system1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments 1:\n",
    "### generating byte-to-integers representations derived from file first blocks to train and evaluate our models - FFNN, CNN, GRU, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from toolkit.utils import get_file_types, get_1st_block_bytes, pad_array, convert_cat2num\n",
    "from toolkit.models import Ffnn, Cnn, Lstm, Gru\n",
    "from toolkit.metrics import evaluate_performance, plot_confusion_matrix\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_path = 'govdocs1/'\n",
    "files_data = get_file_types(dir_path)\n",
    "df = pd.DataFrame(files_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 733699 entries, 0 to 733698\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   file    733699 non-null  object\n",
      " 1   type    733699 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>733699</td>\n",
       "      <td>733699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>733699</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>000000.swf</td>\n",
       "      <td>html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>185302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              file    type\n",
       "count       733699  733699\n",
       "unique      733699      60\n",
       "top     000000.swf    html\n",
       "freq             1  185302"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['swf', 'doc', 'ppt', 'pdf', 'html', 'csv', 'xls', 'xml', 'txt',\n",
       "       'unk', 'ps', 'jpg', 'wp', 'rtf', 'f', 'gif', 'dbase3', 'png', 'gz',\n",
       "       'log', 'java', 'pps', 'text', 'tex', 'gls', 'eps', 'hlp', 'fits',\n",
       "       'dwf', 'sql', 'kml', 'kmz', 'bmp', 'sgml', 'troff', 'docx', 'vrml',\n",
       "       'ttf', 'pptx', 'xlsx', 'data', 'py', 'zip', 'wk1', 'g3', 'tmp',\n",
       "       'pub', 'fm', 'odp', 'sys', 'js', '123', 'exported', 'pst', 'ileaf',\n",
       "       'xbm', 'chp', 'lnk', 'wk3', ''], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: count, dtype: int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing data\n",
    "df.loc[df['type'].isna(), 'file'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASS_NAMES = [\n",
    "    \"doc\",\n",
    "    \"pdf\",\n",
    "    \"ps\",\n",
    "    \"xls\",\n",
    "    \"ppt\",\n",
    "    \"swf\",\n",
    "    \"gif\",\n",
    "    \"jpg\",\n",
    "    \"png\",\n",
    "    \"html\",\n",
    "    \"txt\",\n",
    "    \"xml\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "html    185302\n",
       "pdf     176094\n",
       "txt      72909\n",
       "jpg      53522\n",
       "doc      53274\n",
       "xls      46835\n",
       "ppt      39457\n",
       "xml      26047\n",
       "gif      14152\n",
       "ps       12765\n",
       "swf       3111\n",
       "png       2482\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the dataset to keep the targeted classes\n",
    "df = df[df['type'].isin(CLASS_NAMES)]\n",
    "df[\"type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average file size is: 522.8481363551462\n"
     ]
    }
   ],
   "source": [
    "# get file sizes in KB\n",
    "df['size KB'] = df['file'].apply(\n",
    "    lambda x: float(os.path.getsize(\n",
    "        os.path.join(os.getcwd(), f\"govdocs1/{x}\")\n",
    "    ) / 1024) if os.path.isfile(\n",
    "        os.path.join(os.getcwd(), f\"govdocs1/{x}\")\n",
    "    ) else None\n",
    ")\n",
    "\n",
    "print(f\"The average file size is: {df['size KB'].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19616\\350564064.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  df = df.groupby('type').apply(lambda x: x.sample(n=2000, random_state=42)).reset_index(drop=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "type\n",
       "doc     2000\n",
       "gif     2000\n",
       "html    2000\n",
       "jpg     2000\n",
       "pdf     2000\n",
       "png     2000\n",
       "ppt     2000\n",
       "ps      2000\n",
       "swf     2000\n",
       "txt     2000\n",
       "xls     2000\n",
       "xml     2000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample 2000 files for each targeted file type\n",
    "df = df.groupby('type').apply(lambda x: x.sample(n=2000, random_state=42)).reset_index(drop=True)\n",
    "df[\"type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>type</th>\n",
       "      <th>size KB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22634</th>\n",
       "      <td>482494.xml</td>\n",
       "      <td>xml</td>\n",
       "      <td>31.335938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19325</th>\n",
       "      <td>491204.txt</td>\n",
       "      <td>txt</td>\n",
       "      <td>37.017578</td>\n",
       "    </tr>\n",
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       "      <th>10398</th>\n",
       "      <td>017085.png</td>\n",
       "      <td>png</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>11819</th>\n",
       "      <td>734397.png</td>\n",
       "      <td>png</td>\n",
       "      <td>7.497070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>724968.xls</td>\n",
       "      <td>xls</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5320</th>\n",
