step1_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "955ea486-0749-4d8a-9f75-a1aa529e1f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.svm import SVC\n",
    "import json\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ef0ce67b-f6f7-4f3a-a83d-b15335331ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json('./datasets/final_dataset_waf_version_2.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90e9d397-edf7-414f-a650-7eb2271c1ade",
   "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>pattern</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>crteyreti=wsn&amp;et3tf6shov=tdsviee y fum$oh3;ore...</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>*;q=0.7</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nhgiopie/7.8.4.7.0</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dyylkl.xd9cpu/4ot0ta/ts6xnrp1/hssh/a2cuerht/s...</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/2m6vlb1r37jspc/cwvv/mbar/oqrd0/msc/etceebwgi/...</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108435</th>\n",
       "      <td>/news-and-updates/</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108436</th>\n",
       "      <td>/student-consumer-information/</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108437</th>\n",
       "      <td>/academics/program-finder/?_program_format=online</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108438</th>\n",
       "      <td>/website-feedback/</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108439</th>\n",
       "      <td>/alumni/</td>\n",
       "      <td>valid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108440 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  pattern   type\n",
       "0       crteyreti=wsn&et3tf6shov=tdsviee y fum$oh3;ore...  valid\n",
       "1                                                 *;q=0.7  valid\n",
       "2                                      nhgiopie/7.8.4.7.0  valid\n",
       "3       /dyylkl.xd9cpu/4ot0ta/ts6xnrp1/hssh/a2cuerht/s...  valid\n",
       "4       /2m6vlb1r37jspc/cwvv/mbar/oqrd0/msc/etceebwgi/...  valid\n",
       "...                                                   ...    ...\n",
       "108435                                 /news-and-updates/  valid\n",
       "108436                     /student-consumer-information/  valid\n",
       "108437  /academics/program-finder/?_program_format=online  valid\n",
       "108438                                 /website-feedback/  valid\n",
       "108439                                           /alumni/  valid\n",
       "\n",
       "[108440 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44338259-0c4d-4934-a6f3-685c86f738c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67f19fa7-02e9-45e2-9f1e-4b0a87ea2ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['type'].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dde55764-f2c1-4736-8bb1-2b65de245e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['pattern'].to_numpy().astype(str)\n",
    "y = df['type'].to_numpy().astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76ddb51c-1dcb-4d10-9c3c-44259ed5db57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108440\n",
      "108440\n"
     ]
    }
   ],
   "source": [
    "print(len(X))\n",
    "print(len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "189f4262-332a-4ba6-988e-bd594774a7a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7975e02e-e2c8-447a-8bb6-874cb709a41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(X, y, test_size = 0.25, random_state = 42, stratify = y)\n",
    "le = LabelEncoder()\n",
    "trainY = le.fit_transform(trainY)\n",
    "testY = le.transform(testY)\n",
    "# np.savez('dataset', trainX=trainX, testX=testX, trainY=trainY, testY=testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec2a5dd5-6fa5-4791-8bac-55d659518559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 12 candidates, totalling 24 fits\n",
      "[CV 1/2] END svc__C=1, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 1);, score=0.961 total time= 1.8min\n",
      "[CV 2/2] END svc__C=1, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 1);, score=0.962 total time= 1.5min\n",
      "[CV 1/2] END svc__C=1, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 2);, score=0.986 total time= 3.8min\n",
      "[CV 2/2] END svc__C=1, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 2);, score=0.986 total time= 3.9min\n",
      "[CV 1/2] END svc__C=1, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 4);, score=0.986 total time= 3.7min\n",
      "[CV 2/2] END svc__C=1, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 4);, score=0.986 total time= 3.8min\n",
      "[CV 1/2] END svc__C=1, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 1);, score=0.984 total time= 1.2min\n",
      "[CV 2/2] END svc__C=1, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 1);, score=0.985 total time= 1.2min\n",
      "[CV 1/2] END svc__C=1, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 2);, score=0.990 total time= 5.7min\n",
      "[CV 2/2] END svc__C=1, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 2);, score=0.991 total time= 5.8min\n",
      "[CV 1/2] END svc__C=1, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 4);, score=0.990 total time= 5.1min\n",
      "[CV 2/2] END svc__C=1, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 4);, score=0.991 total time= 5.2min\n",
      "[CV 1/2] END svc__C=10, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 1);, score=0.965 total time= 1.2min\n",
      "[CV 2/2] END svc__C=10, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 1);, score=0.967 total time= 1.2min\n",
