system5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments 5:\n",
    "### generating byte-to-integers representations derived from file last 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_last_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": [
      "The average file size is: 519.8767892054933\n"
     ]
    }
   ],
   "source": [
    "df['size KB'] = df['file'].apply(\n",
    "    lambda x: float(os.path.getsize(\n",
    "        os.path.join(os.getcwd(), f\"govdocs1/{x}\")\n",
    "    ) / 1024.0) 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": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "pdf     174196\n",
       "html    146731\n",
       "doc      53250\n",
       "txt      49990\n",
       "jpg      46969\n",
       "xls      46747\n",
       "ppt      39454\n",
       "xml      18886\n",
       "ps       12705\n",
       "gif      11275\n",
       "swf       2884\n",
       "png       1650\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['type'].isin(CLASS_NAMES)]\n",
    "# since the block size is 4 kb on this computer\n",
    "# we exclude files whose bytes don't fit in the second block \n",
    "# this is to avoid including header file bytes in our data\n",
    "# as we will be using a function that extracts the bytes\n",
    "# from the last blocks of our files \n",
    "df = df[df['size KB'] > 8]\n",
    "df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_27536\\603527055.py:2: 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(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "type\n",
       "doc     2000\n",
       "gif     2000\n",
       "html    2000\n",
       "jpg     2000\n",
       "pdf     2000\n",
       "ppt     2000\n",
       "ps      2000\n",
       "swf     2000\n",
       "txt     2000\n",
       "xls     2000\n",
       "xml     2000\n",
       "png     1650\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample the file types we're interested in\n",
    "df = df.groupby('type').apply(\n",
    "    lambda x: x if x.name == \"png\" else x.sample(n=min(len(x), 2000), random_state=42)\n",
    ").reset_index(drop=True)\n",
    "\n",
    "df[\"type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = os.path.join(os.getcwd(), \"govdocs1\")\n",
    "df[\"last_block_bytes\"] = df.apply(\n",
    "    lambda row: get_last_block_bytes(\n",
    "        path=os.path.join(base_dir, row[\"file\"]), \n",
    "        file_size_kb=row[\"size KB\"]\n",
    "    ), \n",
    "    axis=1\n",
    ")\n",
    "\n",
    "df[\"byte_integers\"] = df[\"last_block_bytes\"].apply(\n",
    "    lambda byte_sequence: pad_array(np.array([byte for byte in byte_sequence]))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "23645    4096\n",
      "23646    4096\n",
      "23647    4096\n",
      "23648    4096\n",
      "23649    4096\n",
      "Name: byte_integers, Length: 23650, dtype: int64\n",
      "all byte sequences of the same length: True\n"
     ]
    }
   ],
   "source": [
    "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>last_block_bytes</th>\n",
       "      <th>byte_integers</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11901</th>\n",
       "      <td>499467.ppt</td>\n",
       "      <td>ppt</td>\n",
       "      <td>3137.500000</td>\n",
       "      <td>b'\\x1f\\x00\\x1b\\x00\\x04\\x00\\x00\\x00.\\x01\\x01\\x0...</td>\n",
       "      <td>[31, 0, 27, 0, 4, 0, 0, 0, 46, 1, 1, 0, 4, 0, ...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12069</th>\n",
       "      <td>466528.ppt</td>\n",
       "      <td>ppt</td>\n",
       "      <td>5785.500000</td>\n",
       "      <td>b'\\xfd\\xff\\xff\\xff\\xfd\\xff\\xff\\xff\\xfd\\xff\\xff...</td>\n",
       "      <td>[253, 255, 255, 255, 253, 255, 255, 255, 253, ...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>226960.gif</td>\n",
       "      <td>gif</td>\n",
       "      <td>19.200195</td>\n",
       "      <td>b'\\xf5Z\\xefr\\x81\\xab\\xb9\\x9c\\xcf\\xf9\\xc2\\xa6\\x...</td>\n",
       "      <td>[245, 90, 239, 114, 129, 171, 185, 156, 207, 2...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9949</th>\n",
       "      <td>412962.pdf</td>\n",
       "      <td>pdf</td>\n",
       "      <td>92.354492</td>\n",
       "      <td>b'062606 00000 n \\n0000062628 00000 n \\n000006...</td>\n",
       "      <td>[48, 54, 50, 54, 48, 54, 32, 48, 48, 48, 48, 4...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17894</th>\n",
       "      <td>210109.txt</td>\n",
       "      <td>txt</td>\n",
       "      <td>962.885742</td>\n",