       "      <td>399203.html</td>\n",
       "      <td>html</td>\n",
       "      <td>11.962891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7415</th>\n",
       "      <td>132721.jpg</td>\n",
       "      <td>jpg</td>\n",
       "      <td>152.493164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12521</th>\n",
       "      <td>086258.ppt</td>\n",
       "      <td>ppt</td>\n",
       "      <td>1487.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10122</th>\n",
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       "      <td>png</td>\n",
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       "    <tr>\n",
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       "      <td>716069.gif</td>\n",
       "      <td>gif</td>\n",
       "      <td>23.551758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              file  type      size KB\n",
       "22634   482494.xml   xml    31.335938\n",
       "19325   491204.txt   txt    37.017578\n",
       "10398   017085.png   png    58.016602\n",
       "11819   734397.png   png     7.497070\n",
       "20626   724968.xls   xls    41.000000\n",
       "5320   399203.html  html    11.962891\n",
       "7415    132721.jpg   jpg   152.493164\n",
       "12521   086258.ppt   ppt  1487.500000\n",
       "10122   719727.png   png   103.920898\n",
       "3596    716069.gif   gif    23.551758"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the file first block bytes\n",
    "base_dir = os.path.join(os.getcwd(), \"govdocs1\")\n",
    "df[\"1st_block_bytes\"] = df[\"file\"].apply(lambda file: get_1st_block_bytes(os.path.join(base_dir, file)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>type</th>\n",
       "      <th>size KB</th>\n",
       "      <th>1st_block_bytes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23550</th>\n",
       "      <td>565661.xml</td>\n",
       "      <td>xml</td>\n",
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       "      <td>ps</td>\n",
       "      <td>34780.132812</td>\n",
       "      <td>b'%!PS-Adobe-2.0\\n%%Creator: dvipsk 5.55a Copy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11473</th>\n",
       "      <td>189048.png</td>\n",
       "      <td>png</td>\n",
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       "      <td>b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11973</th>\n",
       "      <td>338816.png</td>\n",
       "      <td>png</td>\n",
       "      <td>3.418945</td>\n",
       "      <td>b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20980</th>\n",
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       "      <td>xls</td>\n",
       "      <td>51.500000</td>\n",
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       "    <tr>\n",
       "      <th>9064</th>\n",
       "      <td>280713.pdf</td>\n",
       "      <td>pdf</td>\n",
       "      <td>66.274414</td>\n",
       "      <td>b'%PDF-1.4\\r%\\xe2\\xe3\\xcf\\xd3\\r\\n273 0 obj &lt;&lt;/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13677</th>\n",
       "      <td>495546.ppt</td>\n",
       "      <td>ppt</td>\n",
       "      <td>1848.000000</td>\n",
       "      <td>b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6672</th>\n",
       "      <td>392777.jpg</td>\n",
       "      <td>jpg</td>\n",
       "      <td>875.758789</td>\n",
       "      <td>b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x02\\x01...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7433</th>\n",
       "      <td>250854.jpg</td>\n",
       "      <td>jpg</td>\n",
       "      <td>17.752930</td>\n",
       "      <td>b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x02\\x01...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>734975.gif</td>\n",
       "      <td>gif</td>\n",
       "      <td>2.908203</td>\n",
       "      <td>b'GIF87a\\x10\\x01\\x18\\x01\\xf1\\x00\\x00\\xff\\xff\\x...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file type       size KB  \\\n",
       "23550  565661.xml  xml     18.693359   \n",
       "15065   481192.ps   ps  34780.132812   \n",
       "11473  189048.png  png    275.191406   \n",
       "11973  338816.png  png      3.418945   \n",
       "20980  508655.xls  xls     51.500000   \n",
       "9064   280713.pdf  pdf     66.274414   \n",
       "13677  495546.ppt  ppt   1848.000000   \n",
       "6672   392777.jpg  jpg    875.758789   \n",
       "7433   250854.jpg  jpg     17.752930   \n",
       "2556   734975.gif  gif      2.908203   \n",
       "\n",
       "                                         1st_block_bytes  \n",
       "23550  b'<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\\...  \n",
       "15065  b'%!PS-Adobe-2.0\\n%%Creator: dvipsk 5.55a Copy...  \n",
       "11473  b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...  \n",
       "11973  b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...  \n",
       "20980  b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...  \n",
       "9064   b'%PDF-1.4\\r%\\xe2\\xe3\\xcf\\xd3\\r\\n273 0 obj <</...  \n",
       "13677  b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...  \n",
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       "7433   b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x02\\x01...  \n",