      "[CV 1/2] END svc__C=10, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 2);, score=0.990 total time= 2.9min\n",
      "[CV 2/2] END svc__C=10, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 2);, score=0.990 total time= 3.0min\n",
      "[CV 1/2] END svc__C=10, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 4);, score=0.990 total time= 2.8min\n",
      "[CV 2/2] END svc__C=10, svc__kernel=linear, tfidfvectorizer__ngram_range=(1, 4);, score=0.991 total time= 2.9min\n",
      "[CV 1/2] END svc__C=10, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 1);, score=0.988 total time=  56.6s\n",
      "[CV 2/2] END svc__C=10, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 1);, score=0.988 total time=  57.0s\n",
      "[CV 1/2] END svc__C=10, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 2);, score=0.993 total time= 6.3min\n",
      "[CV 2/2] END svc__C=10, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 2);, score=0.994 total time= 6.6min\n",
      "[CV 1/2] END svc__C=10, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 4);, score=0.993 total time= 5.0min\n",
      "[CV 2/2] END svc__C=10, svc__kernel=rbf, tfidfvectorizer__ngram_range=(1, 4);, score=0.994 total time= 5.4min\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2,\n",
       "             estimator=Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;,\n",
       "                                        TfidfVectorizer(analyzer=&#x27;char&#x27;,\n",
       "                                                        max_features=1024)),\n",
       "                                       (&#x27;svc&#x27;, SVC())]),\n",
       "             param_grid={&#x27;svc__C&#x27;: [1, 10], &#x27;svc__kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;],\n",
       "                         &#x27;tfidfvectorizer__ngram_range&#x27;: [(1, 1), (1, 2),\n",
       "                                                          (1, 4)]},\n",
       "             verbose=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=2,\n",
       "             estimator=Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;,\n",
       "                                        TfidfVectorizer(analyzer=&#x27;char&#x27;,\n",
       "                                                        max_features=1024)),\n",
       "                                       (&#x27;svc&#x27;, SVC())]),\n",
       "             param_grid={&#x27;svc__C&#x27;: [1, 10], &#x27;svc__kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;],\n",
       "                         &#x27;tfidfvectorizer__ngram_range&#x27;: [(1, 1), (1, 2),\n",
       "                                                          (1, 4)]},\n",
       "             verbose=4)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;,\n",
       "                 TfidfVectorizer(analyzer=&#x27;char&#x27;, max_features=1024,\n",
       "                                 ngram_range=(1, 2))),\n",
       "                (&#x27;svc&#x27;, SVC(C=10))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>TfidfVectorizer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(analyzer=&#x27;char&#x27;, max_features=1024, ngram_range=(1, 2))</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=2,\n",
       "             estimator=Pipeline(steps=[('tfidfvectorizer',\n",
       "                                        TfidfVectorizer(analyzer='char',\n",
       "                                                        max_features=1024)),\n",
       "                                       ('svc', SVC())]),\n",
       "             param_grid={'svc__C': [1, 10], 'svc__kernel': ['linear', 'rbf'],\n",
       "                         'tfidfvectorizer__ngram_range': [(1, 1), (1, 2),\n",
       "                                                          (1, 4)]},\n",
       "             verbose=4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pipe = make_pipeline(TfidfVectorizer(input = 'content', lowercase = True, analyzer = 'char', max_features = 1024), SVC())\n",
    "\n",
    "# param_grid = {'tfidfvectorizer__ngram_range': [(1, 1), (1, 2), (1, 4)], 'svc__C': [1, 10], 'svc__kernel': ['linear', 'rbf']}\n",
    "\n",
    "# grid = GridSearchCV(pipe, param_grid, cv = 2, verbose = 4)\n",
    "\n",
    "# grid.fit(trainX, trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65fa629b-06f6-4fcb-be05-fa232b448d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9960162301733677"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grid.score(testX, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d8d8938-66a9-4872-85b2-1389f32500b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "          cmdi       1.00      0.98      0.99      2200\n",
      "path-traversal       0.99      0.98      0.98       822\n",
      "          sqli       1.00      0.99      1.00      3464\n",
      "         valid       1.00      1.00      1.00     18192\n",
      "           xss       1.00      0.99      1.00      2432\n",
      "\n",
      "      accuracy                           1.00     27110\n",
      "     macro avg       1.00      0.99      0.99     27110\n",
      "  weighted avg       1.00      1.00      1.00     27110\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.metrics import classification_report\n",
    "# preds = grid.predict(testX)\n",
    "# print(classification_report(testY, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fee413a-12b0-4532-8cc6-35508a6baac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2161,     5,     2,    32,     0],\n",
       "       [    1,   802,     0,    19,     0],\n",
       "       [    7,     0,  3440,    17,     0],\n",
