precision and recall measurements for normalized data is above 99%, which is a satisfying result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVXRD7ll0All",
        "colab_type": "code",
        "outputId": "e85ee93a-7ed2-46e2-b80e-3b8074e853e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "get_logger(logger_file=LOGGER_FILE).info(\"--------------------------------------\")\n",
        "input_data, labels = malware_data_transform(optimize_data='standardize', enable_figures=True)\n",
        "knn_classifier(input_data, labels, 1, (3, 24, 20))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20-01-20 19:17:48 INFO] --------------------------------------\n",
            "[20-01-20 19:17:48 INFO] Running malware_data_transform...\n",
            "[20-01-20 19:17:48 INFO] -Reading csv file\n",
            "[20-01-20 19:17:49 INFO] --Data standardization processing...\n",
            "[20-01-20 19:17:49 INFO] --Figures enabled\n"
          ],
          "name": "stderr"
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        {
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          "text": [
            "[20-01-20 19:17:50 INFO] Function malware_data_transform finished in 1.5547454357147217s\n",
            "[20-01-20 19:17:50 INFO] Running knn_classifier...\n",
            "[20-01-20 19:17:50 INFO] -Running KNN algorithm with n_neighbors=1\n",
            "[20-01-20 19:17:50 INFO] --Finding best 3 features\n",
            "[20-01-20 19:17:50 INFO] ---Starting fitting for weight=distance and metric=chebyshev\n",
            "[20-01-20 19:17:53 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:17:54 INFO] -----KNN accuracy is: 96.37812386816371%\n",
            "[20-01-20 19:17:54 INFO] -----Precision in percent: 97.51242064587359%\n",
            "[20-01-20 19:17:54 INFO] -----Recall in percent: 97.31767784587838%\n",
            "[20-01-20 19:17:54 INFO] -----False positives in percent: 2.4875793541264146%\n",
            "[20-01-20 19:17:54 INFO] -----False negatives in percent: 6.2665915855522485%\n",
            "[20-01-20 19:17:54 INFO] Exec time for 3 features: 4.4405481815338135s\n",
            "[20-01-20 19:17:54 INFO] --Finding best 23 features\n",
            "[20-01-20 19:17:54 INFO] ---Starting fitting for weight=distance and metric=chebyshev\n",
            "[20-01-20 19:18:22 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:18:38 INFO] -----KNN accuracy is: 99.04865386937101%\n",
            "[20-01-20 19:18:38 INFO] -----Precision in percent: 99.23198244531304%\n",
            "[20-01-20 19:18:38 INFO] -----Recall in percent: 99.41604836493542%\n",
            "[20-01-20 19:18:38 INFO] -----False positives in percent: 0.7680175546869642%\n",
            "[20-01-20 19:18:38 INFO] -----False negatives in percent: 1.3878683974201975%\n",
            "[20-01-20 19:18:38 INFO] Exec time for 23 features: 43.71846270561218s\n",
            "[20-01-20 19:18:38 INFO] --Finding best 3 features\n",
            "[20-01-20 19:18:38 INFO] ---Starting fitting for weight=uniform and metric=chebyshev\n",
            "[20-01-20 19:18:42 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:18:44 INFO] -----KNN accuracy is: 96.41434262948208%\n",
            "[20-01-20 19:18:44 INFO] -----Precision in percent: 97.61880147489576%\n",
            "[20-01-20 19:18:44 INFO] -----Recall in percent: 97.27353890529497%\n",
            "[20-01-20 19:18:44 INFO] -----False positives in percent: 2.3811985251042422%\n",
            "[20-01-20 19:18:44 INFO] -----False negatives in percent: 6.405292029686996%\n",
            "[20-01-20 19:18:44 INFO] Exec time for 3 features: 6.325060606002808s\n",
            "[20-01-20 19:18:44 INFO] --Finding best 23 features\n",
            "[20-01-20 19:18:44 INFO] ---Starting fitting for weight=uniform and metric=chebyshev\n",
            "[20-01-20 19:19:15 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:19:34 INFO] -----KNN accuracy is: 99.11143305565616%\n",
            "[20-01-20 19:19:34 INFO] -----Precision in percent: 99.27080108691914%\n",
            "[20-01-20 19:19:34 INFO] -----Recall in percent: 99.46238411965399%\n",
            "[20-01-20 19:19:34 INFO] -----False positives in percent: 0.7291989130808654%\n",
            "[20-01-20 19:19:34 INFO] -----False negatives in percent: 1.2639766650461837%\n",
            "[20-01-20 19:19:34 INFO] Exec time for 23 features: 50.20097541809082s\n",
            "[20-01-20 19:19:34 INFO] --Finding best 3 features\n",
            "[20-01-20 19:19:34 INFO] ---Starting fitting for weight=distance and metric=euclidean\n",
            "[20-01-20 19:19:38 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:19:39 INFO] -----KNN accuracy is: 96.5133405770856%\n",
            "[20-01-20 19:19:39 INFO] -----Precision in percent: 97.58025543665862%\n",
            "[20-01-20 19:19:39 INFO] -----Recall in percent: 97.43899076244313%\n",
            "[20-01-20 19:19:39 INFO] -----False positives in percent: 2.4197445633413874%\n",
            "[20-01-20 19:19:39 INFO] -----False negatives in percent: 5.97026918441141%\n",
            "[20-01-20 19:19:39 INFO] Exec time for 3 features: 4.377427816390991s\n",
            "[20-01-20 19:19:39 INFO] --Finding best 23 features\n",
            "[20-01-20 19:19:39 INFO] ---Starting fitting for weight=distance and metric=euclidean\n",
            "[20-01-20 19:20:23 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:20:54 INFO] -----KNN accuracy is: 99.20560183508391%\n",
            "[20-01-20 19:20:54 INFO] -----Precision in percent: 99.34950955085183%\n",
            "[20-01-20 19:20:54 INFO] -----Recall in percent: 99.5173412397435%\n",
            "[20-01-20 19:20:54 INFO] -----False positives in percent: 0.6504904491481672%\n",
            "[20-01-20 19:20:54 INFO] -----False negatives in percent: 1.132686084142395%\n",
            "[20-01-20 19:20:54 INFO] Exec time for 23 features: 75.58630537986755s\n",
            "[20-01-20 19:20:54 INFO] --Finding best 3 features\n",
            "[20-01-20 19:20:54 INFO] ---Starting fitting for weight=uniform and metric=euclidean\n",
            "[20-01-20 19:20:59 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:21:01 INFO] -----KNN accuracy is: 96.27912592056018%\n",
            "[20-01-20 19:21:01 INFO] -----Precision in percent: 97.35654196019425%\n",
            "[20-01-20 19:21:01 INFO] -----Recall in percent: 97.37319150391627%\n",
            "[20-01-20 19:21:01 INFO] -----False positives in percent: 2.643458039805759%\n",
            "[20-01-20 19:21:01 INFO] -----False negatives in percent: 6.309044606916947%\n",
            "[20-01-20 19:21:01 INFO] Exec time for 3 features: 6.681373357772827s\n",
            "[20-01-20 19:21:01 INFO] --Finding best 23 features\n",
            "[20-01-20 19:21:01 INFO] ---Starting fitting for weight=uniform and metric=euclidean\n",
            "[20-01-20 19:21:44 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:22:15 INFO] -----KNN accuracy is: 99.1766268260292%\n",
            "[20-01-20 19:22:15 INFO] -----Precision in percent: 99.30891499654457%\n",
            "[20-01-20 19:22:15 INFO] -----Recall in percent: 99.51178975797237%\n",
            "[20-01-20 19:22:15 INFO] -----False positives in percent: 0.691085003455425%\n",
            "[20-01-20 19:22:15 INFO] -----False negatives in percent: 1.1302605210420842%\n",
            "[20-01-20 19:22:15 INFO] Exec time for 23 features: 74.2781434059143s\n",
            "[20-01-20 19:22:15 INFO] --Finding best 3 features\n",
            "[20-01-20 19:22:15 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[20-01-20 19:22:19 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:22:20 INFO] -----KNN accuracy is: 96.38295303633949%\n",
            "[20-01-20 19:22:20 INFO] -----Precision in percent: 97.61420444750904%\n",
            "[20-01-20 19:22:20 INFO] -----Recall in percent: 97.23204780383942%\n",
            "[20-01-20 19:22:20 INFO] -----False positives in percent: 2.3857955524909498%\n",
            "[20-01-20 19:22:20 INFO] -----False negatives in percent: 6.494762288477035%\n",
            "[20-01-20 19:22:20 INFO] Exec time for 3 features: 4.498077154159546s\n",
            "[20-01-20 19:22:20 INFO] --Finding best 23 features\n",
            "[20-01-20 19:22:20 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[20-01-20 19:23:03 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:23:33 INFO] -----KNN accuracy is: 99.18145599420501%\n",
            "[20-01-20 19:23:33 INFO] -----Precision in percent: 99.36745848946337%\n",
            "[20-01-20 19:23:33 INFO] -----Recall in percent: 99.46662078458361%\n",
            "[20-01-20 19:23:33 INFO] -----False positives in percent: 0.632541510536629%\n",
            "[20-01-20 19:23:33 INFO] -----False negatives in percent: 1.2575044621126075%\n",
            "[20-01-20 19:23:33 INFO] Exec time for 23 features: 73.17670392990112s\n",
            "[20-01-20 19:23:33 INFO] --Finding best 3 features\n",
            "[20-01-20 19:23:33 INFO] ---Starting fitting for weight=uniform and metric=manhattan\n",
            "[20-01-20 19:23:37 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:23:39 INFO] -----KNN accuracy is: 96.30810092961487%\n",
            "[20-01-20 19:23:39 INFO] -----Precision in percent: 97.43996140991628%\n",
            "[20-01-20 19:23:39 INFO] -----Recall in percent: 97.29580953691598%\n",
            "[20-01-20 19:23:39 INFO] -----False positives in percent: 2.5600385900837264%\n",
            "[20-01-20 19:23:39 INFO] -----False negatives in percent: 6.342801807617818%\n",
            "[20-01-20 19:23:39 INFO] Exec time for 3 features: 6.414351224899292s\n",
            "[20-01-20 19:23:39 INFO] --Finding best 23 features\n",
            "[20-01-20 19:23:39 INFO] ---Starting fitting for weight=uniform and metric=manhattan\n",
            "[20-01-20 19:24:33 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:25:16 INFO] -----KNN accuracy is: 99.22733309187493%\n",
            "[20-01-20 19:25:16 INFO] -----Precision in percent: 99.32728464484079%\n",
            "[20-01-20 19:25:16 INFO] -----Recall in percent: 99.56772832589826%\n",
            "[20-01-20 19:25:16 INFO] -----False positives in percent: 0.6727153551592093%\n",
            "[20-01-20 19:25:16 INFO] -----False negatives in percent: 1.0057933698101063%\n",
            "[20-01-20 19:25:16 INFO] Exec time for 23 features: 96.78699779510498s\n",
            "[20-01-20 19:25:16 INFO] Function knn_classifier finished in 446.4995377063751s\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "As9NdCES7zK_",
        "colab_type": "text"
      },
      "source": [
        "After data standardization – a process of transforming data to have a mean of zero and a standard deviation of 1 – there are similar results and conclusions as with data normalization. Both are giving promising results of accuracy and precision above 99%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJJP3vs9OFRi",
        "colab_type": "code",
        "outputId": "0a5a32d8-0cf9-4540-8351-ca1d16c3dbfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "get_logger(logger_file=LOGGER_FILE).info(\"--------------------------------------\")\n",
        "get_logger(logger_file=LOGGER_FILE).info(\"---------------Stopping---------------\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20-01-20 19:25:16 INFO] --------------------------------------\n",
            "[20-01-20 19:25:16 INFO] ---------------Stopping---------------\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yq7bRifl7av7",