       "2556   b'GIF87a\\x10\\x01\\x18\\x01\\xf1\\x00\\x00\\xff\\xff\\x...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4096\n",
       "1        4096\n",
       "2        4096\n",
       "3        4096\n",
       "4        4096\n",
       "         ... \n",
       "23995    4096\n",
       "23996    4096\n",
       "23997    4096\n",
       "23998    4096\n",
       "23999    4096\n",
       "Name: 1st_block_bytes, Length: 24000, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# blocks on the disk of this computer contain 4096 bytes (4KB)\n",
    "df[\"1st_block_bytes\"].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>type</th>\n",
       "      <th>size KB</th>\n",
       "      <th>1st_block_bytes</th>\n",
       "      <th>byte_integers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9646</th>\n",
       "      <td>480196.pdf</td>\n",
       "      <td>pdf</td>\n",
       "      <td>295.299805</td>\n",
       "      <td>b'%PDF-1.5\\r%\\xe2\\xe3\\xcf\\xd3\\r\\n2823 0 obj&lt;&lt;/...</td>\n",
       "      <td>[37, 80, 68, 70, 45, 49, 46, 53, 13, 37, 226, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>149768.doc</td>\n",
       "      <td>doc</td>\n",
       "      <td>474.000000</td>\n",
       "      <td>b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...</td>\n",
       "      <td>[208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3159</th>\n",
       "      <td>552056.gif</td>\n",
       "      <td>gif</td>\n",
       "      <td>137.440430</td>\n",
       "      <td>b'GIF87a\\xd0\\x02X\\x02\\xf7\\x00\\x00\\x00\\x00\\x00\\...</td>\n",
       "      <td>[71, 73, 70, 56, 55, 97, 208, 2, 88, 2, 247, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15963</th>\n",
       "      <td>195497.ps</td>\n",
       "      <td>ps</td>\n",
       "      <td>4055.626953</td>\n",
       "      <td>b'%!PS-Adobe-2.0\\n%%Creator: dvips(k) 5.86 Cop...</td>\n",
       "      <td>[37, 33, 80, 83, 45, 65, 100, 111, 98, 101, 45...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8126</th>\n",
       "      <td>145145.pdf</td>\n",
       "      <td>pdf</td>\n",
       "      <td>2657.946289</td>\n",
       "      <td>b'%PDF-1.2\\r%\\xe2\\xe3\\xcf\\xd3\\r\\n1 0 obj\\r&lt;&lt;\\r...</td>\n",
       "      <td>[37, 80, 68, 70, 45, 49, 46, 50, 13, 37, 226, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file type      size KB  \\\n",
       "9646   480196.pdf  pdf   295.299805   \n",
       "846    149768.doc  doc   474.000000   \n",
       "3159   552056.gif  gif   137.440430   \n",
       "15963   195497.ps   ps  4055.626953   \n",
       "8126   145145.pdf  pdf  2657.946289   \n",
       "\n",
       "                                         1st_block_bytes  \\\n",
       "9646   b'%PDF-1.5\\r%\\xe2\\xe3\\xcf\\xd3\\r\\n2823 0 obj<</...   \n",
       "846    b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...   \n",
       "3159   b'GIF87a\\xd0\\x02X\\x02\\xf7\\x00\\x00\\x00\\x00\\x00\\...   \n",
       "15963  b'%!PS-Adobe-2.0\\n%%Creator: dvips(k) 5.86 Cop...   \n",
       "8126   b'%PDF-1.2\\r%\\xe2\\xe3\\xcf\\xd3\\r\\n1 0 obj\\r<<\\r...   \n",
       "\n",
       "                                           byte_integers  \n",
       "9646   [37, 80, 68, 70, 45, 49, 46, 53, 13, 37, 226, ...  \n",
       "846    [208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...  \n",
       "3159   [71, 73, 70, 56, 55, 97, 208, 2, 88, 2, 247, 0...  \n",
       "15963  [37, 33, 80, 83, 45, 65, 100, 111, 98, 101, 45...  \n",
       "8126   [37, 80, 68, 70, 45, 49, 46, 50, 13, 37, 226, ...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert bytes to byte-to-integers\n",
    "df[\"byte_integers\"] = df[\"1st_block_bytes\"].apply(lambda byte_sequence: np.array([byte for byte in byte_sequence]))\n",
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pad arrays since for this experiment files can be smaller than 4kb\n",
    "# this means that null byte values could be in these files\n",
    "df[\"byte_integers\"] = df[\"byte_integers\"].apply(lambda arr: pad_array(arr, length=4096))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "byte lengths of each sequence:\n",
      " 0        4096\n",
      "1        4096\n",
      "2        4096\n",
      "3        4096\n",
      "4        4096\n",
      "         ... \n",
      "23995    4096\n",
      "23996    4096\n",
      "23997    4096\n",
      "23998    4096\n",
      "23999    4096\n",
      "Name: byte_integers, Length: 24000, dtype: int64\n",
      "all byte sequences of the same length: True\n"
     ]
    }
   ],
   "source": [
    "# check that our arrays are homogeneous\n",
    "byte_lengths = df[\"byte_integers\"].apply(len)\n",
    "all_same_length = byte_lengths.nunique() == 1\n",
    "\n",
    "print(\"byte lengths of each sequence:\\n\", byte_lengths)\n",
    "print(\"all byte sequences of the same length:\", all_same_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "doc     2000\n",
       "gif     2000\n",
       "html    2000\n",
       "jpg     2000\n",
       "pdf     2000\n",
       "png     2000\n",
       "ppt     2000\n",
       "ps      2000\n",
       "swf     2000\n",