       "      <td>b'967\\r\\n44162\\t-0.28\\t927.5\\t756\\r\\n44163\\t-0...</td>\n",
       "      <td>[57, 54, 55, 13, 10, 52, 52, 49, 54, 50, 9, 45...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             file type      size KB  \\\n",
       "11901  499467.ppt  ppt  3137.500000   \n",
       "12069  466528.ppt  ppt  5785.500000   \n",
       "3608   226960.gif  gif    19.200195   \n",
       "9949   412962.pdf  pdf    92.354492   \n",
       "17894  210109.txt  txt   962.885742   \n",
       "\n",
       "                                        last_block_bytes  \\\n",
       "11901  b'\\x1f\\x00\\x1b\\x00\\x04\\x00\\x00\\x00.\\x01\\x01\\x0...   \n",
       "12069  b'\\xfd\\xff\\xff\\xff\\xfd\\xff\\xff\\xff\\xfd\\xff\\xff...   \n",
       "3608   b'\\xf5Z\\xefr\\x81\\xab\\xb9\\x9c\\xcf\\xf9\\xc2\\xa6\\x...   \n",
       "9949   b'062606 00000 n \\n0000062628 00000 n \\n000006...   \n",
       "17894  b'967\\r\\n44162\\t-0.28\\t927.5\\t756\\r\\n44163\\t-0...   \n",
       "\n",
       "                                           byte_integers  class  \n",
       "11901  [31, 0, 27, 0, 4, 0, 0, 0, 46, 1, 1, 0, 4, 0, ...      5  \n",
       "12069  [253, 255, 255, 255, 253, 255, 255, 255, 253, ...      5  \n",
       "3608   [245, 90, 239, 114, 129, 171, 185, 156, 207, 2...      7  \n",
       "9949   [48, 54, 50, 54, 48, 54, 32, 48, 48, 48, 48, 4...      2  \n",
       "17894  [57, 54, 55, 13, 10, 52, 52, 49, 54, 50, 9, 45...     11  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"class\"] = df[\"type\"].apply(lambda file_type: convert_cat2num(file_type))\n",
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([x for x in df[\"byte_integers\"]])\n",
    "y = np.array([y for y in df[\"class\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (23650, 4096)\n",
      "y shape:  (23650,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X shape: \", X.shape)\n",
    "print(\"y shape: \", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 70% train\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": "markdown",
   "metadata": {},
   "source": [
    "## 2. Models Training and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feed Forward Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 305ms/step - accuracy: 0.2310 - loss: 172.1015 - val_accuracy: 0.3589 - val_loss: 1.9704\n",
      "Epoch 2/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 306ms/step - accuracy: 0.4441 - loss: 1.8041 - val_accuracy: 0.3908 - val_loss: 1.7672\n",
      "Epoch 3/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 307ms/step - accuracy: 0.4983 - loss: 1.5423 - val_accuracy: 0.3882 - val_loss: 1.7123\n",
      "Epoch 4/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 308ms/step - accuracy: 0.5255 - loss: 1.3872 - val_accuracy: 0.3964 - val_loss: 1.6904\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1eb33e4ef90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 98ms/step\n"
     ]
    }
   ],
   "source": [
    "ffnn_pred = ffnn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feed Forward Neural Network performance evaluation:\n",
      "\n",
      "accuracy\t0.39571589627959414.\n",
      "precision\t0.3867950128425131.\n",
      "recall\t\t0.3999961000724173.\n",
      "f1-score\t0.3474188032093912.\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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.85      0.76      0.80       280\n",
      "         pdf       0.11      0.01      0.03       335\n",
      "          ps       0.54      0.02      0.04       311\n",
      "         xls       0.80      0.81      0.80       288\n",
      "         ppt       0.76      0.83      0.79       337\n",
      "         swf       0.34      0.29      0.31       283\n",
      "         gif       0.35      0.41      0.38       301\n",
      "         jpg       0.30      0.30      0.30       276\n",
      "         png       0.41      0.37      0.39       260\n",
      "        html       0.00      0.00      0.00       292\n",
      "         txt       0.00      0.00      0.00       301\n",
      "         xml       0.19      0.99      0.32       284\n",
      "\n",
      "    accuracy                           0.40      3548\n",
      "   macro avg       0.39      0.40      0.35      3548\n",
      "weighted avg       0.39      0.40      0.34      3548\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": 20,
   "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": 21,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 148ms/step - accuracy: 0.3243 - loss: 60.8849 - val_accuracy: 0.4243 - val_loss: 1.6614\n",