       "       [    1,     2,     8, 18181,     0],\n",
       "       [    0,     0,     0,    14,  2418]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# confusion_matrix(testY, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a24c3c9-0adf-4a58-8c26-625d4786ec00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svc__C': 10, 'svc__kernel': 'rbf', 'tfidfvectorizer__ngram_range': (1, 2)}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e92a896a-a932-4a84-bd48-04bb4207a6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;,\n",
       "                 TfidfVectorizer(analyzer=&#x27;char&#x27;, max_features=1024,\n",
       "                                 ngram_range=(1, 2))),\n",
       "                (&#x27;svc&#x27;, SVC(C=10))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;,\n",
       "                 TfidfVectorizer(analyzer=&#x27;char&#x27;, max_features=1024,\n",
       "                                 ngram_range=(1, 2))),\n",
       "                (&#x27;svc&#x27;, SVC(C=10))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>TfidfVectorizer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(analyzer=&#x27;char&#x27;, max_features=1024, ngram_range=(1, 2))</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tfidfvectorizer',\n",
       "                 TfidfVectorizer(analyzer='char', max_features=1024,\n",
       "                                 ngram_range=(1, 2))),\n",
       "                ('svc', SVC(C=10))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pipe = make_pipeline(TfidfVectorizer(input = 'content', lowercase = True, analyzer = 'char', max_features = 1024, ngram_range = (1, 2)), SVC(C = 10, kernel = 'rbf'))\n",
    "\n",
    "# pipe.fit(trainX, trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c8edc4a3-3ebc-479a-aae1-c89ecdad5ccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9960162301733677"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pipe.score(testX, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94996cfe-1a24-4964-ac1d-8599f79a0518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "          cmdi       1.00      0.98      0.99      2200\n",
      "path-traversal       0.99      0.98      0.98       822\n",
      "          sqli       1.00      0.99      1.00      3464\n",
      "         valid       1.00      1.00      1.00     18192\n",
      "           xss       1.00      0.99      1.00      2432\n",
      "\n",
      "      accuracy                           1.00     27110\n",
      "     macro avg       1.00      0.99      0.99     27110\n",
      "  weighted avg       1.00      1.00      1.00     27110\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.metrics import classification_report\n",
    "\n",
    "# preds = pipe.predict(testX)\n",
    "\n",
    "# print(classification_report(testY, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d0ff8408-65d4-46d8-b5bb-9b8bfc2fa92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TWJ708vHA0xfpmLXqAB4/S4N7/erYuThIpE0S7PRYXBJFfg89AS1dulQ5cVPTpk2xePFieHp6qj1OJpNBKpXi8t1kWFqK7I2vIbtKHIFARPpHJpPBtooUqampJdLMnP8aUqkUJl0WQFKh+DN/KnJeIev30SUaqz4TvIYBAIKDgwVrgiAiIpFgDYNWxN2wR0REREWiFzUMREREJY4Pn9IKEwYiIhIHNkloRdzpEhERERUJaxiIiEgUJBIJJKxhKDYmDEREJApMGLTDJgkiIiJSizUMREQkDpK/F22OFzEmDEREJApsktAOmySIiIhILdYwEBGRKLCGQTtMGIiISBSYMGiHCQMREYkCEwbtsA8DERERqcUaBiIiEgcOq9QKEwYiIhIFNkloh00SREREpBYTBiIiEoXXT7eWaLFo9nqRkZHo3r07HBwcIJFIsGfPHpX9AwYMKPAanTt3VimTkpICf39/WFlZoVKlSggMDER6erpKmcuXL6Nt27YwNTWFo6Mj5syZUyCWHTt2wNXVFaampnB3d8fBgwc1uxgwYSAiIpGQQJtkQQKJhp0YMjIy0KRJEyxbtuyNZTp37ozExETl8ssvv6js9/f3x7Vr1xAWFob9+/cjMjISQ4YMUe6XyWTw8fGBk5MTYmJiMHfuXISEhGD16tXKMmfOnEGfPn0QGBiIixcvws/PD35+frh69apG18M+DERERCWgS5cu6NKly1vLmJiYwM7OrtB9N27cwKFDh3D+/Hm0aNECALBkyRJ07doVP/zwAxwcHLB582ZkZ2dj7dq1MDY2RsOGDREbG4v58+crE4tFixahc+fOGDduHABg5syZCAsLw9KlS7Fy5coiXw9rGIiISBS0a474p8OkTCZTWbKysood04kTJ2BjYwMXFxcMGzYMz549U+6LiopCpUqVlMkCAHh7e8PAwADnzp1TlmnXrh2MjY2VZXx9fREXF4fnz58ry3h7e6u8rq+vL6KiojSKlQkDERGJg0QHCwBHR0dIpVLlEhoaWqxwOnfujJ9//hnh4eH4/vvvERERgS5duiAvLw8AkJSUBBsbG5VjjIyMYG1tjaSkJGUZW1tblTL56+rK5O8vKjZJEBERaSAhIQFWVlbKdRMTk2Kd57PPPlP+293dHY0bN0adOnVw4sQJdOrUSes4dY01DEREJA7aNkf83SRhZWWlshQ3Yfiv2rVro2rVqrh9+zYAwM7ODo8fP1Ypk5ubi5SUFGW/Bzs7OyQnJ6uUyV9XV+ZNfSfehAkDERGJgq76MJSUhw8f4tmzZ7C3twcAeHl54cWLF4iJiVGWOXbsGORyOTw9PZVlIiMjkZOToywTFhYGFxcXVK5cWVkmPDxc5bXCwsLg5eWlUXxMGIiISBRKO2FIT09HbGwsYmNjAQDx8fGIjY3FgwcPkJ6ejnHjxuHs2bO4d+8ewsPD0bNnT9StWxe+vr4AgAYNGqBz584YPHgw/vjjD5w+fRrBwcH47LPP4ODgAADo27cvjI2NERgYiGvXrmHbtm1YtGgRxowZo4xj5MiROHToEObNm4ebN28iJCQE0dHRCA4O1uh6mDAQERGVgOjoaDRr1gzNmjUDAIwZMwbNmjXD1KlTYWhoiMuXL6NHjx6oX78+AgMD4eHhgZMnT6o0cWzevBmurq7o1KkTunbtinfffVdljgWpVIojR44gPj4eHh4e+OqrrzB16lSVuRpat26NLVu2YPXq1WjSpAl27tyJPXv2oFGjRhpdj0ShUCi0vCeCkclkkEqluHw3GZaWVuoPEDG7SqZCh0BEVIBMJoNtFSlSU1NVOhLq+jWkUimq9FsHA2PzYp9Hnv0SzzYNLNFY9RlHSRARkSho2w+hpPsw6Ds2SRAREZFa5aKGwVZqCisrVrm/zavsPKFDKBPMjA2FDoGISghrGLRTLhIGIiIidZgwaIdNEkRERKQWaxiIiEgUWMOgHSYMREQkDv96gFSxjxcxNkkQERGRWqxhICIiUWCThHaYMBARkSgwYdAOEwYiIhIFJgzaYR8GIiIiUos1DEREJA4cJaEVJgxERCQKbJLQDpskiIiISC3WMBARkSiwhkE7TBiIiEgUJNAyYRB5JwY2SRAREZFarGEgIiJRYJOEdpgwEBGROHBYpVbYJEFERERqsYaBiIhEgU0S2mHCQEREosCEQTtMGIiISBQkkteLNseLGfswEBERkVqsYSAiIlF4XcOgTZOEDoMpg5gwEBGROGjZJMFhlURERERqsIaBiIhEgaMktMOEgYiIRIGjJLTDJgkiIiJSizUMREQkCgYGEhgYFL+aQKHFseUBEwYiIhIFNkloh00SREREpBZrGHTk9IXbWLLxKC7dfICkpzJsmjsY3To0ETqsUpWXJ8e8Nb/j1yPRePIsDbZVrfBJV0+MGuCj7F2sUCgw96ffsWVfFGRpr9CisTNmj/0YtR1tAAAJic+wYP1hnI65pTxHL98WGBngA+MK4nq7/rg9Aks2hePxMxka1auO78d9DI+GtYQOS1DqPmePn8kQsuQ3HD93A6lpr9C6WV18P+5j1KlpI2DU+oHvJ46S0JagNQyRkZHo3r07HBwcIJFIsGfPHiHD0crLV1loVL865o7/VOhQBLNs01Fs2HMa3435CBFbJuGb4T2wfHM41uyM/KfM5nCs3RmJ2eM+wf4fR8Pc1Bh9x6xEZlYOAOD2/ceQyxX4ftynOL5pIkK+/BAb95xG6Kr9Ql2WIHYdicHkhbsxYVAXnNg4AY3qVUfvEcvwJCVN6NAE9bbPmUKhQL9xq3Hv0VNs/uELRGyaiBr21vALWoKMV1kCRKs/+H56Lb9JQptFzARNGDIyMtCkSRMsW7ZMyDB04v02DTF5WHd80FFctQr/Fn01Hr5tG8G7dUM42lfBBx2bon0rF8Revw/g9Rf6T9sjMDLAB53busOtbnUsntIPyU9TcejkFQBAx3caYOE3/ujg6Qqn6lXh29YdQ/u8h98jLgt5aaVu+ZZj6O/XGv49vOBa2x7zJ30Gc1NjbNobJXRognrb5+zOg8c4f+Ue5k34DM0bOqFeLVvMn/gpMrNy8OvhGAGi1R98P72WX8OgzSJmgiYMXbp0wbfffosPP/xQyDBIR1o0csap6Fu48+AxAODarb/wx+W7eO8dNwDAg0fP8PiZDG1b1FceY2VhhmZuToi5Gv/G86ZlZKKSpXnJBq9HsnNyEXszAR1auSi3GRgYoH0rF5y/8ub7JHZZObkAAFOTf5quDAwMYFzBCGdj7wgVluD4fiJdKVONwllZWcjK+qdqUSaTCRgN/Vfw595Ie5mJdn1nwdBAgjy5AhOHdEMv3xYAgMd/V39Ws7ZUOa6atSUePyu8ajT+4ROs3RmJqcE9SzZ4PfLsRTry8uSF3Ccr3LqXLFBU+q9+LTvUsKuMGcv2YsGkPjA3M8byLcfx6PELJD9LFTo8wfD99A/2YdBOmUoYQkNDMX36dKHDoDfYeywWu47EYFlIf7g42+Harb8wbdEu2FaV4pOurTQ+X+KTF/AfsxIfdGwK/x6tSyBiKk8qGBli45zBGDFzM5w7jYehoQE6tHSBd2s3KBRCR0f6gMMqtVOmhlVOmjQJqampyiUhIUHokOhfZi77DcH9vOHn3RwN6jjgo84tMfjTDliyMQwAYPP3Xzj/7Wj1JCUNNlVU//pJepKKj0csRQt3Z8ydIK6OpFUqWcDQ0KCQ+ySDTRUrgaIqG5o2qImTWybh3vG5uPn7d9i5JAjPUzNQq3oVoUMTDN9Pwnlbx/6cnBxMmDAB7u7uqFixIhwcHNC/f388evRI5Ry1atUq0I9i9uzZKmUuX76Mtm3bwtTUFI6OjpgzZ06BWHbs2AFXV1eYmprC3d0dBw8e1Ph6ylTCYGJiAisrK5WF9EdmZnaBWdQMDQyg+PvPu5oOVWBTxQqnYv5U7k/LyMTF6/fh0chZuS3xyQt8NGIJ3F0cseDrvjAwKFNvU60ZVzBCU1dHRJyPU26Ty+WIPP8nWro7v+VIyie1MEPVypa48+AxLt54gK7tGwsdkmD4fvqHBFp2etTw+dZv69j/8uVLXLhwAVOmTMGFCxewa9cuxMXFoUePHgXKzpgxA4mJicplxIgRyn0ymQw+Pj5wcnJCTEwM5s6di5CQEKxevVpZ5syZM+jTpw8CAwNx8eJF+Pn5wc/PD1evXtXoespUk4Q+S3+ZhfiEJ8r1+4+e4UrcQ1SSmsPRzlrAyErP+20aYfGGI6huWxkuzna4+udDrNp2HJ91ewfA6/a/QZ+0x6INR+BcoxpqOlTBnB8PwraqFJ3bugP4O1kIXoLqdtaYGtwTz16kK88vpr+Ghvd9D8Onb0SzBjXRvGEtrPjlODJeZcG/+ztChyYodZ+zPUcvoGplC9Swtcb1O48wcd5OdGvfGO+900DAqIXH99Nrpd0k0aVLF3Tp0qXQfVKpFGFhYSrbli5dilatWuHBgweoWbOmcrulpSXs7OwKPc/mzZuRnZ2NtWvXwtjYGA0bNkRsbCzmz5+PIUOGAAAWLVqEzp07Y9y4cQCAmTNnIiwsDEuXLsXKlSuLfD2CJgzp6em4ffu2cj0+Ph6xsbGwtrZWuVllQeyN++g+dLFy/ZsFuwAAfbp5YnnI50KFVaq+Hd0bc348iEk/7MCz5+mwrWqFz3u2weiBvsoyQf6d8PJVNsbP2QZZ+iu0bFwbm+cNhalJBQBA5B9xiH/4FPEPn8LDb5rK+R+dXlSq1yOkXj4eePoiHbNWHcDjZ2lwr18dOxcHiSppKoy6z1nyUxm+WbALT1JeT/r1WVdPjBvUWahw9QbfT7r13w73JiYmMDEx0fq8qampkEgkqFSpksr22bNnY+bMmahZsyb69u2L0aNHw8jo9c93VFQU2rVrB2NjY2V5X19ffP/993j+/DkqV66MqKgojBkzRuWcvr6+Gs99JFEohOsOdOLECXTs2LHA9oCAAKxfv17t8TKZDFKpFMnPUtk8ocar7DyhQygTzIwNhQ6BSFRkMhlsq0iRmlpy3+P5vxVNvt4HQ9OKxT5PXmYGLs3qXmD7tGnTEBIS8tZjJRIJdu/eDT8/v0L3Z2Zmok2bNnB1dcXmzZuV2+fPn4/mzZvD2toaZ86cwaRJkzBw4EDMnz8fAODj4wNnZ2esWrVKecz169fRsGFDXL9+HQ0aNICxsTE2bNiAPn36KMssX74c06dPR3Jy0UfKCFrD0KFDBwiYrxARkYjoqkkiISFBJbnRtnYhJycHn3zyCRQKBVasWKGy7981A40bN4axsTG++OILhIaG6qRWQxPi6k1GRESkpf92vtfmhzs/Wbh//z7CwsLU1rJ4enoiNzcX9+7dAwDY2dkVqCXIX8/v9/CmMm/qF/EmTBiIiEgU9G1q6Pxk4datWzh69CiqVFE//Dc2NhYGBgawsXn9QDUvLy9ERkYiJydHWSYsLAwuLi6oXLmyskx4eLjKecLCwuDl5aVRvBwlQUREolDaoyTe1rHf3t4eH330ES5cuID9+/cjLy8PSUlJAABra2sYGxsjKioK586dQ8eOHWFpaYmoqCiMHj0a/fr1UyYDffv2xfTp0xEYGIgJEybg6tWrWLRoERYsWKB83ZEjR6J9+/aYN28eunXrhq1btyI6Olpl6GVRMGEgIiJRKO2poaOjo1U69uf3RwgICEBISAj27t0LAGjatKnKccePH0eHDh1gYmKCrVu3IiQkBFlZWXB2dsbo0aNV+jVIpVIcOXIEQUFB8PDwQNWqVTF16lTlkEoAaN26NbZs2YLJkyfj66+/Rr169bBnzx40atRIs+sXcpSEtjhKoug4SqJoOEqCqHSV5igJj6kHtB4lETOjW4nGqs9Yw0BEROKgZZOEhhM9ljtMGIiISBT4tErtcJQEERERqcUaBiIiEgU+3lo7TBiIiEgU2CShHTZJEBERkVqsYSAiIlFgk4R2mDAQEZEosElCO2ySICIiIrVYw0BERKLAGgbtMGEgIiJRYB8G7TBhICIiUWANg3bYh4GIiIjUYg0DERGJApsktMOEgYiIRIFNEtphkwQRERGpxRoGIiISBQm0bJLQWSRlExMGIiISBQOJBAZaZAzaHFsesEmCiIiI1GINAxERiQJHSWiHCQMREYkCR0lohwkDERGJgoHk9aLN8WLGPgxERESkFmsYiIhIHCRaNiuIvIaBCQMREYkCOz1qhwmDSJgZGwodQpnwZ2Ka0CGUCfXtLYUOgYhKGRMGIiISBcnf/2lzvJgxYSAiIlHgKAntcJQEERERqcUaBiIiEgVO3KQdJgxERCQKHCWhnSIlDHv37i3yCXv06FHsYIiIiEg/FSlh8PPzK9LJJBIJ8vLytImHiIioRPDx1topUsIgl8tLOg4iIqISxSYJ7WjVhyEzMxOmpqa6ioWIiKjEsNOjdjQeVpmXl4eZM2eievXqsLCwwN27dwEAU6ZMwZo1a3QeIBEREQlP44Thu+++w/r16zFnzhwYGxsrtzdq1Ag//fSTToMjIiLSlfwmCW0WMdM4Yfj555+xevVq+Pv7w9Dwn+cTNGnSBDdv3tRpcERERLqS3+lRm0XMNE4Y/vrrL9StW7fAdrlcjpycHJ0ERURERPpF44TBzc0NJ0+eLLB9586daNasmU6CIiIi0jWJDhYx0zhhmDp1KoKDg/H9999DLpdj165dGDx4ML777jtMnTq1JGIkIiLSWv4oCW0WTURGRqJ79+5wcHCARCLBnj17VPYrFApMnToV9vb2MDMzg7e3N27duqVSJiUlBf7+/rCyskKlSpUQGBiI9PR0lTKXL19G27ZtYWpqCkdHR8yZM6dALDt27ICrqytMTU3h7u6OgwcPanQtQDEShp49e2Lfvn04evQoKlasiKlTp+LGjRvYt28f3n//fY0DICIiKo8yMjLQpEkTLFu2rND9c+bMweLFi7Fy5UqcO3cOFStWhK+vLzIzM5Vl/P39ce3aNYSFhWH//v2IjIzEkCFDlPtlMhl8fHzg5OSEmJgYzJ07FyEhIVi9erWyzJkzZ9CnTx8EBgbi4sWL8PPzg5+fH65evarR9UgUCoVCw3ugN2QyGaRSKZKfpcLKykrocKgc+DMxTegQyoT69pZCh0DlhEwmg20VKVJTS+57PP+34uNVJ1HBzKLY58l5lY4dX7RFQkKCSqwmJiYwMTF567ESiQS7d+9WzpysUCjg4OCAr776CmPHjgUApKamwtbWFuvXr8dnn32GGzduwM3NDefPn0eLFi0AAIcOHULXrl3x8OFDODg4YMWKFfjmm2+QlJSkHLk4ceJE7NmzRzkQ4dNPP0VGRgb279+vjOedd95B06ZNsXLlyiJff7Efbx0dHY2NGzdi48aNiImJKe5piIiISoWumiQcHR0hlUqVS2hoqMaxxMfHIykpCd7e3sptUqkUnp6eiIqKAgBERUWhUqVKymQBALy9vWFgYIBz584py7Rr105lmgNfX1/ExcXh+fPnyjL/fp38MvmvU1Qaz/T48OFD9OnTB6dPn0alSpUAAC9evEDr1q2xdetW1KhRQ9NTEhERlRmF1TBoKikpCQBga2urst3W1la5LykpCTY2Nir7jYyMYG1trVLG2dm5wDny91WuXBlJSUlvfZ2i0riGYdCgQcjJycGNGzeQkpKClJQU3LhxA3K5HIMGDdL0dERERKVGF5M2WVlZqSzFSRjKIo1rGCIiInDmzBm4uLgot7m4uGDJkiVo27atToMjIiLSFX16loSdnR0AIDk5Gfb29srtycnJaNq0qbLM48ePVY7Lzc1FSkqK8ng7OzskJyerlMlfV1cmf39RaVzD4OjoWOgETXl5eXBwcND0dERERKXCQKL9oivOzs6ws7NDeHi4cptMJsO5c+fg5eUFAPDy8sKLFy9U+gkeO3YMcrkcnp6eyjKRkZEqv8thYWFwcXFB5cqVlWX+/Tr5ZfJfp6g0Thjmzp2LESNGIDo6WrktOjoaI0eOxA8//KDp6YiIiMql9PR0xMbGIjY2FsDrjo6xsbF48OABJBIJRo0ahW+//RZ79+7FlStX0L9/fzg4OChHUjRo0ACdO3fG4MGD8ccff+D06dMIDg7GZ599pvwDvW/fvjA2NkZgYCCuXbuGbdu2YdGiRRgzZowyjpEjR+LQoUOYN28ebt68iZCQEERHRyM4OFij6ylSk0TlypVVqmIyMjLg6ekJI6PXh+fm5sLIyAj/+9//lBdKRESkT0q7SSI6OhodO3ZUruf/iAcEBGD9+vUYP348MjIyMGTIELx48QLvvvsuDh06BFNTU+UxmzdvRnBwMDp16gQDAwP07t0bixcvVu6XSqU4cuQIgoKC4OHhgapVq2Lq1KkqczW0bt0aW7ZsweTJk/H111+jXr162LNnDxo