notebooks/3-metaeval_simplicity_levels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "\n",
    "from scipy.special import comb\n",
    "from scipy.stats import pearsonr, spearmanr, gmean\n",
    "from tqdm import tqdm\n",
    "\n",
    "from correlation import compute_segment_scores, compute_direct_assessment_correlations\n",
    "\n",
    "# from utils import (\n",
    "#     read_amt_ratings,\n",
    "#     read_sentences_to_evaluate,\n",
    "#     read_test_set,\n",
    "#     collect_references,\n",
    "#     sigmoid,\n",
    "#     read_lines\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the Test Suites Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simplicity-DA (Continuous Scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simplicityDA = pd.read_csv(\"../data/simplicity_DA.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 12.5})\n",
    "plt.hist(df_simplicityDA['simplicity_zscore'], edgecolor='black', color=['#8da0cb'])\n",
    "plt.xlabel(\"Simplicity-DA zscores\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "# plt.savefig('scores_distribution_simplicity_DA.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simplicity Gain (Discrete Scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simplicity_gain = pd.read_csv(\"../data/simplicity_gain.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_simplicity_gain['simplicity_gain'], edgecolor='black', color=['#8da0cb'])\n",
    "plt.xlabel(\"Simplicity Gain scores\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "# plt.savefig('scores_distribution_simplicity_gain.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structural Simplicity (Discrete Scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_struct_simplicity = pd.read_csv(\"../data/structural_simplicity.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_struct_simplicity['structural_simplicity'], edgecolor='black', color=['#8da0cb'])\n",
    "plt.xlabel(\"Structural Simplicity scores\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "# plt.savefig('scores_distribution_structural_simplicity.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics across Simplicity Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_correlations_per_half(df_benchmark, df_segment_metrics, aspects, test_sets, segment_id_cols):\n",
    "    results = {}\n",
    "    for aspect in aspects:\n",
    "        if f\"{aspect}_zscore\" in df_benchmark.columns:\n",
    "            df_benchmark_sorted = df_benchmark.sort_values(by=[f\"{aspect}_zscore\"])\n",
    "        else:\n",
    "            df_benchmark_sorted = df_benchmark.sort_values(by=[aspect])\n",
    "        df_scores_low = df_benchmark_sorted.head(int(len(df_benchmark_sorted) / 2))\n",
    "        df_scores_high = df_benchmark_sorted.tail(int(len(df_benchmark_sorted) / 2))\n",
    "        print(f\"{aspect}: High ({len(df_scores_high)}) - Low ({len(df_scores_low)}) - All ({len(df_benchmark)})\")\n",
    "        for quality, df_scores in {'low': df_scores_low, 'high': df_scores_high, 'all': df_benchmark}.items():\n",
    "            for test_set in test_sets:\n",
    "                print(f\"Computing for {quality} scores - {test_set} references\")\n",
    "                df_metrics = df_segment_metrics[df_segment_metrics.test_set==test_set].drop(columns=['test_set'])\n",
    "                results[(quality, aspect, test_set)] = compute_direct_assessment_correlations(df_scores, \n",
    "                                                                                              df_metrics, \n",
    "                                                                                              aspect, \n",
    "                                                                                              segment_id_cols=segment_id_cols)\n",
    "                print()\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pprint_correlations(results_correlations, aspects, test_sets):\n",
    "    correlations = []\n",
    "    for aspect in aspects:\n",
    "        for quality in ['low', 'high', 'all']:\n",
    "            for test_set in test_sets:\n",
    "                df_corr, _ = results_correlations[(quality, aspect, test_set)]\n",
    "                df_corr = df_corr[['metric', 'corr']]\n",
    "                df_corr.columns = [\"metric\", f\"{aspect}_{test_set}_{quality}\"]\n",
    "                correlations.append(df_corr)\n",
    "\n",
    "    df_corr_metrics = reduce(\n",
    "        lambda left, right: pd.merge(left, right, on=[\"metric\"], how=\"outer\"), correlations\n",
    "    )\n",
    "    \n",
    "    return df_corr_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simplicity-DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_simplicity = pd.read_csv(\"../data/metrics_per_sentence/metrics_simplicity_DA.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS_TO_ANALYSE = ['bleu', 'sari', 'samsa', 'fkgl', 'fkbleu', 'ibleu', \n",
    "                      'amean_bleu_sari', 'amean_sari_samsa', 'amean_bleu_samsa', 'amean_bleu_sari_samsa',\n",
    "                      'gmean_bleu_sari', 'gmean_sari_samsa', 'gmean_bleu_samsa', 'gmean_bleu_sari_samsa',\n",
    "                      'bertscore_P', 'bertscore_R', 'bertscore_F1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_simplicity = df_metrics_simplicity[['sent_id', 'sys_name', 'test_set'] + METRICS_TO_ANALYSE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "EVAL_DATASETS = ['asset', 'turk', 'hsplit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simplicity: High (300) - Low (300) - All (600)\n",
      "Computing for low scores - asset references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for low scores - turk references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for low scores - hsplit references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for low scores - all references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for high scores - asset references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for high scores - turk references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for high scores - hsplit references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for high scores - all references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for all scores - asset references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for all scores - turk references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for all scores - hsplit references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n",
      "Computing for all scores - all references\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results_simplicity = compute_correlations_per_half(\n",
    "                        df_simplicityDA, \n",
    "                        df_metrics_simplicity, \n",
    "                        aspects=['simplicity'], \n",
    "                        test_sets=EVAL_DATASETS + ['all'], \n",
    "                        segment_id_cols=['sent_id','sys_name']\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>metric</th>\n",
       "      <th>corr</th>\n",
       "      <th>p_value</th>\n",
       "      <th>is_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.617463</td>\n",
       "      <td>2.413042e-64</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.572982</td>\n",
       "      <td>1.195520e-53</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.503749</td>\n",
       "      <td>6.297491e-40</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.502735</td>\n",
       "      <td>9.498500e-40</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.499577</td>\n",
       "      <td>3.381328e-39</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.496336</td>\n",
       "      <td>1.228257e-38</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.475908</td>\n",
       "      <td>3.055152e-35</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amean_bleu_sari_samsa</td>\n",
       "      <td>0.418595</td>\n",
       "      <td>7.465983e-27</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amean_bleu_samsa</td>\n",
       "      <td>0.380113</td>\n",
       "      <td>4.626515e-22</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.358708</td>\n",
       "      <td>1.170311e-19</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gmean_bleu_sari_samsa</td>\n",
       "      <td>0.327895</td>\n",
       "      <td>1.665210e-16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gmean_bleu_samsa</td>\n",
       "      <td>0.300198</td>\n",
       "      <td>5.826720e-14</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.165872</td>\n",
       "      <td>4.448623e-05</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.155818</td>\n",
       "      <td>1.269979e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.117057</td>\n",
       "      <td>4.089161e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>1.594498e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.057665</td>\n",
       "      <td>1.583272e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   metric      corr       p_value  is_winner\n",
       "0             bertscore_P  0.617463  2.413042e-64       True\n",
       "1            bertscore_F1  0.572982  1.195520e-53      False\n",
       "2                   ibleu  0.503749  6.297491e-40      False\n",