       "txt     2000\n",
       "xls     2000\n",
       "xml     2000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>type</th>\n",
       "      <th>size KB</th>\n",
       "      <th>1st_block_bytes</th>\n",
       "      <th>byte_integers</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3687</th>\n",
       "      <td>399745.gif</td>\n",
       "      <td>gif</td>\n",
       "      <td>9.458984</td>\n",
       "      <td>b'GIF89al\\x00l\\x00\\xe6\\x00\\x00\\x92uukLL\\xff\\xf...</td>\n",
       "      <td>[71, 73, 70, 56, 57, 97, 108, 0, 108, 0, 230, ...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>547411.doc</td>\n",
       "      <td>doc</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...</td>\n",
       "      <td>[208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7490</th>\n",
       "      <td>124391.jpg</td>\n",
       "      <td>jpg</td>\n",
       "      <td>580.023438</td>\n",
       "      <td>b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x02\\x00...</td>\n",
       "      <td>[255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0,...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7718</th>\n",
       "      <td>685569.jpg</td>\n",
       "      <td>jpg</td>\n",
       "      <td>11.165039</td>\n",
       "      <td>b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x01...</td>\n",
       "      <td>[255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0,...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20300</th>\n",
       "      <td>601280.xls</td>\n",
       "      <td>xls</td>\n",
       "      <td>282.500000</td>\n",
       "      <td>b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...</td>\n",
       "      <td>[208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21820</th>\n",
       "      <td>622215.xls</td>\n",
       "      <td>xls</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...</td>\n",
       "      <td>[208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18445</th>\n",
       "      <td>101302.txt</td>\n",
       "      <td>txt</td>\n",
       "      <td>268.545898</td>\n",
       "      <td>b\"&lt;DOC&gt;\\n[October 31,2005  (Volume 70, Number ...</td>\n",
       "      <td>[60, 68, 79, 67, 62, 10, 91, 79, 99, 116, 111,...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14400</th>\n",
       "      <td>030792.ps</td>\n",
       "      <td>ps</td>\n",
       "      <td>760.114258</td>\n",
       "      <td>b'%!PS-Adobe-3.0\\n%%Title: letters.p65\\n%%Crea...</td>\n",
       "      <td>[37, 33, 80, 83, 45, 65, 100, 111, 98, 101, 45...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>059107.gif</td>\n",
       "      <td>gif</td>\n",
       "      <td>49.410156</td>\n",
       "      <td>b'GIF87a \\x03\\x84\\x03\\xf4\\x00\\x00\\xff\\xff\\xff\\...</td>\n",
       "      <td>[71, 73, 70, 56, 55, 97, 32, 3, 132, 3, 244, 0...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>318471.ppt</td>\n",
       "      <td>ppt</td>\n",
       "      <td>3282.000000</td>\n",
       "      <td>b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...</td>\n",
       "      <td>[208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file type      size KB  \\\n",
       "3687   399745.gif  gif     9.458984   \n",
       "710    547411.doc  doc   440.000000   \n",
       "7490   124391.jpg  jpg   580.023438   \n",
       "7718   685569.jpg  jpg    11.165039   \n",
       "20300  601280.xls  xls   282.500000   \n",
       "21820  622215.xls  xls    39.500000   \n",
       "18445  101302.txt  txt   268.545898   \n",
       "14400   030792.ps   ps   760.114258   \n",
       "2988   059107.gif  gif    49.410156   \n",
       "13908  318471.ppt  ppt  3282.000000   \n",
       "\n",
       "                                         1st_block_bytes  \\\n",
       "3687   b'GIF89al\\x00l\\x00\\xe6\\x00\\x00\\x92uukLL\\xff\\xf...   \n",
       "710    b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...   \n",
       "7490   b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x02\\x00...   \n",
       "7718   b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x01...   \n",
       "20300  b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...   \n",
       "21820  b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...   \n",
       "18445  b\"<DOC>\\n[October 31,2005  (Volume 70, Number ...   \n",
       "14400  b'%!PS-Adobe-3.0\\n%%Title: letters.p65\\n%%Crea...   \n",
       "2988   b'GIF87a \\x03\\x84\\x03\\xf4\\x00\\x00\\xff\\xff\\xff\\...   \n",
       "13908  b'\\xd0\\xcf\\x11\\xe0\\xa1\\xb1\\x1a\\xe1\\x00\\x00\\x00...   \n",
       "\n",
       "                                           byte_integers  class  \n",
       "3687   [71, 73, 70, 56, 57, 97, 108, 0, 108, 0, 230, ...      7  \n",
       "710    [208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...      1  \n",
       "7490   [255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0,...      8  \n",
       "7718   [255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0,...      8  \n",
       "20300  [208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...      4  \n",
       "21820  [208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...      4  \n",