        "colab_type": "text"
      },
      "source": [
        "Below is an output from functions run above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rm5BY4rMUTmj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ENABLE_LONG_LOGS = False    # change to True to enable long logger outputs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjsH08t6Lhmb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if ENABLE_LONG_LOGS:\n",
        "    import os\n",
        "    if os.path.exists('KNN_results_runtime.log'):\n",
        "        !cat KNN_results.log"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBa-16nZ-yDG",
        "colab_type": "text"
      },
      "source": [
        "## Algorithm parameters selection\n",
        "Because running whole script can last a long time, measurements have been carried out earlier and analyzed for best parameter selection."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPqG6X02_wzL",
        "colab_type": "code",
        "outputId": "3ac4f47a-8401-4992-ce65-aa3a821714c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import os\n",
        "if os.path.exists('Malware_Discovery_ML/KNN/results_analyze.txt'):\n",
        "    !cat Malware_Discovery_ML/KNN/results_analyze.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For not optimized data --> k=1 – best result for weight=uniform and metric=manhattan and 23 features\n",
            "[28-12-19 14:07:31 INFO] ---Starting fitting for weight=uniform and metric=manhattan\n",
            "[28-12-19 14:07:46 INFO] ----Measuring confusion matrix...\n",
            "[28-12-19 14:07:57 INFO] -----KNN accuracy is: 97.98140770252324%\n",
            "[28-12-19 14:07:57 INFO] -----Precision in percent: 98.56965602812436%\n",
            "[28-12-19 14:07:57 INFO] -----Recall in percent: 98.54927636113025%\n",
            "[28-12-19 14:07:57 INFO] -----False positives in percent: 1.430343971875646%\n",
            "[28-12-19 14:07:57 INFO] -----False negatives in percent: 3.3948875090718493%\n",
            "\n",
            "\n",
            "For normalized data --> k=1 – best result for weight=distance and metric=manhattan and 23 features\n",
            "[28-12-19 14:15:17 INFO] --Finding best 23 features\n",
            "[28-12-19 14:15:18 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[28-12-19 14:15:50 INFO] ----Measuring confusion matrix...\n",
            "[28-12-19 14:16:06 INFO] -----KNN accuracy is: 99.19594349873235%\n",
            "[28-12-19 14:16:06 INFO] -----Precision in percent: 99.32862799104836%\n",
            "[28-12-19 14:16:06 INFO] -----Recall in percent: 99.52394094107908%\n",
            "[28-12-19 14:16:06 INFO] -----False positives in percent: 0.6713720089516267%\n",
            "[28-12-19 14:16:06 INFO] -----False negatives in percent: 1.1156022635408245%\n",
            "\n",
            "\n",
            "For standardized data --> k=2 – best result for weight=distance and metric=manhattan and 23 features\n",
            "[28-12-19 14:36:12 INFO] --Finding best 23 features\n",
            "[28-12-19 14:36:13 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[28-12-19 14:37:03 INFO] ----Measuring confusion matrix...\n",
            "[28-12-19 14:37:38 INFO] -----KNN accuracy is: 99.2080164191718%\n",
            "[28-12-19 14:37:38 INFO] -----Precision in percent: 99.34780358190413%\n",
            "[28-12-19 14:37:38 INFO] -----Recall in percent: 99.51951329116112%\n",
            "[28-12-19 14:37:38 INFO] -----False positives in percent: 0.6521964180958626%\n",
            "[28-12-19 14:37:38 INFO] -----False negatives in percent: 1.1177227404310068%\n",
            "\n",
            "\n",
            "For standardized data --> k=8 –\n",
            "[28-12-19 15:37:28 INFO] -Running KNN algorithm with n_neighbors=8\n",
            "[28-12-19 15:37:32 INFO] --Finding best 23 features\n",
            "[28-12-19 15:37:32 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[28-12-19 15:38:43 INFO] ----Measuring confusion matrix...\n",
            "[28-12-19 15:39:28 INFO] -----KNN accuracy is: 99.33357479174212%\n",
            "[28-12-19 15:39:28 INFO] -----Precision in percent: 99.38396943937778%\n",
            "[28-12-19 15:39:28 INFO] -----Recall in percent: 99.66522864538395%\n",
            "[28-12-19 15:39:28 INFO] -----False positives in percent: 0.6160305606222253%\n",
            "[28-12-19 15:39:28 INFO] -----False negatives in percent: 0.7849166531801263%\n",
            "\n",
            "\n",
            "For standardized data with k=8 neighbors:\n",
            "[28-12-19 16:27:17 INFO] --Finding best 30 features\n",
            "[28-12-19 16:28:19 INFO] -----KNN accuracy is: 99.35047688035736%\n",
            "[28-12-19 16:28:19 INFO] -----Precision in percent: 99.39686369119421%\n",
            "[28-12-19 16:28:19 INFO] -----Recall in percent: 99.67512269302551%\n",
            "[28-12-19 16:28:19 INFO] -----False positives in percent: 0.6031363088057901%\n",
            "[28-12-19 16:28:19 INFO] -----False negatives in percent: 0.7580645161290323%\n",
            "[28-12-19 16:28:19 INFO] Exec time for 30 features: 62.01845407485962s\n",
            "-------------------------------------------------------------------------------------\n",
            "[28-12-19 17:03:09 INFO] --Finding best 47 features\n",
            "[28-12-19 17:05:26 INFO] -----KNN accuracy is: 99.35530604853314%\n",
            "[28-12-19 17:05:26 INFO] -----Precision in percent: 99.44287777701355%\n",
            "[28-12-19 17:05:26 INFO] -----Recall in percent: 99.63819303263153%\n",
            "[28-12-19 17:05:26 INFO] -----False positives in percent: 0.5571222229864502%\n",
            "[28-12-19 17:05:26 INFO] -----False negatives in percent: 0.8510983221204508%\n",
            "[28-12-19 17:05:26 INFO] Exec time for 47 features: 136.6435329914093s"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYSUpERHBo0O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@log_to_file(logger_file='KNN_final.log')\n",
        "def knn_classifier_final_model(input_data, labels):\n",
        "    log = get_logger(logger_file=LOGGER_FILE)\n",
        "    log.info(f\"-Running final KNN algorithm model with n_neighbors=8\")\n",
        "    weight = 'distance'\n",
        "    metric = 'manhattan'\n",
        "\n",
        "    classifier = neighbors.KNeighborsClassifier(8, weights=weight, metric=metric)\n",
        "    start_time = time.time()\n",
        "    log.info(f\"--Finding best 30 features\")\n",
        "    best_features_data = SelectKBest(f_classif, k=30).fit_transform(input_data, labels)\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(best_features_data, labels, test_size=0.3)\n",
        "\n",
        "    log.info(f\"---Starting fitting for weight={weight} and metric={metric}\")\n",
        "    classifier.fit(X_train, Y_train)\n",
        "\n",
        "    score = classifier.score(X_test, Y_test)\n",
        "\n",
        "    \"\"\" Confusion matrix \"\"\"\n",
        "    log.info(\"----Measuring confusion matrix...\")\n",
        "    result = classifier.predict(X_test)\n",
        "    conf_matrix = confusion_matrix(Y_test, result)\n",
        "\n",
        "    log.info(f\"-----KNN accuracy is: {score * 100}%\")\n",
        "\n",
        "    precision = conf_matrix[0][0] / (conf_matrix[0][0] + conf_matrix[0][1]) * 100\n",
        "    log.info(f\"-----Precision in percent: {precision}%\")\n",
        "\n",
        "    recall = conf_matrix[0][0] / (conf_matrix[0][0] + conf_matrix[1][0]) * 100\n",
        "    log.info(f\"-----Recall in percent: {recall}%\")\n",
        "\n",
        "    false_positives = conf_matrix[0][1] / sum(conf_matrix[0]) * 100\n",
        "    log.info(f\"-----False positives in percent: {false_positives}%\")\n",
        "\n",
        "    false_negatives = conf_matrix[1][0] / sum(conf_matrix[1]) * 100\n",
        "    log.info(f\"-----False negatives in percent: {false_negatives}%\")\n",
        "    log.info(f\"Exec time for 30 features: {time.time() - start_time}s\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eJvgLXIJ4L3",
        "colab_type": "text"
      },
      "source": [
        "Model is performing best with 8 neighbors, 30 features selected by function *SelectKBest* (thus it can vary, but not much) and fed with standardized data. The algorithm is running for about 60 seconds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4tGrMiIAyio",
        "colab_type": "code",
        "outputId": "bc7dee8b-926b-475c-b8d6-b51f909bc03a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "    get_logger(logger_file=LOGGER_FILE).info(\"--------------------------------------\")\n",
        "    input_data, labels = malware_data_transform(optimize_data='standardize', enable_figures=False)\n",
        "    knn_classifier_final_model(input_data, labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20-01-20 19:25:18 INFO] --------------------------------------\n",
            "[20-01-20 19:25:18 INFO] Running malware_data_transform...\n",
            "[20-01-20 19:25:18 INFO] -Reading csv file\n",
            "[20-01-20 19:25:19 INFO] --Data standardization processing...\n",
            "[20-01-20 19:25:19 INFO] Function malware_data_transform finished in 1.136582612991333s\n",
            "[20-01-20 19:25:19 INFO] -Running final KNN algorithm model with n_neighbors=8\n",
            "[20-01-20 19:25:19 INFO] --Finding best 30 features\n",
            "[20-01-20 19:25:19 INFO] ---Starting fitting for weight=distance and metric=manhattan\n",
            "[20-01-20 19:26:19 INFO] ----Measuring confusion matrix...\n",
            "[20-01-20 19:27:09 INFO] -----KNN accuracy is: 99.29735603042376%\n",
            "[20-01-20 19:27:09 INFO] -----Precision in percent: 99.31488078925733%\n",
            "[20-01-20 19:27:09 INFO] -----Recall in percent: 99.68710243097341%\n",
            "[20-01-20 19:27:09 INFO] -----False positives in percent: 0.6851192107426692%\n",
            "[20-01-20 19:27:09 INFO] -----False negatives in percent: 0.7444980773950749%\n",
            "[20-01-20 19:27:09 INFO] Exec time for 30 features: 110.24777269363403s\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFSpE-51C-rP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "if ENABLE_LONG_LOGS:\n",
        "    if os.path.exists('Malware_Discovery_ML/KNN/loggerOutputs/KNN_results.log'):\n",
        "        !cat Malware_Discovery_ML/KNN/loggerOutputs/KNN_results.log\n",
        "    if os.path.exists('Malware_Discovery_ML/KNN/loggerOutputs/KNN_standardized_data.log'):\n",
        "        !cat Malware_Discovery_ML/KNN/loggerOutputs/KNN_standardized_data.log\n",
        "    if os.path.exists('Malware_Discovery_ML/KNN/loggerOutputs/KNN_k8_features.log'):\n",
        "        !cat Malware_Discovery_ML/KNN/loggerOutputs/KNN_k8_features.log\n",
        "    if os.path.exists('Malware_Discovery_ML/KNN/loggerOutputs/KNN_final.log'):\n",
        "        !cat Malware_Discovery_ML/KNN/loggerOutputs/KNN_final.log"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JTTshVegGmU",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion\n",
        "K-Nearest Neighbors is a very accurate and reliable algorithm for malware detection purposes. It classifies malware or benign files correctly in almost all cases. \n",
        "However, KNN lacks the efficiency of execution time when given the large dataset. This is due to the fact that it is not building an actual model, but stores the whole data and makes predictions while executing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ByJ2DFPFkmM",