       "3         amean_bleu_sari  0.502735  9.498500e-40      False\n",
       "4             bertscore_R  0.499577  3.381328e-39      False\n",
       "5                    bleu  0.496336  1.228257e-38      False\n",
       "6         gmean_bleu_sari  0.475908  3.055152e-35      False\n",
       "7   amean_bleu_sari_samsa  0.418595  7.465983e-27      False\n",
       "8        amean_bleu_samsa  0.380113  4.626515e-22      False\n",
       "9                    sari  0.358708  1.170311e-19      False\n",
       "10  gmean_bleu_sari_samsa  0.327895  1.665210e-16      False\n",
       "11       gmean_bleu_samsa  0.300198  5.826720e-14      False\n",
       "12       amean_sari_samsa  0.165872  4.448623e-05      False\n",
       "13       gmean_sari_samsa  0.155818  1.269979e-04      False\n",
       "14                   fkgl  0.117057  4.089161e-03      False\n",
       "15                 fkbleu  0.098361  1.594498e-02      False\n",
       "16                  samsa  0.057665  1.583272e-01      False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (low/high/all, simplicity, asset/turk/hsplit/all) [O:correlations, 1:significance]\n",
    "results_simplicity[('all', 'simplicity', 'asset')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_metrics_simplicity = pprint_correlations(results_simplicity, aspects=['simplicity'], test_sets=EVAL_DATASETS + ['all'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aux = df_corr_metrics_simplicity[['metric', \n",
    "                            'simplicity_asset_low', 'simplicity_asset_high', 'simplicity_asset_all',\n",
    "                           ]].sort_values(by=['simplicity_asset_all'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_aux = df_corr_metrics_simplicity[['metric', \n",
    "#                             'simplicity_asset_low', 'simplicity_asset_high', 'simplicity_asset_all',\n",
    "#                             'simplicity_all_low', 'simplicity_all_high', 'simplicity_all_all'\n",
    "#                            ]].sort_values(by=['simplicity_all_all'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrr}\n",
      "\\toprule\n",
      "               metric &  simplicity\\_asset\\_low &  simplicity\\_asset\\_high &  simplicity\\_asset\\_all \\\\\n",
      "\\midrule\n",
      "          bertscore\\_P &                 0.512 &                  0.287 &                 0.617 \\\\\n",
      "         bertscore\\_F1 &                 0.518 &                  0.224 &                 0.573 \\\\\n",
      "                ibleu &                 0.398 &                  0.253 &                 0.504 \\\\\n",
      "      amean\\_bleu\\_sari &                 0.417 &                  0.239 &                 0.503 \\\\\n",
      "          bertscore\\_R &                 0.471 &                  0.172 &                 0.500 \\\\\n",
      "                 bleu &                 0.405 &                  0.235 &                 0.496 \\\\\n",
      "      gmean\\_bleu\\_sari &                 0.408 &                  0.215 &                 0.476 \\\\\n",
      "amean\\_bleu\\_sari\\_samsa &                 0.388 &                  0.170 &                 0.419 \\\\\n",
      "     amean\\_bleu\\_samsa &                 0.355 &                  0.148 &                 0.380 \\\\\n",
      "                 sari &                 0.336 &                  0.139 &                 0.359 \\\\\n",
      "gmean\\_bleu\\_sari\\_samsa &                 0.355 &                  0.075 &                 0.328 \\\\\n",
      "     gmean\\_bleu\\_samsa &                 0.324 &                  0.074 &                 0.300 \\\\\n",
      "     amean\\_sari\\_samsa &                 0.203 &                  0.050 &                 0.166 \\\\\n",
      "     gmean\\_sari\\_samsa &                 0.222 &                  0.024 &                 0.156 \\\\\n",
      "                 fkgl &                 0.272 &                  0.093 &                 0.117 \\\\\n",
      "               fkbleu &                 0.131 &                  0.006 &                 0.098 \\\\\n",
      "                samsa &                 0.103 &                  0.010 &                 0.058 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df_aux.to_latex(float_format=\"%.3f\", index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a matrix showing the results of the Williams Test for significance in differences in correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aspect_to_plot = 'simplicity'\n",
    "test_set_to_plot = 'asset' # asset/turk/hsplit/all\n",
    "fig, axes = plt.subplots(1, 3, figsize=(20, 4))\n",
    "i = 0\n",
    "for (split, aspect, test_set), (_, df_sig) in results_simplicity.items():\n",
    "    if aspect == aspect_to_plot and test_set == test_set_to_plot:\n",
    "        ax = axes[i]\n",
    "        sns.heatmap(df_sig, cmap=\"Greens\", center=0.0, linewidth=0.05, cbar=False, robust=True, square=True, ax=ax)\n",
    "        ax.set_title(split)\n",
    "        i+=1 \n",
    "# plt.savefig('significance_simplicityDA.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n",
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n",
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yJ/c+X/M6IiEVTcibNhv6YpwxmKBoy4b3XU0QAkPTQDnR24JA+Fcgdu4Z4Qs7X8GWkpAmMC3J+GyBgmk13QarWuHNmmSEom23nSikkoDk8OQs+8Zm+c9Xxzn/1n/jjof2zrk+1xypZdq4xxRCZdBlixamLYmGNIZ6Y3VrGC7e0s/xVJ6iJSnaNqZtY1oS08Yfjy9DSBOs6Y5yfCZfddKsx/jzsbedRW+XgVDS2jZtPVD7VyDufHgfpm1j6BoCoWLdNqRyZtMqerXIQUjXOGMgQV880laiEL+v4vkjU6TyFrpQVXTZosVfff8lvvHUIRCCRFhnfLZASBdMZ4ocnc7yzMFJPnT5adz4lq1lE0OuYJLOl2sUmaLN+p5ITXNo554R7nnmiJdu6/oJ6kETsL5XhQSLts2G3ljZ56Zls388y2ZbVnVoXr5tkM+86wLufHgfj+8bP7Kgm+lDIPwrEIcmM0R0DUuWklzcFbBZFb1W5OBP3n72otT9u0Jw/q3/hqELQkoFVsk2UjI8nePsdd28PJqmaNoIoTL0DF3DsiVf2KkSSd3Cod6YwQvOKm1ogpCTxlswLZ7YP8mW1fGqgugx9xgOc48Q5IpW3eC7oWvYEgaTUQxNUHRmC7dicragkngsWyKE0sRGUqoluVvTsBgIhH8FYmNfF5ZtM54uYqNUX8vJtGv2RVtMOrB6Mf3ZgkXIZ6yatnJ+2xKEUKW5lgSBxHD0ZF2o2oTKwiFXYC0pPUGwpdpeK7LgOvsGkhGGp3LYNM66uWhTn5cr4DpKx9I5xlIFL2qvCRieytEbM5nMFp2EHjUJfPSe3eSKljtprJ/3ja1AIPwnCVpJgnFX7VUJmM4UyTse8Q9dflpLwrsYdGCNYvqV/Htuoo77d1jXKFpWmatdSuVsmy1YbHL8FG7KLSiBt2w1+UnfsVz4IwuV2Y9Hp+r73zRB2YTq5gm4oUaXPlwXGggYmy0ox56AiNOS/NBEhkIpcaBWunzLCBx+JwFaZd5x8+E3r0qwKhHhdZtXcedvvMajt1pKNGK4+eClpzpVhja2tD0ZXx1XMrE6oYg8JeX598loyJs4XJ4CX8EeBcumYCr7vydWvib6Iwt+52MiEkJooi6zbl/M4M6H93mOvDse2ss9zxwBJJGQ8uBLlC9AmTAgURz9A8mIM7aTq54/QBsxnyYTy5XEs1FMv5J/r8vQCWmQjBlIqWryuyM6WafsNhLSSEYNwiGdi7f0873nj3tZdiq0J0sOOyH45QvW8vTB6ZpZkJXmjpSwoS/Gocm5dQO6gPFMkWcPTXqkJV/Y+Qp9XUYZ22/IKSsUAoTjR/AzGC0WAuE/CXCiu/C4WAySz2bqD258y9Yyz/1LIylGU3nCuuCMNd38ydvPBsr9ERdv6eeeZ47Q12VwfCbvsfUMJMKs6YkhpWQ6W+Rz77nIO24jX4ZEmRMzueKcOgHAS/H1k5aYtk0qZ5b5DBASy4ah3hjvumg9//DEAY5N5zg8mfFKhxcDgfCfBGhXN9xW0Ey+fb1+eLVw8ZZ+vrDzFUxbEWn0dBkYuj7HEek//9ruaFkdv39C+tS15wJw49efJZ03kdJX2IMquImFQ4T0UipvPa2o8rot2+bodP0uuraUmLbk4EQGISBbsGpWTKpxHQChHJhmozjiAhAI/0mAViihqmE+K3gjU6NRP7xa43BX51TOJG/aTMwW+dDlm+aMpxrJaLZo8bnvv8Ta7gir4krNvvme3QggnTdrxuMPTGRIRnRPY6iH2x7Yw8hMDkuqkt5EpLEIFX0ndjWEQxOzbOjrmlMx+d67nqAnZrCuR+UC7Dk2g71INn/g8OsQ1GsZ3QolVLXjzoemu1E/uy89+ipI6RBhqv+R0uuHVw2uQA8ko2wZSHDWum429MV4fN9EzfO7zjvTVk4yKWE8XSSdV/3y0nmTVK4x822uicYYO/eMsHck7RGQ5E2b46n6q341CGAqa/LTozOMpvK866L13rOqvK9hffFEtCOFf8+x1JL3TD+RaEZAXU67R275hTl8d/XQap87F43y7dN5E0uWVjpbKht4JmfWfG6tNMh0zz+aynstrN31sWirunxQdr3qzFPfdralbHjN7r1y/9m+KptWLHN3nNGQxkAywj3PHPHuSeV9dT3+4CVktU0N6EjhD2liSRpJLBXmK6DNoJ7A1dM2GjX8FD5hq3xbaz23Vgp43PPnTAvbtss740i1kqdyRadgRyMS0moKqABsG57cP1F1UXHvw5P7J7AtG8sNIfomnIFEuCw/ICTK8wUE5ZRhmlDmQOWzvHhLP4cns/z06DSvjKTIm6X7EbTodtBOAVjuaLVldCvwC9xMtsi+0TR7jqUYS+f56D27a2objUyNLsNJv604n0Cl0N749WfnTCqtdBB2zx8Phyja6rjuy+ye88hkBtNSE0PRqq3Wu4k90ZA25zpdrevVsTRSSgq2yha0Uau+ACK6YE1PjE39XYQ0QdTQ2LauG02IsjH5IwKaKKn0/snW9XlEQzoFSzIxWyQZKXv2QT0/nJhw1nJAq978Zh14O/eMMJUpsH88g4Z0WGyFl2c+mSkSj4Rq8s3V84qfu76XPcemmfR1mtFQPfbGZwvYUrKpv2tOlKCVlOHLtw1yx3su5Lq/f0oRckiJtP3qP6zriRDWNY5O52rqy7pD+RUP6xybzpV1J7rz4X0UTIvx2UIZd37Rkl6TT0MXapW2lHmxustgOlukK6wTj+iYlmQsXSg7v2XjOQvdZ+lqeD2xKANJtV+mYHJ0SvU5cDSNgMYLFj+ctVzQije/Wcor/34beqNekooREqztjXJkKosmVCiqlWYYlU0mswXbsY2FE9NWBno0pFedVFpNPrp82yBbBxO8PJLyNABDUyEyFYfXPX+AoQsMTbCuN8ZMtsiqeJiXRmcJqa7ajKQLTrsuvO5Es/kiuaKNhkALadhFy5sAXFrzdEE11YiFFaWXpute2O4T971AV1hx8Iyli94EoAmYmC2gaXihzJvv2U0mb1J0yEHjYdXLIFO0iOgCIQQ50y7SJnSs2t+uWvFOQCve/Gb9A/79umNhdE0Qdrjqk1HDU0n9tnSjZhjVzIJTV8expCKh1ESJ/tsfIluoBnfLldvQNNVNKGJoaE6Ofsih4xpL55FO/n6mqJyBIV3QF49wxoAaX9EJp0mp+PuREkNXYbi8w54Lc80YN4qn64ItAwkGktGqjTlmchYRQ+OU/i5O6e8iauhIYDZveRNFKqcEX0USLEbSBXKm5fkHnIhE23L7O3Lld728H7z01GWZoroYaHZFbDbbr3I/VRBTcpytTkQ4MpUlpAmklHO0jWZSit0x3/HQXi9xR3M49SYzRWelNBaswV2+bZBkNFS2aoqQhmnbZArl5baaU+E3lipQtFIMJCLIKul5poSZbMEj+bBsSUir7WovWpKZbJHumFF2v917cOltP6A3ZniO0G4nHXk6W/Ti+31dBuOzBaRdmlRsG2KG1lLz0WbR9pVfCGEIIT4vhJhwfv5aCFF1khFC3C2EKAgh0r6fixueA0Wz/A9PHFgR3v5WkAjrvDyaZs+xGfaNppnJFqsKV6VnfXUiosgrhfBy5Hu7DE5dHa+qbbTihHQJL89e18PGvi6nGaZkZCbXNg3ujMEk63pjbFvbzZaBBMloCNOeK6yaECrsJ9Q7NJrO10z+GU0r7v+eaAiJ8iHUg9s0tPJ+79wzwky2yJ5jKe+ZVO7nNkoZ6okR0oUqUcZpPd5EDsJ8sBgr/8eBS4FznL/vR3XjqdV6+2+klB9u5QQSyFs2hXSB2x7Ys2JW/0bYuWeE8dkCpuOMKlo2R6ay9HYZc7LXKv0IrrAPJCJMZ4ts6OuqS8yxsa+LV8fSqmGlQ2CZjIY4dXXC28f1CTy5f4JoSGN1IuJx1o+lVVrrYDLqCf5773pi3nUCldeTyptoQDiklQmPZUt04ar4NlMZs27gvCdmMJkpeiZAPRQse85k5ppH8YhOtmBRsGyGp7PkTYtwqJS27Dp1u2MG3TGDfaNp777OVrQXaxcWQ/h/G/jvUsqjAEKI/wl8htrCPy+4TpyXRqr29VyRuO2BPaRy6mV2WWZCmmAgEZkjSNU8662w8Fy8pZ8n9084HXzViz+aLvC+1/UD5Q7FiC68lx6Uyut2ufna9W9YEC9/reuREjb2x+iOhdk3miZvWh7lVkhXfo3pbPWCHBcRXZDOm44TUMOyrZpaQthxyLmTWWWzzp5YlEhId3L5LTIFi0+/8/w5DlJ38kpGQ4ymCySieqXwL8+OPUKIPmADsMu3eRewSQjRU6MJ528KIX4TOAr8P+BzUsqGeo6b1bVIac8dBzf1VBfKqy2lQEpY0x0hna+e3rqQst7H900wmAwzky2t/N2xEI/vm+BGyn0Cg91RhqdySCRj6bzismvRf9AM/Nfz3rueYCSlsvzcCjohJDFdY21PlKIlMW3l3a8mToYu6E+EGUsXnMYfeKq4Q3noQTUCjVV1wvp9K8moQTJabuv7x+6fvDb1x3n7eet4+sAUIzNlYcJlG+pzdb4p3zb39yRQKfx3AB8FJoDXAt9A3dfPVR5YCHE9cD2A3j3gPbDQIpY8dhJcAZI2HimnjeT4TJ4LN/W1/XyHJjOsikdYnSjx9Ctm3Yz3uf+lH+qFkZlcmarv9x+0uyS5suXYqoTBxGyRmKF557/hK0+Tt0opuv45YE0ygqZp6EJg26WEHr/g++cNf36+H63kaPzcmQO8ZnMfU5mil5T06284hXf+zY+YyjauT2gV7Rb+tPN/DzDm+x1gjn4upXzG9+cTQohPA79JFeGXUt4F3AUQWXeGRIAOnD6YqNx1ReLQZIY1yQjD0zksS3mn3Rfz4i39bT9fo5e68vNk1EDXSqo+lHwCbjhuTbLUx64yrNhq1aFLl+UvKXYZfF1sWR3nxWOl19IV5khIQwjBJ685h9se2MPPjqfA4epz76mr5ksJqxKGp/FUwj8JmZbN8Zk8RdvG0AQ794xw+bZBbFuSyplMZ4seJ6EfmUL7BR/a7O2XUk4Ch4Htvs3bgUM1VP5KNOXWVCmVGv3xsNeFZaVjY18XIV1zaszViypQCStf2PkKr/nUv7e1GKpRKm69z3fuGeHKz/2Q6/7+xzx7cJKeaAjTkhyZyjKTLczZdz5Vh26q7EAywobeKAXT5vYfvMSVn/uh991brtymtCVKQq1rgr4ugw19XVy+bZBbrtxGfzxMOKTyCFzYUtWYDPVGWRWP1NRS3Fi/2/obARt6YxRtyZ/c+zz37TrCwYkM47P5MsG3bMkT+8b5o2/+hEXy9y2Kw+/LwB8LIX7k/P1HwJeq7SiE+DXgAZRW8BrgY8AXGp3A0LWqHV5XMtwVJpU3VeWZw2QLqmItV7Tn5UirhH8VToRVlp4bHajXkdf9HPCEWRdK8KayJqviYaazRY7N5D26ajf+XekPGE3luPHrz9IdM9josPQ8vm+iTDNwzSDLlhydzjtOO8H+iUzZPbjqvLV8a/dRpJN8mIzoZV74Ox/eR0/MIB4OMZbOY1pKEnUBWwaU1pkpmHXzFFzK782ruugKh7C9Umeb//fofj777gu8faczRe5//ijffu4oR53KxMXCYgj/p4BVwIvO3/8I/DmAEOL/QFm77t9HqfIh4AjwN8D/bnSCM9cmy9omBygJ2w1feRpbSiK65qmxAuWNX0jjzJ17RrjtgT3sHUlj6MLpziMpWjafuvbcppt9usKsQm5qkrKRpPMmpw8mmM4Wy56t6w/wE3ZIJ612U38X+8fTPLl/goFEmNWJiDfBZQoma7ujvDo2ixA4uQVqRfVnPD59cJq13RGPxThbtPntN5bs973HZ0jnTM83oAnlZM5bkplsgZCuNZWncGgyQzISomDaXlJR1NA4NpNFSsmeYynu3TXMf/xspCykeOGmXp49ONXSs2oWbRd+KWUR+JDzU/nZ71T8fVm7z7+Scfm2QS7a1OfZ2nuOzSg6antuBVmz2LlnhE/f/yIvjaaxbdXB1rTg4EQW4VSmtZJr4QpzWNcwrVIX3YJl10xG2nNsek483paKM2Ama6IJHF68qDfBFZwWXAXL9rz1UqrxuvfAH4ZzHZeZgunZ7zv3jJDOW2XsuSoRSv1fqaVUg5SSVN5kIBFhLJ0vS4zKFCwMXeN3//EZ9h5Pe9vjEZ1fPHst11wwxKZVXVzx2R8uSlSrI9N7A9SG38Hk7w7jvtytpNL67W1dCCxUPzrXQpZO3fyLx1Lc8dDeOe2wqjnnXEfg6kRExf1t5UrThai6giqVfnzO2ARwbDpH3lT03aalKLmTUZVeG3bCiX5vvY1kdSLq3YNqUQbTsnnm4CSX3vYDZrJFusKax/bryp8lYTARJmLoNTVQ25bM5IrMZE1M2+bdOzZy+w9eYmI2TypvUjDnNvs4bSDOtduHePNZa8omiVP6u3h1POPuvzzj/AFOHGoJmd/Wns4WSeVM+roMlfveYiqt327WRQXNtQ8CqrbDquZjcCcnQxcM9UQ5nspjWurFd523/ky/qUyhrEGHn3wz5xbcONsPjGfQNIGhCzb3d/Gxt53lM1VgKBktyzG48+F9jKRyWLZUJkWhVLE3kc6TN200p3Nv5XVPZIqcMRihEkXLZsa577b0awzK6TldJWS3fWMv1126mbPXdfPjVyf54399nqMzWdZ1x3jPazdy/c+dxm0P7CFTMMmbsm1VfYHwdyAaZcT5be1maKhrTSSVKnpI08oZcxyENNVEo7IdVq1iH78j8MKNJbW52nXtH89gONVAmuPELFilZh26EJiOkLk2vQDGZwsA3P/hy8ruQWWOwUfv2c1kpggOn4GLbFGdw6qib3t5AT7hzhYsZnJFZisSqiYzBe764T4eevH4HNW9OxpS1Y0Szhnq4cl9E9z+g5cIaYLuaIiJTJ7P/8fLfPKac/jcr20PGnVCicNvpXr7W8mIa5TFV28i8avoh6ey2FWWfUMTarUVlLXDclHPx1B5NPe6TEvy6vSsEnIpMQHd4QPQNNBtpXrrmlAxeUu1yPZrBamc6fkiqt0Dd0KYzBSqdtqtp1uHHK1ltqBMjelskYKvfkBKyQvDM9y7a5gf7h0to992i3VAOUvjEZ1jMyrt+es/PoShC+LhEJomiBrquX7x0Vf9vIxtq2HvSOH3c/gtJGy13NFoRfZjvhlx9SYSV0UvmFbVsldDUww4UkJP3GA2b5EtWnWz2epNNocmM+hCNbjQEMpeRzW06OnSyRUt1VdQ11gXN4iEQ55jM+T4NwQqNde2JXtH0l4iTeV9dccghCAkVEusGpm+HjQnamBo6nur42FGfey92YLF9/cc595dw7wyOuttF0IVCGULJraNxzdQtGxyRZu1PTES0RAj6RyGJjg4kfFSplcnwovGVtWRwg/zz//uFDSzIrejSUe9icRV0W/8+rMIobYPJCNkCxbHU3mKNkScCrmRVJ51yQijMzkKTv27rgkSkVBZRWG9yWZjXxfPHppUrDmOoR/SNTRbtd5alYh43Xfuf/6YF3bUhCDvmCOGrjncumDoVH0//GPwog5NdNsNCQFCkjMVwce7d2wE4MD4LPftPsq/v3CsrAjnzDVJrt0+xAPPH2MyU6DL0BlJ5bAdp6kuBLMFk3hB551/8xhTs0VyRRUB0J1W3Uemcpw+EPcWgsf3je/b/+m3t2X171gmHzi5OfzqMfK0QnTZCI0Ycy/fNkh3zGBDr2oicWQqSzpvMpAwFF2WVCvZht4YpoR0wcK2pdIUqhDO1eMBcK/L/WdLxcU/1BujJ2bwyC2/wA2XbeGeZ45QsGw29EZB4qncIU2tzm5zzjXJ6pl3/jGsTkSwkTRDj6/rAstW4/39y08nZ1r8wT/v5rfufopvPnuE2YKFoQveevYavvC+C/nb37iIK89dy/tetwnTmQxVzYAyWwaSERXlsCW9MUM1HwUsaZcKCVDmlBt1QdXBtAUdu/LDyc3h18yK3CzRZT00ww+oCEJm0YXwVqTJjMokHOqNeav4vtE0uiYwdI0tAwlSuSLHpnPc8JWnvXj4xj6VmFNZDbh5VcLj43t1bBbLYeQZSEa8mgCYqzl0x8JkCibDU1lHlZaeiXJ0OoeuCS697QdlZlPlGDRACoGh4XH/OYu85wTUUBNF3rQ5f0MPn/v+XsbSBe8ereuJcs0FQ1x57lp6Kp7bG05bRSys89X/PMjwdJaLNvWXRRvca3Gpx9yW4WFdY213hGMzeTb0xco0vXagY4X/ZOfwa6Tat6vLrjuR3PbAHl4aUYkmp64qn1A9Dn7faoTDe+dfxQuW7dX2u5103IIY12x5zaYentxfKOMBGEkVeO9rVfHRLVdu88ydyslo554Rnjk4iWXbRELKBHHj+nGHE3Aqo7j6VVRAotsSXVBmNlVyEbgOv5t+4XQee2WcV52JoWjZ6DghRieRaCZX5KEXR7zb8fot/Vy7fYjXbu6f0xgkFtZJRJRHf/PqONdeuL7s84/f+3xVKjUhYNvabqBU1FOpLbUDHan2uxx+tcooTwa0U7VvBrMFiw19Mc4YTFC0ZVnxTCpvsr43SsgRqpAmWN8bRddEmckQ1jVsJ4vO7aQjEIR1zTNbvr9nlIFEuGzfgUTYa8nlL4R5aSTN4cksXYbGc4en+MR9L3ipuqYtGZ7KkcopmrIzBpMMJCKKc5ASW29I0xhLF7zz3/bAHr706KtIxy9hOtmPq+MGD780xjsvXI8mFOvQpv4u4hEDhFL3p7JFLypw3lA3X/ng6/jzXz6P15+6yhN8Q9fo6wqzsb+LdT0xktESb18lqnXnsZ0ohv+Zn7pqrmnWDnSk8LuOJ3+bo5MNC+m/1yoaMf66FYNbBhIeR15I19iyOl42QSWjIWwJ3bEQeSdCoLLqVDJMzFCsNKsTkbJjrU5EyjoE3XzPbvaNzdIV1tAFvDSa5nMPvUQqVyAZCXmMukXL5shkxpsUU059wLa13eiaYiJ2U4dBZe/tHUkzWzAxQgJD0xBI+rrCdMcMDk9m2HFqP+/esZFMweLVsQxT2WJZqE53qL1fODrDQy8cB5RmlIiEGOqNsbG/i754GKMJJ0LlBO9WFG7u7yp75h9721nefu1Ex6r9J7u3H9qn2jdCo9BhLb/An7xdZeS5vodTVyd43+tUhd3EbBEhmFOjHw/rVcOB8bDuqfuZvEqJnczYhDS1muZsm8mMScgRardsuWiXiDQ2PlwylcK6SkgyncyavcdmKDi5AJpAJS3pAiEF47N5VhEmEtK56eu7+MmRUvV5JKRhWraXoOOW8kok//z0YW66YivP7J/ki4++2jL/oGtyffr+Fz2Ta8tqlelY+X3Xx/PK6GzbyBk6Vvjh5Pb2LxStEGA041+o52CsPK5bFPOJ+14gpAtmsgUVGrQk67ojHnutn9wiouv0dIXoiUUp2hK3tN20Fee/C8uWRA2V8mM60YAvPfoq52/oLZuk4mGd2bRSlTVUFZ77OxIswHKiBEVbMjydK0v02dAX45oLhhiIR/gf3/2pt106+4cEZE2bXQenuPU7P10Q/2CmaLOhL+ZNrNW+71sI2mb3iWrJG8sdvZu2yTd99EtkCmYZM0wABX+OgH+lrmU2tLp/o3P76/3TeZOjMzkMTWNNd4SQrjGdLRIzdIanc1558JGpHAJY3xdzWmbV5nXRRYm70dCUR359b5fX/OLOh/fxzMFJpNNF1xX8RtAEXHzaKt6xfT0XbupFE4KP/NNudh+eKssBcJOBuqMhzhnqmTNxtvJeulyDLXy/bbx1HWnzw8nv7V8IWu3q2y7/QiXrTtGWjKYLDCYjnLEmSXcsTFc4RE9MMeduXtXFGYNqeySkgdMerBFcWQ7rGpqmEQ3pZV1yvnb9GxhIRjh9MMGpA4mybrm1YGiCP3zrmXzq2nN5zSl9Hr//8VSOVfFyBdmdCD546akLbqK6mE1YG6Ej1X7LlnMKNFYi2pn+2w7/gn/SmckWGXMq445N54mE9LKef5V1AG6Jb860PPqxagt2yOnDJygxOA8kI971SamIQaIhjZdH0tiyeiViJUxb8ndPHKA3HubyMwdJREPEwzqbV8UZSeUQQnMajCoNYag7wo1v2crj+ybm9C8wnKYblfkF1dDObM1W0ZErv8vks9IFvxa3XbWsvbF0nulscU5b7HbCXcVmskWGp7MeWYcELywH5Y4/F90xg1XxMPFwCF2r/loKoL/LQHOOaUtJ0bbZP57h+eEZLFtyaCLLd3cfZSytfAx1OnOXQaKSgv7nd3/KnqMzJJzuxDdctkWVRudNdKf9dn88zJ/98vmAyhkYTRe8HIds0WIqqxyTzXAOnuiQrh8dKfwBWkv/HU3lGE0XCGmCiXSeJ/ePc8NXnuaOh/YCeCG2hU4M7qQzls57+fm6cG3k8vZcH7z01DkvfTikc8d7LuRDl59WddUXqHyEwWQEXXOz+UqfD0/nuOOhvXz2ob3MOM1LWvFoCaFKeSuFVaAOVC1l2e1f4OYtgGI7Us09T5zJNR90pNofoLX030zBoicaIp23EEKFzixbViXgeHUszQ1feZpkNMQZg8mWTCvX2543bXTNbWGt0Z8IMVuwyJmKsjqsa3zj6cMkIyGvgYU/gnDnw/s8O92vsksBOdOiXw8jKqRaQwn6Pc+Wl7snIjqRkMb4rNI6wrooo+XyQ0qIGFqZ/+DOh/fRHTNY2xPz9vOHmCv7F+w5NuNlLlY+l1poxuRajMKeQPg7FK2k/1562w+YSOe97DhQNrVpyzICjpls0SPByOTNOWGraj4GoGzbuy5a73DlK7vbTcF1acUyRdubaNyoQiUB6KHJDNGQhiXxCDyKlo2UKmX2v/386Xzsmz9xuuaqz/3avaEJYmGd1fEwISfZZny2qKi8pOpj6GcF8iMe1suEtZH/pPI5uPkFLmfiTLbI8VQOKZk3B4U/GkMbC3sCtb9D0YqtuLGvi7xV6jEPzioXUk0gXW+zq67rTm28X2Wt5mO4+Z7dfPSe3WXb7nnmCB+89FT6usJYUnHxvzSSYjpbRAjRVBRiY18XPV2Gasdm2wihqu4MXfDxXzqbRCzkpPji0ZOD088hpHHr1ecQj4Q8/sKiZRPWBWu6I5zldAquBg1lVlQ2H6lX9Vj5HPxZjjPZAocnM+SKKhvx2YOTfPSe3S2bVZXFTO1CIPwdjHhY5/BklpdG0hiaqGkr3nDZFkKaUvWllF7Ja9LxaLsvd8GZIFyWWyitctV8DOm8SSpnzhHm7/3kKEXLJl+0VSqu8/KPpHJNhbVuuGwLRdNGSpuCJcmbyrZ//eZ+vvzYfn7vH58tS7nVnJRbIeBNp6/in585TL5oMZUpMDGbZ21PjN//+dMJh3QyBZNkNOTVKGmOTyIkwAgJcmZ5VWOjSbbSZj91dYKbfuF0Nq9KqO5JTkZgOKQhgclMkdse2NPSc64WDmwH2i78QghDCPF5IcSE8/PXQoiqU1Yr+wYowV2FC5bNGYMJNvTFyNRpHn/5tkE+dPlpXkFMSBesiocJh/Qyx1tY17CcrLmBpMrH9zPdVr6AqjCm/LwxQ+eVsVlm8xbhkEbU0AiHNIflx667ikopmc2bTMwWsCQIx2HoVt49+sq40zoLtgzEuWRLP12GeoWjhs4VZw2w51iamWyBod4Yg91RuiIGN1y2hRvfsrVMSBPREGt7Ipwz1MNAIowN5E0JCF6zqYc7H97Hpbf9gDsf3se7Llpf0yHnN4Vcv8WNb9nK165/A5oQhHXhEYyo3AHYNzZLK6imfbQDiyFoHwcuBc5x/r4f1bWnWovuVvYN4GA+XW1vfMtWzt/QWzVF190+nSlg2pL+uEEiUs72W1l7Dg4XnSzPoMkWLUxLegw74MTjhaRo2t5E488k/OClpzI5WyCVUzn9f/fYfnShKvIKZumlF8DPbxvkHduHOGeoGyEEP351gi89so8DExn+/cVRDE1xDLiaiP++uD9uL4KfHU9zbDrvNTbRnI499z13jMFkmFXxiGfKVNOq2tFavBn405bbicUQ/t8G/ruU8iiAEOJ/Ap+hukC3sm8AB/Pl8KvlVW6W7ddf3HNu5kk+EfpXhuQII1Nr+GbsV/iRuFBx5ddIqdMc08Q9/rqeGO993Ua2DCaYzBSYzBS4/yfH+MnwdJmXP6QJemIhzpV7+Xjhq/DQMOGeNRzc9Cv85ZM9TDmNO6RUXHyHJ7Ns6MOr9fffF09rMi3FqOMoLhL1/amsiQAm0gVWJ6JlEwiUOzcnZ/N1J+FTV3Xx8ugswpaeOWVLOH11awk8/ujNsi3sEUL0ARuAXb7Nu4BNQogef7POVvYNUI5KD/NF+ad4R/Ye1jMCd2+FS26CrVfM69j1Jgj3BRwafYT/bn+JRCKOrQ8gU1PckP5buvr/G294+7v59P0vlr30l/Is1+nf5lR9jNWPb+OCzT9HiqcxZ44hfzzET0b+C/cdX8UP946WtarqMnR6uwziYZ0zcs/xW+Y/kciE6e6CaO4VjCc/zvbC+3lEu1Cp1LalWHCk4uFPRo052XKu1jSeNgkJDRt7jtdf4rbjKtIdM3ijfJarh/+Z9f80yh9qzkSXupD947MevZkL/2Tzsbedxc337CadNz1Ow96IwcfedtZCnsvyLOwRQmwEDgIDUsoxZ9sAMAJslFIens++zmfXA9cDbOozXnPgs29d0EveyfCrm2+Uz/I7s39LgRCrenpJ6kWwC/C2z5Tuzd4H4bHbYeoA9J6y8Pt291WQOg5h3wpWyEByDXzgO+zcM+K99JfIZ/lT7cukRZxI9xo0KwuZcbLRNXyf13Jf+ixeNge8wyS1PG82nuM8+TJPR17HS7HzyZs2n8r+ORfGJ+lOllqy7z86ylG7h9+Sn0AgMC3b8/CHNDhlVXxOgdKlt/2A3pjB4MgjXK99h/WMcEgOcKd1FQ/b27lM28UN+nfYKEYZFoPsjW7nzYWHyMoQpogQEwUMTB4y3swZ2V1sECOMG+v41+iv8Exkx5yinGb6JrSIthX2tFv4+1BxyNOllK84204HXgJ6q6z8Te1biR2nJOVTN2+b+5KvILgv1c3H/oA1Ypqe7h6SUUeR8wkiex+E+28GLQxGDIrZhd+3vzoPon3MiR3mpuDDz3nj+9sfvsINxz5BnDxdiR7iEZ2D47PcV3gN/2btYJbSqnmGdoR3aD/i540XiCZ6yJkCclPcpb+H44OX8v+mryOWXAWFFKRHwCqQtTTSMsKl1p2ef+GD9j9znf49EiJHTuti5Jzr2PKuT3oT4MihvczYUXrsaaZlnCxhouQJY/LP1mX8qv4wBULkCBOjwEZtlEm6maDHI+josafokTMcFQOkLIOkXiQiLG4PX89j2kULz9CrP1kvT+EHEEIcAj4spfwX5+93AZ+VUm5ayL5+7BjS5VM39EC4B9ado17ylYpGgthglZ4X6hxTvv/bqoFmziRftODvrsYK9/Cj/Gbum9nKM8VTvK8YFPkFbTfXaI+yTRxE6CHCwiYpMiR6VqFremmcd18FE/tgdhQQIDQsy6Qo4XeLH+FHbOd3tX