german.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T10:01:14.855027100Z",
     "start_time": "2024-02-15T10:01:14.233027500Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "complexity = pd.read_csv('data/TextComplexityDE_aligned_pred.csv')\n",
    "complexity = complexity[complexity.Rating != \"Nicht einfacher / konnte nicht vereinfacht werden\"]\n",
    "deplain = pd.read_csv(\"data/deplain_simplified_pred.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "complexity"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "deplain_ds = load_dataset(\"DEplain/DEplain-web-sent\")\n",
    "\n",
    "level_to_id = {'a1': 1, 'a2': 2, 'b1': 3, 'b2': 4, 'c1': 5, 'c2': 6}\n",
    "levels = {o: level_to_id[l] for o, l in zip(deplain_ds['test']['original'], deplain_ds['test']['language_level_original'])}\n",
    "deplain['level_orig'] = deplain['normal_phrase'].apply(lambda x: levels.get(x))\n",
    "levels = {s: level_to_id[l] for s, l in zip(deplain_ds['test']['simplification'], deplain_ds['test']['language_level_simple'])}\n",
    "deplain['level_simple'] = deplain['simple_phrase'].apply(lambda x: levels.get(x))\n",
    "simp_strength = []\n",
    "for _, row in deplain.iterrows():\n",
    "    simp_strength.append(row.level_orig - row.level_simple)\n",
    "deplain['simp_strength'] = simp_strength\n",
    "deplain"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "   level_simple  level_orig  count\n0             1           6    650\n1             2           4    158\n2             2           6   1038",
      "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>level_simple</th>\n      <th>level_orig</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6</td>\n      <td>650</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>4</td>\n      <td>158</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>6</td>\n      <td>1038</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deplain.groupby(['level_simple', 'level_orig']).size().reset_index().rename(columns={0:'count'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T10:03:37.430231800Z",
     "start_time": "2024-02-15T10:03:37.279231600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "array([2, 1], dtype=int64)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deplain['level_simple'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T10:03:38.588234700Z",
     "start_time": "2024-02-15T10:03:38.497235900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miriam\\AppData\\Local\\Temp\\ipykernel_5804\\3925421906.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  deplain['simp_strength'][deplain['simp_strength'] >= 5] = 4 # group samples with strong simplification\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([2, 4], dtype=int64)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deplain['simp_strength'][deplain['simp_strength'] >= 5] = 4 # group samples with strong simplification\n",
    "deplain['simp_strength'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T10:03:39.410229600Z",
     "start_time": "2024-02-15T10:03:39.369238100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "* 2\n",
      "**toxicity error rate: 0.0% with 158 samples\n",
      "**topic_gnad error rate: 13.92% with 158 samples\n",
      "**sentiment_news error rate: 12.03% with 158 samples\n",
      "\n",
      "* 4\n",
      "**toxicity error rate: 4.38% with 1688 samples\n",
      "**topic_gnad error rate: 33.83% with 1688 samples\n",
      "**sentiment_news error rate: 24.17% with 1688 samples\n",
      "simp_strength\n",
      "\n",
      "* 2\n",
      "**toxicity error rate: 0.0% with 158 samples\n",
      "**topic_gnad error rate: 13.92% with 158 samples\n",
      "**sentiment_news error rate: 12.03% with 158 samples\n",
      "\n",
      "* 4\n",
      "**toxicity error rate: 4.38% with 1688 samples\n",
      "**topic_gnad error rate: 33.83% with 1688 samples\n",
      "**sentiment_news error rate: 24.17% with 1688 samples\n",
      "level_orig\n",
      "\n",
      "* 4\n",
      "**toxicity error rate: 0.0% with 158 samples\n",
      "**topic_gnad error rate: 13.92% with 158 samples\n",
      "**sentiment_news error rate: 12.03% with 158 samples\n",
      "\n",
      "* 6\n",
      "**toxicity error rate: 4.38% with 1688 samples\n",
      "**topic_gnad error rate: 33.83% with 1688 samples\n",