       "18445  [60, 68, 79, 67, 62, 10, 91, 79, 99, 116, 111,...     11  \n",
       "14400  [37, 33, 80, 83, 45, 65, 100, 111, 98, 101, 45...      3  \n",
       "2988   [71, 73, 70, 56, 55, 97, 32, 3, 132, 3, 244, 0...      7  \n",
       "13908  [208, 207, 17, 224, 161, 177, 26, 225, 0, 0, 0...      5  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert categorical classes to numerical\n",
    "df[\"class\"] = df[\"type\"].apply(lambda file_type: convert_cat2num(file_type))\n",
    "df.sample(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "1     2000\n",
       "7     2000\n",
       "10    2000\n",
       "8     2000\n",
       "2     2000\n",
       "9     2000\n",
       "5     2000\n",
       "3     2000\n",
       "6     2000\n",
       "11    2000\n",
       "4     2000\n",
       "12    2000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (24000, 4096)\n",
      "y shape:  (24000,)\n"
     ]
    }
   ],
   "source": [
    "# create feature and label arrays\n",
    "X = np.array([x for x in df[\"byte_integers\"]])\n",
    "y = np.array([y for y in df[\"class\"]])\n",
    "\n",
    "# printing our array shapes\n",
    "print(\"X shape: \", X.shape)\n",
    "print(\"y shape: \", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data to training, validation, and testing arrays\n",
    "# 70% training and 30% temporary\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "# 15% validation and 15% test\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X training shape:  (16800, 4096)\n",
      "X validation shape:  (3600, 4096)\n",
      "X testing shape:  (3600, 4096) \n",
      "\n",
      "y training shape:  (16800,)\n",
      "y validation shape:  (3600,)\n",
      "y testing shape:  (3600,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X training shape: \", X_train.shape)\n",
    "print(\"X validation shape: \", X_val.shape)\n",
    "print(\"X testing shape: \", X_test.shape, \"\\n\")\n",
    "print(\"y training shape: \", y_train.shape)\n",
    "print(\"y validation shape: \", y_val.shape)\n",
    "print(\"y testing shape: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Models Training and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feed Forward Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">524288</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>)             │     <span style=\"color: #00af00; text-decoration-color: #00af00\">6,815,757</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m32,896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m524288\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m)             │     \u001b[38;5;34m6,815,757\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,849,165</span> (26.13 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,849,165\u001b[0m (26.13 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,849,165</span> (26.13 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,849,165\u001b[0m (26.13 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffnn = Ffnn(timesteps=4096, features=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m179s\u001b[0m 338ms/step - accuracy: 0.4917 - loss: 111.3023 - val_accuracy: 0.7172 - val_loss: 1.0686\n",
      "Epoch 2/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m168s\u001b[0m 321ms/step - accuracy: 0.7315 - loss: 0.9249 - val_accuracy: 0.7278 - val_loss: 0.8963\n",
      "Epoch 3/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 330ms/step - accuracy: 0.7339 - loss: 0.7683 - val_accuracy: 0.7222 - val_loss: 0.8497\n",
      "Epoch 4/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 331ms/step - accuracy: 0.7388 - loss: 0.6783 - val_accuracy: 0.7275 - val_loss: 0.7974\n"
     ]
    }
   ],
   "source": [
    "ffnn_pred = ffnn.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    epochs=4,\n",
    "    batch_size=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffnn.plot_learning_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 112ms/step\n"
     ]
    }
   ],
   "source": [
    "ffnn_pred = ffnn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feed Forward Neural Network performance evaluation:\n",
      "\n",
      "accuracy\t0.7383333333333333.\n",
      "precision\t0.6755883705089333.\n",
      "recall\t\t0.732518720346325.\n",
      "f1-score\t0.6850092528815388.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\User\\Desktop\\MSC\\S1\\DISSERTATION\\code\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "metrics = evaluate_performance(y_test, ffnn_pred)\n",
    "accuracy, precision, recall, f1 = metrics[\"accuracy\"], metrics[\"precision\"], metrics[\"recall\"], metrics[\"f1_score\"]\n",
    "print(f\"\"\"{ffnn.name} performance evaluation:\\n\\naccuracy\\t{accuracy}.\\n\\\n",
    "precision\\t{precision}.\\nrecall\\t\\t{recall}.\\nf1-score\\t{f1}.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.99      1.00      0.99       287\n",
      "         pdf       0.98      0.90      0.94       317\n",
      "          ps       0.24      0.98      0.38       287\n",
      "         xls       0.99      0.99      0.99       304\n",
      "         ppt       0.99      1.00      0.99       307\n",
      "         swf       0.96      0.97      0.97       307\n",