        "colab_type": "text"
      },
      "source": [
        "# Decision Tree\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5m4Sk2QkGpHH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import tree\n",
        "import graphviz\n",
        "import numpy as np\n",
        "import time\n",
        "import pandas as pd\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vduax4TQjdii"
      },
      "source": [
        "## Essential variables\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "watphxQmifRc",
        "colab_type": "text"
      },
      "source": [
        "Essential variables for holding the result of each iteration in every feature selection method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5tQEzqmEOUQV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svc_list = []\n",
        "svc_features = []\n",
        "\n",
        "tree_selection_list = []\n",
        "tree_selection_features = []\n",
        "\n",
        "none_feature_list = []\n",
        "\n",
        "svc_accuracy = []\n",
        "tree_accuracy = []\n",
        "none_accuracy = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wjt4enjCjm5k",
        "colab_type": "text"
      },
      "source": [
        "## method: get_average_confusion_matrix\n",
        "\n",
        "### **@description:** \n",
        "> This method creates an average confusion matrix from all iterations. \n",
        "\n",
        "### **@params:**\n",
        ">* *list*       - list with confusion matrices\n",
        ">* *name*       - feature selection method's name\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FgzicooRj_dk",
        "colab_type": "text"
      },
      "source": [
        "## method: get_confusion matrix\n",
        "\n",
        "### **@description:** \n",
        "> This method creates confusion matrix and adds it to the proper list.\n",
        "\n",
        "### **@params:**\n",
        ">* *Y*       - legitimate value\n",
        ">* *predictions*       - supervised learning algorithm's predictions\n",
        ">* *name*       - feature selection method's name\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuPaH-xtnEHO",
        "colab_type": "text"
      },
      "source": [
        "## method: train_decision_tree\n",
        "\n",
        "### **@description:** \n",
        "> This method trains our data with *DecisionTreeClassifier*.\n",
        "\n",
        "### **@params:**\n",
        ">* *X*       - features\n",
        ">* *Y*       - legitimate value\n",
        ">* *name*       - feature selection method's name\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QI_H5iDkovxc",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "l2YXkQkSo8R_"
      },
      "source": [
        "## method: test_SVC, test_extra_tree, test_no_feature\n",
        "\n",
        "### **@description:** \n",
        "> These methods trains our data with  different feature selection algorithms.\n",
        "\n",
        "### **@params:**\n",
        ">* *X*       - features\n",
        ">* *Y*       - legitimate value\n",
        ">* *name*       - feature selection method's name\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUDLR1wYH6So",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_average_confusion_matrix(list, name):\n",
        "    a = list[0][0]\n",
        "    b = list[0][1]\n",
        "\n",
        "    for single_matrix in list:\n",
        "        a = np.vstack((a, single_matrix[0]))\n",
        "        b = np.vstack((b, single_matrix[1]))\n",
        "\n",
        "    a = np.delete(a, (0), axis=0)\n",
        "    b = np.delete(b, (0), axis=0)\n",
        "\n",
        "    a_average = np.mean(a, axis=0)\n",
        "    b_average = np.mean(b, axis=0)\n",
        "\n",
        "    average_matrix = list[0][0]\n",
        "    average_matrix = np.vstack((average_matrix, a_average))\n",
        "    average_matrix = np.vstack((average_matrix, b_average))\n",
        "    average_matrix = np.delete(average_matrix, 0, axis=0)\n",
        "\n",
        "    df_cm = pd.DataFrame(average_matrix, range(2), range(2))\n",
        "    sn.set(font_scale=1.4)\n",
        "    sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}, cmap='Blues', fmt='g')  # font size\n",
        "    plt.title(str(name) + \" feature selection\")\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def get_confusion_matrix(Y, predictions, name):\n",
        "    matrix = confusion_matrix(Y, predictions)\n",
        "    if name == \"LinearSVC\":\n",
        "        svc_list.append(matrix)\n",
        "    if name == \"Tree selection\":\n",
        "        tree_selection_list.append(matrix)\n",
        "    if name == \"None\":\n",
        "        none_feature_list.append(matrix)\n",
        "\n",
        "\n",
        "\n",
        "def show_graph(my_classifier, feautures_names, name):\n",
        "    dot_data = tree.export_graphviz(my_classifier, out_file=None,\n",
        "                                    feature_names=feautures_names,\n",
        "                                    class_names=[str(0), str(1)],\n",
        "                                    filled=True, rounded=True,\n",
        "                                    special_characters=False)\n",
        "    graph = graphviz.Source(dot_data)\n",
        "    graph.render(\"decision_graph_\" + str(name) + \".dot\")\n",
        "\n",
        "\n",
        "def train_decision_tree(X, Y, name):\n",
        "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25)\n",
        "    my_classifier = tree.DecisionTreeClassifier()\n",
        "    my_classifier.fit(X_train, Y_train)\n",
        "    score = my_classifier.score(X_test, Y_test)\n",
        "    predictions = my_classifier.predict(X_test)\n",
        "    if name == \"LinearSVC\":\n",
        "        svc_features.append(X.shape[1])\n",
        "        svc_accuracy.append(score)\n",
        "    if name == \"Tree selection\":\n",
        "        tree_selection_features.append(X.shape[1])\n",
        "        tree_accuracy.append(score)\n",
        "    if name == \"None\":\n",
        "        none_accuracy.append(score)\n",
        "    get_confusion_matrix(Y_test, predictions, name)\n",
        "\n",
        "def test_SVC_loop(X, Y, loop_range):\n",
        "    for i in range(loop_range):\n",
        "        lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(X, Y)\n",
        "        model = SelectFromModel(lsvc, prefit=True)\n",
        "        X_new_L1 = model.transform(X)\n",
        "        train_decision_tree(X_new_L1, Y, \"LinearSVC\")\n",
        "\n",
        "\n",
        "def test_extra_tree_loop(X, Y, loop_range):\n",
        "    for i in range(loop_range):\n",
        "        clf = ExtraTreesClassifier().fit(X, Y)\n",
        "        feature_selection = SelectFromModel(clf, prefit=True)\n",
        "        X_new_tree_based = feature_selection.transform(X)\n",
        "        train_decision_tree(X_new_tree_based, Y, \"Tree selection\")\n",
        "\n",
        "\n",
        "def test_no_features_selection_loop(X, Y, loop_range):\n",
        "    for i in range(loop_range):\n",
        "        train_decision_tree(X, Y, \"None\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIEW_G3ZIHPm",
        "colab_type": "code",
        "outputId": "1927a81f-97fe-4b5b-9cc7-5787a7160fe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"--- main starts ---\")\n",
        "data = pd.read_csv(\"MalwareData.csv\", sep='|')\n",
        "feautures_names = data.drop(['Name', 'md5', 'legitimate'], axis=1).columns\n",
        "X = data.drop(['Name', 'md5', 'legitimate'], axis=1).values\n",
        "\n",
        "Y = data['legitimate'].values"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- main starts ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojlC6Jb9SGoz",
        "colab_type": "code",
        "outputId": "83b4d1c2-1ab7-48e7-f393-8f165eceaf18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "source": [
        "print(\"--- LinearSVC Starts! ---\")\n",
        "start_time = time.time()\n",
        "test_SVC_loop(X, Y, 30)\n",
        "\n",
        "print(\"SVC average confusion matrix : \")\n",
        "get_average_confusion_matrix(svc_list, \"LinearSVC\")\n",
        "\n",
        "svc_average_features_amount = sum(svc_features) / len(svc_features)\n",
        "print(\"Average amount of features in SVC feature selection: \" + str(svc_average_features_amount))\n",
        "\n",
        "training_time = time.time() - start_time\n",
        "average_training_time = training_time/30\n",
        "average_accuracy = sum(svc_accuracy)/30\n",
        "print(\"Average training time : \" + str(average_training_time))\n",
        "print(\"Average accuracy : \" + str(average_accuracy*100) + \" %\")\n",
        "print(\"--- %s seconds ---\" % (training_time))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- LinearSVC Starts! ---\n",
            "SVC average confusion matrix : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IFi2Yy5HDf/LgwQMcypThvS7/o1uPXkrMtytXcOTwn5w9c5q7d2MZEDSYgYMK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\nEQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Average amount of features in SVC feature selection: 27.7\n",
            "Average training time : 24.31192027727763\n",
            "Average accuracy : 99.13122005872356 %\n",
            "--- 729.3576083183289 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LUOnW4-bLow",
        "colab_type": "code",
        "outputId": "cad409bf-32b2-48a8-fc39-11d661ae11ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "source": [
        "print(\"--- ExtraTree Starts! ---\")\n",
        "start_time = time.time()\n",
        "test_extra_tree_loop(X, Y, 30)\n",
        "\n",
        "print(\"Tree Selection average matrix : \" )\n",
        "get_average_confusion_matrix(tree_selection_list, \"ExtraTree\")\n",
        "\n",
        "tree_features_average_amount = sum(tree_selection_features) / len(tree_selection_features)\n",
        "print(\"Average amount of features in Tree Selection:  \" + str(tree_features_average_amount))\n",
        "\n",
        "training_time = time.time() - start_time\n",
        "average_training_time = training_time/30\n",
        "average_accuracy_tree = sum(tree_accuracy)/len(tree_accuracy)\n",
        "print(\"Average training time : \" + str(average_training_time))\n",
        "print(\"Average accuracy : \" + str(average_accuracy_tree*100) + \" %\")\n",
        "print(\"--- %s seconds ---\" % (training_time))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- ExtraTree Starts! ---\n",
            "Tree Selection average matrix : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hz9wf8fBh2wYk30tn696j9OvQuMBtAbRpUotabg78eew8lub6fXT6npu47+LF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\nlAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Average amount of features in Tree Selection:  13.4\n",
            "Average training time : 11.068703389167785\n",
            "Average accuracy : 99.08239646113427 %\n",
            "--- 332.06110167503357 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnZXHoUfiyug",
        "colab_type": "code",
        "outputId": "ba6398cb-aad0-46e1-a982-eb19a559a1f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "source": [
        "print(\"--- No Features Starts! ---\")\n",
        "start_time = time.time()\n",
        "test_no_features_selection_loop(X, Y, 30)\n",
        "\n",
        "print(\"No feature selection average confusion matrix : \")\n",