      "Epoch 2/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 147ms/step - accuracy: 0.5372 - loss: 1.3564 - val_accuracy: 0.4649 - val_loss: 1.6133\n",
      "Epoch 3/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 147ms/step - accuracy: 0.6704 - loss: 1.0166 - val_accuracy: 0.5018 - val_loss: 1.7207\n",
      "Epoch 4/4\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 147ms/step - accuracy: 0.7679 - loss: 0.7187 - val_accuracy: 0.5250 - val_loss: 2.0103\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1eb33d15220>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 45ms/step\n"
     ]
    }
   ],
   "source": [
    "cnn_pred = cnn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolutional Neural Network performance evaluation:\n",
      "\n",
      "accuracy\t0.5326944757609922.\n",
      "precision\t0.5168011473462623.\n",
      "recall\t\t0.5226449316112173.\n",
      "f1-score\t0.5095800856137181.\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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.90      0.81      0.85       280\n",
      "         pdf       0.69      0.79      0.74       335\n",
      "          ps       0.46      0.77      0.58       311\n",
      "         xls       0.75      0.89      0.81       288\n",
      "         ppt       0.82      0.82      0.82       337\n",
      "         swf       0.38      0.34      0.36       283\n",
      "         gif       0.41      0.15      0.22       301\n",
      "         jpg       0.25      0.28      0.26       276\n",
      "         png       0.28      0.22      0.25       260\n",
      "        html       0.43      0.36      0.39       292\n",
      "         txt       0.41      0.39      0.40       301\n",
      "         xml       0.42      0.47      0.44       284\n",
      "\n",
      "    accuracy                           0.53      3548\n",
      "   macro avg       0.52      0.52      0.51      3548\n",
      "weighted avg       0.52      0.53      0.52      3548\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cnn_report = classification_report(y_test, cnn_pred, target_names=CLASS_NAMES)\n",
    "print(cnn_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1372s\u001b[0m 3s/step - accuracy: 0.4577 - loss: 1.5136 - val_accuracy: 0.7037 - val_loss: 0.6931\n",
      "Epoch 2/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1467s\u001b[0m 3s/step - accuracy: 0.7577 - loss: 0.6166 - val_accuracy: 0.8055 - val_loss: 0.5034\n",
      "Epoch 3/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1752s\u001b[0m 3s/step - accuracy: 0.8133 - loss: 0.4696 - val_accuracy: 0.8241 - val_loss: 0.4302\n",
      "Epoch 4/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2530s\u001b[0m 5s/step - accuracy: 0.8415 - loss: 0.4026 - val_accuracy: 0.8204 - val_loss: 0.4213\n",
      "Epoch 5/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1850s\u001b[0m 4s/step - accuracy: 0.8497 - loss: 0.4016 - val_accuracy: 0.7474 - val_loss: 0.6599\n",
      "Epoch 6/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1618s\u001b[0m 3s/step - accuracy: 0.7979 - loss: 0.5633 - val_accuracy: 0.7375 - val_loss: 0.7394\n",
      "Epoch 7/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1583s\u001b[0m 3s/step - accuracy: 0.8309 - loss: 0.5021 - val_accuracy: 0.8506 - val_loss: 0.4340\n",
      "Epoch 8/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1602s\u001b[0m 3s/step - accuracy: 0.8701 - loss: 0.3881 - val_accuracy: 0.8743 - val_loss: 0.3565\n",
      "Epoch 9/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1654s\u001b[0m 3s/step - accuracy: 0.8839 - loss: 0.3345 - val_accuracy: 0.8977 - val_loss: 0.3145\n",
      "Epoch 10/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1584s\u001b[0m 3s/step - accuracy: 0.9015 - loss: 0.2882 - val_accuracy: 0.9019 - val_loss: 0.2828\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1eb27a6c620>"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 542ms/step\n"
     ]
    }
   ],
   "source": [
    "gru_pred = gru.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gated Reccurent Unit performance evaluation:\n",
      "\n",
      "accuracy\t0.9036076662908681.\n",
      "precision\t0.9048275983748896.\n",
      "recall\t\t0.9027029483327121.\n",
      "f1-score\t0.9020788126725039.\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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.80      0.92      0.85       280\n",
      "         pdf       0.98      0.96      0.97       335\n",