1aqTZ9RdlHoYNGzYU+YQBAQEaBaANzsNAusZ5GIqG8zCQrpTmPAz+a87A2Lz48zBkv0zH5sDWJRqrPitSDUNpJgFERESkfzQeJfFvmZmZyM7OVtkmxqyLiIj0n7aPqObjrTWUkZGB4OBg2NjYoGLFiqhcubLKQkREpI+0mYPhv3MxiJHGCcP48eNx7NgxrFixAiYmJvjpp58wffp0ODg44Oeffy6JGImIiEhgGjdJ7Nu3Dz///DM6dOiAgQMHom3btqhbty6cnJywefNm+Pv7l0ScREREWtGniZvKIo1rGFJSUlC7dm0Ar/srpKSkAADeffddREZG6jY6IiIiHWGThHY0rmGoXbs24uPjUbNmTbi6umL79u1o1aoV9u3bp3wYlRg17jEVCYkpBbYHftQWP0z4VICI9NuP2yOwZFM4Hj+ToVG96vh+3MfwaFhL6LBKxK8Hz2LX7+fw6PHrJ8fVrmmDwM86obWHi0o5hUKB0dPXI+rCn5jzdT+0f6dhgXOlyjLgP3IxnjyT4eiWqbC0MFPui7lyF4vWHMDdB8mwrSrFwE/fwwedPEr24krZ6Qu3sWTjUVy6+QBJT2XYNHcwunVootxfuWXhE9FM/9IPX37uXeg+sRDTZ45KhsY1DAMHDsSlS5cAvH7m9rJly2BqaorRo0dj3LhxGp0rNDQULVu2hKWlJWxsbODn54e4uDhNQ9ILxzaMw83fZymX3Utff3H5eTcTODL9s+tIDCYv3I0Jg7rgxMYJaFSvOnqPWIYnKeVzDgSbqlIMD/DFhgXB2DA/CC0a18G47zbi7gPVud237j0NqPkL5tslu1C3VsH53x8lpWDMjPXwcK+NjYu+xGc92mDWkl04e+FPXV6K4F6+ykKj+tUxd3zhSfi/P4M3f5+FpVP8IZFI0KNj09INVM+I7TP3JvmjJLRZxEzjGobRo0cr/+3t7Y2bN28iJiYGdevWRePGjTU6V0REBIKCgtCyZUvk5ubi66+/ho+PD65fv46KFStqGpqgqlZWnchm4YYjcK5RFW2a1xMoIv21fMsx9PdrDf8er+cxnz/pMxw5fQ2b9kZh9AAfgaPTvbatGqisD/vcF7t+P4erNx+gds3Xj5z98+4jbN5zEhvmB6NrwKxCz/PrwbNIz3iFwE87ISpGNRHYdegcHGytMTKwGwDA2dEGsTfu45ffTuGd5vVL4KqE8X6bhni/TcGal3y2VVWHdR+MvIK2HvVQq0bVkg5Nr4ntM/cm2jYriDxf0G4eBgBwcnKCk5NTsY49dOiQyvr69ethY2ODmJgYtGvXTtvQBJOdk4vtv5/HcP/3RN9J5r+yc3IRezNB5UvKwMAA7Vu54PyVeAEjKx15eXKEn76CV5nZaORaEwCQmZWNKfO2YdwXPVGlcuEzKN59kIw1245h7Q/D8VdSwaavKzcfoGWTOirb3mlWDwt+2q/7iygjHj+T4cipq1ge8rnQoQhK7J+5f2OnR+0UKWH497zV6nz55ZfFDiY1NRUAYG1tXej+rKwsZGVlKddlMlmxX6skHThxGanpr9D3A0+hQ9E7z16kIy9PjmrWqj+M1aytcOte8huOKvtu30vCoPErkJ2dCzMzY3z/dT9l7cKCnw6gsWtNtH/HrdBjs3NyMeWHrRgxoAvsqlUqNGF49iIN1pVU76l1JQtkvMxCZlYOTE0q6P6i9NwvB87BoqIpuou8OUKsnznSvSIlDAsWLCjSySQSSbETBrlcjlGjRqFNmzZvfCBGaGgopk+fXqzzl6ZNe8/A28sN9tUqCR0K6Qmn6lWxceEIpL/MwrHTVzBj4U6smDUYDxOfIfryHWxcOOKNxy7/+RBqOdqgS0f2h9HE5r1n8XHnFqJMlqhwBihGx73/HC9mRUoY4uNLvtoqKCgIV69exalTp95YZtKkSSqP7JTJZHB0dCzx2DTxIDEFJ/6Iw8Y5g4UORS9VqWQBQ0ODAp2tnqTIYFOl/E4rXqGCERwdXrejN6hbHTduP8S2fWdgYmyEv5JS4N1nhkr5ibM3o6lbLayYNQTRl+/izv0ktD59FQCgwOvnxfn2+xYDPumAIX3fR5VKlkh5oXpPU16ko6K5iSh/MM9cvI1b95OxZtZAoUMRnFg/c4Vhk4R2tO7DoAvBwcHYv38/IiMjUaNGjTeWMzExgYmJSSlGprkt+6JQrbIlfN7SMUvMjCsYoamrIyLOxymHw8nlckSe/xODPi67/VY0JZcrkJOTiyF9vdHTp6XKvr4jFmFUYDe0bfm6s+Tsif7Iys5R7r9+6yG+XfwrVs0egup2VQAA7q41cSZGdYTRH7G34e5Ss4SvRD9t+i0KTRs4wr3+m79PxIKfOdIVQRMGhUKBESNGYPfu3Thx4gScnZ2FDEdrcrkcm/edxWfdPGFkZCh0OHpreN/3MHz6RjRrUBPNG9bCil+OI+NVFvy7vyN0aCVi2YZDaO3hAttqlfDyVRYOR8TiwtV4LAoZiCqVLQvt6GhXrRIc7F735alhX0Vl3wvZSwBArRo2ynkYenX2xI4DUViy7nd0f98D0ZfvIPzUFcyfWr6eNJv+MgvxCU+U6/cfPcOVuIeoJDWH49/3S5b+Cr+FX8TMUR8KFabeEdtn7k0kEsCAoySKTdCEISgoCFu2bMFvv/0GS0tLJCUlAQCkUinMzMzUHK1/TvwRh4dJz9Gvh7g+hJrq5eOBpy/SMWvVATx+lgb3+tWxc3FQua0efZ6agekLt+NpShosKpqibi07LAoZCM9muhty62BnjflTB2DhT/uxbd9p2FSV4usRvcrVkEoAiL1xH92H/tMJ+5sFuwAAfbp5KkdD7DoSA4VCgd6+LQSJUR+J7TP3JgZaJgzaHFseSBQKhUKwF39DurZu3ToMGDBA7fEymQxSqRTJz1L5WG3SiT8TxTWRTXHVty98+CeRpmQyGWyrSJGaWnLf4/m/FcN/OQ8Tc4tinyfrZTqW92lZorHqM8GbJIiIiEoDOz1qp1ijRE6ePIl+/frBy8sLf/31FwBg48aNbx3hQEREJKT8JgltFjHTOGH49ddf4evrCzMzM1y8eFE5kVJqaipmzSp8SlsiIiIq2zROGL799lusXLkSP/74IypU+Gd8d5s2bXDhwgWdBkdERKQrfLy1djTuwxAXF1focx6kUilevHihi5iIiIh0TtsnTor9aZUa1zDY2dnh9u3bBbafOnUKtWvX1klQREREumagg0XMNL7+wYMHY+TIkTh37hwkEgkePXqEzZs3Y+zYsRg2bFhJxEhEREQC07hJYuLEiZDL5ejUqRNevnyJdu3awcTEBGPHjsWIEW9+gA4REZGQtO2HIPIWCc0TBolEgm+++Qbjxo3D7du3kZ6eDjc3N1hYFH8yDCIiopJmAC37MEDcGUOxJ24yNjaGm5ubLmMhIiIiPaVxwtCxY8e3znZ17NgxrQIiIiIqCWyS0I7GCUPTpk1V1nNychAbG4urV68iIKB8PRmPiIjKDz58SjsaJwwLFiwodHtISAjS09O1DoiIiIj0j86Glfbr1w9r167V1emIiIh0SiL5Z/Km4ixsktCRqKgomJqa6up0REREOsU+DNrROGHo1auXyrpCoUBiYiKio6MxZcoUnQVGRERE+kPjhEEqlaqsGxgYwMXFBTNmzICPj4/OAiMiItIldnrUjkYJQ15eHgYOHAh3d3dUrly5pGIiIiLSOcnf/2lzvJhp1OnR0NAQPj4+fColERGVOfk1DNosYqbxKIlGjRrh7t27JRELERER6SmNE4Zvv/0WY8eOxf79+5GYmAiZTKayEBER6SPWMGinyH0YZsyYga+++gpdu3YFAPTo0UNlimiFQgGJRIK8vDzdR0lERKQliUTy1kcbFOV4MStywjB9+nQMHToUx48fL8l4iIiISA8VuUlCoVAAANq3b//WhYiISB+VdpNErVq1lLUa/16CgoIAAB06dCiwb+jQoSrnePDgAbp16wZzc3PY2Nhg3LhxyM3NVSlz4sQJNG/eHCYmJqhbty7Wr1+vzW16I42GVYq9OoaIiMqu0p7p8fz58yrN9FevXsX777+Pjz/+WLlt8ODBmDFjhnLd3Nxc+e+8vDx069YNdnZ2OHPmDBITE9G/f39UqFABs2bNAgDEx8ejW7duGDp0KDZv3ozw8HAMGjQI9vb28PX1LeaVFk6jhKF+/fpqk4aUlBStAiIiItJn/+3gb2JiAhMTkwLlqlWrprI+e/Zs1KlTR6U23tzcHHZ2doW+zpEjR3D9+nUcPXoUtra2aNq0KWbOnIkJEyYgJCQExsbGWLlyJZydnTFv3jwAQIMGDXDq1CksWLBA2IRh+vTpBWZ6JCIiKgvyHyKlzfEA4OjoqLJ92rRpCAkJeeux2dnZ2LRpE8aMGaPyh/fmzZuxadMm2NnZoXv37pgyZYqyliEqKgru7u6wtbVVlvf19cWwYcNw7do1NGvWDFFRUfD29lZ5LV9fX4waNarY1/kmGiUMn332GWxsbHQeBBERUUnT1dTQCQkJsLKyUm4vrHbhv/bs2YMXL15gwIABym19+/aFk5MTHBwccPnyZUyYMAFxcXHYtWsXACApKUklWQCgXE9KSnprGZlMhlevXsHMzEzj63yTIicM7L9AREQEWFlZqSQMRbFmzRp06dIFDg4Oym1DhgxR/tvd3R329vbo1KkT7ty5gzp16ugsXl3ReJQEERFRmST5p+NjcZbiPkri/v37OHr0KAYNGvTWcp6engCA27dvAwDs7OyQnJysUiZ/Pb/fw5vKWFlZ6bR2AdAgYZDL5WyOICKiMssAEq2X4li3bh1sbGzQrVu3t5aLjY0FANjb2wMAvLy8cOXKFTx+/FhZJiwsDFZWVnBzc1OWCQ8PVzlPWFgYvLy8ihXr22j8eGui8qy+vaXQIZQJlVsGCx1CmfD8/FKhQ6B/Ke1hlcDrP7bXrVuHgIAAGBn985N7584dbNmyBV27dkWVKlVw+fJljB49Gu3atUPjxo0BAD4+PnBzc8Pnn3+OOXPmICkpCZMnT0ZQUJCy38TQoUOxdOlSjB8/Hv/73/9w7NgxbN++HQcOHCj+hb6Bxs+SICIioqI5evQoHjx4gP/9738q242NjXH06FH4+PjA1dUVX331FXr37o19+/YpyxgaGmL//v0wNDSEl5cX+vXrh/79+6vM2+Ds7IwDBw4gLCwMTZo0wbx58/DTTz/pfEglwBoGIiISCV2NktCEj49PoX0AHR0dERERofZ4JycnHDx48K1lOnTogIsXL2oenIaYMBARkSjoah4GsWKTBBEREanFGgYiIhIFITo9lidMGIiISBQMoGWTRHEnYign2CRBREREarGGgYiIRIFNEtphwkBERKJgAO2q1cVeJS/26yciIqIiYA0DERGJgkQi0erJy2J/ajMTBiIiEgUtHjipPF7MmDAQEZEocKZH7bAPAxEREanFGgYiIhINcdcRaIcJAxERiQLnYdAOmySIiIhILdYwEBGRKHBYpXaYMBARkShwpkftiP36iYiIqAhYw0BERKLAJgntMGEgIiJR4EyP2mGTBBEREanFGgYiIhIFNklohwkDERGJAkdJaIcJAxERiQJrGLQj9oSJiIiIioA1DEREJAocJaEdJgxERCQKfPiUdtgkQURERGqxhoGIiETBABIYaNGwoM2x5QETBh05feE2lmw