      "**sentiment_news error rate: 24.17% with 1688 samples\n",
      "Rating\n",
      "\n",
      "* Deutlich einfacher\n",
      "**toxicity error rate: 0.0% with 114 samples\n",
      "**topic_gnad error rate: 20.18% with 114 samples\n",
      "**sentiment_news error rate: 7.02% with 114 samples\n",
      "\n",
      "* Etwas einfacher\n",
      "**toxicity error rate: 0.0% with 135 samples\n",
      "**topic_gnad error rate: 17.78% with 135 samples\n",
      "**sentiment_news error rate: 2.96% with 135 samples\n"
     ]
    }
   ],
   "source": [
    "def compare_task(task, df, simplified_column='_simple'):\n",
    "    same_pred = []\n",
    "    for n, s in zip(df[task + '_normal'], df[task + simplified_column]):\n",
    "        if type(n)==str and type(s) == str:\n",
    "            same_pred.append(n.lower()==s.lower())\n",
    "        else:\n",
    "            same_pred.append(n==s)\n",
    "    different = same_pred.count(False)\n",
    "    error = round(100*different/len(same_pred), 2)\n",
    "    print(f\"**{task} error rate: {error}% with {len(same_pred)} samples\")\n",
    "    return error\n",
    "\n",
    "tasks = [\n",
    "    #'sentiment',\n",
    "    'toxicity',\n",
    "    'topic_gnad',\n",
    "    'sentiment_news'\n",
    "]\n",
    "\n",
    "errors_per_level = {}\n",
    "#for simp, group in aligned_combined.groupby('V_simple'):\n",
    "#for simp, group in complexity.groupby('Rating'):\n",
    "for simp, group in deplain.groupby('simp_strength'):\n",
    "#for simp in ['Vp', 'V1', 'V2', 'V3', 'V4', 'Vs']:\n",
    "    #group = aligned_combined[aligned_combined.V_simple == simp]\n",
    "    print('\\n*', simp)\n",
    "    df_tmp = {}\n",
    "    for task in tasks:\n",
    "        df_tmp[task] = compare_task(task, group)\n",
    "    errors_per_level[simp] = df_tmp\n",
    "\n",
    "def calculate_errors(df, column_name):\n",
    "    print(column_name)\n",
    "    errors_per_level = {}\n",
    "    for simp, group in df.groupby(column_name):\n",
    "        print('\\n*', simp)\n",
    "        df_tmp = {}\n",
    "        for task in tasks:\n",
    "            df_tmp[task] = compare_task(task, group)\n",
    "        errors_per_level[simp] = df_tmp\n",
    "    return errors_per_level\n",
    "\n",
    "errors_per_level_deplain_strength = calculate_errors(deplain, 'simp_strength')#'level_simple')\n",
    "errors_per_level_deplain_level = calculate_errors(deplain, 'level_orig')\n",
    "errors_per_level_complex = calculate_errors(complexity, 'Rating')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T10:03:40.702780600Z",
     "start_time": "2024-02-15T10:03:40.632777100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "{'Deutlich einfacher': {'toxicity': 0.0,\n  'topic_gnad': 20.18,\n  'sentiment_news': 7.02},\n 'Etwas einfacher': {'toxicity': 0.0,\n  'topic_gnad': 17.78,\n  'sentiment_news': 2.96}}"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors_per_level_complex"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T10:03:41.481769600Z",
     "start_time": "2024-02-15T10:03:41.427777600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x430 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "plt.figure(figsize=(6.4, 4.3))\n",
    "\n",
    "colors = {\n",
    "    #'sentiment': 'lightblue',\n",
    "    'sentiment_news': '#f781bf',\n",
    "    'toxicity': '#377eb8',\n",
    "    'topic_gnad': '#4daf4a',\n",
    "}\n",
    "labels = {\n",
    "    'sentiment_news': 'Sentiment',\n",
    "    'toxicity': 'Toxicity',\n",
    "    'topic_gnad': 'Topic',\n",
    "}\n",
    "markers = {\n",
    "    'toxicity': '*',\n",
    "    'topic_gnad': 'o',\n",
    "    'sentiment_news': 's',\n",
    "}\n",
    "\n",
    "for task in tasks:\n",
    "    #plt.plot([errors_per_level_deplain[level][task] for level in [2, 1]], label=task, color=colors[task], linestyle='dashed')\n",
    "    plt.plot([1, 0], [level[task] for level in errors_per_level_complex.values()], color=colors[task], linestyle='dashed', marker=markers[task]) #switch order to match DEplain levels\n",
    "    plt.plot([level[task] for level in errors_per_level_deplain_strength.values()], color=colors[task], linestyle='solid', marker=markers[task])\n",
    "    #plt.scatter(2, compare_task(task, brandeins), marker='^', color=colors[task])\n",
    "    #plt.scatter(1, compare_task(task, wahlprogramm), marker='*', color=colors[task])\n",