      "          ps       0.95      0.92      0.94       311\n",
      "         xls       0.88      0.78      0.83       288\n",
      "         ppt       0.95      0.92      0.93       337\n",
      "         swf       0.88      0.77      0.82       283\n",
      "         gif       0.98      0.98      0.98       301\n",
      "         jpg       0.77      0.88      0.82       276\n",
      "         png       0.99      1.00      1.00       260\n",
      "        html       0.90      0.82      0.86       292\n",
      "         txt       0.86      0.96      0.90       301\n",
      "         xml       0.91      0.93      0.92       284\n",
      "\n",
      "    accuracy                           0.90      3548\n",
      "   macro avg       0.90      0.90      0.90      3548\n",
      "weighted avg       0.91      0.90      0.90      3548\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gru_report = classification_report(y_test, gru_pred, target_names=CLASS_NAMES)\n",
    "print(gru_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1650s\u001b[0m 3s/step - accuracy: 0.3373 - loss: 1.8501 - val_accuracy: 0.5898 - val_loss: 1.1607\n",
      "Epoch 2/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2527s\u001b[0m 5s/step - accuracy: 0.6027 - loss: 1.1109 - val_accuracy: 0.6851 - val_loss: 0.8423\n",
      "Epoch 3/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1520s\u001b[0m 3s/step - accuracy: 0.6910 - loss: 0.8065 - val_accuracy: 0.6981 - val_loss: 0.7285\n",
      "Epoch 4/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1700s\u001b[0m 3s/step - accuracy: 0.6997 - loss: 0.7089 - val_accuracy: 0.7260 - val_loss: 0.6752\n",
      "Epoch 5/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1701s\u001b[0m 3s/step - accuracy: 0.7317 - loss: 0.6467 - val_accuracy: 0.7364 - val_loss: 0.6233\n",
      "Epoch 6/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1553s\u001b[0m 3s/step - accuracy: 0.7444 - loss: 0.6202 - val_accuracy: 0.7773 - val_loss: 0.5607\n",
      "Epoch 7/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1567s\u001b[0m 3s/step - accuracy: 0.7955 - loss: 0.5293 - val_accuracy: 0.8001 - val_loss: 0.5001\n",
      "Epoch 8/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1618s\u001b[0m 3s/step - accuracy: 0.8122 - loss: 0.4808 - val_accuracy: 0.8261 - val_loss: 0.4648\n",
      "Epoch 9/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2122s\u001b[0m 4s/step - accuracy: 0.8007 - loss: 0.4911 - val_accuracy: 0.8077 - val_loss: 0.4684\n",
      "Epoch 10/10\n",
      "\u001b[1m518/518\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1877s\u001b[0m 4s/step - accuracy: 0.8190 - loss: 0.4590 - val_accuracy: 0.8373 - val_loss: 0.4339\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1ebbb14f170>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 37,
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 904ms/step\n"
     ]
    }
   ],
   "source": [
    "lstm_pred = lstm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Long Short-Term Memory performance evaluation:\n",
      "\n",
      "accuracy\t0.8351183765501691.\n",
      "precision\t0.8440579325162312.\n",
      "recall\t\t0.835858895061719.\n",
      "f1-score\t0.8367588116749561.\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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         doc       0.60      0.77      0.68       280\n",
      "         pdf       0.96      0.98      0.97       335\n",
      "          ps       0.98      0.90      0.93       311\n",
      "         xls       0.89      0.67      0.76       288\n",
      "         ppt       0.67      0.65      0.66       337\n",
      "         swf       0.87      0.86      0.87       283\n",
      "         gif       0.97      0.96      0.97       301\n",
      "         jpg       0.85      0.89      0.87       276\n",
      "         png       0.98      0.99      0.99       260\n",
      "        html       0.67      0.81      0.73       292\n",
      "         txt       0.92      0.87      0.89       301\n",
      "         xml       0.77      0.67      0.72       284\n",
      "\n",
      "    accuracy                           0.84      3548\n",
      "   macro avg       0.84      0.84      0.84      3548\n",
      "weighted avg       0.84      0.84      0.84      3548\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lstm_report = classification_report(y_test, lstm_pred, target_names=CLASS_NAMES)\n",
    "print(lstm_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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
}