/l97V/QQqBroXQsVHtcH4FDv8naGEyuRyR2UNoSHLS4DirmJExYuQZYIox0UdRi3r5A9vEAdAjHNTWU7RsNCFYbx0mTJFXtM2K918TaMUM6fBqxt55z8IFv/5k3TbhXwyH35eBPxZC/Mj5+4+AL7Vh33JIC/ITMLx7IWPtfPSeMlcQi1nodebPqQNqcvDDiMHUwfmf85Kb1AtawHtBc5ZN6sIbmR3PeD3qxtN5vme9lW8f28KYXVLX1zHO1fpjvE17kh4xiwDiIku3zBDVnQj87HHoP600zktugm/8hprYNA2k6r5bNPr5uPZ1Rs3v8FrxU4SQSC2MrmuABpYJL9wDfaeBNOnKHXOIwSEsTIYYQ7CaFDESIseoFkUDhC5Y3xMjNBsDM8dAIsKR6Sy2lIQpksdAAkPdUaVxyS414dYT/GbMr8duV4LvPs9wl7rPj93edg13MYT/U8Aq4EXn738E/hxACPF/oKxVd819m4bZWsz0pEMVQcQuqO17H4TcNMwMQygKiUGIdJdPDvPB1iuAz2D/6HZSkyOkkqdR2P7bMHQJ0rZ57sg09z47zCMvj2HZqvpNYPM6sYdr9R/xOrEHTUjCwqSbWRLk0NxkfVsDPQJWATJjUEgr7ab3FNAM0ELqMz0M8QGiEk4rHOS0gdNg3D1G0Ynd6SB0sIrq3ky8Ak5LD5fmU8fiFGOK2VicXLoLw85hG10MJCJKqIvdYJsktQLru6NMp2aw0MiIBOt7YiVTq9E99a/o0T41Yd9/M1Bhfi3GZF0DHcnks2NIl09d764kAm6dWsrhLD28FeWgegEvuUltv/9mKBYgO4ZX/pYYBN1YkM2fdXrUzRZKbbwyBZMHfzrCfbuHedWXxNIdhrcZz3J14bsMiYnSKk+GqKjRcFYLO6WANnQNQmJACdf0QYj2Q9K3uo7+TP0/cCYc+4nSCHHS/dyJAiAUU78LTU0OlYivgddeB7u/OlflvuB9sP+R0v1NDsGL94Jtqkk13A1GuP49bdb8arzfslb7Tyxq0CKvKGy9Yu5Ld/dV6iVO9oIRUbaymVMr6Tv/b8uCb1q2l85b9FWsvTo2y327hvn3nx4vy0I7a12Sa9dnuHz//yas6YREhu5iiiQZ9MqSPD+EBtLEq8ctzEA+AtEe6FoFmXGIJJRwpkfBzCohH38ZwgnIT+PU35YEP9IDxVk1MchqmXIC4qvh8ltg6KLSRBpOQCgMu76iNI9f+t9q9/tvVmPJTql7aptw6rXqe9/7SHWVvtkVvZ4m12Z0vvB3nuIyF+0OxUH5yxbtUT+uI7DJY6tcdotUzizLLjMtm0dfHuNbu4Z57nApKBMOabx52yDXbh9i65okfPMGIlaanvxx4tY0QjSRodazUb3wmXHAVpNVYVZpLIm1SoVProGRPcrzr4ed3txFoKAEPe8LFOkGdPWDWAXTh0sTgh+ariIIUJpI/Wo6Ohx+Cr7+btCjEOuF+ID6ATWxvngv9GyqrdI38s24cEyqOZrcIkS0Olz4NQgZjXdbzmjWFmwVzb5sVZA3LdI5k9m8VZa7P5rK893njvLdnxz1Sn8BNiTg6vDTXMljJDOrENn3E49cRvfkU0QLE4AAPQRmA+E34mp8E/uUyu9vup0+rgR8zTkl73/quNISpt2UEFHSBKRUPgIkzByB7vUwcBYce845rs80kFKtrn64jje7CKkjOM21oZhRE0goovwnoDQA26zvpGtlRa+myS0COlP4heMUkhb0n77Uo1kYFsu726L6aNuSVN4klXN6ze9/DJ79O+T0MM+GL+Re8RZ+dLTEh6cJuHjLKq5dN8VFe/4CTQujGWGSmcP0/OgWQj1/ATKrBEt3CoKE5gh1DbzxJqViZ6coceT6kJ2AzW9Sv7uajRDQs0Gt3GZeCWH3kHJ0WkUVGbBttTq7DkN3LN54pHqf/HCPPzGsvo/t+E0k2JY6nyv8Vl7Z/n5UqvSNVvTF0P4aoDOFH6keXLQH3vI/lnowC4P7kuWm1QvqerKzkws7bpPqY66o1PrZvOmF6Nj/GOmdn+Xf8+dzb+6XOGT24gpiX5fBL523jqvOX8ea7ih88wbVbDNqkRDTSksuhNR59QiQUcIjNOV594RfAz8FhxFXTrVwUkUnqsg+Qlf7cEu5ZhPpVj+zo+q76RGlykvLOYVQtnluWpkBZl6NQwgltNEe6K/ImnWPX8xW8RPYygknpfpcCymnnx+1VPpKgd77IHz/Vhh9US0CibXt0/4aoDOFXwvDhh0nR3pv7ykw9oryyDvJK94KtvfBhV1fDfXRtiXpgslMtui1t3bx8kiae//9Rb6f+h1ylEyqc41hru17lcve9zFVoioE8bBO9+xPicaSkJ+ZO3kNblMqfG66pCrr3UoQpQmWpVSI+CB0rXaSeEbUtXtwHLpaSB3XH/f3azaZMWUaaCFnRfeZDJZDBz57XE0goPYBJfi6UdKI3BV49GfqmjzBd8ahG2rlFyj/Se8mOO9dKkpQyNTXsipX981vUt9LHQeciTHlmChaeFFi+350pvCvOfvkyeorS17RSytS16q2P/yqqzyqv/3DL41y765hXhieAZQpFRVFruh6mWviP+U0YxzyKXRDpztqkIyGFEVW3/rak9fmN8H0IRUas4uQOgbmFKzepjS2x24vrd7u5CFR6rkXjnNsdyGUo81dTSs1m0JahQWNiLLxEY66XuFnkBagKXPAKkL6GMRWqdX3ux9RwqeFIblWCfqMjxNQN0rXGOvzUpmB8ihBNS2rmm/n0c+q5ywtJyeBkoniT3BaJHSm8J9M2HqFUlkLs+qF18PKsx1OtuXhF0yb2bxJOl8eogM4NpPjO7uH+d5PjjGVLcW+TzGmuTqyi7d2v0pCc4gvi7N0RyXJ+96NmPbZpfUmr/2PqNj392+F0VfUi9+9SQnd/Tc78et16qTpEUA4Nrqp7oPnmddUKM6/Qrv3zhWwvzhNhQWzBRwposysKIMz8ekhNd5wHI7/tLS/nYPJAyrM5634YfU93ahuJjRy0lXz7dim8m/oYXVPhKbunVVYeCJWEwiEfzlg4MzqiR3NPPwqjiL79LeQLqiYfL6CAcaWkqcPTPKtZ4d5Yt+4Z1ZrAi49YzXXXjDEduuniEeeAjNMPKzRbU0RM0fBFCB7yqMSb/uMUudzKSezTlchsK7VavLaeoUaX2KtUv9TR9TLHu1R6ngxq67bKqjv2kWlVut6SQOQRWVGGF3qWDB3VS2k1fc03Ynx10ggApQpUFAmQiiq/ARlE4Xj2DNzvjFY0L3RUfvnEXevFucPRdU5ejepiIUbWRH6osX2y06/qEdfLBx/QYV6TgabH+af2FGhSuZmxkl951ZmL5PYp1zieeyZGWYmvpkHun+N+w6FGZ7KeYdYFQ/z9vPX8fbz1nnUXZq4lERE0PPM5zGm96uXMyvALMyNSnz/VqXmazpoEbWSZifU3+7qOPozJbyu088qqpXe6FLXWUAJmVVw7Gnd0QBQAmKbSqC711d3hj12u9I0ZkdVLn/VRJ5KSCc811+K8VeD7azICEgPw4bXze+9qxZ6DavUYUTIubZj6n6scsyiRX63OzO9d31IPvW7qyGShGv/5uSYAKql6Da6rruvwpoZIR3qJWVHKKBDIQeJVXDh++HhT7PXXM+38hfyg8xpFHxz/faNvVy7fYg3nrbKo7cOaRo9MWXPa5VsPLWqB0f3KJvZtfmlXbLXezZBtBtGXnBCfmG13TbVfpoOl92izAPXwWa6vfqcWLxueL8yeLb6qDIt9q/OUxPG9OHqqbs1oSnVv1riT9lujhMx0gN/+HJzh67l3GuUOtz4ubctpbVDhd+QT/1On5rhV58Jv/fYUg+phBMUr80WLFJ//SZmwwPICoHM57Ls1F7HvZOnssdc633UJQpckdzPte98H5tXxb3tEUOnJ6Z4+2qiVs759AHlwMvPQOqoUmPdOJ0bU7dr2d8a9G0q5cTvvA1++GlfONAf73MmAj0MXQPqeK7D7W/fqCYh2/LtXwd62EnsccyUepqC5vgF3MnqPf/U+HnWKsttXdCrIcjt99TCiSZn4hOBdmfruTHgsZdAgNV3BqnL/pTU+kuV8y55KqTHIawSTIYzGt+eOYP7zdcwQ0m4txjjXBN/kStie4kVJ2HVfwUgHlEdc6PN0ELXMk36T1e/R3uU2u0my9imL6wmgZAK74GjRmtgRMtDWvsfgfhaFZKTlYIsS7b8zCE16Xsf+cJ6jRCKlmsvdgMTwf+5pLnn+f1bnexDq+TA1cLq+pZRlKpzhR+ch76MCnvama2390G490PIzDgZYqTpIjM6ifzurfDmW2HzJXDh+7F+eBtPpjZzb+Zcflw8BemwsYcweZP2PO/o3sO58ZR63ws5RPc6ElGDnphBONQic3s44U1E9J8Ov+hUX7uTgpl3BFsqAROCUpxeA1eO3LRbIw6pYVVqe/dVMPKisn1zE+DlH/hWfmmV4vN+AS6klDNu+mBje982nZAdjmYiVWQlFFfRAtfvICW4ZCCuryJUMVlVw94HVcIOutIUrKIyR7rXL3rorlV0pvBLCUVHvVxz9lKPpoQ21mJnH/0C6SxkWIvlV58Ls/Ds3zE1uIP7Rzbw7cxHOOYj7R3UZnh7Yi9vN56mf/YVlW0X34xezJIUKbov+wihZKT6Sf3wmy/hJMyOqdV9YJta6YtO2W5ZvP2AEpTkWsf5VkQJeUxFAKYOOLa/oYRpdgRPuMZeUR779KhSsUMRJeDFrDqPcPwJugHxIcinSuPMTav7ooVU4lBVOBOR61AUzu9aCN7wIWWPR4ZK1YKZEUVL7k5USHUNjZ6nuwBI2zmlk16cOqYS05YROlP43YchNDjrmqUeTAkLKKYBlYQzm3cKaiamwfblogMSwYvmEPceuYCddz1RxnT7GmM/1yZ/xsWxQ07JrAbaWoz0UbqLR0n2DaK98ZPNaSCV5svYXmUfR+JKaCo1mjmVcIayy2cOqePFh9S2cFIVxhQylPkAhKYchuEEZEad0tsKH4HQVXRg1eklh597vlDcScOt9BUIdf/cun5NV9EA3XDCeCG49CMqQefF+8q1mh0fgCe+oCaVUFQJfrRHCX5+Gv5HvyotfsOHVCmwi6kDToruESet2dFQTkDorlV0pvALTb0o0Z5SrvdywDxCdpYtSedMZnLF8iSc7iHITIC0yRHh++YF3Ft8PS/L9c4OkoTI8YvJ/Vx9RoRNe++GTBbyYYivIhKJ0hPKkThlS+t2ZqX54mbF+YtZqq2AlVl3q89UL38+pQQuFFUCkzpa0ua1kBM7t9XEYJtUtd3tIhj9SvDde+qO089ZUDCdaj0nsuAW9hgxFVozZyHaO5f0RAuXazVDFyneA7/jbuqgCmMKJwehkIGH/0Idw50A3AWge30p3VnoKny3zKJSnSn8oahaAaRcXnZUC7XYmYKpymZ9bDhluPD9HPr3v+a+zDk8UMF0e7p+jHd0vcAvJPYTzY/BC+MQSoAQdFlpelPHiNLnZMTd2vp1VJovbgaaPyRWS6Oplel291XqGOEun0lAabUWwhcpqFLVo4dVLv2Ary/B9z4yl7MgN61sfzf8aJmlz3UDrrmjfHwu6Um1/IVYH+TTTuZlRK34Qncy/wBC6vhPfKEk/O4CUCyUrk9ocPa1NW720qEzhd/FCUiBbBl10jwLpmLDSTttqarBsiWPvTLOfbviPJ36HW+7gcnPxV7hHfEXOEscQUScEtJ8CgEkmKanJ044M6mScebJ2APMNV/iAypHXzNKlWytqrH+CUV3bGLbUe8lPq+6W+0nnDnAUd8HzlLC79di3HFK06PzRujQvUG9FyNODwM9ohKOqk3E1fw0dlGlI/edplZw93ozYyWNwoXQ1QThYusVMPw+lbdvm+rcsV7lUxi6aFmt/p0p/GZWJYVEukse52UKy5ZK4PNzU239mJgt8N2fHOU7u48yms5729d2R7n6gnW87dy19Ha9Gf7uaq98VEeSlFN0azlC0oTYGoid3jJjzxxUmi+aAbF+FbJyK9lajVFHupXvwOXYg1L1nV1U6r9tqaQbu+gL3zmoNtFfchPc+yHIjgNOXrxdVPs2O75qfprUseragHAiDn6xkZYyZfzY/4hKcKrMiVjkKr1W0ZnC72F5JihJKZktKDacbLGGWu/s95Mj09y7a5hHXhrz2k4L4LWn9nPtBUO87tT+8t533UOEZkfpiajuNlqoAGaxnIyiTey8ZebLL/75/F/cvQ+qldlLqpHKDtd06OpTtQ2X3AQP/SmM/aycxw+ciaGKprH1ClXw45beupEAzWhe0Gr5abor7p8RAyOh/AGW6UsOksrp58cJZOBdCDpT+F1yRttcVrNprZLZSmQKJg+9OMJ9u4bL2jV3R0O87dy1XHXBEOsresCB4sjrueT9JB76KMIKgxZTtmz6uNIG5quSV4Pfg+8SUz42z4zFx25XTrZwvOQEC0VUHX/fJiUsj92u7OIff0k5CCV47DkDZ6rchmrnLaRg9da5aceVglYr87LaRKcbcwuDillYd75K033iC0rVr+bth9aiPkvA4OOibem9Qog3An8DnAHsBX5XSvl4jX03A68Cs77N/yGlvLqZc5VRd0d64P9buhnVteNnq5TMVmL/+Cz37hrmwZ8eJ1MomQBnrk3yju1D/PyZg1UTbyKGTl+XUWqP7SecsPKl3HctrAg03Beo0YvVzOftaPVVrS7Adcz1nbawFNhmKLH91+HW8NsF5UeoNqks9Lr3Pgj3/p6axGxTaS7V6lDmd57lld4rhOgHvgP8IfD3wG8C3xFCnCalnKrz1Q0NPm+MQmpBX58PXCacaiWzlVBMt+Pct/sIuw5Ne9vDwuQXNhlce+mFnLk2WfW7XWGVfhsLV6Tfui/G/TeD1js3rFjJPlst1biZVOTKkJ9dVPv906/Dxhaq21y2IjeDTg87lYBV7Op6KbDVJqtmwqu1yDjHX6merlsragNqsnEnXc2AwbNq3IdKGa0isyewO081tEvt/2XgiJTyi87fXxRCfNjZ/uU2naM66hFCthm5osVMrkgmb9VV6wHG0nm+89xRvvtcOdPten2Ka+Iv8ovhn9CdSUHuY8Al3udCCOIRnd5YuH76baMXZ6GfQ7ntOnO0lJGHUMJ8/83Ks73/kfpq6+Y3wcHH1feE7jD9FFUDDj8q7eJaWYaVfAJvaxBe9ZNxuoQhEodBp0a6bmXUxp0srWKJX1H47kPlpBntge51pe9Xc/gtsW+gXcJ/PrCrYtsuZ3s9PC+ECAFPAn8opdxTa8eyFt09wv9By4NtBUXL9uz4Rmq9lJJdh6a4d9cwj748VsZ0+/rYYd5hPM1rEiMo/52AQljV22++BE0IumMG3S49ViPUenFG9qjV6cBjKh8iuaZ6Ys7oz0rsQW6+vG2p47rcga7tahd9gu8gOwZWQoW06vHVg5ocEmvKufwsoTQBfALit4trZRkKAVP7SzkHX/s1+LmP1U9kcq/D7dhjmSWewNRwc2Sp7mQ5O6aO4SYPFWZUWnCtSdP/bCqFeoEZoQtFQ+EXQhhAvbKvPJAApiq2TwHV9VkYA14PPAvEgT8BHhRCnCOlnKn2BSnlXcBdoGx+74PujY0uoWW4an06Z5JroNYDpPMm//7Cce7bPczBiVKifW/M4JfOUw68td/8jBJC/2RlRAjNHKYnHqleQ18P1V6c9Kgyg1IOkWUxo0gxQ1FFl2U58f//tcnX2MKXUOPWrbsC7KrUqeOUHAuUuOxyU062ZQO1deqAYvZxm1xAyeavRXpZLctQoux1P6QNOz+tfq90vLlwr8MlEnGrCzWjebJUV6DdXAIoUW75BdvfH9FNzZa2k+V3WvVxtZAR2k40s/J/E3h7nc9PBdJAhQ5HDzBa7QtSyjRqtQeYEkLcDPw6Sv99oIkxOS9dEt7+2aZ2bwS3O006b5KplXVXgVdG09y3a5gHXzxOrljSCs4d6uba9bO8aewfCO87DGNDKh25mPfKb8NY9NoTxFf1IbqM1gdc7cXJTShGG5d2yhVqM69eTilVfnzRl5TiX81tS3XMcUNlH/gO8Bll47vQjRJVlrTV8fxodoXTDOVwi/VVV9fnrJ6iJLRzIMuz7Crh2vAP/akiFgGHWMSZ+KqRpVb6F9wGp36+PZegxG0o+henqf/de2I6jEkuJ0B6pHySOYHdeaqhofBLKa9qtI8Q4jngwxWbtwNNSaaUUgpRr4FbBfQInPLGttyorCPwjcJzLgqmzSMO0+3zwyUlJRrSePNZa7h2+xCnZ55TOd8irF6a9Lh6QcwsXeTpMSxikahq7vjGW+c38GovTm7KocB+Ra00ulZizXFbW7vVeNXg9kLwh8q2XqGcexP7VJgOUcrIE1pzfPWX3KS839OH5nq/oSRkfn4+/4Qxc7QB245Uq+2nBp14f0SFFd38AX/x0V+eocwdK6+uUwhFouk2/ITqztDclLr2aI+TTegU7ehhFWrtGlQmgG2pCcCfpiyluifV8g9OUHeeamiXzf9N4DNCiOuAfwD+C8qY+2a1nYUQrwdmUCHBGErtl0DV0OAcLJC6u1Y7qno4PpPjO88d5Xs/OcpkphQD3tTfxTUXDPHWc9aUmHC++XdK8GURpo6DmSfBLD1ajkhIVy9eLgM7PtJeXn437OXati73nO5Tb+slRhlObkFla+zNb4LRveozlwNfN+CcX4XD/6kmheyU4wEPKR77Oaji/R5+pkRt5QrZvb+nMgnTIyp5R++C/GT9cbuwTMBS11mcVf3+Dv9YVe5dfosSaivv9A1wVHGEMo+Ks4oR6M23VjE5TCdsV1STirtdCys1vWtQdQ4eGStpRXZR+TZcHoLKSXUZoC3CL6WcEEJcjYrzfx4l1FdLKScBhBCbgJ8CZ0spDwJbgD8D1gIZ4D+Bt0opp6sdfw7mQeDpdplN5805jSpqwWW6vW/XMI/vGy9z4L3x9NVcu32ICzf2IiqdjjPDgIaYPU5SZOkR0xgU1fub3OJQdWfaX5Hot23d1R6phGnmqMOuo9WIkAhVhjs7WlrJXIH88Zccog6nms1duc/7VWXHunnsbrvq3V9Vh3SjALlpZfYM+Nh3ChmlqsfXlIcSsxNK0FZvVRNM6ihe+bYrSFWhKcagsjlCqpX4UUcB3f1VNY6c85r5swhFqBT6y6dVTj+oCWj6sLOfUOP1x+LdHAYoNwmgdP9d7sJlVovSmRx+pyTlUzdva5gQ4abZpnJFsoXGjjsXqVyRB144zrd3D3N4Mutt74+Heft5a7nq/CGP6bYa9G9eT/L4k/TIFLouHOIRx2EWjpcqEnNT5Y0f2oG9DzppsntKzSc0Q73w2QmngKZS+IWy9YXjxAvF1UrmwlWJK4U3uUb97rfnc9Oq0YVVgFBM1