      "         gif       0.98      0.98      0.98       308\n",
      "         jpg       0.97      0.99      0.98       295\n",
      "         png       1.00      0.98      0.99       311\n",
      "        html       0.00      0.00      0.00       303\n",
      "         txt       0.00      0.00      0.00       285\n",
      "         xml       0.00      0.00      0.00       289\n",
      "\n",
      "    accuracy                           0.74      3600\n",
      "   macro avg       0.68      0.73      0.69      3600\n",
      "weighted avg       0.68      0.74      0.69      3600\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\User\\Desktop\\MSC\\S1\\DISSERTATION\\code\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\User\\Desktop\\MSC\\S1\\DISSERTATION\\code\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\User\\Desktop\\MSC\\S1\\DISSERTATION\\code\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "ffnn_report = classification_report(y_test, ffnn_pred, target_names=CLASS_NAMES)\n",
    "print(ffnn_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffnn_cm = confusion_matrix(y_test, ffnn_pred)\n",
    "plot_confusion_matrix(ffnn_cm, CLASS_NAMES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4094</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2047</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2045</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">24,704</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1022</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">130816</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>)             │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,700,621</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4094\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │           \u001b[38;5;34m256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d (\u001b[38;5;33mMaxPooling1D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2047\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2045\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m24,704\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling1d_1 (\u001b[38;5;33mMaxPooling1D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1022\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m130816\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m)             │     \u001b[38;5;34m1,700,621\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,725,581</span> (6.58 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,725,581\u001b[0m (6.58 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,725,581</span> (6.58 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,725,581\u001b[0m (6.58 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn = Cnn(timesteps=4096, features=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 163ms/step - accuracy: 0.6836 - loss: 62.9964 - val_accuracy: 0.8925 - val_loss: 0.4073\n",
      "Epoch 2/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 168ms/step - accuracy: 0.9345 - loss: 0.2164 - val_accuracy: 0.9067 - val_loss: 0.5079\n",
      "Epoch 3/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 167ms/step - accuracy: 0.9711 - loss: 0.1027 - val_accuracy: 0.9103 - val_loss: 0.6115\n",
      "Epoch 4/4\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 172ms/step - accuracy: 0.9796 - loss: 0.0710 - val_accuracy: 0.9106 - val_loss: 0.6069\n"
     ]
    }
   ],
   "source": [
    "cnn_history = cnn.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    epochs=4,\n",
    "    batch_size=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn.plot_learning_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 52ms/step\n"
     ]
    }
   ],
   "source": [
    "cnn_pred = cnn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolutional Neural Network performance evaluation:\n",
      "\n",
      "accuracy\t0.9130555555555555.\n",
      "precision\t0.9123263124660443.\n",
      "recall\t\t0.9113487252842093.\n",
      "f1-score\t0.9111757876435879.\n"
     ]
    }
   ],
   "source": [
    "metrics = evaluate_performance(y_test, cnn_pred)\n",
    "accuracy, precision, recall, f1 = metrics[\"accuracy\"], metrics[\"precision\"], metrics[\"recall\"], metrics[\"f1_score\"]\n",
    "print(f\"\"\"{cnn.name} performance evaluation:\\n\\naccuracy\\t{accuracy}.\\n\\\n",
    "precision\\t{precision}.\\nrecall\\t\\t{recall}.\\nf1-score\\t{f1}.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.99      1.00      0.99       287\n",
      "         pdf       1.00      0.97      0.98       317\n",
      "          ps       0.89      0.94      0.92       287\n",
      "         xls       0.98      0.97      0.97       304\n",
      "         ppt       0.98      0.97      0.97       307\n",
      "         swf       0.94      0.95      0.94       307\n",
      "         gif       0.96      0.96      0.96       308\n",
      "         jpg       0.99      0.98      0.99       295\n",
      "         png       0.99      0.98      0.99       311\n",
      "        html       0.67      0.77      0.72       303\n",
      "         txt       0.67      0.58      0.62       285\n",
      "         xml       0.89      0.86      0.87       289\n",
      "\n",