8iks3HyDpqQyb5g5Gtw5NhA5L78xfdxj7j1/CrfvJMDWpgFaNayMkuCfq1bIVOjS9kpcnx+zVB7H90Hk8fiaDXVUp+n7gibGBnctFT+3WzepgxOfeaOJaE/bVpPAfuxoHIy4r91c0M8a04J7o2r4xrKUVcf/RM6zeFoF1u04pywR82AYf+bZAY5casLIwg1PHcZClv1J5nTo1bTDjSz94NqmNCkaGuH77Eb5buR+nYm4py8z+6iN4NqmNBnXs8ee9ZLTzn13yN0AAP26PwJJN4Xj8TIZG9arj+3Efw6NhLaHDKlVsktCOoE0SK1asQOPGjWFlZQUrKyt4eXnh999/FzKkYnv5KguN6lfH3PGfCh2KXjtz4TYGfdwOR9aOxa6lwcjJzUOvEUuR8SpL6ND0ysKfw7D215OYM+5jnNs+GSEjemLxxqNYvS1C6NB0wtzMBFf//Avj5mwrdP+3o3ujk5cbvpj6Mzw/+RYrt57AnHEfo0s7d2UZM9MKCI+6jgXrj7zxdbbOHwojQwP0HLYYHfvPwdVbf2HrgqGwqWKpUm7zvrPYHXZBNxenh3YdicHkhbsxYVAXnNg4AY3qVUfvEcvwJCVN6NCoDBG0hqFGjRqYPXs26tWrB4VCgQ0bNqBnz564ePEiGjZsKGRoGnu/TUO836ZsxSyEnUuCVNaXT+uHej6TEHsjAW2a1xUoKv3zx+W76Nq+MXzfbQQAqOlQBb8ejkbMtfsCR6YbR89cx9Ez19+437OxM345cA6nL7yuCdiw+zQGfNgGzd2c8HvkFQDAyl9OAADaNK9X6DmspRVR18kGX367GdduPwIATF/6GwZ93A4N6jjg8bM4AMDEeTsBAFUqdUXDetV1cn36ZvmWY+jv1xr+PbwAAPMnfYYjp69h094ojB7gI3B0pUfy93/aHC9mgtYwdO/eHV27dkW9evVQv359fPfdd7CwsMDZs2eFDItKkSw9EwBQ2cpc4Ej0S6vGtRFxPg637ycDAK78+RBnL92Fd2s3gSMrHecux6NLO3fYV5MCAN71qIc6NW1w/NyNIp8jJTUDf95LwqfdWsHc1BiGhgYY0OtdPH4mQ+yNByUVut7JzslF7M0EdGjlotxmYGCA9q1ccP5KvICRlb78JgltFjHTmz4MeXl52LFjBzIyMuDl5VVomaysLGRl/VN1LZPJSis8KgFyuRyT5u+EZ5PacKvrIHQ4emV0wPtIS89Eq4+/haGBBHlyBSYP+wCfdGkpdGilYsLcHVj4dR9cP/gdcnLzIJfLMfK7X3Dm4h2NzvNh0FJsmjsECRE/QC5X4MnzdHz05XKkpr1Sf3A58exFOvLy5KhmrdoMU83aCrfuJQsUFZVFgicMV65cgZeXFzIzM2FhYYHdu3fDza3wv6JCQ0Mxffr0Uo6QSsrYOdtx404ifv9xtNCh6J3dRy9gx6Hz+PHbALjWtseVP//C1/N3wr6aFH0+eEfo8ErckE/bo4V7LfQZsxIJiSlo3awu5o7/BElPUxHxR1yRzzN3/Cd4+jwNXQcvxKusbPT3a41f5n+BTgFzkfyMf3CIjUTLURJib5IQPGFwcXFBbGwsUlNTsXPnTgQEBCAiIqLQpGHSpEkYM2aMcl0mk8HR0bE0wyUdGTdnOw6fvIqDq0ehum1locPRO1MX7cGogPfR26cFAKBh3ep4mJiCBevDyn3CYGpSAVOGd8fn437EkdPXAADXbj9Co/o1ENyvU5EThnYt68P33UZw7jQeaRmvm77Gfr8dHVq5os8Hnli4IazErkGfVKlkAUNDgwIdHJ+kyGBTxUqgqITBURLaEXziJmNjY9StWxceHh4IDQ1FkyZNsGjRokLLmpiYKEdU5C9UtigUCoybsx0HTlzC3hVfwql6VaFD0kuvsrJhYKD68TQwkECukAsUUempYGQI4wpGkCsUKtvlcjkMNPjGNjc1Vh6nch6FQqPzlHXGFYzQ1NUREef/SbTkcjkiz/+Jlu7OAkZW+tiHQTuC1zD8l1wuV+mnUFakv8xCfMIT5fr9R89wJe4hKknN4WhnLWBk+mXs99ux83A0tvwwBBbmpkh++rpa2MrCFGZ/f8ET0Pldd8xfdxg17CqjQW17XI57iOVbjsO/R/moXahoZgxnx2rKdSeHKmhUvzpepL7Ew+TnOBVzCzO+9MOrzBwkJKWgTfO6+LRrK0xeuEt5jE0VS9hUsUJtx9dJZ8O6Dkh7mYmHSc/xQvYSf1yOx4u0l1ge0h9zf/odr7JyEODXGk4OVZQ1FwDgXKMqKpqbwLaKFUxNKqBR/dcjJeLuJiEnN6+U7kjJGt73PQyfvhHNGtRE84a1sOKX48h4lQX/7uXj/USlQ6JQ/CeNL0WTJk1Cly5dULNmTaSlpWHLli34/vvvcfjwYbz//vtqj5fJZJBKpUh+lip4bcOpmD/RfejiAtv7dPPE8pDPBYhIP1VuGVzo9mVT+6Evv7yU0jIyMWvlfuw/cQlPn6fDrqoUvX09MH5QFxhXED7Pf9P/x6Jq07we9q8aWWD7lv1nETR9E2yqWGJqUE909HRFZStzJCSlYMPuM1i+5Ziy7ITBXTFxSNcC5xg+fSN+2X8OANC0QU1MHtYdzRrUhJGRAW7eTcLcNb+rDOnct3Ik3vUoODSzcY+pSEhM0eo6n59fqtXxurR6ewSWbDyKx8/S4F6/OmaP/RgtGtUSOizIZDLYVpEiNbXkvsfzfyt2/3EXFS0s1R/wBhnpafiwVe0SjVWfCZowBAYGIjw8HImJiZBKpWjcuDEmTJhQpGQB0K+EgUhMtE0YxEKfEgZ9VZoJw2/ntU8YerYsesIQEhJSoKO+i4sLbt68CQDIzMzEV199ha1btyIrKwu+vr5Yvnw5bG3/mfn2wYMHGDZsGI4fPw4LCwsEBAQgNDQURkb//OFw4sQJjBkzBteuXYOjoyMmT56MAQMGFPs630TQP1XWrFkj5MsTERGVqIYNG+Lo0aPK9X//0I8ePRoHDhzAjh07IJVKERwcjF69euH06dMAXk830K1bN9jZ2eHMmTNITExE//79UaFCBcyaNQsAEB8fj27dumHo0KHYvHkzwsPDMWjQINjb28PX11en1yJ83SYREVEpEGKmRyMjI9jZ2RXYnpqaijVr1mDLli147733AADr1q1DgwYNcPbsWbzzzjs4cuQIrl+/jqNHj8LW1hZNmzbFzJkzMWHCBISEhMDY2BgrV66Es7Mz5s2bBwBo0KABTp06hQULFug8YRB8lAQREVFp0NUoCZlMprK8raP+rVu34ODggNq1a8Pf3x8PHryeZTQmJgY5OTnw9vZWlnV1dUXNmjURFRUFAIiKioK7u7tKE4Wvry9kMhmuXbumLPPvc+SXyT+HLjFhICIi0oCjoyOkUqlyCQ0NLbScp6cn1q9fj0OHDmHFihWIj49H27ZtkZaWhqSkJBgbG6NSpUoqx9ja2iIpKQkAkJSUpJIs5O/P3/e2MjKZDK9e6XZGUzZJEBGRKEig3WyN+UcmJCSodHo0MTEptHyXLl2U/27cuDE8PT3h5OSE7du3w8zMrNhxCIU1DEREJAoGEu0XAAUmEHxTwvBflSpVQv369XH79m3Y2dkhOzsbL168UCmTnJys7PNgZ2eH5OTkAvvz972tjJWVlc6TEiYMREREpSA9PR137tyBvb09PDw8UKFCBYSHhyv3x8XF4cGDB8oHMHp5eeHKlSt4/PixskxYWBisrKyUj0/w8vJSOUd+mTc9xFEbTBiIiEgUJDr4TxNjx45FREQE7t27hzNnzuDDDz+EoaEh+vTpA6lUisDAQIwZMwbHjx9HTEwMBg4cCC8vL7zzzutJ7Hx8fODm5obPP/8cly5dwuHDhzF58mQEBQUpazWGDh2Ku3fvYvz48bh58yaWL1+O7du3Y/Ro3T/Uj30YiIhIFEr74VMPHz5Enz598OzZM1SrVg3vvvsuzp49i2rVXk+LvmDBAhgYGKB3794qEzflMzQ0xP79+zFs2DB4eXmhYsWKCAgIwIwZM5RlnJ2dceDAAYwePRqLFi1CjRo18NNPP+l8SCUg8EyP2uJMj0TC4EyPRcOZHtUrzZkeD1+4h4oWxX+NjHQZfJvXEu3U0GySICIiIrXYJEFERKJgAIlWjzY30GJIZnnAhIGIiERBAmj1ky/udIFNEkRERFQErGEgIiJxYBWDVpgwEBGRKAjxtMryhE0SREREpBZrGIiISBy0nLhJ5BUMTBiIiEgc2IVBO2ySICIiIrVYw0BEROLAKgatMGEgIiJR4CgJ7TBhICIiUSjtp1WWN+zDQERERGqxhoGIiESBXRi0w4SBiIjEgRmDVtgkQURERGqxhoGIiESBoyS0w4SBiIhEgaMktMMmCSIiIlKLNQxERCQK7POoHSYMRKSx5+eXCh1CmfBXyiuhQ9B7aWmleI+YMWiFTRJERESkFmsYiIhIFDhKQjtMGIiISBQ4SkI7TBiIiEgU2IVBO+zDQERERGqxhoGIiMSBVQxaYcJARESiwE6P2mGTBBEREanFGgYiIhIFjpLQDhMGIiISBXZh0A6bJIiIiEgt1jAQEZE4sIpBK0wYiIhIFDhKQjtskiAiIiK1WMNARESiwFES2mHCQEREosAuDNphwkBEROLAjEEr7MNAREREajFhICIiUZDo4D9NhIaGomXLlrC0tISNjQ38/PwQFxenUqZDhw6QSCQqy9ChQ1XKPHjwAN26dYO5uTlsbGwwbtw45ObmqpQ5ceIEmjdvDhMTE9StWxfr168v1j16GyYMREQkDpJ/Oj4WZ9G0SSIiIgJBQUE4e/YswsLCkJOTAx8fH2RkZKiUGzx4MBITE5XLnDlzlPvy8vLQrVs3ZGdn48yZM9iwYQPWr1+PqVOnKsvEx8ejW7du6NixI2JjYzFq1CgMGjQIhw8f1uZuFcA+DERERCXg0KFDKuvr16+HjY0NYmJi0K5dO+V2c3Nz2NnZFXqOI0eO4Pr16zh69ChsbW3RtGlTzJw5ExMmTEBISAiMjY2xcuVKODs7Y968eQCABg0a4NSpU1iwYAF8fX11dj2sYSAiIlGQ6GABAJlMprJkZWUV6fVTU1MBANbW1irbN2/ejKpVq6JRo0aYNGkSXr58qdwXFRUFd3d32NraKrf5+vpCJpPh2rVryjLe3t4q5/T19UVUVFSR4ioq1jAQEZE46GiUhKOjo8rmadOmISQk5K2HyuVyjBo1Cm3atEGjRo2U2/v27QsnJyc4ODjg8uXLmDBhAuLi4rBr1y4AQFJSkkqyAEC5npSU9NYyMpkMr169gpmZmcaXWhgmDERERBpISEiAlZWVct3ExETtMUFBQbh69SpOnTqlsn3IkCHKf7u7u8Pe3h6dOnXCnTt3UKdOHd0FrQNskiAiIlHQ1SgJKysrlUVdwhAcHIz9+/fj+PHjqFGjxlvLenp6AgBu374NALCzs0NycrJKmfz1/H4PbypjZWWls9oFgAkDERGJhDYjJIozrbRCoUBwcDB2796NY8eOwdnZWe0xsbGxAAB7e3sAgJeXF65cuYLHjx8ry4SFhcHKygpubm7KMuHh4SrnCQsLg5eXl2YBq8GEgYiIqAQEBQVh06ZN2LJlCywtLZGUlISkpCS8evUKAHDnzh3MnDkTMTExuHfvHvbu3Yv+/fujXbt2aNy4MQDAx8cHbm5u+Pzzz3Hp0iUcPnwYkydPRlBQkLJmY+jQobh79y7Gjx+PmzdvYvny5di+fTtGjx6t0+thwkBERKKgq1ESRbVixQqkpqaiQ4cOsLe3Vy7btm0DABgbG+Po0aPw8fGBq6srvvrqK/Tu3Rv79u1TnsPQ0BD79++HoaEhvLy80K9fP/Tv3x8zZsxQlnF2dsaBAwcQFhaGJk2aYN68efjpp590OqQSYKdHIiISi1J+loRCoXjrfkdHR0RERKg9j5OTEw4ePPjWMh06dMDFixc1ik9TTBiIiEgUijO983+PFzM2SRAREZFarGHQsR+3R2DJpnA8fiZDo3rV8f24j+HRsJbQYekd3if1eI8KOn3hNpZsPIpLNx8g6akMm+YORrcOTQotOzr0F6zfdRqzRvfGsL4dSznSkvHj1mM4evoK4hOewNTYCE3damF0YFc4O9oUKKtQKDBs8hqcio7DomkB6NT6n8mCZi3fg9hr93DrfhJqO9rg1xVjChx/OjoOyzYewe37yTAxNoJHI2eMG9Id1e2sC5QtKyTQfKTDf48XM72pYZg9ezYkEglGjRoldCjFtutIDCYv3I0Jg7rgxMYJaFSvOnqPWIYnKWlCh6ZXeJ/U4z0q3MtXWWhUvzrmjv/0reX2H7+E6Cv3YF9NWkqRlY7oy3fQp3trbFkYjNWhQ5CTl4chX/+Il5nZBcpu3H0Skrf8On7o2xKd2xWebD1MSsGIkPVo1aQudi4fhVXfDcIL2UuMmvmzzq5FCKXd6bG80YuE4fz581i1apVyGElZtXzLMfT3aw3/Hl5wrW2P+ZM+g7mpMTbt1e183mUd75N6vEeFe79NQ0we1h0fdCz8hw4AHj1+gQk/7MDqmQNgZGRYitGVvFWzBsPPpyXq1rKDax0HfPfVp0h8/ALXbz1UKXfzzl/Y8GskZo75uNDzfD3cD316tEEN+yqF7r9+6yHkcjm+HOCLmg5V4VavBgZ81B437zxCTm6ezq+LygbBE4b09HT4+/vjxx9/ROXKlYUOp9iyc3IRezMBHVq5KLcZGBigfSsXnL8SL2Bk+oX3ST3eo+KTy+UYOu1njOjXCQ3q2AsdTolLz8gEAEgtzZXbXmVmY/zsLfgmyA9Vra3edOhbudWrAYmBBLuPRCMvT460jFfYdzQG7zSriwplOAkr7YmbyhvBE4agoCB069atwJO2CpOVlVXgKWH64tmLdOTlyVHN2lJlezVrKzx+pj9xCo33ST3eo+JbuCEMRoYG+OKzDkKHUuLkcjlmr9yLZg1roV6tfx6NPGfVXjR1q4X3/tVnQVM17KyxetZgLFr3O5p/MAlevaYi6Wkq5n3zuS5CFxAbJbQhaMKwdetWXLhwAaGhoUUqHxoaCqlUqlz++8QwIhKv2BsPsGrrCSyb1u+tbfflxbdLd+P2/STMneSv3HY86hrOxd7BxKE9tDr30xQZQhbuRM/3PbB1yZdY/8MwVKhgiDEzf1Y7twCVX4KNkkhISMDIkSMRFhYGU1PTIh0zadIkjBnzT29emUymN0lDlUoWMDQ0KNAp7UmKDDZVilctWB7xPqnHe1Q8URfv4MnzdLh3n6rclpcnx+RFu7Bi63Fc3jvjLUeXLd8t3Y2IczewYd5w2FWrpNx+LvY2EhKfwavXVJXyo2f+jOaNnLF+7rAinf+XfWdgUdEUXw36QLlt9vg+8O73HS7ffIAmDZx0ch2lTdtmBRHkoW8lWMIQExODx48fo3nz5spteXl5iIyMxNKlS5GVlQVDQ9W2MhMTkyI9RlQIxhWM0NTVERHn45TDvORyOSLP/4lBH7cTODr9wfukHu9R8XzatSXa/6vfBwB89OUyfNKlFfy7vyNQVLqlUCgwa9kehJ+5inVzh6LGf4Y4Dvq0I3p38VTZ9uEX8zD+ix7o8I5bkV8nMzMHBv/5dTQ0eF0hLZeX3RqGUp7osdwRLGHo1KkTrly5orJt4MCBcHV1xYQJEwokC2XB8L7vYfj0jWjWoCaaN6yFFb8cR8arrHLzZaUrvE/q8R4VLv1lFuITnijX7z96hitxD1FJag5HO2tYV7JQKW9kZAjbKlaoV8u2tEMtEd8u3Y2Dxy9iccgAVDQzwdOU131aLCqawdSkAqpaWxXa0dHeppJKcvHgr6d4mZmFpylpyMrOxc07fwEA6tS0RYUKRmjn2QA/7z6JFZvC0LVjU2S8zMKidb/DwbYyGtStXjoXS3pHsITB0tISjRqpdsqpWLEiqlSpUmB7WdHLxwNPX6Rj1qoDePwsDe71q2Pn4iBWI/8H75N6vEeFi71xH92HLlauf7NgFwCgTzdPLA8p6x3y1Nu2//Ww2oHjVqps//arT+Dn07LI55m6cAeiL99Vrn80fCEA4PCGSahuZw3PpnXx/cS+WLfjBNbuOAEzkwpo0sAJK78dBFOTCtpfiEDYJKEdiUKPerB06NABTZs2xcKFC4tUXiaTQSqVIvlZKqysxP1FSkT656+UV0KHoPfS0mRoVtcOqakl9z2e/1vx54OnsNTiNdJkMtSvWbVEY9VnejU19IkTJ4QOgYiIyit2YtCK4PMwEBERkf7TqxoGIiKiksIKBu0wYSAiIlFgp0ftsEmCiIiI1GINAxERiYLk7/+0OV7MmDAQEZE4sBODVtgkQURERGqxhoGIiESBFQzaYcJARESiwFES2mGTBBEREanFGgYiIhIJ7UZJiL1RggkDERGJApsktMMmCSIiIlKLCQMRERGpxSYJIiISBTZJaIcJAxERiQKnhtYOmySIiIhILdYwEBGRKLBJQjtMGIiISBQ4NbR22CRBREREarGGgYiIxIFVDFphwkBERKLAURLaYZMEERERqcUaBiIiEgWOktAOEwYiIhIFdmHQDhMGIiISB2YMWmEfBiIiohK0bNky1KpVC6ampvD09MQff/whdEjFwoSBiIhEQaKD/zS1bds2jBkzBtOmTcOFCxfQpEkT+Pr64vHjxyVwhSWLCQMREYlCfqdHbRZNzZ8/H4MHD8bAgQPh5uaGlStXwtzcHGvXrtX9BZawMt2HQaFQAADSZDKBIyEiKigt7ZXQIei99LQ0AP98n5ckmZa/FfnH//c8JiYmMDExKVA+OzsbMTExmDRpknKbgYEBvL29ERUVpVUsQijTCUPa32+0us6OAkdCRETaSEtLg1QqLZFzGxsbw87ODvV08FthYWEBR0fV80ybNg0hISEFyj59+hR5eXmwtbVV2W5ra4ubN29qHUtpK9MJg4ODAxISEmBpaQmJngyQlclkcHR0REJCAqysrIQOR2/xPhUN71PR8D4VjT7eJ4VCgbS0NDg4OJTYa5iamiI+Ph7Z2dlan0uhUBT4vSmsdqE8KtMJg4GBAWrUqCF0GIWysrLSmw+kPuN9Khrep6LhfSoafbtPJVWz8G+mpqYwNTUt8df5t6pVq8LQ0BDJyckq25OTk2FnZ1eqsegCOz0SERGVAGNjY3h4eCA8PFy5TS6XIzw8HF5eXgJGVjxluoaBiIhIn40ZMwYBAQFo0aIFWrVqhYULFyIjIwMDBw4UOjSNMWHQMRMTE0ybNk00bVrFxftUNLxPRcP7VDS8T6Xv008/xZMnTzB16lQkJSWhadOmOHToUIGOkGWBRFEaY1mIiIioTGMfBiIiIlKLCQMRERGpxYSBiIiI1GLCQERERGoxYdCx8vIY05ISGRmJ7t27w8HBARKJBHv27BE6JL0UGhqKli1bwtLSEjY2NvDz80NcXJzQYemdFStWoHHjxsqJiLy8vPD7778LHZZemz17NiQSCUaNGiV0KFTGMGHQofL0GNOSkpGRgSZNmmDZsmVCh6LXIiIiEBQUhLNnzyIsLAw5OTnw8fFBRkaG0KHplRo1amD27NmIiYlBdHQ03nvvPfTs2RPXrl0TOjS9dP78eaxatQqNGzcWOhQqgzisUoc8PT3RsmVLLF26FMDrGb0cHR0xYsQITJw4UeDo9I9EIsHu3bvh5+cndCh678mTJ7CxsUFERATatWsndDh6zdraGnPnzkVgYKDQoeiV9PR0NG/eHMuXL8e3336Lpk2bYuHChUKHRWUIaxh0JP8xpt7e3sptZfkxpqRfUlNTAbz+MaTC5eXlYevWrcjIyCiT0+6WtKCgIHTr1k3lO4pIE5zpUUfK22NMSX/I5XKMGjUKbdq0QaNGjYQOR+9cuXIFXl5eyMzMhIWFBXbv3g03Nzehw9IrW7duxYULF3D+/HmhQ6EyjAkDkZ4LCgrC1atXcerUKaFD0UsuLi6IjY1Famoqdu7ciYCAAERERDBp+FtCQgJGjhyJsLCwUn9aI5UvTBh0pLw9xpT0Q3BwMPbv34/IyEi9fZS70IyNjVG3bl0AgIeHB86fP49FixZh1apVAkemH2JiYvD48WM0b95cuS0vLw+RkZFYunQpsrKyYGhoKGCEVFawD4OOlLfHmJKwFAoFgoODsXv3bhw7dgzOzs5Ch1RmyOVyZGVlCR2G3ujUqROuXLmC2NhY5dKiRQv4+/sjNjaWyQIVGWsYdKg8Pca0pKSnp+P27dvK9fj4eMTGxsLa2ho1a9YUMDL9EhQUhC1btuC3336DpaUlkpKSAABSqRRmZmYCR6c/Jk2ahC5duqBmzZpIS0vDli1bcOLECRw+fFjo0PSGpaVlgb4vFStWRJUqVdgnhjTChEGHytNjTEtKdHQ0OnbsqFwfM2YMACAgIADr168XKCr9s2LFCgBAhw4dVLavW7cOAwYMKP2A9NTjx4/Rv39/JCYmQiqVonHjxjh8+DDef/99oUMjKnc4DwMRERGpxT4MREREpBYTBiIiIlKLCQMRERGpxYSBiIiI1GLCQERERGoxYSAiIiK1mDAQERGRWkwYiIiISC0mDERaGjBgAPz8/JTrHTp0wKhRo0o9jhMnTkAikeDFixdvLCORSLBnz54inzMkJARNmzbVKq579+5BIpEgNjZWq/MQkbCYMFC5NGDAAEgkEkgkEuXTDGfMmIHc3NwSf+1du3Zh5syZRSpblB95IiJ9wGdJULnVuXNnrFu3DllZWTh48CCCgoJQoUIFTJo0qUDZ7OxsGBsb6+R1ra2tdXIeIiJ9whoGKrdMTExgZ2cHJycnDBs2DN7e3ti7dy+Af5oRvvvuOzg4OMDFxQUAkJCQgE8++QSVKlWCtbU1evbsiXv37inPmZeXhzFjxqBSpUqoUqUKxo8fj/8+juW/TRJZWVmYMGECHB0dYWJigrp162LNmjW4d++e8kFclStXhkQiUT5YSi6XIzQ0FM7OzjAzM0OTJk2wc+dOldc5ePAg6tevDzMzM3Ts2FElzqKaMGEC6tevD3Nzc9SuXRtTpkxBTk5OgXKrVq2Co6MjzM3N8cknnyA1NVVl/08//YQGDRrA1NQUrq6uWL58ucaxEJF+Y8JAomFmZobs7Gzlenh4OOLi4hAWFob9+/cjJycHvr6+sLS0xMmTJ3H69GlYWFigc+fOyuPmzZuH9evXY+3atTh16hRSUlKwe/fut75u//798csvv2Dx4sW4ceMGVq1aBQsLCzg6OuLXX38FAMTFxSExMRGLFi0CAISGhuLnn3/GypUrce3aNYwePRr9+vVDREQEgNeJTa9evdC9e3fExsZi0KBBmDhxosb3xNLSEuvXr8f169exaNEi/Pjjj1iwYIFKmdu3b2P79u3Yt28fDh06hIsXL