    "    #plt.scatter(1, compare_task(task, deplain), marker='>', color=colors[task])\n",
    "    #plt.scatter(1, compare_task(task, deplain, '_simplified'), marker='<', color=colors[task])\n",
    "\n",
    "\n",
    "#plt.scatter(2, compare_task('topic_gpt3', brandeins), marker='^', color='red')\n",
    "for task in tasks:\n",
    "    plt.plot([], color=colors[task], linestyle='dashdot', label=labels[task], marker=markers[task])\n",
    "plt.plot([], color='grey', label='DEplain', linestyle='solid')\n",
    "plt.plot([], color='grey', label='TextComplexityDE', linestyle='dashed')\n",
    "#plt.scatter([], [], marker='^', color='grey', label='brandeins')\n",
    "#plt.scatter([], [], marker='*', color='grey', label='wahlprogramm')\n",
    "#plt.scatter([], [], marker='>', color='grey', label='deplain')\n",
    "#plt.scatter([], [], marker='<', color='grey', label='deplain auto')\n",
    "\n",
    "plt.xticks([0,1],['slight', 'strong'])\n",
    "legend = plt.legend()\n",
    "#handles, labels = legend.axes.get_legend_handles_labels()\n",
    "#handles.append(mpatches.Patch(color='grey', label='brandeins', marker='*'))\n",
    "#labels.append('brandeins')\n",
    "#plt.legend(handles=handles, labels=labels)\n",
    "\n",
    "plt.ylabel('Prediction change rate (%)')\n",
    "plt.xlabel('Simplification strength')\n",
    "plt.ylim(-1, 53)\n",
    "plt.savefig('plots_new/german.pdf')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T13:02:14.487087200Z",
     "start_time": "2024-02-15T13:02:13.737205400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "{4: {'toxicity': 0.0, 'topic_gnad': 13.92, 'sentiment_news': 12.03},\n 6: {'toxicity': 4.38, 'topic_gnad': 33.83, 'sentiment_news': 24.17}}"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors_per_level_deplain_level"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-15T10:03:43.629776400Z",
     "start_time": "2024-02-15T10:03:43.595773200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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WbOS9yn9z/wl/bO9hHTFauD2++b1NhOgMrr322lo3ZQDw+XzceOON3HLLLdhsNizL4tZbb22nEbaN3VW7KfbvQwFeu4cUZwp3DL2rvYclWoGEt+iQqnt3N9UTTzxR77H23tjTmgLhAKXBUjLc6dh0O26bm3R3BsnOZJw2J6X2Mtx26dDZGUh4C9HBRawIZaEyygKl+CMBNE3D6/CSpNvxOX348DV+EtHhSHgL0YFFrDAbSjdgKYXL5iLbm02yIxmbLh38OjsJbyE6kFAkSGmwjIiKkOPNwabbyXRn4ba7ZTnkCCPhLUSCs5RFeaicskApleEqNMDrSEIphaZppLnT2nuIoh3IJh2RkI70roJKqfiW/uJAMTsqdhBSYTI9mfRI6Uk3Xze55+MRTmbeIiEdqV0Fw1aYsmAZpbFbh/mcPlKcKXhsHtx2twS2iJPwFo36YNMiFm5c0KrnnNhjEqcUTKj3+JHUVVApRUWogrJgKRXhCpQCT42gtut27Lr8VRW1ybKJSEhHQlfBiBWOP97j340/4ifNlU6PlAK6J+eT5Ehqzo9OHCHkn3PRqFMKJjQ4S25Lna2rYMSKRC8+BksJRoL0TO2Frul0TeqGQ3fIsohoMpl5i4TU2boKBiMBdlRsZ0PpenZW7iSirFqNoJw2pwS3OCQy8xYJqTN0FQxFQkC0tWpEWZSHKkh2JpPiTMFlk4uPomUa7CpoGIYD+CvQE3AB9wKbgXnAmtjLnjJNs8H6LOkq2PEk+s88UbsKWsqiIlRBabCUqlAlKa5Ucrw50dI/FLrWvv/ZTfTf186ovboKTgX2mKY5zTCMDGAZcDcwyzTNh5s1GiHaweHoKrinag8lgWIiysKh20l3Z5DiTAaiSzAaMtMWraex8H4TeCv2WAPCwAjAMAzjXKKz7xtM0yxruyEKcbBE6CoYsSJUxJZCNE3DUhZehzdal233yrKIaFNNuhmDYRjJwDvAc0SXT1aYprnUMIxbgXTTNG9s6P3Lli1TLfnvrd/vx+2Wvg2HUygUol+/fu09jITkt/xURiqosqpQCrq4uuDQOsYtw9asWYPD0THG2lm0JL8qKyubfzMGwzDygbnAk6Zp/s0wjDTTNItjh+cCjzd2DpfL1aJ1NlmnO/wKCwvxeDztPYyEEogE2FZeRMgKY9N0Up1ppDiTcXWghlAOh0P+Lh1mLVzzrvdYg1dPDMPoArwP3Gya5l9jT//HMIxjY48nAPWfXYgOzFIWpYFSyoKlADh0By67m9ykXHqm9iLbm92hglt0Lo3NvG8B0oGZhmHMjD33a2C2YRghYDtwVRuOT4jDSimFP+KnLFhKWbA8vo6d7EyJbabp2t5DFB1IW94juMHwNk3zeuD6Og4d3zbDEaJ97araRUmgBF3T8DmiNdnSJ1scqsi3OwgvLYKqEIxPbpOvITssRaf0xhtvEAqFKCwsrLfSRClFebCcovKi+IaaZEcyOd4ceqb0oktSFzwOT51VI8XFxbz77rtt+j2IxKaUwiquIrJiO6F/rSbw9JeoskD0WFUIwhZ6nwyw2mb2LTssRZMEXvrmoOdsA3Owj8pDhSIE/7bi4ONDc7EP64qqDBJ8c2WtY67Lmt7AqTmeeeYZfvSjHzFgwICDLhYFIgHKgqXRO9JYEey6jZAVxGFz4HF48ND4hVrTNPnggw/ifVdE56csCyyFZrdhbSgmOHcVlMXaLrhs6N1TUYEwWrIL+7HdsR/bPXqssLBNxiPhLRLS+vXr+f3vf4/dbseyLB5++GH+9re/8dVXX2FZFpdffjlnnHEG06ZNo3///qxZs4by8nIeffRRlixZwq5du5gxYwaXXXYZr7/+OrNmzeLUU09l2LBhmOtMho4aSrAyyJpVa+jTuw8PPfQQ27ZtY+bMmQQCAVwuF/fccw+RSITf/OY35ObmsnnzZo4++mjuuusunn76ab7//nveeOMNLrzwwjq/h1NPPZXhw4ezfv16MjMzefzxx7EsizvuuIONGzdiWRY33HADZWVlLFmyhNtvv51nn32Wr7/+mqeffpp33nmHoqIievfuzXPPPYfdbicnJ4fZs2ej6/Kf5ramgmGsLWWozSVYm0uwtpRiP7UP9uHdINWF3iMNPT8VPT8VLScJTT/Mdf3Vd+xoy49Vq1aplmjp+8Wha++f+Zw5c9R9992ngsGgWrJkiXr55ZfVDTfcoJRSyu/3q3POOUeVlJSoqVOnqnfeeUcppdSsWbPUM888o5RS6uSTT1ZVVVXq408+VtOvna62lG1RAwYMUFu3blV7y/eqYcOGqTVr1ijLstTJJ5+sSkpK1PXXX68++ugjpZRSS5YsUb/+9a/V5s2b1bHHHqvKyspUOBxW48ePVzt37lSff/55fDz16d+/vyoqKlJKKXXhhReqb775Rr366qvqwQcfVEoptXfvXjV58mRVVVWlzjrrLKWUUldeeaU677zzVCgUUtdff71au3atuu6669T8+fOVUkrNnTtXlZSUNPvn2t6/r4nMKvWryK6K6OOqkKq65yNVddeHququD5X/6S9U8F+mimwqPuTztuRn/tVXX32l6slVmXmLhPSTn/yE5557jiuvvJLk5GT69+/PypUrmTZtGgDhcJitW7cC+1u35ubmsnv3bkJWiIiKsKl0E7uqdhG2wjh0B2lpaXTr1g0Ar9cb70SYnJxMIBBg9erVPPPMM/zlL3+J/uWINakqKCjA5/MBkJ2dTSAQaNL3kJ6eTteu0eqU6rayq1evZunSpaxYsSL+fVRWVtKrVy9WrFiB3W5n6NChfPnll2zbto0+ffrw+9//nmeeeYY5c+bQu3dvJk6c2Bo/4iOetbsCa1MJ1qYS1OYS1D4/er9MnBcfjea2Y5/QGy3bi949Fc2deFGZeCMSAli0aBEjRozg2muvZd68ecyaNYvjjz+ee+65B8uyePLJJ8nPz4+/3lIWiuiFoYpQBQqFTdPJ8GSQ5Ewix5vT6Hb13r1787Of/Yzhw4ezbt06vvzyS+Dg1rCwv2VtQ+p6X+/evcnNzWX69On4/X6eeuop0tLSmDhxIg899BATJkwgPz+f2bNnc9xxxwHRi6/XXXcdmZmZ3H777SxYsCDeg1w0jQpbqKIy1L4qbENzAQi9a6I2l4LXgV6Qim1kHnrPtPh77GPz6zlbYpDwFglp8ODB3HzzzTz11FNYlsVjjz3Gu+++y5QpU6isrGTixIn4fD4sZbHPvxetBPxhPxCtGBk9ajR3/vourrnmmiY3hLr55pu58847CQQC+P3+BptUFRQUsHr1al588UUuv/zyJn9fF110EbfddhtTp06lvLycKVOmoOs6J598Mrfccgt33HEHubm5XH/99dx5550ADBkyhKuvvpqkpCS8Xi/jx49v8tc7kllbS4kU7sLaXIIqKoOIApuGPigHza7jOK0vuOxoGXVXFCW6JvU2aSlpCdvxJPLPXClFSaCE0mApgUgATdPwOXykuVJx22VLf0MS+fe1uZRSqGI/alP0wqL9lF5oXifhTzcR/nA9Wrfk6IXFglT07iloSXX3dm8r7dUSVoiEoJQiGAnisrvQNI2yUBmaBjnebHyOZGy6rV3GtWjRIl588cWDnr/00kuZNGnS4R/QEcTaWU74441Ym0ugfH/Jnm1oLprXiW1kN2zH5qE52ufPRluT8BYJLRgJ7q/JVhF6pfTCptvoltSt3QK7pgkTJjBhQvvc3/NIoQJhrC2l0eWPzSXYjumKbXAX0DTUtjL0XgeU7MWWQDRX5463zv3diQ7LH/azu2o3VeEqNIj3ya7+i5kIwS3ahgpbaHY9uvnrhW9QO8pBARpoXXzErkujZyfh+tWYdh1re5LwFglBxRpC6ei47C50TSeiwmR6MklxJmPXpQd1Z6SUQu2KlextLkVtLkHL9eG8YDCaw4aWnYR+VGZ0Zt09pdPPpg+F/CREuwpbYcqCZZQGSwlGgiQ7k8m15+K0OSlI7tEhqwBE/VQ4gtpdhZ4brZsPvbIca0Nx9KDPiZ6fgt43M/5653md6+Jqa5LwFu1mZ+VOSoMlKAUeu5su3hySHL74cQnujk9VhqJby2OVIKqoDHQN183j0Gw6thHdohcY81PR0t3ye34IpEGCOGwCkQB7qnbHexw7dQdprnR6pPSge3I+Ka7UVlvLbkpXwZaQroIHU0ph7a0kvGwbyh8GIPLVVkJvfEfkiy0A2MZ0x/HjgfH32AblYBuai95Ba63bk8y8RaMiy7cTXrateW9WEFERwiqMpSzsQMDmwnFMHmmxnW5toaGugq1BugpGqfIAke92xmfWVERb62pJTmz9MtGHdMHZMw2tWzKaXS4ytyYJb9FmlFIEIn4UoKPj1B3omq1JM6zW7io4e/ZsJk2axDHHHMOGDRsYO3YsZWVlrFixgl69eklXwSZQgTDW5mjJnt4jFVvvDFR5iPD769DS3Oh9MvaX7GV7AdDTPJAmG6faRH0dq1rzQ7oKdjzN+ZkHw0G1p3K32lu1RymllGVZalflLlUVqlKWZR3SuVqjq6Df76/V/a+6q2AwGJSugk1khSMq+G9T+Z/+QlXdHe2wV3X3hyq0eEP0eMRSVqm/2eM5EkhXQZGQLGVREaqgNFhKVagSBSQ7oxcdNU0jy5PVrPO2pKtgfaSrYP2UVbNkrwQtyYHjtH5oNh1rQzGaz4XtxGy0/BT0vP0le5quQbKr1ccjGifhLVpkd9UuSgKlOHQ76e4MUpwpOGwtr8k+1K6CB9I07aCuf9JVcD8VsdBs0aWX0HtriCzfDoFI9KDPiW3A/n90ndNHycXEBCTVJqLJIlaYYn8xm0o3xTv4pbrSyPPl0SOlJ5mezFYJboh2FXzssce49NJLef3113nsscfwer1MmTKF888/HyA+G67LyJEjueqqqw7p7t0333wzf/7zn5k6dSo333wzhmHU+9qaXQUPxUUXXcQPP/zA1KlTueiii8jLy4t3Ffzmm28YN24co0ePZtWqVZx66qnA/q6Cl112Gbt27WpWV0EVsVBVIVRViMDzSwnMWhK9rRegJbuwDc7B8aP+OK8bjWvGWBxnHBV/rwR3Ymqwq6BhGA7gr0BPwAXcC6wCXiS6SfU74BrTNBucgkhXwY6n+meulKIyXElpsDTaJ1sp3HYXWe5sPA65EJWIlFIQscCmo2kaqjyIKo0u9Xy/aS19CkHPT8F+Qg/ZsXgYtFdXwanAHtM0pxmGkQEsi33cZprmR4ZhPA2cC8xt1shEwqr+R12h2F6xHU3TSHWmkuJMwWWXNc5qidBVUCkFoQgELVQwAsFI9Ea5mR5w2cFpQ0txRn8tduG6YmDjJxVAdTfLAFURP/5wFVVhP4GIn6pw9HN/2E9VpIpAOPZcJPZcuIpAJIA/7GeY8xgG0PqTz8bC+03grdhjDQgDI4CPY8/NB05FwrtTqApXsWTrJyzYtICzfeeilELXdPJ8eThtTnRNVtkO1B5dBZVlQdACmxZtdxqyULurogdtGrht0eft0d8vzWkDZ6zGupMugSilCFvhg4I0HrIRP/7w/gCOhmwgfqwqFsQ1X1f9uPoOTU3htrlx29247Z7440N5/6FoMLxN0ywHMAwjmWiI3wb8yTTN6tGUAamNfZFAIEBhYWGzB+n3+1v0ftGwbf4iPi3+hOWlywmqAFmObBw+B36/P/6aQKhpFRaibehBCz2s0MMKLXZdMeLSCHujoaz7bCi7hopnc2wGfoDqXaftKaIiBK1g9EMFCFhBglaAoBUkYAUIqtgxa/+xQI3n4s+rQPw8ASuARcMXkGuya3acuhOX5sKpO3HqLly6C4/uIVVPxel24Yo979Sc8ccu3YVDd+LSqj+PvUZ34tAcdU5w2iq/Gl3wMgwjn+jM+knTNP9mGMaDNQ4nA8WNncPlcrVozVrWvFvfXv9enLoDnzOZHZu38+2WFZyYfyITe5zKgIwBfP/993g8sqZ9uCmlIGzFg1dLckbreksqQKno0oc3OpO2O2w49FhaN/G3yuFwNPnvkqWs+H/9/TWWDQ6ayUaqlxACtZYNDpzJVr8uZIWa/PPQNR2P3YPH7sFlc+NxuPHaksiwZ8aec+Gxe+KzXY/Njcvujj5niz1nd0ffW2NGfDhbCrdwzbveYw2Gt2EYXYD3gWtN01wUe/obwzDGm6b5EXAG8GGzRiUOu5AV4qvtX7Jw4wKW7vyKSwdezvn9fszx3cYxuusYPHILsXajKoLRfiDBSLxfNU4dLckZrfbI8kaXSepY9lBKoVAoZWEphYW1/7GyUOx/XBGq4IXv/hoP2eqArR22+x83lYYWDdBYYLpjAZrkSCLLk1V/kNZ4j8fuxm2LvcbuxmPzYNftUu1Sj8Zm3rcA6cBMwzBmxp67HnjMMAwnUMj+NXGRoJRSvLjyr3ywaRElwRIy3Bmc1/fHjOk6FgCHzYGDxOuX/b///Y8bbriBvn37Rtc0w2EuvfRShgwZwjnnnMOgQYNqvf7FF1/kySefZN68eeTk5ADRZYIZM2YwevRojj/+eD799NM6v9auXbv485//HL/pb1uxIhFUIALBMIQtQmn2aM+XQAQ9DBGXImxXhGwRwrqFqqw4KJCVsrCoHc5NrYisCFXwr6J5NWao0cB0292kudJqz2QPCNL47PaAAPbY3DhtLgnZw6yxNe/riYb1gU5qm+GI1lIeLGfVnpUc23