      "    accuracy                           0.91      3600\n",
      "   macro avg       0.91      0.91      0.91      3600\n",
      "weighted avg       0.91      0.91      0.91      3600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cnn_report = classification_report(y_test, cnn_pred, target_names=CLASS_NAMES)\n",
    "print(cnn_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_cm = confusion_matrix(y_test, cnn_pred)\n",
    "plot_confusion_matrix(cnn_cm, CLASS_NAMES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gated Reccurent Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ bidirectional (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">136</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">28,968</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,781</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m136\u001b[0m)            │        \u001b[38;5;34m28,968\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m)             │         \u001b[38;5;34m1,781\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,749</span> (120.11 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,749\u001b[0m (120.11 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,749</span> (120.11 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,749\u001b[0m (120.11 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gru = Gru(timesteps=4096, features=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1421s\u001b[0m 3s/step - accuracy: 0.5985 - loss: 1.3513 - val_accuracy: 0.9219 - val_loss: 0.2952\n",
      "Epoch 2/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1517s\u001b[0m 3s/step - accuracy: 0.9279 - loss: 0.2505 - val_accuracy: 0.9517 - val_loss: 0.1627\n",
      "Epoch 3/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1591s\u001b[0m 3s/step - accuracy: 0.9493 - loss: 0.1654 - val_accuracy: 0.9494 - val_loss: 0.1937\n",
      "Epoch 4/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1925s\u001b[0m 4s/step - accuracy: 0.9473 - loss: 0.1885 - val_accuracy: 0.9322 - val_loss: 0.2327\n",
      "Epoch 5/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2452s\u001b[0m 5s/step - accuracy: 0.9198 - loss: 0.3114 - val_accuracy: 0.9394 - val_loss: 0.2622\n",
      "Epoch 6/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1750s\u001b[0m 3s/step - accuracy: 0.9467 - loss: 0.2265 - val_accuracy: 0.9586 - val_loss: 0.1702\n",
      "Epoch 7/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1704s\u001b[0m 3s/step - accuracy: 0.9576 - loss: 0.1673 - val_accuracy: 0.9625 - val_loss: 0.1371\n",
      "Epoch 8/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1706s\u001b[0m 3s/step - accuracy: 0.9620 - loss: 0.1369 - val_accuracy: 0.9661 - val_loss: 0.1206\n",
      "Epoch 9/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1673s\u001b[0m 3s/step - accuracy: 0.9675 - loss: 0.1169 - val_accuracy: 0.9669 - val_loss: 0.1148\n",
      "Epoch 10/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1682s\u001b[0m 3s/step - accuracy: 0.9721 - loss: 0.1029 - val_accuracy: 0.9658 - val_loss: 0.1067\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1c2fb9a3e00>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gru.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    epochs=10,\n",
    "    batch_size=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gru.plot_learning_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 833ms/step\n"
     ]
    }
   ],
   "source": [
    "gru_pred = gru.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gated Reccurent Unit performance evaluation:\n",
      "\n",
      "accuracy\t0.9669444444444445.\n",
      "precision\t0.9668822061533948.\n",
      "recall\t\t0.9666802966189384.\n",
      "f1-score\t0.9662653175839507.\n"
     ]
    }
   ],
   "source": [
    "metrics = evaluate_performance(y_test, gru_pred)\n",
    "accuracy, precision, recall, f1 = metrics[\"accuracy\"], metrics[\"precision\"], metrics[\"recall\"], metrics[\"f1_score\"]\n",
    "print(f\"\"\"{gru.name} performance evaluation:\\n\\naccuracy\\t{accuracy}.\\n\\\n",
    "precision\\t{precision}.\\nrecall\\t\\t{recall}.\\nf1-score\\t{f1}.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.97      1.00      0.98       287\n",
      "         pdf       0.98      0.99      0.99       317\n",
      "          ps       0.98      0.98      0.98       287\n",
      "         xls       0.98      0.98      0.98       304\n",
      "         ppt       0.99      0.97      0.98       307\n",
      "         swf       0.99      1.00      1.00       307\n",
      "         gif       1.00      0.99      1.00       308\n",
      "         jpg       1.00      1.00      1.00       295\n",
      "         png       1.00      1.00      1.00       311\n",
      "        html       0.92      0.81      0.86       303\n",
      "         txt       0.94      0.93      0.93       285\n",
      "         xml       0.84      0.94      0.89       289\n",
      "\n",
      "    accuracy                           0.97      3600\n",
      "   macro avg       0.97      0.97      0.97      3600\n",
      "weighted avg       0.97      0.97      0.97      3600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gru_report = classification_report(y_test, gru_pred, target_names=CLASS_NAMES)\n",