bbPHFLn6N1YijxICcefV5GCzGgFg44s8fUV3X5/huPLqDFxh2KOje1/n4VafW1T3Xd3ojn+PB4vH6jzuOm6vZsVjZi77/jLpWw/3SjnE/A3DA13la5dSlSBkvPOuX6U+SRIzcXySvJZEtRJiGg2zbYSe4+nuHfXMD/YM0Lepx1csKGHa7ev59LTV9UNwxm6RnfMIPlzH0D7pwdQLZh8gRLdKNmu7VgFKjP93KQTt3+h6w9IrlG5+cPPqFXQLJSiDrpRUouhtJL5ewf6VzPvYl3HniytfJ7g+8p1U0dKmoifD6CYVYI5c8hh1tFA+ux6oTmeeYdKyzU3/EQd/ac6/fQyeM1C/M/b/TsUVdqEZarj2BWOQ01XYTvdybTTI0404pAqIvMjP1PeUMXveI10QyyvHK+RbhXdcPkMkmuWHdV85wo/lHmWmymXrYaCabPzZyPcu3uYF4+WsgW7wjpXnL2Gay4Y4tTV8TpHUDn3vV3hks1/5luVJ3v8FTwSR2mVvMN+Qor5olHSyds+U+4X2fugUsMj3RBysusSg+o7u76iPrvkpuq9A0GNPz9TLrzu5OWufK5D0FXT3Vi4y+Vn5srrD+IDvnZY/gVN4NF2+cOQfp9FYlCNxcyrCUpaICsWRY9PMAxkSrZ+JSyHACQxqMbm+hwqTQzbUiZAfKDUk2AOgclpcMkdy0rIa6Ez1f4hXT51Qw9mpI/ZgQtIvfMrTdvxLo5OZ/n2buXAm8mVVoLNq7q4dvt6rjh7sJRLXwNRQ6fXn3PvR2Xednq0tCL4vdDV0Eyxh6tupobLW1HrhuqNVy23vVhQHmmXNCMcV8U1lbn1j362xG8n7dJKqYeVLe7PQYfSdU6+qo7rlujqoVJIMLZ6LovO9z6iVn1Pw/B79H35Bzi5BGauNBbP99ANr72uRMPl8gYKrcTek3F4Aev5C/pOKanmRlwdJzVcykb0Qzfg3V9bKgFf2Wq/hcZRu4dsJgTRU3xMrw2+Z0t+vH+Ce3cN8+SrE96rpWuCN52+mmsvHOL89T1zHXgV6AqH6O3yUV7XFNZ5rAj1cu6hdJ7UcUX11SjpxP1OsVBazbWQeqlzU9DVTxkvn6sd+HsH9qwHW6qONVXr+Z0YuutAczvZ+ttPGeG5LDqPOeG8Vaerv0f3lGLjfoTCap/8DEzsp9yul4okwx/j9zdACSdg9phy6JUJv6PV9GxS1yXtkmruMgRZPvPI/z2rwmzoUHSk8JvoZKXjQd3/SMP9pzNF7n/+KN9+7ihHp0sv10AiwlUXqFZV/fFwnSOUcu57YoYK17loVCAzn/LXWrRShXTpPOlRh13HUYdd0kuhq1Cdv5Hk1AEnlVYoDcGParx8A2eWt9ZOj6hQ1YbXlWsTd19VnnefHHLUZelT2+3a7af89rJdLBd8v5ovpRL8yQPqeEKHng1qTNVy5v33/e6rlA3v0mn5NV0jpiaqyutyM/SkpEQ+4poyhvp/GZWSzxcdKfxlyKeqbpZSsueYcuD9x89GyphuL9rUy7Xb13PJaavKiTKqQAhBMqqq64xqzr52V2bVygsf3aO0Cvc8ybWlpBm/A0tKFaq76P2lbb2nlKeblq6uOi9ftUIc/zFr5d33blIe/fSIEuRwl6ISc+/T9z5SvZb++7cq2ix/YoxddGjDbDXGiX3l1+j6CiLd9WPnfjZdlyTERbi73PfiXlc44TAMu++Mz/xAquO1Gq9fwrr9Wuh84a9ArmjxH3tG+NauYV4aKaWxxiM6v3j2Wq65YIhNq7rqHEFB1wRJp7lF3Qmi3ZVZtYo9BKUkHHCSRoDp/SW13z13uLs8h+CSm1Syi9+O95pf6OVOOJcV181Ycwtx/Mesx+676nQlkG4oExprRo/drur6q/kvRBjyExU3yfEjzI6qCaJe1KSSTddL8NEcU8wnhA/9aanTjqaD1Ep9AISuOgzFB9Q53TBnM2h3J6c24aQR/sOTGe7bPcwDzx8nnS/N7qcPJLhm+xBvPmuQWBNtqeo2q6yGdldmNWqL5T+PbpQop+sx2Wy9QoXzHv2sk6wDns0bH5xrx3/vI5AYADFY/ZitsPu2UjqcGPS1qnaiA5pDmaW7WoDjfLMtxwHYIGri3k8tDP2n1SbM2Pugyo0QIZ95IFW8Pzs2l3SklUjNEtft10JHC78lNZ6QZ3HvPc/x1IFJb7uhC35u6wDXbh/i7HXdDR146jsavV2qWWUz+3tod2VWPSKJaudZfcbcSaHa5OM6xCpZZTVRLgh+m9ffrMJ/zFbYff302i4qNSP3eJFuZct7ZkNcjbOYcTQCZ/J26xXC8cZJMy6bbiX1VuV3yjrtOKFG21a+Bn8BUiQJ0miN1myZcvp1pPBb6Pyj9Wa+bV3CCH3gCP6a7ghXnz/E285bS19XfQeei4ih0xsziNfrUFsPC63MqsstV4kWJoVqk08jVtlKm9fMK9u6mFfeeveYrbD7uh59u1jexMLvkPQfL5yEZKi0Oj92eynvwLZLoUNQFN3N3N/dX1UreO9mdX+q0WhPHVB+lJkjTm6C5mQc5hWbb6wPtv/63FqEZtT3Ja7br4WOjPNH150u177/du/v123u49rt6+cy3dbBnHDdUqBdHHn+0Fa9yacal54/zdifqpqfKV+BK3sANHtOl8/O7XTjNsCMrYJrv1CucVQ7nj9HITdRylHoGoRYT+P71QzHn38/tweAm1Ogh9XKnxlzajYkXq8/t5Zh9Wn1C83a9ZwVVnacXyJIkuFK/cdcs+oI63/lzqa/m4iE6OmqCNctFZq1BRt5ipsNKTZagfzqaaS73HHXiOaqFrZeoTSCfEo50kJuZmGo/DprHc+vWR0aVzn8ybXKFIHGtnOzKnelb2DMqWLsXq8IUmZH8bz+LrGImVOazEhFElC1a2jG9DjB6EjhXyMm+Ub0z4lQAP3Uhvs3DNctFZp5MSs9xRP74Bu/oVTzmuSRNdDIP7FY6ml+ppxeOz+j1Gu3Nbf/GuqZQdU0l0a2c7PXVGm+YSuWqGiPKu6pTD9GlkKsxQx10azpcYKxjCShefSQJiLzKlW0zo3XNUFfV5hN/V2sTkSWl+CDejHdFtQuKl9Mv3bgrkCup3vsFTUR/OUZSoj2Plj7XK5QFTKqam1mWKm+ftXzkpvUZFDIqBW/HTUIldfplsi6mYiu3bz3wdJElzpeblO719XM/apEK9e09Qqlvn/4OZX4oxtqu8uP4KHCVDaz9e+9/xm62ZQucegSYplJQwuQtipoCc8tuglpGqsSETb1d9EXDzftBzjhaObFnDpQiu+nR1BZerqa9NJH1SQwO6Y0Ar+g+OEXquQ6tQIJTXUB/t5HShPH1ivUZJBco1T9ysmhHdeZOl4i0DRz6vpmhhVV2L9ep2x7u6i0gqn9av+H/rT5+1WJ+V6T/1ya4azyVd4jLaQiEvUE2f8MXQTe/oXASb3Mz3pbDF2jp8sg2Wq4bjHQTEZXM5ECv9rqesrL6toF4CS8xAeq27/VmGmy4yV1fKEpyY3gv87xl9Q2zWHRcUt1ha7qCQqzSrBdMlFpq/i7x303j8hKrWuq94z858pNQd4CI1mecOSOsVHGX+Dtbx/OG4rKez94ivpDhAh/+MflJbUnAvVenGa8u82me/qP5ZJklNFxOTFpt8otvrpEELL3QZU6e/x59XcoqoqB0iNOTT8weLb6rJoHfDHw6U3qXLpTXFRWYuvY0giVTQelsNuGHe0dW7VKRy1Uzm1Quf9jt6u0ZzdDMFSR8Vev2cgy9PZ3rtovBBFRZK0YY0Nf14kX/Hq2aSMbb++Dqrvs4acUvdbhp9Tf1VR2v9oajpdWTP+jc1cgK1/Oe3/vh1RNgPu+mDm1Qpk5PPJJF9UcjXdfpZxs9fwJze7nwm0o6sbsyz5zbGykYxrYpYKlA481d/xm4a90tJwSZJfyq9Zz+MB34D1fV/e4b4uKCrhlwItheiwyOnLlv2AoIp/4rz3EhMNe84mxEzuARrHjSq90fkbtb+bglEuUkM0c8THYOC/5wDb43R/VP/feB5VtnEsBUk0G7our6fBrX1Ev1d1XqUnFpasqq0l3iDZ6NvpCZr7x11qpvD56B0rEnm7SS7Mr2t1XlSoGC7N4GozQVA2B59ATJQou9/fkULuosNQzykyUaglwhmIXYdPFjeP2S0S3zUpf+Q0sYsKxf/28cicKjRw4jbzbUwfUi6ZpziKuAZoio2iErVeohJu+TRDpU6unmVOTx1nXlufLu4SdeqhUigpqkoj1l9JxXcfZ5jcp4fzGr5cSXlzNpVhQq6Jf23n0s2p7K17sS24qEY70bvZ9IByuQ/dPDY9dVwilXrfTS957SkkDciFtpZk0csT5owIf+M6Sr+DzRUcKPwJlb3WtVlTLJxqNQk6V3m23SMQqKg82MKcBhRDNz+lbr1CrsJVR3zPiyot/+D/Lw2JuBR+UJoBQDDa9QXHm+9XQC96nVvHUcaVuu5RV+Rn1/cKMQ4TpE3TbdHgCfKjmxfabBo/drs6VXOP0EHAu2iXa0AxH6KUKoYUiKtHG1VDa5SW/5KaSxiRxiomkqndYRgy7i4nOFH4trBxA/vTQE4lGISe/jWfmSlzuUB4vtopqu2vb9p/e/BjcPP0156rinvhA+ap4yU1Ohp6lylLdc0SSJTXVv3rtf6TkpwhF8Hj30iPqeGauZK+70CNzmXcqvdjV/CO7v6q0DJdVyIMzSWo6xNeqsSaHSoJf7fh+tOJ/cCsdNb1EOxYfUFrJCWqXtdRoi/ALIdYJIe4TQgwLIaQQYnsT33mjEGK3ECIjhNglhLi4+TMusZ+iGQeOK1ynXIL3kmtayTMPji3udL6J9ZdYd+vBfcEPPKbq310OeihfFbdeoSZHt9xXAKvPnNsj3oXflIkP4N1jM+/EukNqVfQj1qu214u7V3N+WkX44adVslElk6601UqfGFCTS7NxfXeSGXtF2fIHH1cJUDtvq30vL79F+Ug2XayiJP1bloUj7kShLQ4/IcQa4FeAp1ANOC6UUu6qs38/8Arwh8DfA78JfBo4TUo51eh8O4Z0+dT13RDthnd+aXk/rL0PwtffDeiOBuCol9E+Z+JY27zTqFrYT4iSWryQcF2lEzM3rarZpK3addVy7nlOwBrOr2rOz6lDPgekG96j9Pe6C0rX4pKLNHKu3X1VBeuwmOsEPTmwvAp7pJTHUd16mk2u+WXgiJTyi87fXxRCfNjZ/uXmzmor4fneH8DW51oc8QnE1ivm0ni7hS0DW1sTVP8qmlzjdJKRJUabhaTiVivTrdRohi6qIYi3lDzglXXulQkurhnholL2oXyFbzbpqBpPoRZSk8xJwLe3GFiqDL/zgV0V23Y526tCCHE9cD3Aph7fBDN1oN1jaz/efGv10FmrglpZddezwWlakV14U4hmsufqZcrVoqmqnFTMnOPJDzlsvxWSL/T5XUs1nsJmvfcrFA2FXwhhAPXqX/OyddshAUxVbJsCkrW+IKW8C7gLlNrf4vmWFgsl/HBRuYpGupUQtSszb76pvfVKk9023x6Vdly1AwtHFRuvawYJTUVvavkkGqEaT+EK8963imZW/m8Cb6/z+anA/hbPmwb6K7b1AKMtHqdz0I6c+XZThrULjUqT/dfuagkipIQydUxlJoacdF5/m+5aqJUa7fIU2sUSTdkK8t63iobCL6W8ahHO+xzw4Ypt24HPtnykpS7gaRfcF3rkxdLLW9nZp10aRLvRSuFK5TWsOs3pC9BbXuJbixqrnolx+S11/BIBKtG29F4hhDN1kwVejxLwgpRzm6P5vP03A/8A/BfgL4DT3bbe9aC8/Qn1R7QHPtbhNt3O20oNNF0VWGjKMagbixt+agef/EIKV5ql2Zrv/icflmV6b9b5ARXuywKXAQghNgkh0kKITQBSygngauAmYBq4Ebi6GcH3IHTFA7e2po+wM7D3wVJvPAEeJz2ocNtikj40KlBqFgspXGm11n2Z1sZ3Itrm7ZeyskVq2WcHUU4+/7ZHqePdrwujS1VULQd7d6F47HaV6KIZYPo771jV++61+9zt4pOfr0+j1Vr3ZVob34nozPRe21o2ZZELxtQBXy+5yiKT8OK+2MthFW2VnWexqMZWIDpT+Jc6vbed6D3FSZuVlB6Hc33RnsV9sefDibcYCCfURDS2p7GPY5nWxnciOlP47aJDgPF77SN3WCq4Ja7xAVVQg+Psi/Ysfq75Uq+irs/BLKgahJ5TnEq/BjhJSmqXGh1J5rFjvSGf+p0+ZRevPhN+77GlHtLCsJTkEEt57sBzPx8sr9z+Ew5pq8QQocPEy0s9moVjIQlACw3Vufu6x2gmyaZdWKY97FYKOlPtdyEtxfW2UtGOUF27wn3zwXLxOaxQdLbwAyeV869VtKMZxFI2lFhqn4OLVklITxJ0vvDPTSBcOWhHqG4pw33LwXO/lJrPEqMzbf4ynCS5/fNBOxJeljppZjGahLSCdiY6dRg6f+VfyWp/O9Tm5aJ6LxWWQ6LTEuEkEP4VjHaozctB9V5KrGCnY+er/Vqk8T4nM9qhNi+16r2UWK4cCScAnb/yd69d6hEE6GSsYM2nc1d+oSvapwABFooVqvl0pvAbXbD2vFIq6EpDOwg4Aqx4dKbab2Zh9GdKTVsBtlkZVnBcOkB70ZnC72EFxviXMiMvwEmFzhT+UEyRW0Z7Vt5Lv4Lj0gHai84Ufhcr8aVfwXHpAO1FZwv/SnzpV3pGXoC2oXOFf6W+9Cs4Lh2gvWhLqE8IsQ64E9gBrKNxl97NwKuAn7PpP6SUVzd1QpfAc6WGuFZoXDpAe9GuOL8NPAD8GYqzv1lsaKYl9xysOTugeQoQYIFYqhbdAQIEWGIstc3/vBDimBDiPiHEtiUeS4AAKwoNhV8IYQghonV+5rPUj6H6+Z0KbANeAh4UQnTXGcf1QoinhBBPje57bkXRLQUIsBhoZuX/JqU+fNV+Tmn1pFLKtJTySSll0bH5bwYM4JI637lLSrlDSrljIBkJ0loDBFggGgq/lPIqKaWo87N/oYOQqnlAa5Q8QVprgAALQtuq+nwtugHCzt+1WnS/HpgB9gIx4E9Qwv94SyddiRl+AQK0CUvSohvYAnwHNQG8CpwDvFVKOd3SGVdihl+AAG3CkrTollJ+Dfjagk64UjP8AgRoEzqTzGOlZ/gFCNAGdKbwBxl+AQIsGEud5BMgQIAlQiD8AQKsUATCHyDACkUg/AECrFB0pvAffyHI7Q8QYIHoTOHXQkFuf4AAC0RnCj8Euf0BAiwQnSv8EOT2BwiwAHS28Ae5/QECzBudK/xBbn+AAAtCZ6b3Brn9AQIsGJ0p/EFuf4AAC0bnqv0BAgRYEALhDxBghSIQ/gABVigC4Q8QYIUiEP4AAVYoAuEPEGCFojOFP6jqCxBgwehM4Q+q+gIEWDDaIvxCiLcLIR4WQkwKIUaEEPcIITY0+M4bhRC7hRAZIcQuIcTFLZ00qOoLEGBBaNfK3wPcBmxENd+cAb5Ra2chRD+qacfngT7gC8B3hBC9LZ01qOoLEGDeaIvwSym/KqX8rtOAcxb4K+D1Qoha6cO/DByRUn5RSpmXUn4ROOZsbx5BVV+AAPPGYtn8Pwe8KKU0a3x+PrCrYtsuZ3tzCKr6AgRYEBoW9gghDECvs0ve6bLr7n8h8CngV+t8JwFMVWybApJ1xnE9cD3Apj4jqOoLEGCBaKaq75vA2+t8fiqwH0AIcR7wAPD7Usp6bvg00F+xrQcYrfUFKeVdwF0AO3bskEFVX4AAC0NDtV9KeZWUUtT52Q8ghDgXeAj4mJTyKw0O+xywvWLbduAnLV9BgAAB5oV2hfrOAb4P/ImU8stNfOWbwAYhxHVCiLAQ4jpgnbM9QIAAJwDtcvjdDAwAnxVCpH0/mwCEEJv8f0spJ4CrgZuAaeBG4Gop5WSbxhMgQIAGED5fXcdgx44d8qmnnlrqYQQIsBQQ7TpQZ6b3BggQYMEIhD9AgBWKjlT7hRAp4GdLPY4FYDUwttSDmCc6eezQ+eOPSinPbceBOpO9F34mpdyx1IOYL4QQT3Xq+Dt57HByjL9dxwrU/gABVigC4Q8QYIWiU4X/rqUewALRyePv5LFDMH4PHenwCxAgwMLRqSt/gAABFohA+AMEWKHoKOEXQhhCiM8LISacn7+uwxa0ZBBCRIQQXxRCvCqESAkh9gghftv3+d1CiEJFHURrHIaLiEbjW+7PoWLcaSFEUQjxnO/zZXf/hRC/L4R4SgiRF0J8q+Kzuvd7vs+jo4Qf+DhwKXCO8/Mm4I+WdETVEQKOAm8BuoEPAP9bCPFW3z5/I6VM+H4eX4Jx1kO98S3r51Ax7gTwIvD1it2W2/0fBv4M+GKVzxrd7/k9Dyllx/wAh4B3+f7+VeDAUo+rybH/K/BJ5/e7gb9a6jHVGWvd8XXScwBeB5jAULPXt8TjvRX4Viv3e77Po2NWfiFEH7CBcu6/XcAmIUTPUoypWQghoqiX8Dnf5t90VLQXhBB/IIRYbs+i6vg68DlcB9wvpRyu2L7c7z/Q+H4v5HksywuugYTz/5Rvm/t7Te6/pYYQQgBfAl5Crf4AdwBnojgQrkPxGiwnJtJ64+uY5yCE6ALeg7r/fiz3++9Ho/s97+fRScKfdv73z2bu76kTPJam4Aj+36JetHdIKW0AKeUzUspRKaUlpXwC+DTw7iUcahkajK+TnsOvARngu/6Ny/3+V6DR/Z738+gY4ZeK5ecw5dx/24FDUsrppRhTPTiC/wWUuv/WBmO0T8yo5g1vfB32HD4I/J2sTSHvYtne/0b3eyHPo2OE38GXgT8WQqwVQqxFeTQrVbrlgs8DbwSukBX0ZEKIXxNCdAuFHcDHgH9ZikFWQxPjW/bPQQhxJnAJ8P+qfLbs7r8QIuT4hkKAJoSICiHCzseN7vf8nsdSezdb9IQaqNV00vn5PBBa6nFVGecpgARyKLXM/fk/zucPo+yyNIqX4A8BbanH7Rt/3fF1wnMA/gL44Xyub4nGe6vzzvh/djZzv+f7PILc/gABVig6Te0PECBAmxAIf4AAKxSB8AcIsEIRCH+AACsUgfAHCLBCEQh/gAArFIHwBwiwQhEI/wqEEGKnQxqRdshGXhBC/Krz2WYhhBRC9Fb5nvtZJVnGXzmff0AIsavK9+529wmwfLBs2FcCnHDcIqX8K6cG4ZeAbwohnmzyuxuklFOLN7QAJwLByr/CIRW+i0p3PXOJhxPgBCIQ/hUOIYQmhLgWiALPLvV4Apw4BMK/cvG/hBBTwCyKZOTPpJSjTX73gBBiyvfzW4s2ygCLhsDmX7n4/6SUfwUghDgd+LYQYhr4tya+e0oNm7+IqjCrhOF8FmAZIVj5AyClfBnFdnPVAg91ANgghKicAE4D9i/w2AHajED4AyCEOAXl8f+Jb3PEIZRwf5rREp8EjgN/JoSICyHCjklwDnBv+0ceYCEIhH/l4jY3Tg/8CHgI+KTv82NA1vfzcd9nhyvi/P8MIKUsoCaRM4F9zjF+C7hSSnl40a8oQEsIyDwCBFihCFb+AAFWKALhDxBghSIQ/gABVigC4Q8QYIUiEP4AAVYoAuEPEGCFIhD+AAFWKALhDxBghSIQ/gABVij+f7S4+u2rj9guAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RCejNQPvpuZJL1BKsTamodq6kyvXXJKOsTc33hU0DEhGTuYKDJ5RGT8fsjoNAwc3olbd+b176R+Xnay0FHYxrtpF9+LUX8qE7nmQ8U6p53e8fwZM0TPGloxbPjmYoOB6GAMswEFQzB7qNs1GKqlVWYakREi0scyyUArgT3q562t90rBq02r42WVOAogUjGTFZk4yQLbnEIyZx26Q/ZnHu2hS7L9/Ebfcf6GqtjSq4TENgGbWPZruU0LU7R/j47ksraT3w7VT/V0PMj9Tv2TvKWKaI48lKHUDJ9XCkJBExeXZMFdzM5suMZ4o112uXVVhqhD72MkY3wbF6aE04V3AYzxQrhAFx2+CR33vNvOvUm7dQ9StPTOcr7ZmazF9H37XZ3WkQqtV3dFyPEzMFSn4PdsQUbOhXFMST2TKpqMlwKloJqqWjFlJKMiW3ojGfOjrNJ7/37LzYwHAqQl/cZiQdq7SGPnZ4CiGU1p4rOMrlQLkcOne3yadAro8DHJ3KM5iwKy7EbF7VtUtJT3LkHSL0sVciTqdxYctggufHM0xkSxgo7q6S6+F4kj17R2s+38y81b7wwYkcMctgbSpaKUCp1366Y8vxZCWwFSxfbUYqcO3OEXYfneav73uOfJ0PXHIlhyfzWH5EO2oZPDuWBWAoYbNvOo8rldk5Plfkt+94kj/bfSnve9X5/M39B8iVXAwBa5I2fXF7HtuK63kYQjCdL7OxP44QMDpbIFf2iPlBxHSsaoJniyqwt3kwwXSuxNpUFFBCfXwm72t9uShECu0QmuLLGKfTuHDTNduZyqm6aGHodkbBUNLuuC5Z1ze/dNsQ6/urVWVQyzV2/Sfu45mTcxQcr6IpdR27DkLdcvczDV2KPXtHueOxYzhu47CXLowZTiuqIlMITEMwni1RIVcR6ripXJlb79nLe199AT/52PV87tdewsvOXUPMthpytkUtEyEEBoLxTJF0zGbDgKozP38kVSPUa5JR+uM2D9z8Kr5045VcsK6v4kKMZ4oYCIQQRC3zjHOwa/ds24e/1fQCoWAvY5wOAf21O0dIxyxVR+1JLEOwcSDGmmRrZpFGCFZLzeZLlfE7R6dUFdrByer5ZODH8VS/teN67B/LNPRHtVXiNAtnA45fPVZyVXRe+NH5agmtimYbQlWnQW1GIBg9D26Ww+moX8ii0nXN4gsw/74H70nJ9ZCojSwZMTkwluHwZI7HDk/13OcOduwBTWtzQ8FexmhVfthJUG3HSJoNA3F2ru9j+3CqZUVYOyQjJocmshyazON5ks0DMcbmikznyhQapJFACffaVJRTc0Vsw2gYyGtkldTDMgzmCmVcT1JwPIr+9ZpFh1oNFghulumYzcaBmL8pqLz17ss3IaXk4ESW/afmmM2XGlbBBTvf9OcH4hZT+TKOKyuNOb0OqNW7Z80QCvYyRrO2SaCjETS9qEvWQlJyPaKW4u7CNztdKecRGtTD8otZ1vVFa17XLoUWtEiz2nEBUdvg6FS+qqHrjlGlrqrj69w1iZYZgXrr4+RMgbLnce6aKmdZ2ZNsHoiDgKPTBSKm0TBgqV2V2375Ckb6YswWnUAkXvnovTbJO9kIIRTsZQ/98Gjf7tqdIx2PoOnFyJvgtQqOh+PKSu+32YZn2xAwko5xwUgKy2ycvtKCNpiwqTsE2xS872d3sKFP1bcb/i7S6KqelAwkbD782gsbPvyO6/HY4Sk+ctePSdgGJcfj6HQBBGweiFP2JJ/e8xwlR1EY9cUj7BhJs21NgoFEZB7fWdBaAvjYmy5CSrUO7fakY3blur2aWNrIPWuEMCq+AtFNT2+3rYX11Wq6pnyuUMbzdIOk8nEdv2OsHgIYTNi8YH1fhUn0t+94kmNTeRxPTfaImIKIafCRu35MOmqRsJUvXqqrFf/Ko0cZmyuyvi9KXzzC3pOzakMRapBA3DYpuSq6/fHdlyoSiPtrWyPnCmWOTRew/GKdfNlldK7ISDpSM0qoGWdZfe67YeHPmy7i8q2DNdedzfvXNXpHO3zTNdv5yF0/xnFLLY8LBXsFoBm3t+tJxuZUjtoUgnPXNh5B06y0tP719X0Rvv3jUzieR9Q0cD2PTNFVHF3Z6oOkRdmpE2qBahBBQLbk1pj80j9ACJVmyrgwnS+xJhmt5Oc//vMvVkUz39nHp/c8V1mHlNInchB+ME5dN26bbB9OVXLqjUgg4rbJyRlFArEuXWUtcT3JTK5cI9jNOMuCMYlWKcj6655SAS7W91ev2ypduWfvKLfes7cSADx3TaKmBqDouOzc0MdvXXs+X/7REQ5N5oca/sEJBXvZoxm3d7Gseo99OcLxJGOZ4rwctRYS15NELaUVf/8bT7P76HQNB/bBiQwPHShiCrAtA1fCREYVhUxkS5TdZqGqKkwDEFQKYfQ6brv/AP1xmw1+7bcedjebd1ibitU88ACf3vMcnlR95Hl/zA/AiZk8G/pjSsgl9MVUWWfZVdF3/d11bvyz33+ebMnFk5K1fi5bI2oZFJxakzZqG+Qdh2dOzBK1DNIxax6dcitrqb4OXkpV4BJMmwVN8/pN9rfveLJCJAHw7FiWD93xJLe85UVcsmWATEGV+r50+xAv3T7E9uFU02BJKNjLHM24vU8UXSyfCEFNg4xi1dVC79k7WhESy1DD5UbniliG4BPf2U/UNpQWiwhm8+qhcSVILUwCskXV2KxrrlvB9WDrUBzLFDVTM+qFQZnOqodbC7ltCGbyZW67/wCuX+Kpq930tUuuJFt0OH84SabocGK2gG0YbParw7SZC3DHY8cYTkfZaps8O5phKueQiJQrwp2OWTg5WcMcOltwGYzbFMpuUzrldgwoQddHd45p1LsEQdP8tvsPMFdwMA0VYdc3dK5Q5q/+/Tn+4hcubXP3axEGz5Y5mhWpFF2P84dTlVSWbnEM+oO33X8Ax1O0SEIIPClr2EGL/hys2XxZ5Yj9z1WqjCUUXSXVW4filTE+zSD99R6cyDGdK1UCRfUBn4hpVCrUHL9KrexJ5goO+04pbVkx8wND8WK2wY51fXz4tRcy62sv/d2CAcT64OL6frXJqJJPJcwRq5Z4MFdyGUlH2DSY4LyRNC/c0M/mwTgPHahNFXeTaag/tpFLEEz7OZ5XaRuVUt1315OcnMm3vO+NEGrsZY5mGiIZUVpmruBU6sDTfjOGxpGpnPKV/fbKep8Y1GunZlVKp+wq4as/yjIElmmwcSDG4ckcLQhG8SScM1irQbXvOZ4pMJMrky97FfIGAUjfrR1M2ORKLumYVaF3Ci5mXTrK/tE5349V7aaOpwgKLaOA43kcncqTjlms76taDOmYzaYBycnZYqUktJ54sFGXWaOAZDcMKPNMc5RpHnQJgmm/sdkCnicrUz2lVAys6/vizW94E4SCvczRbMbUz+4c5htPnVTMJkKZt2OZEr/40mo8ZctgAtfz1Khbajm8g6Z1wZ+d1QiqlVFRItmmYMtgnEOTzTVIMmLSF1c0d9pv/tKNV7L76HTF109ETLIlV43IDZSSzhUcjCYbkIE6tuR42Kbymx0HhP+9XKnID4VQ57HNYk1gzDINLt862LRXvBuSwW4yDa1M8+A13nnlOfz05Cwz+TKG3wrrSeiL2Lz9JVs6ulYQoSm+zNEsF31ytsRIOkLENPB8Pq+RdKTGdLzpmu3YpsmalE293DZSuqLu/xHT8E1dg92Xb2Jsrqhyv00goNIYAbUa76EDk2wejHPhBuU6JCNmhaTBNgwipqEE15XELAPbqJ7T9IkIT8wUyRQcRmeLlQBTxWuQavMpOR6e5zGeKdWYyzP5MtO5UsNa9Xfc/jD7Ts1ydCrPeKbQUTHPQtppG5nxRcfjLZdt4gUb0vzOz+3knDVJdN3sOUMJbv65nbx0e3WzllLy5NFpfu+uH7e8Vti2uUKhTcfgMHYpJTP5Mg/c/KrKa8GWzPFMcV4HlUbMMij7fq/uWrRNA89T43UGEpFKa+XRKb+ryhdM/QQNxC22DFVTbsHWzvo20lypykumrQ4QvvUhSdgGqajFbKCdUn9VvZFJKSsBNlB0w5ZpVDjEd65Pkyk6JCMqst+IaklnBoKjd9Ixix0j6Y7onZq1qLZLMR6ezDKSjvG2XVtqBLcZyq7Hnp+OccejR9k/qmj5D97y+rBtc7WhU9MxaAru2TvKr33uRw3PV3Q8EhGTouNVgmyWIehL2hWiwUTE4sBYpmZAgQBsQ9EYCSEYmyswV1CayDQEN1y6sbLe58czjGXm86hpuqWgHVF0PBxPmeZRS7WnWaaoBOF0ZD0I21JBRgFELcFAIsLd77+Gd9z+MGVPzss9f/b7z5OMmkxkqnGKoaTNtjWplvRO9dTMijaq2qJan0oMRr+v2DbIn45cQrlJN1s9ZnJlvvkfx/n6E8eZCLDDXLih4cCcCkLBXgFotPu3m+/cCNfuHPG1jJ9GqtO4a1NRjs/kMYUgZqhosvKtvZqpkUG5jJgGHpJ8yeGdL99WU1jSn7C547FjXLJ5gJuu2c5//T+PNCRHbJRKcyW4/sNvChVEWpuK1Wj7+lNpKmIp1XA/7QYcmcphimr+XKUHI2SKDvmSi2GIymzt8bkSZbfhMJoK2vGkffb7zzOcjtZsJJlCmU9+dz9/3mHa6tBEljsfO8a9PzlVKZoxBFyzY5i3XrGJizb2t/x8zwW7m7nXQojP+ccF6+Ouk1I+1Ot1rVTMm8hxZIp3/cMjXDCSajnfOfj54KawLh3l5GyxQuHruB6OT3uUjlmscSJM5cokolYN20i9daBNY8NQ7KYlV1b86OBxuZLDLXc/w2Ay2rTIpW1+XILw1CwvLUSg0l9SqtJS1QgiSUTUWF7TUFTI77j9YU7NFii70o/uKwHWI4nwBRP/+3hC+emt0G7CSrbkstXfCF3fvbFMwYk2aSspJY8cmuLOR4/yw4NTldeTUZPXXbyBN1++qSba3wpnQmN3O/f6M1LK95+BdSx7dMJVXelXdiXHZwo+Gwo8P55tOd9Zn7++aq3geKoJQj9wlkHcEGweTDCTL3Pu2hS3NFiHtg6Er+YlYBsCx/PUVBLP4bHDU/TFVAmn1oyJiMFkrsy2VjmyFtDa3JUQEarJAlSkXhfn9KciTOXLeFJyrt9LrSmMy55E+J/R3GaVAhB/SSrFVM3fN8sQaGhrSU9YEVQZXnUqctR3Scquh20aJCMmmwcbl/wWyy7feWaUOx87WhkpDLBxIMZbLtvM9Reva9umWY8zIdjh3OsO0I5FVKNi9s1kMVAdTvX0uM0Eu1nVmp2MMOgTLnTCRV6bj80r01iIKg2SgJhtUnJcxjKe0ox+pdtYpoxt0PbBbFbZFqiVqQTNBKrpY8e6qp9pGILZglMJLJlC0J+wSEQspBDYhsTxlHAnIgbrU8pyGUzYdbUAdk0tQKv70WjCSsnxePn2Ie59ZlTl6YUKfE3mPN54Sa35PJkt8Zl/f4779o/VuCiXbu5n9xWbuXL7mhpyxm7QU8FuN/e6ycC9dwoh3gmcAP438AkpZWeRhRWMTvnMasw+Md/sa8WG0qyueSZf5p7/3jw41Ag6CBfckE5M5yn7z93aVFSZvJ6HKyVWIFpv+Iyjps/m0giGH5FrFlMyBOxc3wfAbL7E0el8TXxB58f7/cj33pNzjM+ViFpmpdItaqh89/bhFGNzBUxDMJYpYZuCjf2xriZwBu/Hbfcf4MhklrWpCD9/xWa+/KMjrEnaZIpuRWOnoiaPH5nhV4DnRjPc8dhRvvPMaM39SERMEhGTd7xka0eR8lbotcZuN/e6XrA/Bfw2iuLlJcBXUCnLT9SfWAhxI3AjwNatW3u13iVDp62XFbPPqJp9Uipan3ZsKGdismNQe9eTHB6fyWMbal6WNpMxq8Icsww/2l2tAdeprrJHjcqu1+DBtJ5lGuwYTtVYHbZflqq/a9RSefGxuSLD6SjHpwt4qDWNzRUYy1TrAE7NFjk6nWfHcIrfe/0Lu2qpfMWOtbxwU1+lQQPgE9/dx0AiwmCiumZPehycyPCBrzzJE0emK68bQnGWD8RtLH+22Jd/dGTZCXZXc6+llI8Ffn1YCHEL8E4aCPZqmo+9Z+8os3lFwh8NCEazdNXHgFvufob9YxlsIVjfH6XouExmVdHFO25/eN4I3NvuP8D+0TnmCg6DCbtCm9uLyY5aW9XP4dJFJomImlMNai7XeKZU6cLS/duWAaahus1aud+67NQ21Sgh/R3qBbC+JFRH+AuOSypqsSZlM5ktE7cNZgsOpoDxTImIabC+P4ZpCAaT0Y6F2vUk33rqOP/7BwdV11lfnLe/ROWkN/TFmcgWidsmnieZLZSZzJVxPFkR6nOGEkzm1OZiimqdWMw2ODnbfW14PXoq2D2Ye73qTXBtyiajJvmSS8n1OD6Tp+i481oENerNvnqBfX48w01feJR0zGJtMlIpxljfF8M2i0xmyziux451fW396W6Gz9Wn3NIxi7FMib644vsezxSZyJZwXOWL40ffS64i/rMMgSdFw2i5pDpIL192KTkez5ycIxkx+fVXnjtvTfXWSV/cpui45EoqkLZtTYr/783q3r7rHx7BFFRSXMenC2zo74zk0fVUEdC9T5/kL7+7X+X6YxYT2SKf/N5+3scO3v6SLfzFd/YxnSuTLTk1G9eucwbZfcVmXrJtkA9+5Sl/A6i+Xyh7bWvD4xGTVLRNzKLXlWdCiI8BbwBe57/0beDrUsp5wTMhxC8A96C0+RXAHcCnpZR/1uoaK7nyLKjlguNrkxGLT739sq4G6iUiVoXTGlSU2gMcV7J5MF7pA64n92+GTgcUBIVfk/ZnS8raePn2IR46MMn+U7PMFd3KqB6BwEOysT/O8ekcrlSCpYVam956qD3A2qQi5B/LFLFNUWkgQSiS/4s29tdUdHWy9nfc/jCPH5lCelSoljypXJzLfKL/Rhub5wv0jB99/8A/PVnRyhr5skvMMtk8FOe+fWOV7yGAl2wb5KafOa+GDOOHByb55PfU5hCzDQpl5aa871U75pniUdskFbFIRs0gzdSiVp41nXvdYEb2b6HMaws4BnwG+PMzsKZlg6BvnY7ZpGN2pRS0UzMweI4Kp7VR7V82BIzNFSuC3SkXeScBvXp2E9fzsE2TP7rh4sox76W6+RyezKl6cwSuKzkUoCo2AkpFUq1N14Umj/zea7j+E/f5baUBBSRhtuBwcCJTk0nopOvqyFSOdekox2cK4FFpk3Rk7TABnan4vbt+zM2FnVy8ub+SagM4MZunL6buk5SSTNFlKlei4CiqZYChZIQbXryRN16ygYFEZN79fun2Id7HDr78oyOcnM2zPmDOA37QzSIZtZqSPTZDzwVbSlkG3uP/1L/3G3W/X9Pr6y939CKgFTyHjpbrSDmo9Eqwa6rT87cL6AWJG2yzyrKyJsW8aP6+U7NKA7mq59oA3LrgWDACrgNpnqfKWc9fq4YR7BvNNPXBZ/MO6/utyrU76brS925jf7wy+sg0BNuHkjXDBKSUfiurx2e//zx/8bbairENfXHGMgWKjse07z9rnD+cYvcVm7j2BSNtBVKzoWhYhkEyapKMWsQ6YCNthrO2u2uhw+5OF72gBA6eI2IauFJWIuVrU1E8iS/s3Z2/3YACzW5iCqWBDb96bSZX5uhUrnJPr/ijf2MyW66M6FUD6udfzzQEMcvAFFRaL4UBAwmb171oA+/+4mNNhVoAuZLLoYlsV8T8+t5ZpuKI2zqUYCQdq7CbxiwDx1Mbo6aTqg9mHZvOk4iaHJ9WgUEt1FHL4L++8lxu+5XLec1F6zvWsqYh1BSS/jhb1yRYk4qellDDWVor3mlxyJlAK5Ox08BV8BwzOfVgDSVtUlGLfNllIGEznIrOIxVoh3b150emlFmta5d9slCKEsozBW76wqMMJmwKPheb6wFG8zJSnQYb6YuSLbr0x+2Kn37HY8fIlZrT7Er/+npzCdIitbqHze7/NRcMs74vxqGJLJmiU5N/3jSQRErJU8dmuOPRozz47ERNZ1rMNjlnKMGvvnxbx2kq0xCVIFjcNmvSeb3AWdm2WZ+mgc4DTGcKpzNZs9G0zIVuUK3Odf0n7mPfqUzD1IWuHTcNoUbu+hHnRqQJ9Z/bMpSo+Z7vuP1hDk5kODFTbPlZ3RyysV/xrBXLLmOZUk0Tim2aLe+hlJLZvMN0vsTnvv88n3/4UKViTM0eg6vPX8OJ2WKlqg1gJB3lzZdtYn06xl1PHufEbG3KqxEMIUhEeyrMYdtmEJ0Uh3ST9ukFTmeyZrfc4Z2eS9+Dj9z1Y7YMJsiWXNUT3UBWJT6Vj889LmW14UJHfGF+4YllinmCd2Qqx4w/ULAVXAlDccU+qqrRCtimaOv/gy/QBYfpXLWN9PEjMwwlbLIllV4TQiCE5P5nJyqfu3BDmp+/YjNX7xjmiw8f4k/ueQbPU1zorutVUl5auM+AMHeEs1Kw2wWwlsJU72YIwGKg0T04Np1HSmoaJjR0mkprcw/1iwqSVckbQA/Rg3V9UbatSc27p+moxdGpzoo0pvNlEhGLU7NKu6vGDEX+70nFunJqtlgp4vmZFwwzV3SYzpYrw/40TszmSUYtyq6kUPYIWrM/c8EwuwPtkj88MMkXfngYKRUZo+NKpvNlBuLw5R8d4Wd2DquIdsSqpNUWE2dl8KxdAKvTETq9xOlM1jwTaHQPQGnbmGXOswEtvx7ck2pDWpNU6R1DqHZKw1fV2jL3JJyaLTKVLdYEvvbsHWUs09oEF4F/BYp9tOgohs+CowJfQQ9ASnh+fI6PfP3H/PMjRxifK9YItZSSHx2cJFd0OTSZZ6bgVHLq6ajFC9f38QdvfGFND/SXf3QE15MYPkuqYahxvLmSy3imwIZ+VUewFEINZ6lgt5tpdTpzqReKXkTLe4mGw9/q2hyDMAw1MEAIiNsGg4kI/XGb4XSUqClwZW1Zof74dL5cM1BQDxdoBS2zlqGKSxzXwzJFU4fTEDCTdxACvvh/j1ReL5ZdvvXUCd71+Ue4+c7/YK7o+OcVDKcibOiPkYpZvPPl58w754nZvGrvlNUNxjBUVV2QHmqpcFaa4tDaLz0TzROdrEdHa/ePzvlsnFUr4Uy4AK14uRrVskcsVZBimQK/AKziM5ddNZPrPdeex3tffQHvuP1hpnIlJrIl1W4qZEWLWobyg8uOmqFlCMF7v/w4n3r7ZRWXJGYZFJoQHhh+NDxiqTrvsbmiT3FUrsnfg9Jcpikou16lDnsyW+LrTxzjX548wUy+6su/eEs/L9o4wFNHpzk1V2A4FWsYDBNCsHkggUAymXUqEXrX50hfqs24Zo1nY1S8HU4nQn0mrw2tUzm9uI4m+Cu7LuNzpYpqXZOM4PqBMd0a2Yr875W3fo8pv07cMASFci15oSlEpVIuaqkg26aBBAnbYKZQZnS2OC/3HSw31QUtmwYT5EoO6/tiZIoOhyZyNcG5qGVUJqH0xWxKrhq8p6P1liF41c4R3nr5ppr+7kaIR1ThSCpicf++MX7/G09Tdl1mcmWKdRvbIqGpnR8KdgBBDZaKqAhmpujMS/ucyYh5s1ScbQhyZa9nm02z6+g2x2a17NC8bPNT39lXmZcFSoPFLAMhBEWnlitNC6khVOmk5XOsRUyDA+NZVb4pFTlCTRTdqE40EQL++8/u4KEDk4zOFYhaJjP5ktoUPImHSolJqbV29Ux9MYs3vXgjN1y6kTWp2tndQUQsVdaZilpY/nywTp6RRUKY7mqH+iiwEhyvpga60XG9jpg3i47vH83U8Il1kw7r5jpBvq5mteyNrvep7+zjk997tjJgQGvEgqNM4KAgV1lJfSJAv2pOk0CkoiaFsldpAU1ETMZ8hk5Nb2QZgrUpxaN+xdYBbn/gAI4niZgC26xaA1qWPf8/5wwleOsVm7nuwhGiTaq7bNMgGVUNF1Grekynz8hyQCjYPjrNI59OvrkTNPPvgabD3OsnN57OdSKm4NmxzLxhf+3iC5/9/vO+UKt4rIGHNr4LZY+IAYMJi1zJI2oZSqtLVbI5nI6SjtnkSk7lOvXzrccC1LtBaqi9J2Y4OJGlL2YxU3AoOJJGifYdIyl+/epz2XXOYMNcshbmRMRsWs55pv/2vcRZGRVvhE4j4Wc6Yt4sOr7dJ+nT0EPVBdRYDt3WTOvrHJvK8dxYlnzZo1D2cF1J2VVD+2by5bYBoWzJrfB8O65XQ+QfswxcIBmxOHdNgr64zY7hJEPJCOv7Y6SiVk0W4OXbhzg6lecnJ2Z4bnSOI5Pz760Ejk4VKPnso0JUaZWDSERM1vdFSUYsXrJtqEaoDaFqtDcOxNkylGAoGWlZo70U2ZKFItTYPjqNhJ/piHmzWmagpo6726Hqra7z9PGZyvRKDQ9809ZgOBVtW8uejCi/3xCqmQOqZaY71qUZzyg6oqhtVsxY3aoZrGl/yp/xpQn1C2WvKfuGRG0iKg7Q+CjH9UjHrEojhxAqRpGKWf6Yoc7zzL3825/pysZQY/voNI+8GPnma3eO8KUbr+SBm1/Fl268spKaC+beGw1V71Z76OuAYiupFH6IalTm/JEUmaJT8S9H5woNLYRff+W5eLJa8QVK8Ib9wNRMruwTDlYLXvrjNgOJSOV7QnXofcQ0sA0Do80TWnRlU6EGNS5oOldiY3+cNckoW4cSFSuh2/LOXv3t293LXiDU2D46bdTvZozqmVhjJ5Mbm6GZlsiWXCxjfqmoJ6vnbOdf6hTPZ7//PGXXQaAaJeIRkwNjGXJ+x9fJmTy5kkvBUSWbB8aylXJPPc/bNtVIHyHAkM2IiauI+iSJzTCRLfMrVw7Tn2hd+NLJfdOMMd12zgXP89jhKQQLt7Y6QZjuWiKcrinWKg/90IHJhkUnzfLj7/3y48p/l9T4xsHOqw/+8xOVSLUOqqVj1rwhgMG1BXPh2v+VKDMxKIYRU9AXV9H3XNHx53krVtbgehrBMmDrYIJDk7mGPd8CVZN+7trW87i6vc/dphmD5zk8mfMtIsFG3+pqNFCxAzQ1OUJTfAnQC1OsUVmsLi4ZnSuocUCHp3jXP/yI6z9xH7fes7dp/bs2o/FTVfpp2TwYrxTGZIpuha1FTSXJc3Qqx0y+PI+sQq8tW6z60cOpKjVQvW4tu5KpbIls0SEVt9VIXMdrK9QA6Zilpn00ecR15dxCA1y96hsInkcNPVB95GNzqi6+15WNoSm+BGhn1nZDuFBP1NdsCmSx7BHxq7CCGvfoVI73vlppMl1cko4pJlBtXl//ifvUyFpX4giJJVR+eDrvsKE/WrM57T46XbEYio7HpoEYfXEl1JO+n63FVRvZuuFCUSgJbEtQLtUKdaOOMhMVXNvYrzjNo5aiMtL5cssQuD5l8UKFplddd8HzaGpkIdVU0TMRpwkFewnQ6mE5nQKYZlMg9dC6ouMp6h9f465xIpVxNu999QUNSyH37B1l/1gGU4gKeULZFzBTwNqUGhKXiFiMZwp8es9zbB6MYwpVP35oMo9t5DENY94UkBoB95tEJnOlmgqxmK2i8idm8gijKtymoRowHE+SKTpIX0i0cNuGgUTROJ2O0PQqEh48T5//tz81V0BIURl+2Ms4TSjYS4BWD8vpFEE0mwIJuhrMpxTyndypXJlbAg98I0vhtvsP+EKihMky8adrKiEKQke+qwMElbCWPSh7Xk2tdxAScAIVYsmoScK2sAwqUX/bJxaMmKpEVVe2GahNShNAeFKZ5Z708BCcN5zk5ut3LlhouhlX3MrSqj+PZYp5XYW9ROhjLwFapU1OpwhCn1dPgfQ8iYdU/cJCEQda/vws21Bjc4P17438/n2nZlnXF1UaVUocV/naEqUtZwPdUUVf2DUlsl3Xt+3JWh++HpYheOMlG/inG6/kg9ddAEIwlStxdEq5Eo6n+MXWplRDiqtZHwBTGKxLRytMrfGIxd+9cxd3v/+a0xKcdi2+Gu3iJp2ep1dY6vnYHR+7mtAqZbbl/oWbfs2mQJ6aK+K4shKBhSrHm0YzS0ExehpsHIipIXx+5Ev1QivGTlDHGEJQctxKS6clxbxEVaOslAAGEopt5JFDU1x7Yo7XX7qBU7N5PnOfSoHFbIOorUpRDUOwYySFlJL9Y1mipmCkT323Eeiapz2IZlq33bk6sbR6SWHVDks9H7vbWdqrBs3+yN2Yfq3OGyQl3DaUYCJbwjRq518Fz9nM749YagKlbQos00AZuCpNA3BypsDJ2SLb1yaJWYaa1IEKkLWLagsUOcO6vhjJqIUhBNO5In/4zZ/wie/uZyJTRErpM5EK+mIRhpKihnSyUZfa6VSDdRrfqN8A9o/OzRtKv5Tlpks9HzucpV2HXhXA1G8c7ZhMtX/+SvkEbyncyTrvFCfECP/a/zaufM3bKtM1g9oRIBVVuez+uE2+HKXseIzOFSqavRUk4HlwbLqAIfyhfo6HYQgGE7a/SfiMp37A7z8nn+bt2a/DX87CwDl8+Pxf5b89snbBG2EQncY39uwd5UN3PEmm6OB6akaZ46qWWh1MhKWltlqy+dgLnKV9VuBMmGztznnTNdv55lf/gZvKt+MImxmZZFBO8r7ibcSNnVx743UtaZuf9zusLGEymIgw4QfSmkGnujzgZ4wnuNn6MtvFCaQJB+V6PpH5JQ5zCQCv4HF+w/wW54kjpIt5MmY/iDQcfYRLDz/It9Ln82nzV/hW/qLTqgTsNLV1y93PMJ0rYwqhBjN4Kkg5NlckEbFOe4PpBZZyPnZXs7RX23zspth3Lzz4SZg+BAPnwFXvgwuu6+3nGhx77c7ruHTgXrITEXJehIhlsCY1SNwoqWMvuK7iJmSLZaKWSdb3wT+05TgzY1/loey53OteQZ4qccGFxhFAsk9uxfQ5wUCZ4K4H1xhP8Gf2bQyQ8Y18OF8c54/5axzzN3Cl5A+tz1EWNnFKCDyG5DTMTIMwQViks4f5cPqzfPhlvwgHH4B7DsHDXdw7H52mtp6fyCl6Jj/zIASYqL7ykXRs0UuNG6GnJaW+Fp4EzpdSPue/dj6wHxhooLE7OrYeq6GktCH23Qt3fwiMCNhxKOfBK8FrP976Ae3mc62O/fYHlLBkx8AtgRmB5DBID97/FI7rcc9/nOTvfvA8J2bUELnrhk7yyE8Pcl/xAjw/yRKhzNXGU1xjPs2u1CReucCfFd7AD9yLA0SEajTRF6w/4lLxLAYSz4+XK0/e4Cm5A4lkmGnyRNkpDmEKrzaVI0wwTLXO3AT0b+3sHjTYBDstH33BR+5GSsnPGE/yLvEvbBajHJHD/J18I5/9o99d2N9+YVg8aiQhxBHg/VLKO/3fdwN/IaWcp2a7OTaIJRHsRg8DLEy7NsPn3gBzpyAS0BClHKTXwa99szefCx5bnIXMKDgFiCQhNgCzx3xNaCiBli75NRcz+yv/Sq6kxuK6nuSB/ePc8ehRfnJitnLqQZHhBuMB3mA8yIDII5PDjKRjpJ1pZqxB3m39YWW87lDSZjbvcI/4LdYxiSdMQPgjbSW2cHGkiYVLUdjkRZIhOVkhUKyBGQHDBrcI6y5ufQ8abWyFGUiuhdIcU5GN3Oa8oaVZ/9q/vJ/1Y9/nD62/p4RNgQgxWSRpeWx4+1+d3jPQHRaVGunvgf8phPiB//vvAp/twbFLh+DDEBtUgnHXuwEBsf7qa3d/CGijXVth+pA6VxB2HKYP9+5z+tjiLMwcVd9BWFDKKkGQik/XkYJZmSLjRXCcNBQdMgWHb//4BF/70QFOBdzO881Rdice5dryA0S8AglRpI8cidI4OJshkmawcIIvvXoOHvwU+bHnea68htvM13HMHWGtMYOqE1P10xYeBh5S2BQxsSkTl1ONhRrALatNyKqNSje8Bw9+Uv0d9SYoHchPqPux9gIGy5N8WH6WD7/543BB46aRm6/fSfKf/gclzyYvIwghKBkxNiRFxW1Zaiz1fOymxy4r1D8MkQTM+PzUfRuqr5Wo/mEX4isPnDNf85bzMNAmptDN5/SxmVFAKEJwzxcKp0CBGLNigIxrKC2YGuJYwear33uWe546Qt5VhrDA4+XWs+w27+NS9mM4BikjTz/TRCj7Gt+EqUO+ExpVm2FsgHh6DReX8/wv70sc2HQDhZ8cJ+XNqhJQw8D0XMAgPrhRrXn2GLjNB/SBBMMCEYGJZ6tuRKQP1p5Xe2j9JpgZBQyQrlpn/d+xAa7dOUI+Mc2JYhzbVeN9hlNRklGz/Sa80BhKl1jq+dhNj11WaKQRvQY1NFpDNNLwnWjzq94Hd71HbRqeox7WaB/8XJO9Tj8kYz9VGic2BKnhqn+p3YXgsaPPQCmjtJxhg6f6orPxTcxmc4ozbGAHUsJTpfXcMXMRD5bORU4dAwxiFHmt8UPeYj7AJjGOYadIu1n6vVksywYh/SJwAZ5flWZElLDli8rkF4mKAG3PPAZv/1v47kdhfL/SvBqzx/3uj/rcWV2PtmHChTfA03eqTQrU9UpZOPfq2o/Wb4Juyd94AtHwDqyk+PC5bG/k/rTahBf6XCwAYa14Jwg+DNov9RxAqN+jfeo4rSUbafg2WqCK+phHgxjIvnuVIIw9o66TWq8ezNyEEqaRnbWaIPhA9W2CzBhkTyqObWuITGITrt0H9gRlN8Oe2XO4I38Z+8vDlUuOiGnebH6f14uHSIkChpD0kaXfGccc3AJT08qstWNKeLPj6oPCVNecPaZK1aYPK0E0I5AYVr9fcF3Vyrn7Q+peOyXlM7e7R8KEa25W0fBoH+Sn/PeFshqeuQv2/Xz1Xlz1PnWNEkqAhanuWXJj9ZydWEn152m0mdbjtJ6L7hAKdifQf8Rs1jfd8DspUA9m/xZf+/l/2G9/YGG+8oOfVAGsSLIamS7OwXf+YL6Qzp0CTKXN5o4p4enf2jhgFnigXCnIJLaQkYMU8xlIbAQ7ykwBvll8BV93X85EvvpYvDCdZ3f8Ua6e+jqmkJh49IssaZHD1J3Vhg3rLlKWgA5K5SaqGwkoC0ELpDTU77NHYO0LatdZLvnHtTK9fZhRuPqDcO3N8JdfqJrgmk9JooQ2KDgXXAd8XN3T8b2+hhdQLkJUdiagwfM8+En1dx3Y2t6sXmgMZQEIBbsT6D/iV98FSOWPJn1tNndSmYxbXlr9wz64QF95+hBgKkHVGkd66gHcd69/bl9Ipas0n0A9nNkxGDqv4UPiTR0mGx0h60TJS1uJVywOLhyKnMud41v4t+JFlKR6HAwB1+wY5q0bJ7no6c8AEUxD0scc/WQxhK8RQa3RK8HP+u6CftAjSbCSyjzNjlKjZb2yCtgJqiM4QbkJxVmahMiqiKQgvVFtYtferF6L9sG0ypnjGcqNwfftGwlOOQv95/ib0Hhza6cVtKXRKRYaQ1kAQsHuFBdcpyLgA9tqH8ZoHxSma7XkQsw0UH/4o49QCWqBP3M2UtU6etc3I0rrCZ+szC3Ne0hyJRXJziZfhMxMQMSunPKRzAh3Fq7nh9PV45M2vP7Szfznyzapuuev3YRlWPRHPdKOxMhlAov1BbVvc22uOGhZ3PVuteE05CyTysIozlVf8sp6bIcSzhrf2verhaG+a1DT7bu3aknpL+iWlHDH11bviY4zHPmhOk96vfL3k8NgJ9unFRuhm2DYQp+LBSAU7G7Q6Y67EDMN1DFffpuqs/Sk/2ALSI5UH2K9htSISlfpYJEwwSvhXPleMrkScwWnQuHLZb8K999CsWjynfJF3Dl3MQfdNZXLbjRneEv8Ca6PPkninA9C33nYpkF/5qek4wmEISCWgLyl/GhQgvCK9ymNue9elR/XD/e2q5XPW/F3NQIbomEqEz69rvqaGQVvzo9fNIs1GGpTC9537cIgaq0DYajYw1Xvq40z6A1j9pj6N9a/MJO422DYQp+LBSAU7G6woB23iwKgC66D9GaYOVSNLBumyrMmfV9029Xw/b9QD78wAYnneWTXvJDsK36X3JqrIFuqOe3k8C7uWvPbfGNfjhmvmuu91D7K7r6fcGXsMKaQkMtj/9vvMBC3SA+th1gEnLwS5pmjai34xSupYdh4+fyHe/IAHH4IErqow9e0hgWe9ptlZSOquXe6eqxh0ZSfxRZ+7UDws9qFKWfVGqVUropXVq7Ag5+E3GQ1cGVFfZ9fKIsi1t+5SRzU0IUZ5RbEBtR7nQTDujXfF4hQsLtBpzvu6aQ1YmmYs+ZVfyGEOu+TX4TYELI4S9aBrEiR2/UbyJf+13mnenY0w52PHeV7e0d9uqEYliH4TztH2H38z9iRLFTcimhpmoH8cyRFARIXqTUXZgCpzOWKWy2VCWvY6j5AbaS3MKMOLM0qASpLtTHUpweHtsOr/7D2fghfcI2I77s71ZQZ/rVBCVLwsxKYeV6fhIplYNiQ3qC+y9Rz0OcLrrZ2JOAUlfB3YhLX/11nj6vPWtFqZuQMBcO6RSjY3aKTHfd00hrFWRVlr6nX3qiE68FPkifJXHwTuZitaqtLBTj2CKAE25OShw9McMejx3jiyHTltH1GgTdFH+eG4ROsufBtULAgUyQWtRgwCiQK+0EUVWBQF2oUM1CcUVobodbSt0lpOOmnrpC1kV63pDYlt6SOnfeQCyUE9UKtv3t8jQpmybqouOE/qtE0TD6n/PfhFygLZu5E4EBJjSmuv4sRgcxJiPer6/dvhtkTgKfcgU5M4vq/qxVTG0NmdH7Kc4kRCvaZwOmkNbQPveb8ykvlYoG55BYy42M40WGQAV/VjsLsCfIll399+iRfffwYR6fylbfPScNb5b/x6vhzxCImFIpw/y0kLno9A3u/SMwzwIyrenFhVKP9hRnIjyuLwU6q992S+g52vLaqa+6U0sqZ0Wpay4qpDcC0wXEBoSLlqREVEW+0yUXSSgsalmr9qomk+353flJp4lJWmf0Hv09Td8cpqEq01IiyMmYOKw1rx9Ua0uvaN9gEUf93TQ4rP90pqI3uDAbDukUo2AtFq2hop0G2Rufw/XivKMhYfWSKkoKXhEtvhMc/D5kJiFT95NGCzded1/LN2x8mU6yauy/ZNsjuKzaz67H/gchWP5OMmgy4OaIn/x1ed8v89FSsX50gO6YeVium/MhyVr0uXZWvLmWUmfzS/wo/+jsVB8BQPq7nqAKTwoxfQWfDwJaqVqto+zqUMr4At2HGEkIVrzSNuAfglpXZnRyG4QshPrjwwFX93zXWr/LfTlZlRs5gMKxbhJNAFoJ2bZKdtFE2OEa6JXLX/RnZMmR/+Hnk5PPVtM2a82DTLtj7LyAiPCM3c8fsRdxXPL/aLmkZXHfhOt5y+SbOXZtU1/n8GyHaR8JwGDTzRIUaX0thGt7/VPPvNPo0IJRAZkaVpms0Hi/aD+VcwBf2TW235Gt7f2PQlgDM77rad68qGBl9uvk9b0QsbthNIugBWDEVtDNM+IUv1N7/dmmq+mO2Xa1iHN221Z45NE34hxp7IWjnQ3cSZPPPIe0EOWmTNVJkHRf5g8/Dm2+Dy4D7b1EmryzDyR/jHnuSB+xXcEfxpfykvL5yqqFkhBtevJE3XrKBgUQkuFLi/SMM5g8TswKvd5KiiySVQEb7lMYLlmcL4f9fKh+8BlJp3v6tgAev+3M/k5BT2jNzUgmDFVGCA4FKuhZopIB0WW8zwRZ+ZZ4VBSveeFONDcL4c/CVX1bfdfgFVVO6PgD65BfhUp/M4Qynq04XoWAvBJ340G2CbKWpo8xF1pFxYrh647WkH9ABHvwUZCfALZHxInzbexlfc6/mVHGoco7zh1PsvmIT175ghEgdx3ciYjGQsIn9zK/7guW0T9EdfwxOPqWCZlZM/Wsn/V5nnUJrpiQEFeZ/6SkB3ryrtoRz6jklKP1blal+94f880fmB8s6QiBQVgMDTKsa6NMWgkZwY67EEqTy23UGQ6+rfvM++ED3RSxLgFCwF4IFlgZ6niRTcpgrOBSTF/n+ckBQykXVBnrwQZg6wDFvDV91XsE93kvIo3xkgcdV9rO8de1hLn3Hx+aNgq0ItOYm7zRFt+dWuP9PUblzX5CdPDgDSns7+WphxzxZEnWvy9rNQ5fCDp5X1w0FTD4La3f6lXS1+ff50CMI6rV03e8CVXEW7WucygpuzNkxKvUCXrkqwHpdQSyTVFYnCAW7Hp34Xl0Uqmi630zBIeszkACVajB1jqgSallCvvidPPXg3dxR/n940N2J9P3nmnbJSA7KgzWlrfGIIhGM1Q0bADpL0T38aVRKSz8SlurDKE7Dhw8rwb/v1saa1bDUWjzHJ2owVKHNg59UDTED56jW0vSG2s/ZcSWP5bzSkKXM/HMDVSvBU5uO6Re7VDYCGViHoczutec138iCG7NbqtYLmJH561qEuu4zgVCwg+i0sKQDLZgvuWSKDrmS05itc9tVwIdVpHv2BOX0Jvas/SXu+H6M/aOvrhw2wqRqlzQeJiUKfiqICsFD1DYZTNhVAr6FNvIXM0poNDxXCWphRpWLbrsaEmtU0M0NBMpsP5frBQReSpg7ro5LDav7WJxVqa