U0mqaxtXwLg7IGM7FgEsfkDO8wd6IZM2YMs2fPBqCiooJp06Zx33330bdvX1555ZU633P55Zdz8cUXA7Bu3TpuvPFG5s5t+Jp6dnZ2reBW8WCM/aosrJqPG5nhxoMWC0tZeMIO0oJJOJQdjWgVj18PsqO0hIhmoWmgqu+LG4l+aJqGjoau6Wiajq7p0c91Ow4t9jw6uqbVOB4tDzzosaajoRHyhnjr7L+38HdFJAIp8uxELGXx7e5vWbjxfT4rWkLYCvPX018iw53BLaNntqhapK5yuEGDBjFq1ChCoRCvvvrqQceHDRvGsGHDqKys5P/9v/9X69jll19+yGNISkriwgsv5Pnnn48/V71kEA3MaMVByApRGarEUhZFu4twup3s8+/DUha7q3ax9MuveeGZF7CURVVlFbfcews2u859t/6B2X+dzS+m/ILBwwazYd0GAGY+NJMkX1K949IATdOxaTouy4En4sBluXFGbJR4A4TtCpdmQ1kaVY4Ilh2UXUPTXXTRctGoDlgtFsb7Q7e1acjsuLOQ8O4kVu1Zxaylf2Jn5Q6SHElM7DGJiT0mke5KB0iYMj+FAgXBSKD2LLWOGW5xoBh/2M+2im37Z7JexbZdRaxZu5qfXPwTUNFCrL79+3Ll9VdSEijhnRfe4Z/v/hPdpuPz+Zh+83R2V+3GUhYlgRLMNSa/veu3ZOdk89oLr7F40WImnTEJHR2fw4e/ws/ZZ53N0GOGcsfv72Dt12s5/YzTa4Wqjo6mojNjzaZD2ELtrty/Xm3XwGMjw+vrtHcvF+1LwruDCkQCfL7tM9Jd6QzJHkpuUi7dkroxbeCljOk6Fpet+RtpIiqCpSzCVrQHt6UsLrzkwtg6qxVfUrCw2FO1G0spzrjgjDrXZDeUrMdSFuN+NK7WVvWNpZsaHIOmaVSFKqNlZZFgfMlg1/ZdDB0+jMqKKp56/qn9M9ZYoCY7k7n88su54KILosFavZyg6dh1O33S+jKwx0BeeuwlvF4vO3bsYPjw4eR4c3DYHOR4c7DpNsYcMwa3201BXgF6RCfJkQQRBcFIbCNMMFoVkuRAS3Wj7Dp4HfGa6uq+IUK0FQnvDkQpxdritSzc9D6Lt3xMRaiCcd1OID+5gGAkwBWDf44/XMWqPSujO7zCAariVQU1d4dVVxPUvWMsaAX5Tf5NOEucjY5J04itre5fk40uIdhw6HWvydb+fP/j6Brv/sd7fHtIciTRI6UHAOXl5bz/zvs8+uijfPXpV2R7sw8aj9PmxGlzNlg5M3PmTBYsWIDP5+Pmm2+ut/+JCkaiAV39+e5KsFR0ncRpQ/M4ozsYif5jo6W6G/15CdFaJLwPg5q7v2ru3mp8i23tsN1UupGKcAUANs2GhsYnRf/lk6L/Nmkc1XWx1RUC7lgpV6Y7c3+pVqx0yxf0kePNiV/oOnCGWx2wGnWXr7WWzz//nGnTpqHrOpFIhOuuuw6n08natWvj7WGr/eEPf2jSOc855xwuueQSPB4PWVlZ7Ny5M3pAKVRZINrEaXs5yulCBaO389I0DdLc0R2Mdl0qK0S7a7AxVWvpSI2pIlYk1peg7l4F1UFaFQvgQI3H9fU+8Ef8RNTBu93q49SduO1udE0nGAmS58vD6/BSEapAQyc/OZ9kZ0odtbK1NydUP++xew55e3tn3xilItGNMCoUQUuOlrlZxVVQGQaHHp1ZO23gsKHZO88SSGf/fU1E7dWYKmHt3/1Vc9dX4OCZbI1NBwcGcHzZoMbrDmX3l02zHRyYNjdprnS6Ju2vlXXba892D9qYUKMPwt6qPXy45UM+2LSIvf49pDpTmT70Gvql92vDn+aRQQXDqIpQdCNMJDZp0QCvE+waWrILUtzRGwwIkeASOrz9YT9PLHuMjbs3om3Tam2xDUSa3mujevdX9Rbb6sc+RzJZnux6g9RdY3NCfHtujdmuQ2/djS1byrZwzQe/QEdnRJeRXD1kOiNzR7X61+nslIpeWCQU3RCjJbuis+iIit5wwGlDS4o1a3LsXwKRi4yiI0no8FYogpEgDt1BVlJWjeUAV3z3V31bbPeHb2Lu/lJKUbi3kIUb38dlc3H10F+Q58vjl8OuZVSXY8n0ZLb3EDsMpVS0b3XYQhVXQciqUbKnRy8yArjtaG7Zbi06h4QOb4/dwy2jb+tU63R7qvbw4eZFLNq0kK3lW/HYPUwsiPZ91jSN03ue0c4jTGzRGw3ULNmLREM5xQW6BmjR8r3q9eoas2kJbdGZJHR4dxYhK4RNs6FrOnPXvs076/7JoMzB/OSoCziu2/HSEKoB1WGt2fXobspdlfvL93RqXVDUdA0ty9t+gxXiMJLwbkMbSjawcNP7fLT5Q24a9TuGZA/lR33PZ3KvM+nmy2vv4SUkZal4FQiB6Lo1Nh0tJyk6c/bao8XlTpuU7IkjmoR3KwtFQizY9D4LNy5gbfEa7JqdY7uOJskRvVlulierkTMcWapL9oitRasSP1SF+eOTs1m55nt279uDP+Anv6CA9PR0HnvssUbP+eWXX5KcnEz//v0JBAI88sgjLF++HE3T8Hq93H333XTt2rVVv4+///3v/PDDD9x4441Nfs+zzz7LmDFjMAyDd955h5/+9Kf1nvuxxx4jPz8fy7LQNI1rrrmGsWPH1uq8WK2pPyfRsUl4twJLWeyq3EWXpC5omsbr3/+NNFc6Vx59FeO7jyfF1ejNho4YKmxBILx/vTpWsqflJEXL9ZKc4HHwu7tnoulas0Lx7bffZvLkyfTv35/77ruP3r1787e//Q2ABQsWcMMNN/DGG2+0yfd3KK666ioAtmzZwptvvllveAOcddZZ8Z/B7t27ueSSS5gzZw5Qu/OiOHJIeLfAjoodfLB5EYs2LsBC8dypz2PX7Tx68uOkudI7zX/ply9fzjfffNO8N1sqegcYXYsud1jRO8UM7T+YoYOH7C/Zs8XK9Zx1N3EKhULccccdbNy4EcuyuOGGGxg0aBAXXHABs2fPxmazMWPGDGbOnMl///tfVq5cSd++ffnggw+466674ueZNGkSI0dG9zx8+umnPPLII7hcLtLS0vjDH/5AYWEhzz77LA6Hg+3bt3PRRRfx+eef8/3333PppZcyZcoUJk+ezMiRI1mzZg2pqanMmjWr1lhfeeUV5s2bh6ZpTJ48mUsvvZRf/epXHHfccZx77rlMmTKFe++9l1deeYXJkyfz/vvvs3btWp544gk++eQT7rnnHvr168fHH3/Mhx9+yJAhQ2qdPysri9NOO42PPvqIgoKC5v2+iA5PwrsZVu7+jtfNv7F813I0NIZmD2Nij0nR8jQN0t0Z7T3E9qMAy4qFdo3nNS26IUbXohcZU1zoGU2/UPvmm2+Snp7OH/7wB/bt28fUqVP517/+xQMPPMDMmTNRSvHggw8yYMAATjjhBCZPnozdbicrK+ugf0TT09NRSjFz5kxee+01unTpwksvvcRTTz3F+PHj2b59O//4xz9YuXIl119/PQsWLGDHjh1ce+21TJkyBb/fz9lnn82oUaN48MEHeeONN0hNjf7vau3atfz73/+Oz/SvuOIKxo0bx7333suUKVP49NNPufDCC2vdSGL69OmsXr2aa6+9lq5duzJ37lxuuukm3n77ba6++mpM0zzo55GZmcm+ffsoKCiItxCodtJJJ3HllVc2+WcrOiYJ7yaINoRaQ7o7gyxPFv6In20V25nS/xJOKZhIjjenvYfYpoYOHcrQoUNrPRetArHiJXuawxa936KlUDvKD95i3sINMKtXr2bp0qWsWLECgHA4zN69exkyZAjJycl13psxPT2d0tLSeB14tXfeeYdx48bh8/no0qULAKNGjWLWrFmMHz+efv364XA4SE5OpqCgAKfTSWpqKoFAdGOY3W5n1KhRAAwfPpzFixczbNiw+DiLiori/cpLSkrYuHEjvXv35pxzzuGFF17gT3/6U73f5xlnnMH555/Pz3/+c3bs2MGgQYPqDO+ioiIGDhwIyLLJkUq2lDWgJFDCP9fO5VcfXsNvPp7Bv36YB8AxOcN5dtJfuKj/lE4f3NVq9sCx9lWhdlSgdlaiigPgD6Oq1651DS3Xh56VhJ7iRnM7WmXnYu/evTnzzDN55ZVXeO655zj99NNJS0vjvffeIykpCbvdznvvvRcdg6ahlMLhcDBu3Lhad9yZP38+L7/8Munp6ZSXl8ebUn3xxRf07Nkz/v6GhMNhvv/+eyB6g9iaFwt79+5N3759efnll3nllVc4//zzMQyDzZs3M2/ePKZNm8Yf//jHWufTdR3LipY/er1eRo8ezX333cc555xT59ffuXMnixYt4qST5IZWRzKZeddBKcWspX/i062fEFZhjko/il8