    "print(gru_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gru_cm = confusion_matrix(y_test, gru_pred)\n",
    "plot_confusion_matrix(gru_cm, CLASS_NAMES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Long Short-Term Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ bidirectional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">136</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">38,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,781</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m136\u001b[0m)            │        \u001b[38;5;34m38,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m)             │         \u001b[38;5;34m1,781\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,861</span> (155.71 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m39,861\u001b[0m (155.71 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,861</span> (155.71 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m39,861\u001b[0m (155.71 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm = Lstm(timesteps=4096, features=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1554s\u001b[0m 3s/step - accuracy: 0.5933 - loss: 1.3817 - val_accuracy: 0.8997 - val_loss: 0.3626\n",
      "Epoch 2/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1584s\u001b[0m 3s/step - accuracy: 0.9072 - loss: 0.3224 - val_accuracy: 0.9156 - val_loss: 0.2473\n",
      "Epoch 3/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1615s\u001b[0m 3s/step - accuracy: 0.9370 - loss: 0.2021 - val_accuracy: 0.9489 - val_loss: 0.1693\n",
      "Epoch 4/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1666s\u001b[0m 3s/step - accuracy: 0.9559 - loss: 0.1422 - val_accuracy: 0.9500 - val_loss: 0.1414\n",
      "Epoch 5/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1641s\u001b[0m 3s/step - accuracy: 0.9641 - loss: 0.1192 - val_accuracy: 0.9636 - val_loss: 0.1201\n",
      "Epoch 6/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1646s\u001b[0m 3s/step - accuracy: 0.9701 - loss: 0.0973 - val_accuracy: 0.9383 - val_loss: 0.1744\n",
      "Epoch 7/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1677s\u001b[0m 3s/step - accuracy: 0.9707 - loss: 0.0955 - val_accuracy: 0.9619 - val_loss: 0.1183\n",
      "Epoch 8/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1645s\u001b[0m 3s/step - accuracy: 0.9704 - loss: 0.0983 - val_accuracy: 0.9675 - val_loss: 0.1123\n",
      "Epoch 9/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1643s\u001b[0m 3s/step - accuracy: 0.9777 - loss: 0.0761 - val_accuracy: 0.9703 - val_loss: 0.0942\n",
      "Epoch 10/10\n",
      "\u001b[1m525/525\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1678s\u001b[0m 3s/step - accuracy: 0.9787 - loss: 0.0716 - val_accuracy: 0.9675 - val_loss: 0.1233\n"
     ]
    }
   ],
   "source": [
    "lstm_history = lstm.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    epochs=10,\n",
    "    batch_size=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm.plot_learning_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 579ms/step\n"
     ]
    }
   ],
   "source": [
    "lstm_pred = lstm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Long Short-Term Memory performance evaluation:\n",
      "\n",
      "accuracy\t0.9711111111111111.\n",
      "precision\t0.9708866509969534.\n",
      "recall\t\t0.9708939257217853.\n",
      "f1-score\t0.9707987377283892.\n"
     ]
    }
   ],
   "source": [
    "metrics = evaluate_performance(y_test, lstm_pred)\n",
    "accuracy, precision, recall, f1 = metrics[\"accuracy\"], metrics[\"precision\"], metrics[\"recall\"], metrics[\"f1_score\"]\n",
    "print(f\"\"\"{lstm.name} performance evaluation:\\n\\naccuracy\\t{accuracy}.\\n\\\n",
    "precision\\t{precision}.\\nrecall\\t\\t{recall}.\\nf1-score\\t{f1}.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.94      0.96      0.95       287\n",
      "         pdf       0.99      1.00      1.00       317\n",
      "          ps       0.98      1.00      0.99       287\n",
      "         xls       0.93      0.95      0.94       304\n",
      "         ppt       0.94      0.90      0.92       307\n",
      "         swf       1.00      1.00      1.00       307\n",
      "         gif       1.00      1.00      1.00       308\n",
      "         jpg       1.00      1.00      1.00       295\n",
      "         png       1.00      1.00      1.00       311\n",
      "        html       0.94      0.94      0.94       303\n",
      "         txt       0.97      0.94      0.96       285\n",
      "         xml       0.96      0.97      0.96       289\n",
      "\n",
      "    accuracy                           0.97      3600\n",
      "   macro avg       0.97      0.97      0.97      3600\n",
      "weighted avg       0.97      0.97      0.97      3600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lstm_report = classification_report(y_test, lstm_pred, target_names=CLASS_NAMES)\n",
    "print(lstm_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm_cm = confusion_matrix(y_test, lstm_pred)\n",
    "plot_confusion_matrix(lstm_cm, CLASS_NAMES)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}