2L48OHK/Zs3b8bUqVPx3Xff4caNG5g1axamTJmCDRs2aBwPEekxBVE5FBAQoOjZs6dCoVAo5HK5IiwsTGFiYqIYO3ascr+tra0iKytLeczGjRsVLi4uCrlcrtyWlZWlMDMzUxw+fFihUCgU9vb2ijlz5ij35+TkKGrUqKF8LYVCoWjfvr1i5MiRCoVCoYiLi1MAUISFhRUa5/HjxxUAFM+fP1duy8zMVJibmyvOnDmjUjYwMFDRp08fhUKhUEyaNEnh5uamsn/ChAkFzvVfABS7d+9+4/65c+cqPDw8lOvTpk1TGBoaKh4+fKjc9vvvvysMDAwUiYmJCoVCoahTp45iy5YtKueZOXOmwsvLS6FQKBTx8fEKAIqLFy++8XWJSP+xDwOVW/v374eFhQVycnIgl8vRt29fhISEKPe7u7ur9Fu4dOkSbt++DUtLS5XzZGZm4s6dO0hNTUViYiI8PT2V+4yMjNCiRYsCzRL5YmNjYWhoiPbt2xc57tu3b+Ply5d4//33VbZnZ2ejWbNmAIAbN26oxAEAXl5eRX6NfNu2bcPixYtx584dpKenIzc3F1ZWViplatasierVq6u8jlwuR1xcHCwtLXHnzh0EBgZi8ODByjK5ubmQSqUax0NE+osJA5VbHTt2xIoVK2BsbAwHBwcYGam+3StWrKiynp6eDg8PD2zevLnAuapVq1asGMzMzDQ+Jj09HQBw4MABlR9q4HW/DF2JioqCv78/pk+fDl9fX0ilUmzduhXz5s3TONYff/yxQAJjaGios1iJSHhMGKjcqlixIurWrVvk8s2bN8e2bdtgY2NT4K/sfPb29jh37hzatWsH4PVf0jExMWjevHmh5d3d3SGXyxEREQFvb+8C+/NrOPLy8pTb3NzcYGJiggcPHryxZqJBgwbKDpz5zp49q/4i/+XMmTNwcnLCN998o9x2//79AuUePHiAR48ewcHBQfk6BgYGcHFxga2tLRwcHHD37l34+/tr9PpEVLaw0yPR3/z9/VG1alX07NkTJ0+eRHx8PE6cOIEvv/wSDx8+BACMHDkSs2fPxp49e3Dz5k0MHz78rXMo1KpVCwEBAfjf//6HPXv2KM+5fft2AICTkxMkEgn279+PJ0+eID09HZaWlhg7dixGjx6NDRs24M6dO7hw4QKWLFmi7Eg4dOhQ3Lp1C+PGjUNcXBy2bNmC9evXa3S99erVw4MHD7B161bcuXMHixcvLrQDp6mpKQICAnDp0iWcPHkSX375JT755BPY2dkBAKZPn47Q0FAsXrwYf/75J65cuYJ169Zh/vz5GsVDRPqNCQPR38zNzREZGYmaNWuiV69eaNCgAQIDA5GZmamscfjqq6/w+eefIyAgAF5eXrC0tMSHH3741vOuWLECH330EYYPHw5XV1cMHjwYGRkZAIDq1atj+vTpmDhxImxtbREcHAwAmDlzJqZMmYLQ0FA0aNAAnTt3xoEDB+Ds7Azgdb+CX3/9FXv27EGTJk2wcuVKzJo1S6Pr7dGjB0aPHo3g4GA0bdoUZ86cwZQpUwqUq1u3Lnr16oWuXbvCx8cHjRs3Vhk2OWjQIPz0009Yt24d3N3d0b59e6xfv14ZKxGVDxLFm3prEREREf2NNQxERESkFhMGIiIiUosJAxEREanFhIGIiIjUYsJAREREajFhICIiIrWYMBAREZFaTBiIiIhILSYMREREpBYTBiIiIlKLCQMRERGp9X+lo+ebr1k2AQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# cm = confusion_matrix(testY, pipe.predict(testX))\n",
    "\n",
    "# disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "# disp.plot(cmap='Blues')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b021b69-2e42-408f-9db7-257435b8aff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['request_predictor_improved.joblib']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import joblib\n",
    "\n",
    "# joblib.dump(pipe, 'request_predictor_improved.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4350be3e-0f6c-4655-a009-b3f095e2ec01",
   "metadata": {},
   "source": [
    "# Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0575226-32d6-4117-b2f2-02307c68037c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9c71630-c752-436d-9416-0aa1373f642a",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = len(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f82b8d66-26f1-4451-a01c-3206ed07094c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8819ea49-76c0-470e-bd16-bf76312416f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=5000, char_level=True)\n",
    "tokenizer.fit_on_texts(trainX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "35a64431-9c3a-429b-84f9-21802157f527",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sequences = tokenizer.texts_to_sequences(trainX)\n",
    "test_sequences = tokenizer.texts_to_sequences(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f40c392-e993-4305-9211-83c2e616f145",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len = 512\n",
    "train_padded = pad_sequences(train_sequences, maxlen=max_len, padding='post')\n",
    "test_padded = pad_sequences(test_sequences, maxlen=max_len, padding='post')\n",
    "vocab_size = len(tokenizer.word_index) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28d5d826-34c3-4f04-ba3d-ae2425309a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Embedding(input_dim=vocab_size, output_dim=128),\n",
    "    LSTM(128),\n",
    "    Dropout(0.5),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(num_classes, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ea2d358-a71e-4fbf-b5f6-c91473554756",
   "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",
       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</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: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\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",
       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)         │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ ?                      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\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\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\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\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\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": [
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d259091b-2276-4b6b-87af-5832aa30357b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1017/1017\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m966s\u001b[0m 947ms/step - accuracy: 0.6676 - loss: 1.0856 - val_accuracy: 0.6745 - val_loss: 1.0546\n",
      "Epoch 2/10\n",
      "\u001b[1m  37/1017\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m19:40\u001b[0m 1s/step - accuracy: 0.6653 - loss: 1.0715   "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_padded, trainY,\n",
    "    validation_split=0.2,\n",
    "    epochs=10,\n",
    "    batch_size=64,\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b573ccd-0c5c-4a75-93e6-85ca6bc63de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss, accuracy = model.evaluate(test_padded, testY)\n",
    "print(f\"Test Loss: {loss}\")\n",
    "print(f\"Test Accuracy: {accuracy}\")"
   ]
  }
 ],
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