        "get_average_confusion_matrix(none_feature_list, \"None\")\n",
        "\n",
        "training_time = time.time() - start_time\n",
        "average_training_time = training_time/30\n",
        "average_accuracy_none = sum(none_accuracy)/len(none_accuracy)\n",
        "print(\"Amount of features : \" + str(X.shape[1]))\n",
        "print(\"Average training time : \" + str(average_training_time))\n",
        "print(\"Average accuracy : \" + str(average_accuracy_none*100) + \" %\")\n",
        "print(\"--- %s seconds ---\" % (training_time))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- No Features Starts! ---\n",
            "No feature selection average confusion matrix : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hrN/WrqH1W221kkNRHI/4ixs3rtOps+4cRK8DfabaEEVsQURGRhIeHk5UVJQy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\nCEQgAACB/gJFxhPqwh0jTgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Amount of features : 54\n",
            "Average training time : 3.593021869659424\n",
            "Average accuracy : 99.19322747643331 %\n",
            "--- 107.79065608978271 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Y6NfA_cpTma",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Go79LnOZpUOW"
      },
      "source": [
        "## **`Conclusion`**\n",
        "\n",
        "Using those 3 feature selection algorithms it is noticeable that dividing efficiently based on maximum information gain is key to decision tree classifier. From all 54 features *ExtraTreesClassifier* reduces them to 13, and *LinearSVC* to 27 - on average, without significant loss in efficiency! Indeed they took a little bit more time than standard training with all the fetaures because first we need to select usefull data to train, but running time difference of these examples on my laptop was not as noticeable as here, so it might be the issue with Jupyter Notebook.\n",
        "\n",
        "To sum up, I strongly believe that using Decision Tree Classifier is the best way to start supervised learning with as it is the most intuitive one, and gives us oppurtunity to improve our results with different mechanisms like abovementioned feature selections algorithms.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nko-cXGnCV3-",
        "colab_type": "text"
      },
      "source": [
        "# Naive Bayes\n",
        "*author: Michał Szczepaniak-Krupowski*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlShNMmNo-1q",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "The last algorithm we have implemented is Naive Bayes Classifier. As previously, sklearn models will be used. The necessary data is located in *MalwareData.csv*. A few libraries that will be needed:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tvh4rKIOCyi9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import ComplementNB\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sn\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsXhT_8TCxI-",
        "colab_type": "text"
      },
      "source": [
        "Before importing dataset it is good to define and implement functions responsible for data management, visualization and training models. Visualization to begin with:\n",
        "\n",
        "## **def** *plot_conf_matrix*\n",
        "\n",
        "### **@description:** \n",
        "> This function's task is to create confusion matrix of accuracy in graphical form\n",
        "\n",
        "### **@params:**\n",
        ">* *y_real*       - real output's value taken from dataset\n",
        ">* *y_pred*       - output's value predicted by the model\n",
        ">* *name*   - model's name\n",
        ">* *name2* - name of feature selection method\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Kd5UACqDB4s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_conf_matrix(y_real, y_pred, name, name2):\n",
        "    cm = confusion_matrix(y_real, y_pred)\n",
        "    df = pd.DataFrame(cm, range(2), range(2))\n",
        "    sn.set(font_scale=1.4)\n",
        "    sn.heatmap(df, annot=True, annot_kws={\"size\": 16}, cmap='Blues', fmt='g')\n",
        "    plt.title(str(name) + \" model selection, \" + name2 + \" feature selection\")\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EY8ssoMxC9tI",
        "colab_type": "text"
      },
      "source": [
        "Now it is time to create training functions. There are 3 variants. Each one of them uses  sklearn's train_test_split as a separator of data trained and data tested. Then predictions are made using given classifier and an accuracy is being calculated. If function contains optimization of a classifier (SelectKBest and Variance Threshold will be used), then it will be placed before train_test_split function.  \n",
        "\n",
        "## **def** *default_training*\n",
        "\n",
        "### **@description:** \n",
        "> This function trains data using only given model imported from *sklearn.naive_bayes* library\n",
        "\n",
        "### **@params:**\n",
        ">* *x*       - features matrix\n",
        ">* *y*       - legitimate value matrix (0 - infected, 1 - benign)\n",
        ">* *model*   - model that will be used to train and precict values\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHDg21v7DYNZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def default_training(x, y, model):\n",
        "    start = time.time()\n",
        "\n",
        "\n",
        "    x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.5, test_size=0.5, random_state=0)\n",
        "    model.fit(x_train, y_train)\n",
        "    y_pred = model.predict(x_test)\n",
        "    print(\"\\n\" + \"Number of mislabeled points out of a total %d points : %d\" % (x_test.shape[0], (y_test != y_pred).sum()))\n",
        "    score = model.score(x_test, y_test)\n",
        "    print(\"Accuracy \", score * 100, \"%\")\n",
        "\n",
        "    end = time.time()\n",
        "\n",
        "    print(\"Start time is \" + str(start) + \" seconds\")\n",
        "    print(\"End time is \" + str(end) + \" seconds\")\n",
        "    print(\"Exec: \" + str(end - start) + \"seconds\")\n",
        "    print(\"Matrix size: \", x.shape)\n",
        "    model_name = \"\"\n",
        "    for x in range(len(str(model))):\n",
        "        if str(model)[x] == 'N':\n",
        "            break\n",
        "        model_name = model_name + str(model)[x]\n",
        "\n",
        "    plot_conf_matrix(y_test, y_pred, model_name, \"default\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qC-JX1KCDVTf",
        "colab_type": "text"
      },
      "source": [
        "## **def** *kbest*\n",
        "\n",
        "### **@description:** \n",
        "> This function trains data using given model with SelectKBest feature selection method which aims to improve speed and accuracy\n",
        "\n",
        "### **@params:**\n",
        ">* *x*       - features matrix\n",
        ">* *y*       - legitimate value matrix (0 - infected, 1 - benign)\n",
        ">* *model*   - model that will be used to train and precict values\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kvq7ywnDjFu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def kbest(x, y, model):\n",
        "    start = time.time()\n",
        "\n",
        "    sel_chi2 = SelectKBest(chi2, k=8)  # why k=8 is below\n",
        "    x_chi2 = sel_chi2.fit_transform(X, y)\n",
        "\n",
        "    x_train, x_test, y_train, y_test = train_test_split(x_chi2, y, train_size=0.4, test_size=0.1, random_state=0)\n",
        "\n",
        "    y_pred = model.fit(x_train, y_train).predict(x_test)\n",
        "    print(\"\\n\"+\"Number of mislabeled points out of a total %d points : %d\" % (x_test.shape[0], (y_test != y_pred).sum()))\n",
        "\n",
        "    score = model.score(x_test, y_test)\n",
        "    print(\"Accuracy \", score * 100, \"%\")\n",
        "\n",
        "    end = time.time()\n",
        "\n",
        "    print(\"Start time is \" + str(start) + \" seconds\")\n",
        "    print(\"End time is \" + str(end) + \" seconds\")\n",
        "    print(\"Exec: \" + str(end - start) + \"seconds\")\n",
        "    print(\"Matrix size: \", x_chi2.shape)\n",
        "    model_name = \"\"\n",
        "    for x in range(len(str(model))):\n",
        "        if str(model)[x] == 'N':\n",
        "            break\n",
        "        model_name = model_name + str(model)[x]\n",
        "\n",
        "    plot_conf_matrix(y_test, y_pred, model_name, \"SelectKBest\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGteaWobnO_2",
        "colab_type": "text"
      },
      "source": [
        "Adnotation: Amount of k features being chosen is fully depend on user. Before the last meeting, during which we were presenting our results, I had examined that the best result for *k* is in the range <5,8> (all 4 values had the same, highest accuracy). That is why I set 8 as a *k* value."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iiiei4ezDhkg",
        "colab_type": "text"
      },
      "source": [
        "## **def** *variance_tres*\n",
        "\n",
        "### **@description:** \n",
        "> This function trains data using given model with Variance Threshold feature selection method.\n",
        "\n",
        "### **@params:**\n",
        ">* *x*       - features matrix\n",
        ">* *y*       - legitimate value matrix (0 - infected, 1 - benign)\n",
        ">* *model*   - model that will be used to train and predict values\n",
        "\n",
        "### **@returns:**\n",
        "> *nothing*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlsTiN60DzVw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def variance_tres(x,y,model):\n",
        "    start = time.time()\n",
        "\n",
        "    sel_variance_threshold = VarianceThreshold()\n",
        "    X_remove_variance = sel_variance_threshold.fit_transform(x)\n",
        "\n",
        "    x_train, x_test, y_train, y_test = train_test_split(X_remove_variance, y, train_size=0.5, test_size=0.4, random_state=0)\n",
        "\n",
        "    y_pred = model.fit(x_train, y_train).predict(x_test)\n",
        "    print(\"\\n\" + \"Number of mislabeled points out of a total %d points : %d\" % (x_test.shape[0], (y_test != y_pred).sum()))\n",
        "\n",
        "    score = model.score(x_test, y_test)\n",
        "    print(\"Accuracy \", score * 100, \"%\")\n",
        "\n",
        "    end = time.time()\n",
        "\n",
        "    print(\"Start time is \" + str(start) + \" seconds\")\n",
        "    print(\"End time is \" + str(end) + \" seconds\")\n",
        "    print(\"Exec: \" + str(end - start) + \" seconds\")\n",
        "    print(\"Matrix size: \", X_remove_variance.shape)\n",
        "    model_name = \"\"\n",
        "    for i in range(len(str(model))):\n",
        "        if str(model)[i] == 'N':\n",
        "            break\n",
        "        model_name = model_name + str(model)[i]\n",
        "\n",
        "    plot_conf_matrix(y_test, y_pred, model_name, \"Variance Threshold\")\n",
        "\n",
        "    if x_train.shape[0] == x.shape[0]: print(\"Nothing has changed, method does not optimize in this case\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MCKsI4tDx0V",
        "colab_type": "text"
      },
      "source": [
        "Having all that implemented, it is time to import dataset that will be used to train predictor. To do so, I used pandas library, which, by using predefined separator (in this case I am using '|'), divides into groups external.csv file's content. Next thing to do is to split them between features' and outputs' values.\n",