OvZYTup8IJM6NDdqUUih/jQuLKLSs2N1kNMBli/euPrBkry3W+i+66CJuu+02pk6dSnl5OVOmTGHbtm08+uijvPrqqyilmDJlCkcffTRDhw7lT3/6E927d+f3v/89999/PxdddBEAqampPP7442iaxr333st1112HpmmkpqZy//33s2bNmiaN57nnnqOoqIhu3boxY8YM5s2L/sPev39/xo4dy8UXX0wwGGTIkCFkZWUxbdo0Zs6cyciRI7n88stZtGhR/FyZmZmEQiEeeughfvvb33LBBRcwZcqUWrdlmzdvHsuXL0fXo/Xu999/P2lpaQAHLZtUj8/tlha1nZl0FYzZWr6Vb3Ys5aw+0dnOsyuexq7bmVgwiYJYP+nOTJUGorsUgdCCdazxldK/R2xGWb0EkpJ4bQbawymnnML8+fNxuZp/w4uGrFixgjlz5vDggw+2+rk7027ljkK6CraBylAlnxZ9wsKNCyjcuwpd0xnddSzZ3myuGjK9vYfXZpSlUDsrsDaXRD82lUBpANf1Y9BS3egFqVBZiZbpia5XS5e9w2bOnDm89dZbPPLII+09FJHgmhTehmGMBv5omuZ4wzCOAeYB1f+/fMo0zfYvmj1E3+3+lrs/uxN/xE+erzuXDbqCk/NPIaMTVoqoUARrayl6phct2YW1ciehuYXRg8lO9PzUaGDH7rVoM7LQCnehuY7of9vr9cEHH7TZuadOncrUqVPb7Pyi82j0b6dhGDcB04CK2FMjgFmmaT7clgNrbdUNoXK8XTix+0n0Tu3DSfnjOSV/Av0zBnSq5QAVimCt3RufVavt5WAp7Gf0wz4qD71XOo4f9UcrSEVLddf7vR9YpSE6tsOxRCoOn6ZMrdYB5wPVl+xHAIZhGOcSnX3fYJpmWRuNr0VCVogvt33Bwk3v8/WOr7GwOL3nZE7sfhJeh5drhl3X3kNsMaUUak8V1uYSNK8Dm5EFEYvQmyvBpqHlpWAbmx+bXacAoPmc2IbkNnhet9vNnj17yMzMlADvBJRS7NmzRy5idiJNumBpGEZP4HXTNMcYhnEFsMI0zaWGYdwKpJum2eDe5WXLlqmWXNzx+/3N+kP34pbnKawoJMWeysiUkYxIHUWWs3P0FkleW4lnRxD37iC2YPT3sCLPxc7j0gBw7gsRTLHHdy4eKqUUkUhEZmudiKZp2Gw2+cf4MGtufgFUVla26gXLuaZpFlc/Bh5v7A0ul6tFV7ibcrW2PFjG4i0f8+HmD5k55nZSXKlckjWNkBViWM4x2LS6t10nOuUPYW0uxdpcAlUhHGcaAAS++Ab8OvqALmj5KegFqbgyvWTKX0whEkoLq03qPdac8P6PYRjXmab5BTABqP/sbcxSFit2LWfhpgV8VrSEkBWiV0ovdlftJsWVypDsoY2fJEGFlxYR+XIramfsUoMGWl5KfB3aeckQNHvH/AdJCNFyzQnvXwCPG4YRArYDV7XukBoXsSLYdBs7K3dy+5Lb8Dl8nNrjNCb2OJU+aX0O93CarXorebxkb3MpriuHo/lcoBRashPbwGy0/FT0vJRaTZskuIU4sjUpvE3T3ACMiT3+Gji+DcdUp0AkwGdFS1i4cQFeh5dbRt9GblIudx93LwMzB+G0OQ/3kA6ZCoYB0Jx2Imv3EnprZWz3IpDiQs9PRYUsNMA+Mg9Gyg0bhBB1S/hC3h+K1zF3+9t8u24FFeEKuni7cGrP0+PHh+Uc046ja5gqC8Rn1NbmEtS2MuxnHoV9eDe0TA+2IV3iNdZaqlQBCCGaLuHD+4vtX7C09CvGdT+BCQUTGZx1dEL2FlFKoXZXQkSh5/pQVSECsz+LHrTraHnJ2I4vQO8WLdfT0z3ok49qxxELITqyhA/vs3qfzVFhg+GDh7f3UA5ibSnF2lgcX7OmKozeLxPnxUejeRzYzzoKPceH1tXXKp31hBCiWsKHt8/pw2Nr/7urq6pQdOmjJIB9VHQtOvT+WtSW0ugSiJGFVhDbZh5jH96tvYYrhOjkEj6821Nk/T6slTujob2rMvqk04ZteFc0m47jLAMtyRG976IQQhxGEt6AsizU9op4LxDHmf3QvE7U1lIiK3ei56diG9wlemGxW3J8CUTPSWrnkQshjlRHdHhb28oIL/wBa0sJhKJ3MiHVhSoOoHmd2EZ3x3Z8gWwnFkIknCMivFVZAGtTSfzCon1092hjJruOqgphG9YVPT8FPb92yZ7mkI0wQojE1OnCWykFgTCa24EKhgk+/RWq2B896NDR81Ig1qdaz07CdVWdPV+EECKhdfjwVuEIqqisxsy6FL1nGs4LBqM57eh9MtAyPdFZda6U7AkhOocOF96qMojaXYlekAZA8OXlqC2lAGhZXmwDstH77r8bjuNM2QgjhOh8Ej68VYkf34YqQmvNaMne7kqw67huHodm07EfXwCAnp+C5pWSPSHEkSHhwzuyfDvZX5YScVei56fE+4EQqwCxGZ3j5gpCCHEoEj68bcNy2eAso8/owVKyJ4QQMQl/9U5LcRNKtUtwCyFEDQkf3kIIIQ4m4S2EEB2QhLcQQnRAEt5CCNEBSXgLIUQHJOEthBAdUJPqvA3DGA380TTN8YZh9AVeBBTwHXCNaZpW2w1RCCHEgRqdeRuGcRPwF6C6V+os4DbTNE8ANODcthueEEKIujRl2WQdcH6Nz0cAH8cezwcmtvaghBBCNKzRZRPTNN82DKNnjac00zRV7HEZkHrwu2oLBAIUFhY2b4SA3+9v0fuFEKK9tFV+Nae3Sc317WSguLE3uFwuBgwY0IwvFVVYWNii9wshRHtpSX4tXbq03mPNqTb5xjCM8bHHZwD/bcY5hBBCtEBzZt6/AZ4zDMMJFAJvte6QhBBCNKZJ4W2a5gZgTOzxauCkNhyTEEKIRsgmHSGE6IAkvIUQogOS8BZCiA5IwlsIITogCW8hhOiAJLyFEKIDkvAWQogOSMJbCCE6IAlvIYTogCS8hRCiA5LwFkKIDkjCWwghOqDmdBUUQogjllIKQhFUZQgqQ7V+VZUhqKr5OExSTxu0we0IJLyFEEc0FYo0EL51hHNlCCKq7pNpgMeB5nWA14GW7iHijbTJuCW8hRCdhopYdQbxgeGrajxPyKr/hB57NIg9DrRUN3rX5GgoV4dzzaD2OsBtR9O0Wqfwt9EtHCW8hRAJSVkWVIXrDt9YAHNgSAcamOW6bPuDNsmJlp2E5q0dvpqnRhB77Gh64l4WlPAWQrQ5pRT46w/iWksV1b/6w/Wf0KHvD1yvAzI96AeGb83jHgeaLXGDuDkkvIUQh0QpBYFIo+Fba0ZcFYJ6lomxaZDk3D/z7eqOBnF8acJ+cBA7bIf1e05EEt5CHMHqrJyILVUcVE1Rc53YqieJda32EkR2Enpds+Aa4YxDP2idWDROwluITkSFIo3PgptbOZHhQe+eclD41vrVaZMgPkwkvIVIUE2qnDggqA+5csJTx/pwA5UTInE0O7wNw/gaKI19ut40zStaZ0hCdD4HVU7UV8J2qJUTns5ROSEOXbPC2zAMN6CZpjm+dYcjROJrl8qJA5cqOlnlhDh0zZ15DwW8hmG8HzvHLaZpft56wxLi8Gi0cqKuIG6scqJmEHf1RS/Y1bFOLJUToiWaG96VwJ+AvwD9gPmGYRimaTYwvRCibUUrJ+peJz48lRN2cMgFO3F4NDe8VwNrTdNUwGrDMPYAXYHNdb04EAhQ2IIton6/v0XvFx2TFlHoAQtb0Ir+GlDoQQtbIPoRfbz/OT1goddzvU4Blksj4tSxXHr012wbEZd9/3MuHcupE3FpRFw6yq7BQUEciX34o5+GgJLYhxB1aKv8am54/ww4GvilYRjdgBRgW30vdrlcDBjQ/LZahYWFLXq/aH8qYjVpFtzkygm3ff8SRFrtWbBUTohE0pL8Wrp0ab3HmhvezwMvGobxCdFJzc9kyeTIoSxVu2ytrSsnDmz+I5UTIoHtqwiyaU8F+yqChCIW3dvokkazwts0zSAwpZXHItpBnZUTVQfMjA/a6tzEygmPAzI8tbc6S+WE6CCUUlQGIuyrDLKvIkiv7CR8bgfmtlLmLy9iX0WQ4orosX2VQR6/dCQ9s328/+02Zs//HoBUr4NHzunaJuOTTTqdiFIKgk1sEt+cyonUBionqsvapHJCJDh/MMK6nWUUV4aiwRv7OGNoV/rlpvDNhr3c+fdv2VcRJBjev3T32KUjObZPJlv3VfLO0i2kJzlJS3KSk+rG6JqC0x6dhJxg5NAjK4n0JCfpXid7ita3yfch4Z2gmlw5ceAOuyZUTuCxo2V76w3i+K9SOSESnD8Uic9+05Oc5KZ5KK4I8sqn6w+aGV85vi9nHZPHht0V/Py5/9U6j8uhMzAvlX65KaQnORnRKyMevum+6K9G12QATh7QhVMG5tY7pm7pHrqle+Kf7ylqm+9dwvswUeEIVIYPXoI4cJ24ZglbuIELdjWXINI96HkpdZawxV/nkiAWHYNSCnNbGcWVwVoz4wF5KZwyMJeKQJhpTy2huCJIZXD/tZSfj+/D/53cl4hSvPm/TdGZsddJepKTntk+clLcABRkenn4kuGkeR2kJ7lIT3Lgce6Pwp7ZPm4/7+h6x5cof48kvJuhTSsnUlzoub6DKyc8B1RO6InxB0iIhoTCVjyEdV2jb5fo7PWFj9exrbiq1tLFsX0yuemsgQBc/df/Eajxd8Zu0/jpsQWcMjAXj8PGkPw00qpnxrHliz45PgAykpx8fNvEekPW67Jz/FHZbfydt70jPrwbrZyo4+Jdg5UTzjru1tFQCZtUTogOaNOeCnaW+GvNjH1uO5cc3wuAX89Zyrebiymr0RZgZK8Mnrh8FADvrdhGRSAcn/0OzEuhXyzYNU3jgQuH4XXZ40sXvhqlnrquceePh9Q7tkSZGbe1ThXebV45kd5I5YTHgWaXIBYdR8RSlFZFZ7/l/hBDCtIBeG9FEcs3FtdauvA4bbx49VgAHppXyJc/7ImfR9dgUPe0eHgPyEulW7qX9KRoOKclOeiWtn8d+PVrj28wZMf26/gz47aW8OGtyoM494aIrNnTpDs8N6lywuOALr5Gm8RL5YToqHaXBdgcqzWu/iiuDPLrMwag6xrPfbCWv3+1mZLKYPwat8OmsXjmJDRN46sf9vJfc2d05htbksitEb5XT+jL5Sf2JiN2PNnjwFZjKe//Tu7b4PiOlNlxW0ro8FYVQQKPfkZeRBFi7/4DGrUrI7KkckJ0Tkopyv1h9lUE2VsRxOiajMdpZ/mmfSz8bnutaop9FUFenn4cWcku/vHVZv7y0bpa50rx2LnqlH6keBwUZHkZPyAnfkGv+kOpaEeAW88dhKYNrndcg7untfF3LhqT0OGN14HjwqPZvHETBUavGpUTstVZdGwV/jA/7CqvPTOuCHLeqHx6ZCXxX3MnD767in2VQcI17nTz16vGMDAvlY27Kvj3sqJ46OalexmUl0r134pTh3RlSEF6/Hia14G9xmao04Z047Qh3eodn/z9SnwJHd6apmHrm0FVaAd699T2Ho4QB1FKURWMUFwZnRnnpLjJSXGzs8TPa59tiFVTBNhXEV1X/vXk/owf0IVVRSVc99JXtc7lddoY0y+LHllJZCe7GNMvq1Y1RUaSk4JMLwBnD8/jnBHd6x1XQWYSBZlJbfq9i/aV0OEtRHuJWIrV20r3L0mUR38d0TOD447KZmepn6v+8j/2VQZrlbRdf5rBxcf1pDIYZu5XW2IzXwdZyS76dvGRkeQE4KjcZGZNHU5GrBY5LcmJu8Y1lv7dUrn13PonLDIzFhLeotMLhi2KY2vGLrtOr1g98FMLV7OnvPZGkAmDunDtqQYRS3HFs7XvL+Ky6yQ57Rx3VDbJbjvDe2ZEa41rfPTtEj13j6wkPrptYr1jSvU6OU4qKkQLSHiLDmv9znJ2lvrjM+PiyiCZPhcXjOkBwFXP/491O8qpCOwvBx0/IIcHLjoGgH8vKwKN+NJEfqaXPrFaY6dd5+FLhpPqdcSPe2rcGd3jtHP7+Ym/C090XhLeot2FIxYllSH2VgQJhCIMzk8D4J9Lt1C4taRWNUVOips/xzZ63D33WwqLSuPnsekao/tkxsN7WEE6A7qlROuMvQ4yfC7yavSceOc3JzUYsp1hF57ovCS8RZvZUVLFlr2VsYt1AYorQlQEw9xwen8AZs8vZP7ybZRWheLvyfA5+fdvTwbg87W7Wb5pX3zma3RNoUeNi3A3nNEfpYgvWSQfcMOFX046qsHxyexYdGQS3qJRlqUo80dnxsUVQQZ1T8Np1/li3R4+LtwRr7SoLnf7x4yTcDttvLZkI69/vjF+Hk2DNK+Taycdhd2m0ycnmUmDVa1qigyfK/76P1wwtMGAHRrbDSjEkUjC+whWWhXih53lteqM91YEmTauJ11SPcxfXsTj75uUVIaI1Gg1++avxpGfmcSa7WUs+G57fObbMyuJ9B4ZhC0LsHHuyO6c0D87vmyR6nXW2oXXUKkbyMxYiIZIeHcCSikqAuH4zDgv3UtmsotNeyp4+4tN8RrjfRUBiitD3Hn+0YzsnclX6/dwyxvLa50r2W3n9KFd6ZLqoWuahxOMnFrVFOlJTrKSo7PjKcf14JLje9Y7rl7ZPnpl+9ryWxfiiCXhncBCYQtzW2mtaoq9FUFOMLIZ0SuT9bvK+dXLX7GvovYuvNt+NJizjsmjtDLEu99sja8Z56Z5GJCXSorHAUSXHR6dNqLGLjwnjhqNtYb1SGdYj/qXJmRmLET7kfA+TPzBSHRmXBkkyWWnR1YSobDF04vW1Kqm2FcR5LyR+Vx+Ym9Kq0Jc+Zfad/xwO2x0S/MwolcmaV4no/tkxYM3wxfdBn1UbgoAg7qn8sEt9dcaZ/pcZPZ11XtcCJG4JLxbaM32UnaXBWLBG62qyM9M4tzYeu5FT3zC9mI//tD+HuBnD8/j1nMHY9M1/rF0C8lue3z22zvHR4+saEVFqtfBw5cMr3FHkNp3/EhPcnLbj+pvHiQzYyE6LwnvmFDYYl9ldM04bCkG5kW3Jr/x2UbW7iyrdT+83jk+HpoyHIDfv7GMLXur4udx2DROGZQbD+9je2di07X9SxNJznjPCV3XWHTLhHrHZLfpUmsshKhTs8LbMAwdeBIYCgSAK03TXNuaA2stW/dWsnVfVbzOeG9FEKVUvAb47rnfsvj7nZTXuONHj6wk3rhuHACfrN7F+l3l8dlvt3QvR+Umx19767mDsdv06CaQJBfeA+4V+evJAw7TdyqEOJI0d+b9I8BtmuZYwzDGAA8D57baqOoQjliUVIXis9/hPTPQdY3F3+/kszW745UU+yqCVATCvBvbPff8x+ui26BjdC16d+fq8B7QLQWfyx6vM06rUU0B8PhlIxsc1zE9M9rmGxZCiAY0N7zHAe8BmKb5uWEYDSdcC9z19gr+8+02LLWp1vNH56dht2ls21fF7rIAdpuGXdejv9p0fvHCl2gaVAUj9MtNxq5rOGx6tM5Yg1+88EVbDVkIIeJGdNEY0Ab/AW9ueKcAJTU+jxiGYTdNs84bQgYCAQoLC5v1hVSgnGSXjl3XsOkadj3awyLgryIIpDohNdN50PuqKivjj3XAikAgdNDLhBCiTYVCzmbnX0OaG96lQHKNz/X6ghvA5XIxoJn/9Nw5AAoLC5v9fiGEaE8tya+lS5fWe6y5tzr/FJgMEFvz/raZ5xFCCNEMzZ15zwUmGYaxhOjtgK9ovSEJIYRoTLPC2zRNC5jeymMRQgjRRM1dNhFCCNGOJLyFEKIDkvAWQogOSMJbCCE6IAlvIYTogDSlVOOvaqGlS5fuAjY2+kIhhBA19RgxYkSdrUUPS3gLIYRoXbJsIoQQHZCEtxBCdEAS3kII0QFJeAshRAck4S2EEB1Qwt+A2DCMHGApMMk0ze/bezxCCNEUhmH8HjgHcAJPmqb5fGueP6Fn3oZhOIBngKrGXiuEEInCMIzxwHHA8cBJQH5rf42EDm/gT8DTQFFjLxRCiARyGtGb1MwF3gXmtfYXSNjwNgzjcmCXaZr/ae+xCCHEIcoCRgI/JXrvg1cNw9Ba8wskbHgDPyN6t56PgGHAy4Zh5LbriIQQomn2AP8xTTNomqYJ+IE6t7k3V8JesDRN88Tqx7EAn26a5vb2G5EQQjTZJ8D1hmHMAroCSUQDvdUk8sxbCCE6JNM05wHfAF8QXfO+xjTNSGt+DWlMJYQQHZDMvIUQogOS8BZCiA5IwlsIITogCW8hhOiAJLyFEKIDkvAWQogOSMJbCCE6IAlvIYTogP4/7cTOJL31ndwAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "colors = {\n",
    "    #'sentiment': 'lightblue',\n",
    "    'sentiment_news': '#f781bf',\n",
    "    'toxicity': '#377eb8',\n",
    "    'topic_gnad': '#4daf4a',\n",
    "}\n",
    "\n",
    "for task in tasks:\n",
    "    plt.plot([errors_per_level_deplain_level[level][task] for level in [4, 6]], label=task, color=colors[task], linestyle='dashed')\n",