        "\n",
        "## *import* dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ak5SaqEVOBT5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "    # generate dataset\n",
        "    dataset = pd.read_csv(\"MalwareData.csv\", sep='|')\n",
        "\n",
        "    X = dataset.drop(['Name', 'md5', 'legitimate'], axis=1).values\n",
        "    y = dataset['legitimate'].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTWxxt4wPD7g",
        "colab_type": "text"
      },
      "source": [
        "Matrix X carries all of the features from .csv file except those which names are \"Name\", \"md5\" and \"legitimate\". First two have been disqualified because of their uniqueness: every file is supposed to have different name and checksum to be recognized by the OS, so these information are not helpful in predicting file's legitimacy, they are just extending algorithm's running time.\n",
        "\n",
        "Matrix y holds values which indicate whether the file is infected or not\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8BUWlvwFSBS",
        "colab_type": "text"
      },
      "source": [
        "Another step is defining models that will be used. These are sklearn's:\n",
        "* **Gaussian Naive Bayes** - A special type of NB algorithm that is specifically used when the features have continuous values. It is also assumed that all the features are following a gaussian distribution i.e normal distribution.\n",
        "* **Multinomial Naive Bayes** - The naive Bayes algorithm for multinomially distributed data. The distribution is parametrized by vectors for each class.\n",
        "* **Complement Naive Bayes** -  An adaptation of the standard multinomial naive Bayes (MNB) algorithm that is particularly suited for imbalanced data sets. Specifically, CNB uses statistics from the complement of each class to compute the model’s weights.\n",
        "\n",
        "\n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXcZhaU8Phou",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define the models\n",
        "    model2 = ComplementNB()         \n",
        "    model1 = MultinomialNB()\n",
        "    model = GaussianNB()\n",
        "\n",
        "    models = [model, model1, model2]\n",
        "    model_names = [\"GaussianNB\", \"MultinomialNB\", \"ComplementNB\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubtWP4EQJy9R",
        "colab_type": "text"
      },
      "source": [
        "## *Training and accuracy*\n",
        "\n",
        "After all these done, training can finally begin: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CnF-ROLJzQk",
        "colab_type": "code",
        "outputId": "8900ee49-ed17-4d52-9b75-aab5ab9a8625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(3):\n",
        "        # Default_training\n",
        "        print(\"\\n\", str(model_names[i]), \" default training method\")\n",
        "        default_training(X, y, models[i])\n",
        "\n",
        "        # Optimized Training Methods\n",
        "\n",
        "        print(\"\\n\" + \"Now the most interesting, optimized part: \")\n",
        "        print(\"\\n\", str(model_names[i]),  \" Variance Threshold method: \")\n",
        "        variance_tres(X, y, models[i])\n",
        "        print(\"\\n\", str(model_names[i]), \" SelectKBest features method: \")\n",
        "        kbest(X, y, models[i])\n",
        "        print(\"-----------------------------------------------------------\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " GaussianNB  default training method\n",
            "\n",
            "Number of mislabeled points out of a total 69024 points : 20642\n",
            "Accuracy  70.09445989800649 %\n",
            "Start time is 1579740512.737878 seconds\n",
            "End time is 1579740512.9967728 seconds\n",
            "Exec: 0.258894681930542seconds\n",
            "Matrix size:  (138047, 54)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/fz7BwcEAXL16lbVr1zJhwgTeeOMNAOrWrcsrr7zCihUreP/99wH46aefOH36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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Now the most interesting, optimized part: \n",
            "\n",
            " GaussianNB  Variance Threshold method: \n",
            "\n",
            "Number of mislabeled points out of a total 55219 points : 16445\n",
            "Accuracy  70.21858418298049 %\n",
            "Start time is 1579740513.2865195 seconds\n",
            "End time is 1579740513.6088989 seconds\n",
            "Exec: 0.32237935066223145 seconds\n",
            "Matrix size:  (138047, 54)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yevTobLexefPmhISEsGHDBmJjY3F0dCQoKIgBAwYoEzltbGxYs2YNy5YtY9++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\nACB1ggQASJ0gAQBSJ0gAgNT9BrokgCruD/bGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " GaussianNB  SelectKBest features method: \n",
            "\n",
            "Number of mislabeled points out of a total 13805 points : 4081\n",
            "Accuracy  70.4382470119522 %\n",
            "Start time is 1579740513.884986 seconds\n",
            "End time is 1579740513.9825113 seconds\n",
            "Exec: 0.09752535820007324seconds\n",
            "Matrix size:  (138047, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZUxjKREQkjj1ljWEoExGROJ59rTEMZSIiEscTvTSGoUxEROI4fK0xDGUiIhLH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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-----------------------------------------------------------\n",
            "\n",
            " MultinomialNB  default training method\n",
            "\n",
            "Number of mislabeled points out of a total 69024 points : 3304\n",
            "Accuracy  95.21325915623551 %\n",
            "Start time is 1579740514.2145646 seconds\n",
            "End time is 1579740514.3624883 seconds\n",
            "Exec: 0.14792370796203613seconds\n",
            "Matrix size:  (138047, 54)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HhzN27FitabNnz6Zp06YMHToUgICAAGJiYpg7dy5BQUEA3Llzh3Xr1jF69Gja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\nESEKAICPCFEAAHxEiAIA4KP/AtFiPAXcgy3mAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Now the most interesting, optimized part: \n",
            "\n",
            " MultinomialNB  Variance Threshold method: \n",
            "\n",
            "Number of mislabeled points out of a total 55219 points : 2638\n",
            "Accuracy  95.22265886741882 %\n",
            "Start time is 1579740514.7996187 seconds\n",
            "End time is 1579740515.0337682 seconds\n",
            "Exec: 0.23414945602416992 seconds\n",
            "Matrix size:  (138047, 54)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ldk1MTBAVFYWNGzfi8OHD7GSxZs2afdGB279/fxw7dgwRERHIzc2Fnp4e+vTp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\nA52ohZox3p1CAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " MultinomialNB  SelectKBest features method: \n",
            "\n",
            "Number of mislabeled points out of a total 13805 points : 523\n",
            "Accuracy  96.21151756609923 %\n",
            "Start time is 1579740515.3544238 seconds\n",
            "End time is 1579740515.4502153 seconds\n",
            "Exec: 0.09579157829284668seconds\n",
            "Matrix size:  (138047, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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evVGSoBdCCKEOOeveKEnQCyGEUIf06I2SBL0QQgh1mMoxemMkQS+EEEId0qM3\nShL0Qggh1CFn3RslCXohhBDqkJPxjJIEvRBCCHXI0L1RkqAXQgihDhm6N0oS9EIIIdQhPXqjJEEv\nhBBCHdKjN0oS9EIIIdQhPXqjJEEvhBBCHXLWvVGSoBdCCKEO6dEbJQl6IYQQ6pBj9EZJgl4IIYQ6\npEdvlCTohRBCqEN69EZJgl4IIYQ6pEdvlCTohRBCqMLEVILeGEnQCyGEUIWJDN0bJdn9EkIIoQ6T\nXDxyYffu3XTu3Jnq1atTr149hg0bxrVr1/Tqbd68mYCAALy9vWnbti3h4eF6dZ48ecKcOXOoX78+\n1apVo1evXly4cEGv3r179/jwww+pWbMmvr6+fPTRR8TGxuau4UZCgl4IIYQqTExMDH4Y6vDhwwwd\nOhQXFxdCQ0OZNGkSUVFRvPvuuyQmJmrrbd++naCgIFq0aMGSJUuoW7cuo0aNYt++fTrzmzFjBqtX\nr2b48OHMnz8fc3Nz+vXrR0xMjLZOWloaAwYM4NKlS8yaNYvp06dz8uRJhgwZgqIoL/9BvWIydC+E\nEEIVeTF0v3XrVsqUKcOsWbO08y9btixdunThjz/+oFGjRgDMmzePgIAARo8eDUCdOnWIiooiJCRE\nWycmJoZ169YxceJEunbtCkC1atVo1qwZYWFhjB07FoCdO3dy8eJFtm7diru7OwAODg706NGDiIgI\n7fwKCunRCyGEUIWpqanBD0OlpaVhZWWlsxNhbW2tU+fGjRtERUXRtm1bnfLAwEDOnDmjHXI/cOAA\n6enptGnTRlunaNGiNGnShIiICG3Zvn378PDw0IY8QI0aNShbtqzeCEFBIEEvhBBCHXlwjL5jx45E\nRUWxcuVKNBoNN2/eZNasWbi6ulK3bl0AoqKiAHB1ddWZ1s3NTef1yMhI7O3tKV68uF69a9eukZGR\noa2XNe3T9bLmVZDI0L0QQghV5GboXqPRoNFo9MptbGywsbHRPq9Tpw4hISF89NFHTJ8+HQAPDw9W\nrFiBhYUFAPHx8dpp/83W1lbndY1GozcakFXvyZMnJCUlUbRo0Rzr2djYEBkZafAyGgsJeiGEEKrI\nTdCHhYURGhqqVz506FCGDRumfX7ixAmCgoLo3LkzTZs2JS4ujvnz5zN48GDWrFmDpaWlKm1/nUnQ\nCyGEUEVugr5v37507NhRr/zpXvn06dPx8/NjwoQJ2rI333yTxo0b89NPP9GtWzdtz12j0VCyZElt\nvayefNbrNjY2JCQk6L1nfHw85ubmFClS5Jn1NBqNdl4FiQS9EEIIVeQm6J8eos9JZGQkTZs21Skr\nVaoUxYsXJzo6GgAXFxcg8+0LpqUAACAASURBVFj8v4/TZw2zZ73u6urKgwcPiIuLo1ixYjr1KlSo\noD1J0NXVNdtr669cuULjxo0NXkZjISfjCSGEUIWJqYnBD0OVKVOGc+fO6ZTdunWLhw8fUrZsWQCc\nnJxwcXHRu0HO1q1b8fb2xs7ODoD69etjamrKtm3btHUePXrE3r17adiwobasUaNGXLp0Sed4/J9/\n/smtW7cK3KV1ID16IYQQKsmL6+h79uzJtGnTmDZtGs2aNSMuLo4FCxZQokQJWrdura03fPhwRo4c\nibOzM/7+/uzZs4eDBw+yaNEibR1HR0e6d+/OF198QaFChShTpgzLly8HMg8lZGnZsiWenp4MHz6c\nUaNGkZ6eTnBwMNWrV9fZISgoJOiFEEKoIq+C3tzcnDVr1rBp0yasrKyoVq0aX331lc5lcq1btyY5\nOZmFCxeybNkynJ2dmTNnjl4PfPz48RQpUoSvvvqKhIQEvL29WbFiBY6Ojto6hQoVYunSpXz22WeM\nGTMGExMTGjduzMSJEwvk/fxNlIJ4Pz9hkMLVh+Z3E4QRenhc/0xnISxV6PY59P/B4Lp3l3V9+TcU\nBpEevRBCCFUUxN7uf4EEvRBCCFVI0BsnCXohhBCqyM097MWrI0EvhBBCHdKhN0oS9MKoNfR15+Mh\ngVSv7MTjlCds33+O8V/+yN3Yf+5a5Vzajr/Cp2Y7fakGY4hPfKxT5lnRkSmD29LQ1wOrwhbc+Psh\ni3/Yzzdrf9PWsbO1YsKgANo09KaUvQ0xDzRs33+OzxZv4/7DRITxifn7b5YvW8L5c2e59NdFkpOT\nCd+5h7Jly+nUq1bVM9vpv9+wmUqVK+vOMyaGb0K+4kBEBBpNPCUdHAho3ZYRI0fn2XIUZDJ0b5wk\n6IXRqlfdla3zh7Lr8AV6jFmKna0Vn3zQjvBFw/B/J5jUJ2k69YOX7eCXfWd0yhKSknWe16jizLZF\nw4j44wpDpq4hPvExbs4OFC3yhk69DV8Nwq28A9MW/MLFqBgqu5Ri8uC21KjiTKO+c/JmgcVLiY6+\nzs4d26hSpSrVa/hy+NCBHOu+1aETXbp20ykrX6GCzvNbt27St1cPypYtR9CEiZQoYc/tW7eIjr6e\nF81/LUjQGycJemG0JvyvNdF3Yuk6ajHp6Zk/H/nX1RgOrh5Lvw51Wbx+v079q7fuc+zMtRznZ2Ji\nwtJpvfnt2CW6jV6iLY/4/bJOPTdnB+q+6coH09ayfNNBAPb/cZkMRSFkYnfcyztw+fpdlZZSqKWm\nby1+jTgEwKYN658Z9I4ODvhUe/OZ85v+6cc4ODiydMV3mJubZxbWUq25ryUJeuMkZ04Io1XbuyJ7\njl7UhjzAifPR3H+YyFtNq+V6fg193ansUpqvV+19Zj0LczMAEh7pjgbEJ2QeAjDNxe07xauj5olg\nN6KjOXTwAD169von5MVz5cUtcMXLkx69EYiMjCQiIoKoqCidX1tycXGhYcOGOj/S8F+Snp7Bk6eG\n5wFSn6RRxbW0XvnUYW8RMqE7jx6nsv/EFT4J3cK5K7e1r/tXz/xhizcszNkXNprqlZ15mJDEhh1/\nMHHeTySnPAHgfOQd9v9xmXEDA4i8cY+/rv5NZZfSjB8UwPYD5/jrakweLbF4VX74fh3frliGmZkZ\n3j7VGDJ0ODVq+mpf//PkCQAs37DkfwPe5Y/fj2NZuDCNGjdhTNB4ihUrntOs/9OkR2+cJOjzUXJy\nMhMnTiQ8PBxzc3OcnZ21v+YUFRXFTz/9RHBwMG3atOHzzz/njTfeeM4cXy+Xr8dQ27uiTplz6eKU\nsrfhSVq6tiz1SRpLNhxgz+EL3H+YiEcFR8b2b8mv346iQe/Z2mAuXTLz16pWznqXhd9HMOnrn6lZ\nxZnJg9tSzrG4znB+h6ELWD69DwdXj9WWhUecpefYZXm5yOIVaNvuLRo2aoKDgwO3b98ibPkyBr7X\nl4VLllOrth8Ad+9mHpr5ePIEAtu1570Bg7gRHc3XX80lKvIKq9dtkEvJsiFBb5wk6PPRF198wcGD\nB5k9ezYtW7bEwsJC5/XU1FR27drF9OnTmT17NpMmTcqnluaPb9b8xorP+/HxkEDmr/2N4rZWfDO5\nBxkZChkZ/9y5+e/7GoZ/tk77/ODJSHYdOs8fGyYS1L8V7036DgDT/98IrQs/zrQFvwCZx97NzEyY\nPqIDnhUdtTsF86e8Q22figydvpaLV/+mUsVSTHq/LWtm9+ftEYuQO0cXXJ/PnK39/xo1fWnStBlv\nt29H6NdfEbZqLQCKknm4yLdWbSZM/hgAvzp1KWptTdBHIzl0cD/1GxS8XzHLaxL0xkl2SfPRL7/8\nwvjx4wkMDNQLeQALCwvatm1LUFAQv/zySz60MH+t2/Y7M5ZsY0TvpkTvncnJjRO5fTeO7QfP8/d9\nzTOnvRkTx6E/o6hZtby2LDb+EQB7jlzUqbv7cObzNz2dAAioX5VurX15b1IYyzYe5OCJSJZtPEj/\nSd/RuoEXbRt5qbmYIp9ZWRWlQcNGnDv7zxUbtv//W+V1/Ovp1PX//+cXs/mtckHmdfSGPsQrIz36\nfJScnIy9vf1z69nb25OcnPzceq+jqfN/4Yvlu6hYzp57sQncjU3g5MZJHPoz8vkTg07P+3zknWfW\nzfj/ul7uZQD441y0zuu/n7sGQKWKpdj6m+5lfKLg+3dv1NXV7Tl1pY+UHenRGydZW/NRjRo1+Oab\nb7Qn4GUnPj6e+fPn4+vrm2Od111ScirnrtzmbmwCLfwrU8mlFEs35HzpFIBTqeL4v+nC7+f+ueZ5\n58HzJKc8oUVd3ZuitKhXBYA/zmfWzRot8PUqr1OvllcFAG7djXup5RHGJTExkYh9v+Hl7aMt86n2\nJvb2JTl0UHc9O3gg85JOL2/vV9rGgsLU1MTgh3h1pEefj6ZMmULv3r1p3LgxdevWxc3NDWtrawAS\nEhKIjIzk8OHD2NjYEBYWls+tffWqeZajZb0q/HnxBgD+b7oysm8z5qzYxZFTV7X1Zo7qiKmJCUdP\nX+Xe/5+MN+a9lmQoCrOW7tDWi41/xOzlOxk/MADNo2R+O36JmlWcmTAwgJU/HyHqxn0Aftp7ik+H\ntmPZtD7MWLKNv67G4FnRkQmD2nDjTiw/7z31aj8IYbBdO7YDcP78WQAO7o+geHE7itvZ4VurNmEr\nlnHt6lVq1fajpIMDd27fJuzb5dy/f58ZwV9o51OoUCFGjBzN5InjmPbpFJo1b8mN6OuEfP0VvrVq\nU9uvTr4sn7GTHr1xkt+jz2cJCQmsXbuW/fv3ExkZiUaT2Zu0sbHB1dWVhg0b0r17d+0OQG4U9N+j\nr+xSitBJPajiVpo3zAtx8WoMC9btY+XPR3Tq9Wlfh0FdGuDiVJKihd/gQfwj9h2/xGeLwrO9sc3w\nXk0Z1KUBTqWL8/d9Dau2HGXGkm2kpf1zvX45x2JMfL8NjWt5UMrelr/vx7P36F98tjCc2/dyHoEp\nCF7n36PP6fa2vrVqs+zblfz2615WLF3MtWtXSUxMxMqqKG9Wr87A/w3B28dHb7otP29mxbKlRF+/\nhq1tMZq3bMWID0dRxMoqrxfllVPj9+g9xm43uO6l4ICXf0NhEAn611hBD3qRN17noBcvTo2g9wza\n8fxK/++vWa1e/g2FQWToXgghhCpk5N44SdALIYRQhZxkZ5wk6IUQQqhCgt44SdALIYRQhQzdGycJ\neiGEEKqQy+uMkwS9EEIIVUjQGycJeiGEEKqQnDdOEvRCCCFUISfjGScJeiGEEKqQoXvjJEEvhBBC\nFZLzxkmCXgghhCqkR2+cJOiFEEKoQnLeOEnQCyGEUIX06I2TBL0QQghVyFn3xkmCXgghhCqkQ2+c\nJOiFEEKoQobujZMEvRBCCFVIzhsn0/xugBBCiNeDiYmJwY/c2rx5M506dcLHxwc/Pz/effddYmNj\nta/v27ePjh074u3tTfPmzVm5cmW281m2bBlNmzbFx8eHTp06cfjwYb06iYmJTJkyBT8/P6pXr877\n77/PzZs3c91mYyFBL4QQQhV5FfQLFizg008/pUWLFixZsoTPPvsMNzc3njx5AsDJkycZMmQIlStX\nZsmSJXTq1InPP/+ctWvX6sxn2bJlfPnll/Ts2ZNFixZRoUIFBg0axMWLF3XqjR49mr179zJ58mS+\n/PJL7t69S79+/Xj8+PHLfUD5xERRFCW/GyHyRuHqQ/O7CcIIPTwemt9NEEbIUoUDuY2/OmRw3d8+\n9DeoXlRUFO3atSM0NJQmTZpkW2fAgAHEx8ezfv16bdnkyZP59ddfiYiIwNTUlNTUVPz9/enatStj\nx44FID09nXbt2uHu7s68efMAOHXqFF27dmXx4sU0atQIgNu3b9OiRQsmTJhAz549DV5GYyE9eiGE\nEKowMTH8YahNmzZRpkyZHEM+NTWVI0eO0KZNG53ywMBA7t27x7lz5wA4ceIECQkJtG3bVlvHzMyM\n1q1bExERQVafd9++fVhbW9OgQQNtvTJlylCjRg0iIiIMb7gRkaAXQgihitwM3Ws0Gm7evKn30Gg0\nOvM8deoUnp6ezJ8/n3r16lG1alU6d+7MsWPHAIiOjubJkye4urrqTOfu7g5kjggAREZGAujVc3Nz\nIykpiZiYGG09FxcXTE1N9eplzaugkbPuhRBCqCI3PfWwsDBCQ/UPIw0dOpRhw4Zpn9+7d4+zZ89y\n8eJFJk6cSNGiRVm+fDkDBgwgPDyc+Ph4AGxsbHTmk/U863WNRoOFhQWWlpY69WxtbQGIi4ujVKlS\naDQarK2t9dplY2OjnVdBI0EvhBBCFaa5SPq+ffrSsWNHvfKnA1tRFJKSklizZg2VK1cGoFatWjRr\n1oxly5YRGBj4co3+D5CgF0IIoYrc3ALXxsZGL9RzqlesWDFtyAMULlyYatWqcfnyZW2P/Okh/6zn\nWa/b2NiQmppKSkoKb7zxhrZeVi+9WLFi2np37tzRa4dGo9HOq6CRY/RCCCFUYWpi+MNQbm5uOb6W\nkpKCs7Mz5ubmesfPr1y5AoCLiwvwz7H5rGP1WSIjI7GyssLR0VFb7+rVqzx9QdqVK1e08ypoJOiF\nEEKoIi+uo2/SpAlxcXHas+cBkpKS+PPPP6latSoWFhbUqVOHbdu26Uy3detWSpYsSdWqVQGoUaMG\n1tbWhIeHa+ukp6ezbds2GjRooG1To0aN0Gg07N+/X1vvzp07nDhxgoYNG77Q55LfZOheCCGEKvLi\nFrjNmzfHx8eH4cOHM3LkSKysrFi+fDnJycm8++67AHzwwQf06tWLSZMm0a5dO06cOMH69euZMmWK\n9ux5CwsLBg8ezJdffomdnR1VqlRh/fr1REdHM2fOHO37VatWjcaNGzNx4kTGjRtH0aJFmTdvHqVL\nl6ZTp07qL+ArIDfMeY3JDXNEduSGOSI7atwwJ3DRcYPrbv1fLYPrxsbGEhwczJ49e0hJSaFatWqM\nHTsWb29vbZ19+/Yxd+5cIiMjcXBwoF+/fvTp00dvXsuWLWPVqlXcv38fd3d3xowZQ926dXXqJCYm\nEhwczPbt20lNTcXPz49Jkybh5ORkcJuNiQT9a0yCXmRHgl5kR42gf2ux4UH/8yDDg168HBm6F0II\noYrcnHUvXh0JeiGEEKrIzXX04tWRoBdCCKEKyXnjJEEvhBBCFS/yO/Mi70nQCyGEUIXkvHGSoBdC\nCKEKM0l6oyRBL4QQQhUydG+cJOiFEEKoQq6uM04S9EIIIVQhPXrjJEEvhBBCFZLzxkmCXgghhCqk\nR2+cJOiFEEKowkwO0hslCXohhBCqkJg3ThL0QgghVCH3ujdOEvRCCCFUITlvnCTohRBCqEJOxjNO\nEvRCCCFUITlvnCTohRBCqELOujdOEvSvsXtHQvK7CcIIrTkZnd9NEEbovVrOLz0PGbo3ThL0Qggh\nVGGa3w0Q2ZKgF0IIoQrp0RsnCXohhBCqkEP0xkmCXgghhCrkZDzjJEEvhBBCFZLzxkmCXgghhCrk\nEL1xkqAXQgihCrnXvXGSoBdCCKEKubzOOEnQCyGEUIV06I2TBL0QQghVyFn3xkmCXgghhCok542T\nBL0QQghVyMl4xkmCXgghhCok542TBL0QQghVyNC9cZKrIYQQQqjCJBf/XtSjR49o2LAhnp6enDlz\nRue1zZs3ExAQgLe3N23btiU8PFxv+idPnjBnzhzq169PtWrV6NWrFxcuXNCrd+/ePT788ENq1qyJ\nr68vH330EbGxsS/c7vwkQS+EEEIVhUwNf7yo0NBQ0tPT9cq3b99OUFAQLVq0YMmSJdStW5dRo0ax\nb98+nXozZsxg9erVDB8+nPnz52Nubk6/fv2IiYnR1klLS2PAgAFcunSJWbNmMX36dE6ePMmQIUNQ\nFOXFG59PZOheCCGEKvL6Z2ovXbrEunXrGDduHFOmTNF5bd68eQQEBDB69GgA6tSpQ1RUFCEhITRq\n1AiAmJgY1q1bx8SJE+natSsA1apVo1mzZoSFhTF27FgAdu7cycWLF9m6dSvu7u4AODg40KNHDyIi\nIrTzKyikRy+EEEIVpiaGP17E1KlT6dmzJxUqVNApv3HjBlFRUbRt21anPDAwkDNnzmiH3A8cOEB6\nejpt2rTR1ilatChNmjQhIiJCW7Zv3z48PDy0IQ9Qo0YNypYtqzdCUBBIj14IIYQqctOh12g0aDQa\nvXIbGxtsbGz0yjdv3sz169dZtGgRZ8+e1XktKioKAFdXV51yNzc37et2dnZERkZib29P8eLF9ept\n3bqVjIwMTE1NiYyM1E77dL2s9ypIJOiFEEKoIjfX0YeFhREaGqpXPnToUIYNG6ZTlpCQwOzZswkK\nCsLKykpvmvj4eAC9HQRbW1ud1zUaDdbW1nrT29ra8uTJE5KSkihatGiO9WxsbIiMjDRwCY2HBL0Q\nQghVmOXiYHDfvn3p2LGjXnl2vfmvvvqK8uXL89Zbb71M8/6zJOiFEEKowjQXl83lNET/tMuXL7Nu\n3TqWL1+uHepPSkrS/jcxMVHbc9doNJQsWVI7bVZPPut1GxsbEhIS9N4jPj4ec3NzihQp8sx6Go1G\nO6+CRIJeCCGEKvLipPvr16+TlpZGnz599F7r06cPlSpV0h4CiIqK0jlOnzXM7uLiAmQew3/w4AFx\ncXEUK1ZMp16FChUwNTXV1svu2vorV67QuHFj1ZbtVZGgF0IIoYq8uDNejRo1+O6773TKLly4wIwZ\nM/j000+pWrUqTk5OuLi4EB4eTosWLbT1tm7dire3N3Z2dgDUr18fU1NTtm3bRo8ePYDMG/Ds3buX\nt99+Wztdo0aN+Omnn4iMjNTuOPz555/cunWrwF1aBxL0QgghVJIXP2pjZ2eHn59ftq9VrVoVb29v\nAIYPH87IkSNxdnbG39+fPXv2cPDgQRYtWqSt7+joSPfu3fniiy8oVKgQZcqUYfny5UDmOQNZWrZs\niaenJ8OHD2fUqFGkp6cTHBxM9erVadiwoerLmNck6IUQQqgiP3/UpnXr1iQnJ7Nw4UKWLVuGs7Mz\nc+bM0euBjx8/niJFivDVV1+RkJCAt7c3K1aswNHRUVunUKFCLF26lM8++4wxY8ZgYmJC48aNmThx\nYp7fFCgvmCgF8X5+wiCJKfKnFfp+OH0jv5sgjNB7tZxfeh7Lj0e/0vcThpEevRBCCFXIrVaNkwS9\nEEIIVRTEYe3/Agl6IYQQqpCYN04S9EIIIVSRF2fdi5cnQS+EEEIVEvPGSYJeCCGEKkzz4o454qVJ\n0AshhFCFnHVvnCTohRBCqELOujdOEvRCCCFUITFvnCTohRBCqEJ69MZJgl4IIYQqzCTojZIEvRBC\nCFVIzBsnCXohhBCqkA69cZKgF0IIoQpT6dMbJQl6IYQQqpAevXGSoBdCCKEKE+nRGyUJeiGEEKqQ\ns+6NkwS9EEIIVUjOGycJeiGEEKqQoDdOEvRCCCFUIcfojZMEvShQDh3cT9jypVyNikSjiad4cTt8\n3qzO/wYPxcXVTafugf37+HbZEi5eOI+pqQnO5SswfOQYavvVAeDjSePY+vPmbN+nfIWKbPp5W54v\nj8i9qNPHObr1Bx7cuk7yo0QKW9tS1r0K9d/ug33Z8gCsmT6aGxdPZzt9RW9fugbNACDlcRIHf1zJ\n31GXiLl2hdTkJHpM+ALnKtWe2Ybzh39lyzefU7S4PR+ErFV3AQsw+ZVa4yRBLwoUTXw8latUpUu3\nHhS3s+PvO3f4dvkS+vXqxvcbf6Z0mbIAbFy/jlkzptOte08G/G8wGRkZXPrrIsnJj7XzGvC/IXTu\n0l1n/rdv32JC0GgaNW76SpdLGC45MYFSFdyp3rwdRaxt0Ty4y5Et37Py4+G8N3MxtvaOtHx3OCmP\nH+lMd/vyBfauXohbjbr/mpeGM/t24FjBjQreNbh0/MDz3/9RIntXLcDK1k71ZSvoTGXs3ihJ0IsC\nJaBNIAFtAnXKqnp583b7NuzetYPefd/j9q2bzAmewYcjx/BO777aev71GuhM5+TkjJOTs07ZkSOH\nAAh8q0MeLYF4WVX8m1LFX3dHrLRLJZaOfY+/jkVQu00Xbc/+3079ug2zQuZUrttYW2Zj78iIRZsA\nuHb2hEFB/9u6JZR0dqFoMTuunT35cgvzmpGhe+Nkmt8NEOJl2RYrDoCZWeZ+60+bN2FiYsrbXbs/\na7Js/bLlJypXqYqrm7uqbRR5q7C1DQCmpmbZvv4kJZm/jkbgWr0OhYvaaMtz+2trNy+d5dzBPbTs\nO+zFG/saMzUx/CFeHQn6AuL27dts3pz98eT/ovT0dJ48SSX6+jU+nzqFEvYlCWjdFoA/T/xBhYoV\n2bH9F95q04La1avSvm1Lfli3+pnz/PPkCW5EX5fefAGRkZFOetoTYv++yY7lX2Fla0fluk2yrXvp\n94OkJifh3aDFC79feloa25d9Re22XShequwLz+d1ZpKLf+LVkaH7AuLMmTOMHz+eDh0khAD69uzK\nhfPnAHByLs+ipd9iV6IEAPfu3eX+vbvMmzubD4aNpJyTM7t3bmfW59NIS0vnnV59sp3nL1s2U6iQ\nOa1aB2b7ujAuKz8ext9XLwNQ3LEM3ScEY2VbPNu6Zw/soohNMVyq1X7h9zu69XvSnzyhbrseLzyP\n150cojdOEvSiQJr2eTCJiYncunmDlWHLGTLoPZaFraZM2XIoSgaPHj1i9rQZNG3eEoDafnW4c/sW\n3y5bTI+evfWGbFNSUti1YzsNGjWmePHsw0IYl7bvB5H6OIm4u3c4Fr6B72eOo9eUL7EtWUqnXsLD\n+1w/e5KarTpiapb90P7zPPz7Fod/WkPHDz+hkIWFGs1/LUnOGycJ+nzWrl07g+o9evTo+ZX+Qyq6\nuALg7VONevUbEti6Gd8uX8KEyZ9ia1sMuI5fXX+daer41+PQwf3cv3eXkg6OOq/t+20vCQkaGbYv\nQLJOuCvjVhmXarVZOLIXR7aso9V7H+rUO39wD4qS8VLD9rtXfoNzlTcp41aZ5EeJQOZQPigkP0rE\nzNwcc4s3Xnj+rwu5Ba5xkqDPZ1FRUbi5uVGlSpVn1rt16xZ37tx5Ra0qWKxtbHBycuZGdDQALq7u\nnDl9Ksf6Jqb6p6Zs/XkzxYoXp179hnnWTpF3LK2KUtyxDA9jbuu9dmb/LhycXXAo7/rC879/KxrN\n/Rjm/a+j3mvz/teRmq060rz3kBee/2tDct4oSdDnM3d3d8qXL8+MGTOeWW/Hjh0cP378FbWqYHnw\n4D7Xrl6lddvMY+tNmjXnpx83cPjgAZq3DNDWO3RwP46OpbC3L6k3/ZFDB+jS7R3Mzc1faduFOh7F\nP+TB7RtU8W+mU34n6i8e3LpO057vv9T83xo6gfTUJzplR7as4+9rl+kwbDLWdvYvNf/XhZxkZ5wk\n6POZj48P+/fvN6iuoih53BrjN/rDoVSqXAV3D0+srKyIvn6N1SvDMCtkRq8+7wJQv0EjfGv58fm0\nj4mLi6NsuXLs3rmDI4cO8vG0z/Xmue2XLaSnp8uwfQGx6ctPcKzghoOzCxaFi/Dwzk2Ob9+EqZkZ\ntdt01ql77sBuTM3M9K67/7fIU8d4kpLMvRtXAYi+eJqkxHjM37DE9f9P3ivrpj/idmb/DswKmT/3\nLnr/JTJyb5wk6PPZgAEDaNSo0XPrNWrUiD179ryCFhk3b59q7NqxjVXfreDJkyeUcixFzVq1ebf/\nIMqULQdkXhs9Z943hM6by6L5IWg0GipUrMj0GbNp3Vb/nIitP2/G1c2dylWqvurFES+gjFslLh6N\n4Pi2DaSnpWFjVxKnytWo+1Z3nRPx0tPSOH/4Vyr61MrxbHyAnSu+RnM/Rvv84KbvgMyb6Qz+alXe\nLchrSHLeOJko0k18bSWmyJ9W6Pvh9I38boIwQu/Vcn5+pec4fjXe4Lq1Ktq+9PsJw8gNc4QQQqjC\n1MTE4Iehtm3bxpAhQ2jUqBFvvvkm7dq1Y82aNWRkZOjU27dvHx07dsTb25vmzZuzcuXKbOe3bNky\nmjZtio+PD506deLw4cN6dRITE5kyZQp+fn5Ur16d999/n5s3b+buwzAiEvRCCCFUYZKLh6FWrFiB\nhYUFY8eOZeHChTRv3pzPPvuM2bNna+ucPHmSIUOGULlyZZYsWUKnTp34/PPPWbtW95cFly1bxpdf\nfknPnj1ZtGgRFSpUYNCgQVy8eFGn3ujRo9m7dy+TJ0/myy+/5O7du/Tr14/Hjx9TEMnQ/WtMhu5F\ndmToXmRHjaH7E9c1BtetUd7m+ZWA2NhY7Ox0fylwxowZrF27lt9//x0LCwsGDBhAfHw869ev19aZ\nPHkyv/76KxEREZiampKamoq/vz9du3Zl7NixQOattNu1a4e7uzvz5s0D4NSpU3Tt2pXFixdrz5+6\nffs2LVq0YMKECfTs2dPgZTQW0qMXQgihiry41/3TIQ9QuXJlUlJSiIuLIzU1lSNHjtCmTRudOoGB\ngdy7d49z5zJvlX3iigvGEgAABy5JREFUxAkSEhJo27atto6ZmRmtW7cmIiJCe1XTvn37sLa2pkGD\nf37tskyZMtSoUYOIiIhcfR7GQoJeCCGEKkxMDH+8jD/++INixYpRokQJoqOjefLkCa6uujdEcnfP\n/AXKqKgoACIjIwH06rm5uZGUlERMTIy2nouLC6ZP3VjLzc1NO6+CRi6vE0IIoYrcBLhGo0Gj0R/q\nt7GxwcYm52H9M2fOsGnTJj744APMzMyIj4/XTvf0fADt6xqNBgsLCywtLXXq2dpmnv0fFxdHqVKl\n0Gg0WFtbZ9uurHkVNBL0QgghVJGbIfmwsG8JDQ3VKx86dCjDhg3Ldpp79+4xfPhwvL29GThw4Au3\n879Ggl4IIYQqctOj79u3Lx076v92QE69+YSEBAYOHIilpSULFizQ3q46q0f+9OhA1vOs121sbEhN\nTSUlJYU33vjnB4iyeunFihXT1svud0U0Go12XgWNBL0QQghV5ObQ+/OG6P8tJSWFwYMH8+DBA9at\nW6fzU9LOzs6Ym5sTFRVFw4b//CjVlStXAHBxcQH+OTYfGRmp8yNikZGRWFlZ4ejoqK136NAhFEXR\n+TnrK1euaOdV0MjJeEIIIdSRBxfSp6WlMWLECP766y+WLFlC2bJldV63sLCgTp06bNu2Tad869at\nlCxZkqpVM29tXaNGDaytrQkPD9fWSU9PZ9u2bTRo0EAb6o0aNUKj0ej8BsmdO3c4ceKEzo5EQWL2\nySeffJLfjRB5IzU9v1sgjNG5GMOvdRb/HdXLvvyw9L2EJwZfXudgY2HQPD/55BO2bt3K8OHDcXBw\n4O+//9Y+ihYtioWFBU5OTixcuJA7d+5gZWXFli1bWLFiBWPGjMHHxwfIvJTOzMyMhQsXYmlpSUpK\nCvPmzePEiRMEBwdjb5/5C4SlSpXi7NmzfP/99zg6OnLnzh2mTJmChYUF06ZNK5C/cCk3zHmNyQ1z\nRHbkhjkiO2rcMOfC7UcG161cxsqgek2bNuXWrVvZvvbdd9/h5+cHZF7/PnfuXCIjI3FwcKBfv370\n6dNHb5ply5axatUq7t+/j7u7O2PGjKFu3bo6dRITEwkODmb79u2kpqbi5+fHpEmTcHJyMnj5jIkE\n/WtMgl5kR4JeZEeVoL+Ti6AvbVjQi5cnJ+MJIYRQRW4urxOvjgS9EEIIVbzsHe9E3pCgF0IIoQrJ\neeMkQS+EEEIdkvRGSYJeCCGEKkxl7N4oSdALIYRQhcS8cZKgF0IIoQ5JeqMkQS+EEEIVcnmdcZKg\nF0IIoQo5RG+cJOiFEEKoQoLeOEnQCyGEUIUM3RsnCXohhBCqkB69cZKgF0IIoQrJeeMkQS+EEEIV\n0qM3ThL0QgghVCJJb4wk6IUQQqjCVHLeKEnQCyGEUIUM3RsnCXohhBCqkMvrjJMEvRBCCHVIzhsl\nCXohhBCqkJw3ThL0QgghVCHH6I2TBL0QQghVmEjSGyUJeiGEEKqQmDdOEvRCCCFUIR164yRBL4QQ\nQhVyeZ1xkqAXQgihCunRGycJeiGEEKqQoDdOEvRCCCFUIUP3xkmCXgghhCqkR2+cJOiFEEKoQnLe\nOEnQCyGEUIckvVGSoBdCCKEKOUZvnCTohRBCqMJUct4oSdALIYRQhwS9UZKgF0IIoQoZujdOJoqi\nKPndCCGEEOL/2rl/l1TDOAzj9ylQWl7BscDBahFE2pwUNBAyCJqaXNre2aDJxcC26H9ocGwUmnRq\ncXMLfxQ2ZvggCA52tiA849EHvl2f8XG5B/GCV32wHlu+BwAAgPUh9AAAGEboAQAwjNADAGAYoQcA\nwDBCDwCAYYQeAADDCD0AAIYRegAADCP0MGs0Guny8lJHR0fKZrOq1+uaz+e+Z8Gz19dX1Wo1nZ2d\nKZVK6fT01PckYK246x4mOedUqVS0u7ur+/t7TSYTNRoNTSYT3d3d+Z4Hj15eXtRut5XJZLRcLsUt\n4LCO0MOkZrMp55weHx8Vj8clSdvb26pWqwrDUIeHh54XwpdCoaDj42NJ0vX1tXq9nudFwHrx6B4m\ndTodZbPZ78hLUqlUUiQSUafT8bgMvm1t8bGH34V3PEzq9/s6ODj4cRaJRJRIJDQYDDytAoDNI/Qw\nyTmnIAhWzoMg0HQ69bAIAPwg9AAAGEboYVIQBHLOrZw75xSLxTwsAgA/CD1M2t/fV7/f/3G2WCz0\n9vamZDLpaRUAbB6hh0m5XE7Pz8/6/Pz8Pnt6etJisVA+n/e4DAA2i//Rw6SLiws9PDwoDEOFYaiP\njw/d3t7q5ORk5df4+F3m87na7bYk6f39XbPZTK1WS5KUTqe1t7fncx7w3/354looGDUcDnVzc6Nu\nt6toNKpyuayrqyvt7Oz4ngaPxuOxisXiP19rNBo6Pz/f8CJgvQg9AACG8R09AACGEXoAAAwj9AAA\nGEboAQAwjNADAGAYoQcAwDBCDwCAYYQeAADDCD0AAIb9BbSRa/aAqczoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-----------------------------------------------------------\n",
            "\n",
            " ComplementNB  default training method\n",
            "\n",
            "Number of mislabeled points out of a total 69024 points : 3304\n",
            "Accuracy  95.21325915623551 %\n",
            "Start time is 1579740515.6933663 seconds\n",
            "End time is 1579740515.8436346 seconds\n",
            "Exec: 0.1502683162689209seconds\n",
            "Matrix size:  (138047, 54)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EECKLnLM1GEm2QgghskjL1mAk2QohhMgiLVuDkWQrhBAii7RsDUaSrRBCCACM\njCXZGookWyGEEAAYSTeywcjPGCGEEFmM8vDKh8ePHxMQEICbmxtnzpzRmrZ582aaNWuGh4cHQUFB\n7NixQ2f+p0+fMmPGDOrWrUu1atXo0qULFy5c0Im7f/8+gwcP5v3336dGjRoMGzaMhw8f6sSdPn2a\njh074unpib+/P7NnzyYjIyN/G/kCSbZCCCGArJZtbl/5ERERoTeZ7dq1ixEjRtC4cWMWL15MrVq1\nCA0NZf/+/VpxU6ZMYc2aNQwcOJB58+ZhampKSEgId+/eVWKePXtGz549+euvv5g2bRoTJ07kxIkT\n9O3bF41Go8TdvHmTkJAQbG1tWbhwIX369GHp0qXMnDkzX9v4IulGFkIIAbyZbuS//vqLyMhIwsLC\nGDt2rNa0WbNm0axZM4YOHQqAn58fsbGxzJkzh8DAQADu3r1LZGQko0eP5pNPPgGgWrVqNGzYkBUr\nVvDFF18A8Msvv3Dx4kW2bduGi4sLACVKlKBjx45ER0cry1uyZAkqlYrZs2djZmZGrVq1SEpKYu7c\nufTs2ZMiRYoUyHZLy1YIIQQAxsbGuX69rvHjx9O5c2cqVKigVX7z5k1iY2MJCgrSKm/ZsiVnzpxR\nun8PHjxIRkYGLVq0UGKsra2pX78+0dHRStn+/ftxdXVVEi2At7c3ZcuW1WopR0dH06hRI8zMzLTW\nmZ6ezpEjR157O18kyVYIIUQWA5+z3bx5M9evX+fzzz/XmRYbGwuAk5OTVrmzs7PW9JiYGOzs7Cha\ntKhO3LVr18jMzFTisud9MS57WSkpKdy+fVtnnfb29lhYWChxBUG6kYUQQgB560ZWq9Wo1WqdcpVK\nhUql0ilPSkrim2++YcSIEVhZWelMT0xMVOZ/nq2trdZ0tVqNjY2Nzvy2trY8ffqUlJQUrK2tc4xT\nqVTExMQoddK3zuyy7HUWBEm2QgghgLwl2xUrVhAREaFT3r9/fwYMGKBT/t1331G+fHlat26drzr+\nU0myFUIIAeQt2Xbr1o22bdvqlOtrJV6+fJnIyEiWLVumtIZTUlKU/ycnJystWLVazXvvvafMm926\nzJ6uUqmUFunzEhMTMTU1xdLS8qVxarVaWVZ2y1dfC/35uIIgyVYIIQSQt2SbU3exPtevX+fZs2cE\nBwfrTAsODqZSpUpKKzk2NlbrHGp2l6+joyOQdU43Pj6ehIQErZHCMTExVKhQQRm85eTkpPfa2ytX\nrlCvXj0ALC0tKVOmjLKObHFxcTx58kRZZ0GQAVJCCCEAMDI2yvUrL7y9vVm5cqXWa+TIkQCMGzeO\niRMnUq5cORwdHXVuYrFt2yHfaHwAACAASURBVDY8PDwoVqwYAHXr1sXY2JidO3cqMY8fP2bv3r0E\nBAQoZYGBgfz1119aifTkyZPExcUpl/0ABAQEsGfPHtLT05Wy7du3K5cBFRRp2QohhAAMd51tsWLF\n8PX11TutatWqeHh4ADBw4ECGDBmCg4MDtWvXZs+ePRw6dIiFCxcq8SVLlqRDhw5Mnz6dQoUKUaZM\nGZYtWwZkdW1na9KkCW5ubgwcOJDQ0FAyMjIIDw/Hy8tLKyn37NmTrVu3MnjwYLp27UpsbCzz5s2j\nW7du0o0shBCi4L3teyM3b96c1NRUFixYwNKlS3FwcGDGjBlaLVGAkSNHYmlpyXfffUdSUhIeHh4s\nX76ckiVLKjGFChViyZIlTJo0ieHDh2NkZES9evUYPXq01naWK1eO77//nsmTJ9O7d29sbW3p3r07\n/fv3L9BtM9I8f98q8c6x8CrYN1z88z36Q3cEqBAAhfPZfCrRY12uY+8t/SR/K/uXkZatEEII4O23\nbP+XSbIVQggBSLI1JEm2QgghAPJ1z2PxcpJshRBCZJGGrcFIshUGsyWiL03qVGHq4l2Mm7cNgEXj\nutC1tZ/e+EtX/6Z6u4kAjP6sBWP6tNAbl5r2lKJ+QwDo0sqXxeO75liHCo1Gcjde9y4yftUqsmfZ\nEIyNjbGuMZCMjMw8bZswnAPR+1m2ZBEXzp/H2NiI8hUqMDh0OL5+Wdc8qhMT+XZGOPv2/B+paWlU\nq1ad4SNG4uLqprOs2JgY5kXM4o+jv/PkyRNKlS5N+w6d6Ny1m06skG5kQ5JkKwzik2bv4+FaVqd8\nyuJdLNlwUKusfJnirJzane37zyhl3/90mKjD57XiLC3M+Dmin1bcroPnCAyerhVnZGTEhu8+41rc\nA72JtlAhY+aM7sjd+CRKv1dw19GJ/Fu/LpKpkybQoWNnevfpS2ZmJpcuXiA1NRUAjUbDgH59uH07\njrBRX6JSqVi6ZBE9uwezbuMWSpYqpSzr3Nkz9Pq0GzV8avLV+ElYW1tz4/p15TaBQpckW8ORZCsK\nXBEbC6YN/ZARMzayYkp3rWlXbz3g6q0HWmUN/CoBsHrr70pZ3L0E4u4laMV1DPLB1NREK+7Bo2Qe\nPErWiqvj5YRdUWsmLtC+E022IcGNMDKClVuOMKJn07xvoDCIuLhbfDN1MkOGDqdLcIhSXqeuv/Lv\nX/ft4eSJ4yxetoKavlk9JJ7VvWjRpCHLly0hbNQYADIzMxkzcgQ1/Wrx3ey5yvzZ8wj9JNkajpwN\nFwVu4qA2nI+5zbpdf+YqvnPLmvx5/gYXYv9+aVyXVr78/UBN1G+69zvVWl4rX9LSn7Ju1zGdaRXt\n7Qjr2YxBU9by9FlGruon3ozNmzZiZGzMx+075hjz6769vFeihFbStLGxIbBefX7du0cp++Po78TG\nxhDcrbu+xYgcGOp2jUJatgUiJiaG6OhoYmNjtZ5Q4ejoSEBAgM6Dif+X1a7uSOeWNanZfkqu4mtV\nc8TZoQSh09a/NM6+ZBECa7gS8cO+l55fLWxuSrtGXuw8cI5Hat3uwjmjO7Dp/05w6HgM9Xx0z/GJ\nt+fE8T+pWNGRXTu2s2jhPO7cvk2ZMmXpEhxCh06dAYi5cgVnZ1edeZ2cndn682ZSHj/G0sqKE8ez\nfuilpaXRpeMnXDh/DhuVimbNgxgcOozChQu/0W37p5CWreFIss2H1NRURo8ezY4dOzA1NcXBwUF5\nCkZsbCxbtmwhPDycFi1aMHnyZMzNzd9yjQ3LtJAJc8Z05LuVe7h8/V6u5unUsibpT5/pbYU+r0NQ\nTUxMjLW6kPVpXd8TWxsLvXEdWvjgVbkc3Ud9n6u6iTfr/v173L93j5kzwhkwKBT7cuWI+mUXUyaN\nJyPjGZ27diMxMZEyZXXHAtjaZj39Ra1WY2llxf37WZ+/L4YOoUOnzgwaMpTz584yL2I2f/99R6tr\nWfyXJFvDkWSbD9OnT+fQoUN88803NGnSBDMzM63p6enpREVFMXHiRL755hvGjBnzlmr6ZoSGNMLC\n3JRpS3fnKt7crBAfNvFmZ/RZ4hMevzS2c1BNTly4ydnLt18e19KXu/Fqdh08p1VeVGXJtKHt+Cpi\nK/dfOMcr3g2ZmRoeP37M+ElTadS4CQC+frW4HRfH0sWL6NRF9/FsOdFkZt2FNqhVa/oNGASAT01f\nMjIymDVzBrExMTj+i3qcckuSreHIOdt82L59OyNHjqRly5Y6iRbAzMyMoKAgRowYwfbt299CDd+c\ncqWKMqJHU8bP24a5aSFsrS2wtbYAspKqrbUFxi+c52kZ6EFRlSWrt728tVqjankqOZZizStataXs\nVDTwdWPdzmM6Xc1f9WvJ3w8S2fjLcaVuhc2zfmvaWltgWVj3/RNvVvazSWvVrq1VXqt2XeLjH3D/\n/j1UKhXqRN0HfScmZg2my+5Zsv3PsvxqvbCsOnUBuHhBe6S7+A+jPLxEnkjLNh9SU1Oxs7N7ZZyd\nnZ1y6cL/qgpl7bAobMbyySE604Z0a8SQbo3wbT+F03/FKeWdW/ly/1GSTiv0RZ1b+ZL+9BlrX9HV\n3LGFD4UKmejtQq7sWBpPV3tu7w/XmRb36zS27jvFJ6GLX7p8YVhOzs6cPnUyx+nGRsY4OTvz2+FD\nOtNiY2IoXboMllZWyrJexkjulKSXtGwNR5JtPnh7ezN37lzc3d1zfO5hYmIi8+bNo0aNGm+4dm/W\n6Uu3aNJzlk75L0sG8cO2o3y/+Tdibt5XyksUs6FxrcosXBfNs2c5D3gyLWTCx03fZ/eh8zqX+Lyo\nU0tfTv91SyuhZxv+zQZsbSy1yrq29qVraz+afzabe3quxxVvVoOGjflp4wYOHzxI46bNlPJDBw9Q\nslQp7N57j3r1G7Llp00c++MoNXxqApCcnMz+X/fRPKilMk9d/wDMzMw4fOgg9eo3UMoPHzwAQNWq\n7m9oq/5ZXux9EgVHkm0+jB07lq5du1KvXj1q1aqFs7MzNjY2ACQlJRETE8Nvv/2GSqVixYoVb7m2\nhpWY/IQDf17WO+3GnYc60zq8pBX6vBYB7hQvYvXKLuTqlexxdynDiBmb9E7Xl4ADargAcODPK3IH\nqXeAf0AgPjV9mTDuKx4lPMLevhxRu3fx2+GDjJ+YNbq9Xv0GVKvuxaiw4QwZ+gUqlYplSxah0Wjo\n/mlPZVlFihSlR6/PWLRgHtbW1tT09ePc2bMsnD+X1h+0xaF8+be1me80adkajiTbfChfvjzbt2/n\nxx9/5MCBA2zYsAG1Out8kkqlwsnJic8//5wOHTooSVhk6dyqJmcv3+bkxVuviPMlPuExO6LPvjSu\nSytfnj7NIHLHHwVZTfEGGRkZ8d2cecyeOYP5c+egTlRT0bEiU6ZNp0XLVkDWjfLnzF3AjOnTmDxh\nHOnpaXhWq86S5SspVbq01vI++7wflpZWrIv8gRXLl/Hee+/RrXsPevfp+zY27x9Bcq3hyMPj33Hy\n8HjxInl4vMhJfh8e7zYid1cSAFyaJndfywtp2QohhACkZWtIkmyFEEIAMkDKkCTZCiGEACTZGpIk\nWyGEEIB0IxuSJFshhBCAXPpjSJJshRBCAJJsDUmSrRBCCEC6kQ1JbhAqhBACyBogldtXXvzyyy90\n7NgRX19fPDw8aNSoEdOmTSMpKes2qRkZGSxevJguXbrg5+eHj48PnTt35rffftO7vKVLl9KgQQM8\nPT1p166d3rjk5GTGjh2Lr68vXl5e9OnTh1u3dG+ic+3aNXr06IGXlxd+fn5MmDCBJ0+e5Gn7ckOS\nrRBCCCCrGzm3r7xITEzEx8eHCRMmsGTJEoKDg9m4cSODBmU9/jA1NZWFCxdSqVIlpkyZwrfffkvJ\nkiXp3r07+/bt01rW0qVLmTlzJp07d2bhwoVUqFCB3r17c/HiRa24oUOHsnfvXr788ktmzpzJvXv3\nCAkJ0UqkarWa4OBgHj9+zKxZswgLC2Pbtm2MGjXqNfdgzqQbWQghBGC4buSPP/5Y629fX1/Mzc0Z\nO3Ysd+/exc7Ojj179mg90KVu3bpcu3aNZcuWUb9+fSDrGeHz588nODiYHj16AFCzZk1atWrF/Pnz\nmTUr62Eop06d4tdff2XRokUEBgYC4OrqSuPGjdm0aROdO3cGIDIyErVazebNmylWrBgAJiYmDBs2\njL59++Li4lJg+0BatkIIIQDDtWz1KVq0KABPnz7FxMRE58lpRkZGVKpUiXv37illx48fJykpiaCg\nIKXMxMSE5s2bEx0dTfbdh/fv34+NjQ3+/v5KXJkyZfD29iY6Olopi46Oxs/PT0m0AE2bNsXMzEwr\nriBIshVCCAFktWxz+3odGRkZpKWlcfbsWebOnUuDBg2wt7fXG5uZmcmJEydwcnJSymJiYgC0ygCc\nnZ1JSUnh7t27SpyjoyPGLzy32NnZmdjYWK3lOb/w7GMzMzMcHBy04gqCdCMLIYQA8nbpj1qtVp5y\n9jyVSoVKpdI7j6+vrzIoyt/fnxkzZuS4/FWrVnH16lUmTJigtU4zMzMKFy6sFZvdKk5ISKBUqVKo\n1Wq9T1pTqVQkJiZqLU9fXV+MKwiSbIUQQgB5u13jihUriIjQfQJV//79GTBggN55Vq1axZMnT7h8\n+TLz58+nT58+LF++HBMTE624o0eP8s033/Dpp59So0aNvG3EO0qSrRBCCCBv3cPdunWjbdu2OuU5\ntWoBKleuDIC3tzdVq1blww8/JCoqimbNmikxFy9epG/fvjRq1Ijhw4frLDs9PZ20tDTMzc2V8uxW\naJEiRZS4O3fu6KxfrVZrnRtWqVR6W+dqtRpHR8cct+N1SLIVQggB5K0b+WXdxblRuXJljI2NuXHj\nhlJ248YNevbsSZUqVQgPD9epT/a52piYGKpUqaKUx8TEYGVlRcmSJZW4w4cPo9FotJZx5coVrSTq\n5OSknAfOlp6ezo0bN2jXrt1rb5s+MkBKCCEEYPgBUs87ceIEmZmZygCp+/fv8+mnn2JnZ8e8efMw\nMzPTmcfb2xsbGxt27NihlGVkZLBz5078/f2VxBoYGIharebAgQNK3J07dzh+/DgBAQFKWUBAAEeO\nHOHRo0dKWVRUFOnp6colQwVFWrZCCCEAw90buUePHvj5+eHi4oK5uTkXLlxg6dKluLm50ahRI1JT\nU+nZsyfx8fGEhYVx5coVrfmrV68OZI0U/vzzz5k5cybFihWjSpUqrF+/nhs3bmgNtqpWrRr16tVj\n9OjRhIWFYW1tzaxZsyhdurRWi7VDhw6sXr2avn370rdvX+Lj45k6dSotWrTQGaWcX5JshRBCAIZL\nth4eHvz888/K7RLt7e3p0KED3bt3x8zMjFu3bil3gOrXr5/O/JcuXVL+nX0zi1WrVvHgwQNcXFxY\ntGgRlSpV0ppnxowZhIeHM27cONLT0/H19WXWrFlYWFgoMSqVihUrVjBx4kQGDBiAubk5QUFBOueK\nC4KRJvsqYPFOsvDq/7arIN4xj/7QHQEqBEDhfDaf6n13ONexvw6unb+V/ctIy1YIIQQgT/0xJEm2\nQgghAHmerSFJshVCCAFIy9aQJNkKIYQAwFiyrcFIshVCCAHk7XaNIm8k2QohhABAcq3hSLIVQggB\nyAApQ5JkK4QQApABUoYkyVYIIQQARki2NRRJtkIIIQA5Z2tIkmyFEEIAMhrZkCTZCiGEAOQ6W0OS\nZCuEEAKQAVKGJMlWCCEEIJf+GJIkWyGEEIC0bA1Jkq0QQggATCTbGowkWyGEEIB0IxuSJFshhBCA\nXGdrSJJshRBCANKyNSRJtkIIIQAZIGVIkmyFEEIA0rI1JEm2QgghADCRk7YGI8lWCCEEgDzzx4Ak\n2QohhADk3siGZPy2KyCEEOLdYGSU+1de7Ny5k759+xIYGEj16tVp1aoVP/zwA5mZmXrjz549S+XK\nlfHy8tKZ9vTpU2bMmEHdunWpVq0aXbp04cKFCzpx9+/fZ/Dgwbz//vvUqFGDYcOG8fDhQ52406dP\n07FjRzw9PfH392f27NlkZGTkbQNzQZKtEEIIIGuAVG5febF8+XLMzMz44osvWLBgAY0aNWLSpEl8\n8803OrGZmZl8/fXXFCtWTO+ypkyZwpo1axg4cCDz5s3D1NSUkJAQ7t69q8Q8e/aMnj178tdffzFt\n2jQmTpzIiRMn6Nu3LxqNRom7efMmISEh2NrasnDhQvr06cPSpUuZOXNmnrYvN6QbWQghBGC4S38W\nLFiglTz9/PxISUlhzZo1DBkyBDMzM2XaunXrSEpK4sMPP2TVqlVay7l79y6RkZGMHj2aTz75BIBq\n1arRsGFDVqxYwRdffAHAL7/8wsWLF9m2bRsuLi4AlChRgo4dOxIdHU1gYCAAS5YsQaVSMXv2bMzM\nzKhVqxZJSUnMnTuXnj17UqRIkQLbB9KyFUIIAWSNRs7tKy/0tVIrV65MWloaCQkJStnDhw+ZOXMm\no0ePxtTUVGeegwcPkpGRQYsWLZQya2tr6tevT3R0tFK2f/9+XF1dlUQL4O3tTdmyZdm/f79SFh0d\nTaNGjbSSfcuWLUlPT+fIkSN52sZXkZbtO+72oVlvuwriHbP25M23XQXxjupWo1y+5s9L97BarUat\nVuuUq1QqVCrVK+f/888/KVKkCMWLF1fKpk+fjre3NwEBAZw6dUpnnpiYGOzs7ChatKhWubOzM9u2\nbSMzMxNjY2NiYmJwdnbWmd/Z2ZnY2FgAUlJSuH37Nk5OTlox9vb2WFhYKHEFRZKtEEIIIG9dnStW\nrCAiIkKnvH///gwYMOCl8545c4ZNmzbRr18/TExMADhx4gTbt29n27ZtOc6nVquxsbHRKbe1teXp\n06ekpKRgbW2dY5xKpSImJgaApKQkpUxfXGJi4ku3Ia8k2QohhADy1rLt1q0bbdu21Sl/Vav2/v37\nDBw4EA8PD3r16gVARkYG48aNIyQkhHLl8tc6f1dJshVCCAHk7ak/ue0ufl5SUhK9evWicOHCzJ8/\nXzkvu27dOu7fv0+nTp2Urum0tDQgqzVrZmZG4cKFUalUSov0eYmJiZiammJpaanUTV+cWq3G1tYW\nQGn56usKfz6uoEiyFUIIARj2do1paWl8/vnnxMfHExkZqXXeNTY2lgcPHhAQEKAzn4+PD8HBwYwe\nPRonJyfi4+NJSEjQGikcExNDhQoVMDbO6gh3cnLSe+3tlStXqFevHgCWlpaUKVNG6VbOFhcXx5Mn\nT3B0dCyIzVZIshVCCAEY7nm2z549Y9CgQVy6dIlVq1ZRtmxZreldunShUaNGWmU//fQTO3bsYPHi\nxZQqVQqAunXrYmxszM6dO+nYsSMAjx8/Zu/evXz44YfKvIGBgWzZsoWYmBhlANTJkyeJi4tTLvsB\nCAgIYM+ePXzxxRfKiOTt27crlwEVJEm2QgghAMNdZzt+/Hj27dvH8OHDSU1N5eTJk8o0Z2dnypcv\nT/ny5bXmOXr0KCYmJvj6+iplJUuWpEOHDkyfPp1ChQpRpkwZli1bBmSdQ87WpEkT3NzcGDhwIKGh\noWRkZBAeHo6Xl5dW67lnz55s3bqVwYMH07VrV2JjY5k3bx7dunWTbmQhhBCGYah7Ix88eBBA7x2j\nVq5cqZVQX2XkyJFYWlry3XffkZSUhIeHB8uXL6dkyZJKTKFChViyZAmTJk1i+PDhGBkZUa9ePUaP\nHq01CKxcuXJ8//33TJ48md69e2Nra0v37t3p379/PrZWPyPN8/euEu+cRykFf49O8c/28/nbb7sK\n4h2V3+tsR+34K9exk1u45mtd/zbSshVCCAEYrhtZSLIVQgjxH/LweMORZCuEEAIw3GhkIclWCCHE\nf8jD4w1Hkq0QQghAztkakiRbIYQQgHQjG5IkWyGEEAAYIdnWUCTZCiGEAKBQXp6xJ/JEkq0QQggg\nb4/YE3kjyVYIIQQg52wNSZKtEEIIQEYjG5IkWyGEEIBcZ2tIkmyFEEIAYCIDpAxGkq0QQggAjOXS\nH4ORZCuEEAKQc7aGJMlWCCEEIKORDUmSrRBCCEAGSBmSJFshhBCAdCMbkiRbIYQQgDw83pAk2Qoh\nhABArvwxHEm2QgghALk3siFJshVCCAEgV9kakCRbIYQQgIxGNiRJtkIIIQBp2RqSnA8XQggBgLGx\nUa5feXX9+nXGjh3LBx98QJUqVWjZsqXeuLS0NObMmUOjRo1wd3fH39+fMWPG6MQtXbqUBg0a4Onp\nSbt27fjtt990YpKTkxk7diy+vr54eXnRp08fbt26pRN37do1evTogZeXF35+fkyYMIEnT57keRtf\nRlq2QgghAMO2vi5fvsz+/fupVq0amZmZaDQanZjMzEz69u3LtWvX6NOnD+XLl+fvv//m7NmzWnFL\nly5l5syZDBkyhCpVqrB+/Xp69+7N+vXrqVSpkhI3dOhQzp07x5dffom1tTWzZ88mJCSErVu3YmFh\nAYBarSY4OJgyZcowa9YsHj58yJQpU3j48CEzZ84ssO2XZCuEEAIw7GjkBg0a0KhRIwDCwsJ0EijA\nxo0bOXnyJDt27KBkyZJKeevWrZV/p6enM3/+fIKDg+nRowcANWvWpFWrVsyfP59Zs2YBcOrUKX79\n9VcWLVpEYGAgAK6urjRu3JhNmzbRuXNnACIjI1Gr1WzevJlixYoBYGJiwrBhw+jbty8uLi4Fsv3S\njSyEEALIOmeb21deGRu/Ot2sX7+eZs2aaSXaFx0/fpykpCSCgoKUMhMTE5o3b050dLTSYt6/fz82\nNjb4+/srcWXKlMHb25vo6GilLDo6Gj8/PyXRAjRt2hQzMzOtuPySZCuEEALIatnm9qVWq7l165bO\nS61Wv9a6nz59yvnz5ylbtiwjRozAy8uL6tWr07dvX27fvq3ExcTEAODk5KQ1v7OzMykpKdy9e1eJ\nc3R01Enyzs7OxMbGai3P2dlZK8bMzAwHBwetuPySbmQhhBAAmOShG3nFihVERETolPfv358BAwbk\ned0JCQk8ffqUxYsX4+3tzezZs1Gr1cyYMYPevXuzefNmChUqhFqtxszMjMKFC2vNb2trqyynVKlS\nqNVqbGxsdNajUqlITExU/lar1ahUqlfG5ZckWyGEEEDeuoe7detG27Ztdcr1Ja7cyMzMBMDKyoq5\nc+cqydTBwYGPPvqIqKgomjdv/lrLfhdIshVCCAHk7ak/KpXqtRNrTsszMjLC29tbq9Xq4eGBtbU1\nly9fpnnz5qhUKtLT00lLS8Pc3FyJy26FFilSRFnenTt3dNajVquVVnB2nL6ub7VajaOjY4Ftn5yz\nFUIIAYAxRrl+FTQLCwvKli2rd5qRkRFpaWnAf8/VZp+7zRYTE4OVlZUyuMrJyYmrV6/qXGJ05coV\nrSTq5OSks6z09HRu3LghyVYIIUTBMzLK/csQ6tevz59//klqaqpSdvr0aZKSkqhatSoA3t7e2NjY\nsGPHDiUmIyODnTt34u/vr1y+FBgYiFqt5sCBA0rcnTt3OH78OAEBAUpZQEAAR44c4dGjR0pZVFQU\n6enpyiVDBcHk66+//rrAliYKXOpT3Qu/xb/bpftJb7sK4h1VvYztq4Ne4vK9FIxy+Z9rCas8LfvJ\nkyfs2bOHK1eucOjQIR48eECpUqW4cuUKFhYWqFQqXFxcWLNmDb///jtFihTh9OnTfPXVV5QuXZpR\no0ZhYmKivBYsWEDhwoVJS0tj1qxZHD9+nPDwcOzs7AAoVaoUZ8+eZe3atZQsWZI7d+4wduxYzMzM\nmDBhAqampgC4uLiwceNGDhw4QMmSJTlx4gSTJ0+mQYMGdOrUKV/783lGGn238RDvjEcpGW+7CuId\n8/P5268OEv9K3WqUy9f8u87dz3Vss6rv5WnZt27domHDhnqnTZkyhXbt2gFw8eJFpkyZwsmTJzEz\nMyMwMJCwsDAliWZbunQpq1ev5sGDB7i4uDB8+HBq1aqlFZOcnEx4eDi7du0iPT0dX19fxowZQ7ly\n2vvp6tWrTJw4kT///BNzc3OCgoIYPny4cpepgiDJ9h0nyVa8SJKtyEl+k+3u87lPtk2r5C3Z/tvJ\naGQhhBCA4c7FCkm2Qggh/sNIHrJnMJJshUHtjdrNL7t2cPH8WR49ekjJUqWp16AR3Xp8hpWV9gCL\ns6dPsWThXM6ePsWzZ88oa29PSI/PaNyshRJzO+4Wc2ZO54/ff+PZs2dUcfdgwOBhVK7qrrWsxIQE\nli6ax8HoX4l/cJ9ixe2o4x9Ij959KfrcPVCF4anj7/Pbtkj+jv2LuzdieZaeRt/vVlPkvVJKTPTG\nFRzctErv/Campoz4fqfyd0pSInt/XMTl40d4mvqEEg6OBHzUDUdPnxzrcOuvc6wcPxg0GsJW7sbY\nxERr+tP0NH7bGsm5Q3tRx9/D3NKKMo5ufDjka0wKmeZzD/xzvMaT80QuSbIVBrVm1XJKlSpNnwGD\nKVGiFH9dusCSBXP589hRFn//g3Lf0kMH9jMidABNmrdk/ORwCpmacjU2hrT0NGVZiQkJfNa9C5ZW\nVowY8zWFCxfmx9Ur6Nc7hKWr1lLRMev6O41Gw7DB/bh5/Rq9Ph9ABUdHrsbGsHj+HC6cP8uSFT8a\n9OkmQtuju3FcOLKf0hVdKefmztUzf+rEVK/XAqcXkmV6Wiprw0fi4v3fQS/PnqazZtIwniSpadCx\nF9a2xTj5607WTR9Dx7BplK9SXWfZGc+esXPpTKxsi/I44aHe6WvDR5Jw729qt+6IXdnypCQlcPXM\ncTIzMzHRmeN/l7F8LwxGkq0wqOnfzdNqSXrX8EGlsmX82JEcP3aUGjX9ePz4MRO/Gs2Hn3RkyPCR\nSmxNv9pay9q4PpKHD+OZv3Ql9uUcAKhR05d2LZuyZEEEk8Kznj1588Z1zpw6QdiYr2nz4ScAvF+j\nJsZGRoRPHs+N69coX6GioTdd/IdDJU8Gz98AwMl9O/QmW1Xx91AV1x5wc+ZAFJkZGXj6N1HKLv4e\nzf2bV+k8erqSWB2rFZ3AFAAAELlJREFU+bBkZG/2/riY7hPm6iz7yPZ1aIBqgc04vOUHnem/71jP\n31ev0Dt8CariJZTySjUDdGL/10k3suHITS2EQenrss3u8r1/7x4Ae6N28ejRQzp1DXnpss6dOYW9\nQ3kl0QJYWFhS3et9Dh7Yz7Nnz4Csp4cAWFlZa81vY5N1a7nse7CKN8MoF49W0+fMgV+wsi2q1T0c\nd+UChczMtVqwRkZGOHrU4E7sJZIePtBaxqO7tzm0eQ3NQgbqdB1nO/5/P1PZN0Ar0f5bGRvl/iXy\nRpLtG3L79m02b978tqvxTjjx5x8AVKiYdSu0UyePo7K1JebyX3T++APq1PCgdbMGLFk4l4yM/176\nZGxsjKme82emZqakpaYSd+smAI5Oznh512DZ4vlcOHeWlJTHnDt7mqWL5lGrjr/S3SzeXer4e1w/\nf4qqtRtqJUkjY2NM9CRNk//coOD+rata5TuXfUdl3wAcKnvqXU/ig7uo4+9TpERpdiyewfQerZkW\n0pw1k4dz99qVAtyif4bc3tBCWsB5J8n2DTlz5gwjR458deD/uHv37rJ4fgQ+vrWUFu6D+/dJS01l\n7KgvaNGqDbMXLKVFqw9YvngBc2Z+o8zrUKEiN29eJzEhQSnLzMzk/NkzAKj/cyNyIyMjvo1YgEOF\ninTv8gkN6vjQo2sHytqXY8r0WW9wa8XrOnvw/9BoMvEMaKJVXry0PWlPUngQd12rPO7yeQCeJCdp\nLePvq5dp0LF3jutJfhQPwG9b1/Lo/t+0GTCaNv1Gk6JOYPWkoSQ+uFtQm/SP8LZv1/i/TJKteGNS\nUh7zxeD+mJiY8OW4SUp5ZmYmaWlpfNr7czoHd+f9GjXp028Qrdt+xIa1P5CclHUAbfdRezSZmYz7\nMoxbN2/w4P59vg2fzJ3bcQAYPde3NWX8V5w7fYoRo79i/pKVjBj9FRfOn2XU8MHSjfwPcObg/1Gy\ngjMlHLRvBF+1dkMsbGzZuiCcezdiSUlK5NCWH7hx8fT/t3fv4TXfCR7H30kkkYsTgiRN3HIt6UQo\nHjGMEEw96LRU6cW4lG4r6Hhcqm06ZUc0zUyNzUjV0q5BWWtml3ZDlWqfpHbLWm3HpSzOESE6LpHk\nCMmTE8n+cZrTOU54kolfzlGflyd/5Pv7/n75nucJH9/v73sBcEx8q6yw8umm1QyZ8BxBIe1u+3Pq\n9/Tx9fdnwvylxPXqz4P9BjFx4TJqqqs5tOcjgz6hZ/Jqwpc0jSZINdOjjz7aqHrXr183uCWeraqq\nigW/msWF4nOsem8DYeE/LPsICbEfiXXrhKj+Away7c//hsV8mp69ehPVqTNLlv2Wt99ayvhfjATg\nwR6JPPXsZDZtWEeHDvYJNv/1RT67d+1g5er36dffPpO1d5++RHbqzK9mzmBf/ucMHtrwtnHifhfM\nJyi5UMTwX6a7XGsdFMwTc5eQtzqb916191jbhUfys3GTKfjzHwlu2x6A/D+tI7htKD1SUqm6XgHA\nTVs1AFU3Kmjl64df6wACgu3v8TslPISv/w/Hupnah9E+svN9N5TclMPjpWkUts1ksViIi4sjMTHx\njvWKi4sbPFvxflBjs/Hawrmc+PYof3j3feLiE5yuR8fG3fF+77+ZYJM2/OekDh1G0dlCfH196dS5\nC9nL/pHwiAgiHogEwHzqJACJDyU5Peeh778vPGNR2HqwwwW78fZpxUM/TWvwepfuScxcsZHSi8XU\n1tbSPqIT+3dspZWfPxHR8QBcKT7LpSILK14Y53L/P734BAl9fsr4eb+hbdgDtPLzd6lTz+t+mwl0\nn33clqSwbab4+Hi6du1KVlbWHet98sknHDx4sIVa5Tlqa2tZnPEyhw4e4O2cVfykZ7JLndShw1iz\n6g8c+HKfUxDv/+8v8Pf3JybOOYx9fHwck5wuX7rE3t0f8+zk5xzXQ7/fsPzY0cNOveVjR+1DjR3D\nwu/eB5S76maNjeP7Pyc2uR9Bpra3refl5UVoRCcAqqsq+ebznfxk0HD8Wts3jh8xKZ2qGxVO9xwu\n2M2RL3bz9Ku/dQwt+7RqRVyv/hSdOEx1VaXj/vIrFym5cM5pje/9QBOfjKOwbaaePXs6nZd4J/fj\nmQ+/y1rK3j2fMHXGCwQEBHL08F8c18LCwwkLjyA2Lp7Rv3icNe/mUltbx4Pde3DwwH4+2vbvTHv+\nRQID7TtN1dhs5OYsp3efvgQFBWMxn2bDv6wlOjaOZyZPdTx3aNoI/jk3h9/8+lWmPT+TbtHRFJ45\nw/tr3iE8IoLUNPVqW9rxAwUAfHfGPupg/sv/ENimLYGmELr2+OE/YKe+3k9lxTWSbpkY9bc+3/Ie\nEdEJBLYxUXrxAvt3bMXbpxVDJ0531Anv5jpacva4/Xeva49kpxnOP3tiCn98YxZb386g/6gnqbFV\n88V/bMQ/MIg+P3+8eR/8HqNRZOPo1J9mKioq4tSpU7c9OqpeVVUVJSUlREVFNen59/qpP4+PGs5f\nv2v4lJrpL6Tz/IuzAbDZqnl/zbvs/M8PuVpyhQcioxg/8RkmPvNLR/2amhoWzZvDt8eOUnHNSlh4\nBCNGjmLqc/9A61uOwrr41+94b/U7/O/BA5RcuUz7Dh3p138AM16cRdg93rO9F0/9efPZ4Q2Wd+nR\nk0mv/97x/Z+W/5rzJ4/y0jtbb7tNYt6a33Hm8CGuW8sIMrUlod9ABj8xxfH+9Xbqt4RsaLvGC+YT\nfPava7lgPoGPjw9dE3uR9swLhEY07e+ruzX31J+DlvJG1+0X07yzc+83ClsPd6+Hrdx992LYSsto\ndtieaULYRitsm0LDyCIiAmhvZCMpbEVEBNBkZCMpbEVExE5paxiFrYiIAFr6YySFrYiIAFr6YySF\nrYiIAApbIylsRUQE0DCykRS2IiICqGdrJIWtiIgAmoxsJJ1nKyIidgYeaPvpp58yfvx4evfuzcCB\nA5kzZw6FhYUu9bZv387IkSNJSkpi9OjR7Ny506WOzWZj+fLlDBo0iOTkZCZNmsTx48dd6l2+fJm5\nc+fSp08f+vbty4IFC7h69WrTG38XKGxFRASwv7Nt7J+m+PLLL5k9ezYxMTHk5uby+uuvY7FYmDZt\nGhUVP5zOtGvXLhYtWsSIESNYu3YtAwYMYN68eeTn5zs9Lysri02bNvHSSy+xatUqfH19mTp1Khcv\nXnTUqampYcaMGZw8eZLs7GwyMzP5+uuvSU9Pd8uhMBpGFhERAIw6vjcvL4/IyEiys7Px+v7FcFRU\nFE8++SSHDh0iNTUVgJycHEaOHMn8+fMBSElJwWKxsHLlSkedixcvsmXLFjIyMpgwYQIAycnJDBs2\njPXr1/Pyyy8DsHv3bk6cOEFeXh7x8fZzjsPCwnj66acpKChwPK+lqGcrIiJ2Bg0j19TUEBQU5Aha\ngDZt2jjVOXfuHBaLhdGjRzuVjxkzhiNHjjiGf/ft28fNmzcZNWqUo05wcDBDhw6loKDAUZafn09C\nQoIjaAEefvhhoqKiXHrKLUFhKyIiQNOGka1WK+fPn3f5slqtLs8dO3YsFouFjRs3Ou7Lzs4mNjaW\nAQMGAGCxWACIjY11ujcuLs7putlspkOHDrRr186lXmFhIbW1tY569ffeWq/+WS1Jw8giIgI0benP\n+vXryc3NdSmfPXs2c+bMcSpLSUlh5cqVLFiwgMzMTAASEhJYt24dfn5+AJSX24/3M5mczyUOCQlx\num61Wl16xfX1bDYbN27cIDg4+Lb1TCYTZrO58R/0LlHYiogI0LTR4SlTpjB27FiX8lvDEuCrr75i\n0aJFjB8/nrS0NMrKyli1ahUzZ85k8+bNtG7duhmtvjcobEVExK4JaWsymRoM1oZkZmbSv39/Xnvt\nNUdZr169GDJkCB9++CETJ0509GCtVisdO3Z01Kvv0dZfN5lMXLt2zeVnlJeX4+vrS2Bg4B3rWa1W\nx7Nakt7ZiogIYD88vrFfTWE2m+nevbtTWUREBO3ataOoqAiAmJgYAJf3qfVDvvXXY2NjKSkpoays\nzKVet27d8Pb2dtRraLj49OnTjme1JIWtiIgAxu1pERkZybFjx5zKiouLKS0tJSoqCoDOnTsTExPj\nsolFXl4eSUlJhIaGAjBo0CC8vb35+OOPHXWuX7/OZ599xuDBgx1lqampnDx50ilwv/nmG4qLi1t8\n2Q+Az5IlS5a0+E+VRquytfzia/Fs/3fZdWhMBKBXZPOGR0tv1DQ6bUODfJv07PXr11NWVoavry9H\njhxhyZIleHl5sXjxYgICAgBo3749ubm52Gw2vL292bBhA3l5eSxdupRu3boB9mU+JSUlrFu3jtDQ\nUMrKynjzzTe5dOkS2dnZBAcHAxAdHc3evXvZsWMHERERnD59msWLFxMbG8v8+fOdliG1BK86d2yl\nIY1WeuOmu5sgHuajby+4uwnioab07dys+y2XqxpdN6Zj4yc11dXVsXXrVjZv3kxRURFBQUEkJycz\nb948l6U+27ZtY/Xq1RQXF9OlSxdmzZrlsvbWZrORk5PDtm3buHbtGklJSWRkZJCYmOhU7/Llyyxb\ntoyCggK8vLwYMmQIGRkZjl5yS1LYejiFrdxKYSu309ywPXOl8WEb3eHHP4P4btJsZBERAXTEnpEU\ntiIiAujweCMpbEVEBFDP1kgKWxERAXR4vJEUtiIiAqhnaySFrYiIfE9paxSFrYiIAMYdHi8KWxER\n+Z6GkY2jsBUREUBLf4yksBURETtlrWEUtiIiAihrjaSwFRERQO9sjaSwFRERgBY/du5+orAVERFA\nw8hGUtiKiAigYWQjKWxFRATQ0h8jKWxFRARQz9ZIClsREQEUtkZS2IqICKBhZCMpbEVEBFDP1kgK\nWxERAbT0x0gKWxERsVPaGkZhKyIigN7ZGklhKyIigA6PN5LCVkRE7BS2hlHYiogIoGFkI3nV1dXV\nubsRIiIiP2be7m6AiIjIj53CVkRExGAKWxEREYMpbEVERAymsBURETGYwlZERMRgClsRERGDKWxF\nREQMprAVERExmMJWPFphYSHTp0+nd+/epKSksHTpUiorK93dLHGzs2fP8sYbb/DYY4+RmJjImDFj\n3N0kkTvS3sjisaxWK5MnTyYyMpKcnByuXr1KVlYWV69eZcWKFe5unrjRqVOnyM/PJzk5mdraWrTr\nrHg6ha14rC1btmC1Wtm+fTuhoaEA+Pj4sGDBAtLT04mPj3dzC8Vd0tLSGD58OACvvPIKR48edXOL\nRO5Mw8jisQoKCkhJSXEELcAjjzyCn58fBQUFbmyZuJu3t/7pknuLfmPFY5nNZuLi4pzK/Pz86NKl\nCxaLxU2tEhFpOoWteCyr1YrJZHIpN5lMlJeXu6FFIiJ/H4WtiIiIwRS24rFMJhNWq9Wl3Gq1EhIS\n4oYWiYj8fRS24rFiY2Mxm81OZdXV1RQVFRETE+OmVomINJ3CVjzW4MGD2b9/P6WlpY6yPXv2UF1d\nTWpqqhtbJiLSNFpnKx7rqaee4oMPPiA9PZ309HRKSkp46623GDVqlMssZbm/VFZWkp+fD0BxcTEV\nFRXs2rULgKSkJKKiotzZPBEXXnXaekU82JkzZ8jMzOTQoUP4+/szevRoFi5cSEBAgLubJm50/vx5\nhg0b1uC1rKwsxo0b18ItErkzha2IiIjB9M5WRETEYApbERERgylsRUREDKawFRERMZjCVkRExGAK\nWxEREYMpbEVERAymsBURETGYwlZERMRg/w8HIehNbvI7rgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Now the most interesting, optimized part: \n",
            "\n",
            " ComplementNB  Variance Threshold method: \n",
            "\n",
            "Number of mislabeled points out of a total 55219 points : 2638\n",
            "Accuracy  95.22265886741882 %\n",
            "Start time is 1579740516.1862817 seconds\n",
            "End time is 1579740516.4141312 seconds\n",
            "Exec: 0.22784948348999023 seconds\n",
            "Matrix size:  (138047, 54)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Yc+fOSa1bGX9/f8bb21vmsrNnzzIikYjx9vZmSkpK2PSNGzcyIpGIefjwIacckUjEbNmy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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " ComplementNB  SelectKBest features method: \n",
            "\n",
            "Number of mislabeled points out of a total 13805 points : 523\n",
            "Accuracy  96.21151756609923 %\n",
            "Start time is 1579740516.7442021 seconds\n",
            "End time is 1579740516.8466368 seconds\n",
            "Exec: 0.10243463516235352seconds\n",
            "Matrix size:  (138047, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GOH/+PO7du5fv9NjYWCQkJMDX1xdPnjwR15meno4WLVrg/PnzBXZLajpvTk4O9u3bB3d3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\nSJIkDAJJkoRBIEmSMAgkSRIGgSRJAr4B8Jq8GavVnSEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "-----------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sHU-4I2ameL",
        "colab_type": "text"
      },
      "source": [
        "## Conclusions\n",
        "\n",
        "As can be seen, Gaussian Naive Bayes is far more inaccurate than others: 70% score in contrary to over 95% of two remaining models.Taking this fact into account, it will no longer be considered.\n",
        "\n",
        "Authors of Complement Naive Bayes Classifier said: \"[CNB] was designed to correct the “severe assumptions” made by the standard Multinomial Naive Bayes classifier. It is particularly suited for imbalanced data sets.\"\n",
        "So conclusion is: in that particular situation the dataset that is given is not that imbalanced to have this algorithm applied, because the effects are the same as Multinomial's NB Classifier. Nonetheless in case of a different dataset it is recommended to use that as well, or even in particular. \n",
        "\n",
        "A last thing to do is finding the most accurate and least time-consuming algorithm. As mentioned above, MNB and CNB return the same outputs, so the best result is reached by Multinomial/Complement Naive Bayes Classifier with SelectKBest (where k belongs to <5,8>) Feature Selection, which gives 96.21% of accuracy executed in 0.10 seconds\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snLg9blFk7eG",
        "colab_type": "text"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSOUO6WGbayt",
        "colab_type": "text"
      },
      "source": [
        "# Project summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XC-g-7HxoCE-",
        "colab_type": "text"
      },
      "source": [
        "In terms of time efficiency, *Naive Bayes* occured to be best, while *KNN* has proved to be the most accurate. Despite this, in our opinion, the optimal of these three algorithms is *Decision Tree*, because it obtained efficiency close to *KNN* over thirty times faster, and is only 3 seconds slower than the Naive Bayes.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IKPQCnwMb9rS",
        "colab_type": "text"
      },
      "source": [
        "Algorithm || Time || Accuracy\n",
        "----||---------------||------------\n",
        "KNN || 110s  || 99,36%\n",
        "Decision Tree || 3.59s || 99,19%\n",
        "Naive Bayes || 0.1s || 96,21%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aL3u7-MxdXEV",
        "colab_type": "text"
      },
      "source": [
        "As a result of the research, we come to the conclusion, that Machine Learning techniques and algorithms apply to malware detection problems.\n",
        "\n",
        "The whole project carried out by the team is held in the *static analysis* manner, which means Machine Learning CAN be a great tool to discover malware files, but only if the signature of malware and proper features are known.\n",
        "\n",
        "To apply this approach to real-world problems, we need far more advanced algorithms and approaches, such as *dynamic analysis* and deep-learning algorithms.\n",
        "\n",
        "The project topic still remains open for further and more-depth research."
      ]
    }
  ]
}