    "    plt.plot([1, 0], [level[task] for level in errors_per_level_complex.values()], label=task, color=colors[task], linestyle='solid')\n",
    "\n",
    "plt.plot([], color='grey', label='DEPlain', linestyle='dashed')\n",
    "plt.plot([], color='grey', label='TextComplexityDE', linestyle='solid')\n",
    "\n",
    "plt.xticks(range(len(errors_per_level_deplain_level.keys())), labels=errors_per_level_deplain_level.keys())\n",
    "legend = plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-13T14:10:43.343370400Z",
     "start_time": "2024-02-13T14:10:43.120381Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# For topic classification: https://huggingface.co/Mathking/bert-base-german-cased-gnad10"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2095/2095 [00:00<00:00, 9392.93it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from Levenshtein import distance, ratio\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "distances = []\n",
    "num_errors = []\n",
    "aligned_combined = pd.concat([deplain, complexity])\n",
    "\n",
    "for _, row in tqdm(aligned_combined.iterrows(), total=len(aligned_combined)):\n",
    "    errors = 0\n",
    "    for task in colors.keys():\n",
    "        if row[task + '_normal'] != row[task + '_simple']:\n",
    "            errors += 1\n",
    "    num_errors.append(errors)\n",
    "    distances.append(distance(row.normal_phrase, row.simple_phrase)/len(row.normal_phrase))#, score_cutoff=100))\n",
    "    #distances.append(ratio(row.normal_phrase, row.simple_phrase))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-13T14:11:00.656442600Z",
     "start_time": "2024-02-13T14:11:00.401442600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Levenshtein distance')"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "dists = {\n",
    "    0: np.array(distances)[np.array(num_errors) == 0].tolist(),\n",
    "    1: np.array(distances)[np.array(num_errors) == 1].tolist(),\n",
    "    2: np.array(distances)[np.array(num_errors) == 2].tolist(),\n",
    "    3: np.array(distances)[np.array(num_errors) == 3].tolist(),\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(dists.values(), showfliers=False)\n",
    "ax.set_xticklabels([f\"{k}\\n$n$={len(dists[k])}\" for k in dists.keys()])\n",
    "plt.xlabel(\"Number of successfully attacked classifiers\")\n",
    "plt.ylabel(\"Levenshtein distance\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-13T14:11:01.642481800Z",
     "start_time": "2024-02-13T14:11:01.398443100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "import json\n",
    "with open('lev_dist_german.json', 'w') as fp:\n",
    "    json.dump(dists, fp)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-02-13T14:11:09.712778700Z",
     "start_time": "2024-02-13T14:11:09.680766700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Label distribution"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x720 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(nrows=len(tasks), ncols=2, figsize=(12, 10))\n",
    "for ax, task in zip(axs, tasks):\n",
    "    for col, identifier in zip(ax, ['_normal', '_simple']):\n",
    "        col.set_ylim([0, 1800])\n",
    "        sns.histplot(data=deplain[task + identifier],discrete=True, ax=col)  # RUN PLOT\n",
    "        if col != ax[0]:\n",
    "            col.set_ylabel('')\n",
    "#plt.xlabel(\"Number of successfully attacked classifiers\")\n",
    "#plt.ylabel(\"Levenshtein ratio\\noriginal $\\\\rightarrow$ simplified\")\n",
    "#plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-24T08:12:48.717708400Z",
     "start_time": "2024-01-24T08:12:47.145706500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x720 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(nrows=len(tasks), ncols=2, figsize=(8, 10))\n",
    "for ax, task in zip(axs, tasks):\n",
    "    for col, identifier in zip(ax, ['_normal', '_simple']):\n",
    "        col.set_ylim([0, 250])\n",
    "        sns.histplot(data=complexity[task + identifier],discrete=True, ax=col)  # RUN PLOT\n",
    "        if col != ax[0]:\n",
    "            col.set_ylabel('')\n",
    "#plt.xlabel(\"Number of successfully attacked classifiers\")\n",
    "#plt.ylabel(\"Levenshtein ratio\\noriginal $\\\\rightarrow$ simplified\")\n",
    "#plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
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