9gEK2ch7U7lEAXZ32fuMHGMbgNijnInVJataZoRUNQGe4laG0uBzdmM6LWL4RKi9WvaxHqus8Ewqh4EAvlHPNRdHzNXHRxvAYR5AaYyZX5l6eOc9cTx5kIlIK+0DrBbuPfuVo+jin0w+6bjIZJ9A0fZ/DiV9UwanZNhhjcBGaPKS1lRXxt6Oejham6xmYOK8Guj267RZV7rhc24VsOph0w4SUMnlu7tkt/EZ75Bpz6sX98xNf8XvX79m/xySdcWqe3hN/4MQi/vb/1ja8QT+yF0pz6bom1tfcMFqWu+zQQRsU7wgIKS1xPkik4zBXLlBrNr2mCQxNZ7nzsGP/2k1OVz+l2yd0bJ3nh018E14GcUBRD/oMeMSRDV/0yictfM/+kzaL13/1oYyLGoHVSzKjiD8dPU2kBSqxV5/EcyE/XCrYdV+ccPLeqoQ1DCYfWvI4flZZ+aalpV3O+wfRRJKU0J6iml/ykn0ePqtZYgNQ6yJyipXCnRlS5ahDNrJh5qa8GAry8BLljhIIdRIdBMdeTZEsO2aJDvtR5NFdKySOHprjz0aP88OBU5fVk1OT1L9rAmy/bxDo9JmaNocx0zwG3hG0YDI5sInX1u5s/bA0pnMow9pwKXAXdi/qor/6OxRnfnDWVUGtON58TrQblvNIZtl+55rmN+RF0DtqMQmII3v2gev1zb6iuQVdxSb82XTeEWPFqmW2sX5WbNqKl0mvU3VwanbhXi9SYsZgIBTuIFkExx/XIllyyRYdCuY0wH3zQ952PQ99Gipe8k+9kt3PnY0c5OFGl99w4EOMtl23mtRevJx6pC3ptuwq2XYVtGgwmI6SiHfypGm1Mcycba/FGUd/+LVBIKyGvP0+kTwmULsnU92bofF/YtJlcZx0K4beW+s0jQesnuBEFK97KeaV13+DXBWgXCar12Z7fnWUGUnGpEbVhffsDiuziqvctKh3RckIo2EHUBcXc/nPI7PpvZEdeSWEy1/bjgBLq+28BEWHSWsddJ8/nG8/PMiP3VQ65dHM/u6/YzJXb11TmWNfDNAQDiQh9MWteSqspmm1MfXWR3HZR30bnsSOw6wPzizNAFXdUUk51ZrLuidY14sF8cqMSTd2j3Yy4Ijms/G1/CgjSVcJ+4Q1w9P8qdyComUu5xtH4FZK2WihCwa6D3PFqcuf8Jz+i7aen2mnoIB7/PM86G7mzeAXfy51HGaWJLVxe9cKNvPXyTexYl276cdMQ9Mdt+mJ2ZYRMjY8YSato7dxJJUtD51c7pRpF6007EMX20S7q2zLqf/P8RUdSYBTUOcBvv/TzwoPbm0eVG20ghWkVkPujker3e+ENShNPPqv2jb5N6pqlTHVtzTSzO7Wi01YLRc8EWwjxCuAzwA5gH/CbUsqHmhy7DXgeyAZe/ncp5Rt7tZ5uUSi7zBVapKfawJOSh56b4M4jr+SJcvWh6SPLm+wfckP8Kda89qtNP28eepD+J2+jb3Y/xmBAkII+IiaM7fX7sy0VMR7/qWphvOEztb5icCMo+GWfQQG7cLeKRo/vrwrQz/3JwvzO1DrVaaXZR5PDfr7ZVj51s6jyBdfB8V9UOfRiRgXKECpwhqH+P7YX9jyjfP21O6vrr2/7bEYgaUTU8Ss0bbVQ9ESwhRBDwDeB3wH+AXgn8E0hxHlSyukWH93c5v0zCsf1yBSdWhqhLjG/XVIJ9TniFG+1vs911pNEZQEcU5np266q+bxpCAaO/4C+7/82woxAfKA2wKOZO0vjquSxYur6xRoelV7teRtBbNDXolKlsRpFo4d9YSlnWRD23auohr0y4LOPzhyB+JDabKC6yWhihmA0+skvqq6yckE1k2gYttoo9N+lMKM2jGY+crPApyYnbBb1bpX3XyRy/zOBXmnsNwPHpJR/6//+t0KI9/uv/32PrtETSCnJllS+OVdq0x7YAqOzBb72+DG+9R8na9sl1wl2T/8du9iLMH2aHiFUF9Tjn68IthBquHp/3Mb4l0/5kysaBHg0c2cwBQXV3LCu+NI+YyOTFFRu9zd/oP4fjEbXX6/bB/fBTyrfOJr0+7FL/gQRv9ijVURab1rZU/MLUyp5cf87B8tN633kffcqfjVdi6551oOuRbM8frP1NVr7Xe+pzPha7oLeK8G+hNo51/i/X9Lmcz8WQljAD4HfkVLubXbg6Y7RPV1TW+OZE7Pc8ehR7ts3hj6Nbpd86xWb2LYmCZ/9GDgR9XAavjkaScHsCYQQpKIWgwkby9Tc5HVpquKsepAmn/PphZs0Ozh+S6dhVX3GTnLx04eUDxwch5McXth4Gn09IWr7rQvTKn8+5wutGVHCXs+4Wppl/qQPH8GiF7es1qvN/CDzqBbA/q0+HfRhZbYHXYtGaBUxh9r36mZ8nckpHr1AW8EWQthAgwLkCoqoWdfTda9Po+ZcN8I48DLgcSAJ/B5wrxDiIinlbKMPSClvB24HVXnWbt3QG1MbaDpdsmm75Jrz5hEiUCqQGljLwEB8XifWvEkjM0eVcGifsFEfNPiEfaiqKe0z6nN55Sq9kjCrRRv77lVdY+Us1XSRbz4P72x8nUaTR/SDHe1rHJyKptXxKMLFCuOJFVNFJp97g/rXKdER4aMwa818PfaoXjh1N1diqL3AtdwE5WnP+FpKdKKxvwa8vsX75wIZYKju9X5grNEHpJQZlJYGmBZCfAj4JeAq4J4O1tQUungkU+gg39wGlemSjx/j1Gyx8nqrdklgXoNHyp1lwJghcs0HodHxwejw3Kmq+Z4cVsLZtDkCFXDSgTN9rrverQJQwvQzUGXlB++5Vfm0weYJTeQA83ueNQHC+F5lNehmCT15xIiozzQKTkm7yn0WhFNQP+PPQWojTB9s/t00zEi1+8uw1XV0g0nmlDpPEN3SUDWLmPdgxtdSoa1gSynf0O4YIcRTwPvrXn4x8BedLEJKKYUQC7aPvUolmEu+HOigWiCOTef56mPHuOfHJ8n7m4MArjpvDW+9YjOXbu5vnVv2GzxST36Wgbn9RAY3wVW/25qSVgfKJqr5bkV4kKIxmYBP1OcUa4NSF1ynTN7inJ/jjVRzyA9/GpLrfCZQW70vPSWc/VtrN5DgKCFhAUVf8P1otZ48UphWZArB4NS2q5t3gmkUJlXcoR10Y4rmgCvMqLp1M6o0amZMjQoSVItcOk1ntevSu/tDMJdR7oJOGWqq4W6uswTolY/9NeDjQoh3Af8H+BVgg//6PAghXgbMotJicZQpLoGG6bFG0EGwbDDffBqQUvLU0RnuePQoDz43URGjmG3w2os38JbLNrFpMN72PEIIxSt22auJvKRBPXczXHCdIjo4+EBgUa4q8TSi4PkWgzB8GfdAGuqBrGcAyYwqPzC4+UipUkoD26pTREzL1+iu0kSNyAik67N9+p1Tms+8lFGkDKC0uk4/7btXBZnaESdozd30Rvr18brCbGxv9XPCUD6vECoNNn1YlaNmRlUO3LDgRbur52pZK96iS+/4L1Y53PU9y55Sm5qmHG42CTWIJYiu90SwpZSTQog3ovLYf4US2DdKKacAhBBbgZ8AL5RSHga2A38MrAdywP8FXiOlnOnkeo4rOTSRw+tBZ1rZ9fj3n45xx6NHeXa0qrFG0lHefNkmXv+iDaRind2mVMxiMBHBNhuY253g4U9XH+gKpDKlX/Q2NTlSc3xJX4uX8ypNJIwqA0izFsloSv2r67I9X1sbDcj7K0Exw2/O8O+1ZkjRazOs2lz6g5/05291iCDNUhDBjUEYtZuAFCoO0b9ZCVd8jR9Z93zyxT7lcmy8XB3fKjLfKl9/8AFlyejYx/Thanms/v7tsIijc4PoWYGKlPL7NImC+8KcCvz+JeBLC72WK+VpC3XTdskNaXZfsZmrdww3LfesRypqMZCINPa3u0GlJ9qrkhbo2uu33q7GwT74STj0A5RQaXPaFza3qB7C2JBqoLCTtSbmle+p5q/TmxR9kSzBmgvgZz86Py88eaB2SmY9zIi/KXjVXPr0IV+r+xq+3cPfyd+xspH490KTQWg+7+KsahYZDkzu1BNAYOGpvWBwLTOqNl3LUusZfkH1GguNvK8EwV4pODSR5auPHeNfG7VLXrGZF27s6/hcyajS0Kct0BrRlHpYavx3CZb/UGjtcstWdZxpQTmQ79XECKlhZcKm1803MTdeXu1DFgIwVPXZdz9avQao47/yy1UzvJ4NVPdNa9/TMP1rnaP8Xg9laZwuDKtKYWz4kXHp+p1kRcXM4hbV5MwgmkW3a96rQ73JHElXI/46u1DPtNIueLaIo3ODOCsEu6t2yQ4wj1esV7jyPXDfLdVBexqGVR2jC+ohFwKcBoIzdRAQalRQI19O+/KHH676rxjKh73rPXDDp6sbSCRVHRQQSdZG6d0S1U4uqQQt6l/zrvf4POONgn4+ov0NGE7r4UehNZuLbigJMriAEjjdy63RLLpd/55GI5NZjziCKg+czlY0O089FnF0bhCrWrCLZZfvPDPatF3y+ovX1TKQtEHMNhlK1vGK9TIwcu3N8Pg/qiiv9NQDm1yrhCBouo1cqAJm2VMNTuKbv+W8mty5dmdtXfW+e/2AULCyywVpKZO2/jrzHspioLQ1KLRCmdUXXKc2h+9+tMqKUjnEqJrV0lU15tn6cT4iEGfwv4feINyA8BpWtUc7O6Zcj2hKfa85n+LYtFUDyZNfbF8r3qxiz4qoqr3CNBRd5ebUkyS2egYWcXRuEKtSsCezJe564hjfePIEM/mqVuukXbIRIpbBUDIyfxM4E4ERIWHdi+ZHtIOm27ar4eiPfIHQbkB9EYtQaarJA/PLOCt83FQpg7VfP324+qCO/VQJe2xImfe6fTPap4RMa0nDoqL1P/cG9dD+5g/gr1/hC7eofg99UbcE6fOVJTB9WAm9YatAoPQj78Koo0MyqLCU6lw/KEIIt6yEcMwvK+3bql7rlAu8mclcmK6W4jZiWoH2wblFGp0bxKoS7GdHM9zxqJou6fj1npYheNXOkbbtko3QluSgm8BIp5q9vgotM6rM4UiySrD/5BdVtZkeKqdR0YhCURTpcbL1ZZxmFLwcKrruf1Z/LjuuNL3hC7D0IHMccmMqhfZzf6LONa9SzlOCEHywC3qofb05Ln2tLKs86poDvDCj3AnpqpliushF+Kk9UA0j0oXJ5/3BBn3VwFkjiuNOuMA7MZkbRdA7qbtfAoaWFS/Y1emSR3niSNVn64/bvPHSDdxw6UbWpKJdndMyDAaSNuloG5KDTgMj3Wh2bbpls34ZIz6lblK9Hkn55xmAYlbRA2vUR491VH3uuKo7/9wblLB6brXrKwjpv6792Owpvxbdj37rDrBWlXJBnrW5o8z3sw2/TXS70oZ40LelWlwS6/eF3qnWmOs2Vbek7l+wkEbPETv3ajjyUPeBqgqp4TPqPPWkhu1M5iUKjrXDihXsptMl1yR46+WbuO7CdUS7DG6ZhmAgHqEv3iFrSaeBkW40uzbdvvou0FMrk8PVGujx/aqmuzjbIvgkVQDO84s4HD+/O3fKFyahIuo15Z6iqvGFoGIJ6KCVU1Kf/84f+Jxlvnk5+Zx6kPUaQf0+tld9Z+FRmfEtPSXUwxfW8p7Vj/cxbcCuauGJZ5XZb0XV5hKkKRZ+Jdwzd6lKuG5IFYIbbt8mFc3PTahNTY/6aadplyg41g4rUrDH54q8rdl0yXMGO6cS8iGEYi0ZiAdYSzpBp4GRbnf1C65TQjKwrdbXtuNKMMp5X5sHfNYKa4mflqpPU+lJF7GBKlOoYSth0YPgMZTvLGVtfllbAk5eDc7bc6sK9AX5yOofbIHfLHLM959901s6KpjX6h5G0+o7aX61WL+qCY/0KZdAC7XOo1cq4kR3pAr1G256RAXgOqSbbrr+ZUDk0KME7OJiMlciU3SIWAavf9EG/vev7eLWt17CS7YNdSXUQgj64jZbBuMMJSPdCTWoB/u1H1cPQmG6+bzlgXOqtEEa7Xb1Zp8ZOl89OE6Bmj+fbt80TFh3sTJ1deTbsH1/9pjSRqWMqr02fK2dGQVE9Xg3UGnWCHv+BD5zldJ4V73PF6acuoaOFg+drzaQvk1VbnFhqCh9PYtK/T284TMqsq5fG9oO1/yOmu5h+N9ZCzVQGfdbnOvs76Exfajqt2t0a0Z3+gwsMlbkwIDkpgvkzX/zNd7QYLpkpzjt8s9u0C2Rf6vPXPqL8JO7YPQnVHOsvlAHx8U++Ek4+ogSKC0Mni9cQ9tVkEzPtBbCzw17/u9mg+KSOl/ZiquHWK9H85Gt3aGq2KCz79xtunDfvapwRn9XXd0WX6sEv5uBeZ+5SmUNtC+ve7270dhLi6aaaEVq7HPXJvnlK89ZkFCnohabBxOMpGOLI9SwsF290Wcu/UW/7bKsxt7oNFOlOsuEV36gGv3Wc6rL+eoYHs8vsoj1K4vBilCzQUQSqhQvkqq2cwI1Qi38vmS3rHLibllp4oFzqoGtTr5zsIMsGFTcd6/6+dwb4C9fpP7VGYELrlPf0fDdDtNWQm1HujN/g5ROkmqvd2F6yc3oXmBFauwXvfhyede993f1mUTEYjBpE7V6XC22UCyksKXeny3OVlNem19ae46/foXfESWqgStQ19JllkG35dTT6t91F1Vfmzmh5mXNg6HWIP2ikXUXV9/qYiRS05FKpq2CZK20favpHZ1AX1s6VUonYSqSDJ231li+3GdNNfaKDJ51g6htMpSIzCfkX0p0W9iiH6xDDypfMr1OaeNoH6SlGkxQTxaoN2zhWyXaZM2OKVO8PnpsNHgUSrMoo66+ykxW+b2tulLcbnzUZkHF8b2q9rub3LDW8J0KXytKpyCWqDvrdLEiTfFOYJsGI30xNg3El49Q64fvK79U1RaaZkcXkTT6jDZX7bjSLDNH/XTXrE+eb8w3ZUtzKj8sAkExYVeDZYWZ2oBXtE9Fo0s5yM+oqjMnXyX7rwiwUD/lorICynmVjtIUx92kepoFCCXdBbVamfTdXrtVqrLd32oZYdUJtmkI1qSibB6MdzYWZ7EQfPg8T/m7WkCh+YMbfLCSw1TmYM0cg8mD1cmUpbnah27gHCWglT5mvwrNiqmUV3KtX4K5F2YOqd9f8utKkGf9dVgxde78FPRtVMP3rJjaSAqTtf3as8dgbrRxqqeZv9wsor52R3dZhFbC1+21G6UqTzdyvgRYRk/+6WHBuegzDW1GH/mhEoL0epU71h1Kuqe42YPbaL7V3AlfYIXSwlAlHoik1UO35cpaNhYA/MYSO67M92hSbQDaj33yiypopssyCzPVQXnTx1R03Ssp4Y4PqI0mWPbqZOFNn2ocIGtqyjaoo4bucsPBe1RZT1FFvMf2qfvW6bUbpSqXYQFKO6wKwU7FLIYSkSqd71IjWKZYmPHTSX6b48wRxfjhTqnfnWJzbQGN51vNHK0SGUhHdWYJQz3QEV/AfvyVxmsrZaqCYgzO92N1ZZu+FqhNwC2CEVeMIjNH1LU0PVC0r+qf1gtGu6q7pnXUAaGLpkHatcP2Gg0LkI5faKOr6MoqpRdNVjV5/bX1GhsNNIBlW4DSDstEEhaGRMRi02CckXRseQm1NrlLGeXfBnuXPUeVgvZtqlaHBVNB9abjtqtrTca5UXVO4ae5kOp3z1PH5EaV1p2X7dD1437Ky4w2NjF1ZZtGrN+vVfcZSrQr4JVh6lDVldBarH79Yz9dmCl7wXUqsv66P/f7v8vN/WdtVs+eCMT4/Oo7jGrNff21O/HNg2m7uROqfr6YqZr5yxTLRBq6gxCwoT/O+v7Y4qWvmvlq9QhqqErzf51r4BSrhRBv+0f1AAfH8wQfNN12qPPBTlYVUwhT1XubEXV+6aj8c2qd74ubgWuLamrLMNWDOvyC1pVt9b5nen21kyu4SUweVMUyU88pgbnr3bXrL86qMbv11+nUlO0keKWFD7+HW1e8WbGAtdTg2p0Gxi64Tm0ekYRiee3b1FmAbgmxIgU7YhqLG+nuJuraKNiiYdpUKrgaFWw0e9B02+H7n1IatG8TlUYPYfq0QYYy8RNr1bn0vzpVpfPYW15R7bWeOawsgKAAv/oP5xeWDF+orqFLTy1bdVupMSXgOspEz4wqPnOvXF2/5l9rF6Rqhk6DVxdcp3L5g+eqctlYv9rgpFttEa2/9vQhtdaJZ9XmNPGs+r1dEHMFRMdXhY99xtFNd1bQJzYjgbprX3MaJqx9QeMCjk6aRfT5+zcHZmWZipAwPli9dt8GFdDSHWDCVOb0kQchMaJmRpu2Ejqv7Ke0IsqPbTSc7u4P+dS/ltpQDEP9X+fHY/0q0AZqvXryppVUGYDpQzSc6tkO3QSv6v1hw1ZTQ1Ij1YGEwe8V7VNZAWFSM2mk0USUZdqe2QwrUmMvOrpJeQTTKOmNoAn2DZ8RJD5U290URCe5VX1+Yak2xYFtSqv+7Efnp3D0lI+h7bD+Rb4FLVThie6f7t+qHvxyVrVmNrJItKkbSSqTX5u6etiAJvfT3WSai9wpqMo1r+xbDIYq4+wU++6F3KQy88d+Ws29N9P4zRpKfvMHytrRLo9GTRxCNnndx0IaeZYQPRFsIcQGIcQ3hBDHhRBSCPHiDj7zCiHEk0KInBDiCSHEy3uxljOCbv6owYcLT/GGrbtICcLmXbXjeOrRSW61/uG1Iopm+NsfUBaE9sd1SkxKpdmLs9WyyfrJlZPPdubHvuXvlKWQ3qi0nTDVd9STNSvwrRPPrf5uWH5OfKLKiNoKe25VzR7jP1Wa13OU62BFWtfZ66BbI0Guhy7i0d1npq1+bzRSqd3fptMYzCKhVxrbQ83c+s+dHByYp/1XwCDwadQ87YEerae36LSYQSP4cL37QaUxXvfn6r1vf6D5H77TZpFWEeMnv6gi6ZGECh4Zlm9i6hSZW9WwhRkY31ctMAkS/TfzY4PrG9qu/HptknuOup5mPalpHMHvMjNUSq0VKoSLrr8hSLXhJNYpd6NXpZwD5yhhXnM+jLxQ/Wva7Tfs+r/NQirfzjB63gTiz+C6TEr5RItj3gX8dynlxYHXngY+LqX8+3bX2LVrl3zkkUd6sdzO0YzIrpPmgFYtmAcfWHhzQbMmiuwpFb31fJ+xQgjoR8eTfpHM7BH1Gd3yKUR1ukanzRzB+1KYVj512tfgJ56kknayfHoqz2dR+chokxP63+vwQz7NMtXPmbaiLnr/U53fo3Zr77adttl6G/0dznz7Z9NKrKXysS+hy3naQogbhRCPCCEeGRsbO4NLa4J6Ew8636UbRVTLJaWVTmeXb+b7FzN+TlrM9xcjaUgMKoJCPWc6vaF67Nyp7iLXwfvylr9T7ZPasqlMpvSLaTyfImno/Pbfy4xSM+pICOVa9NKn7RVJwjIsO20bFe9kPrbsXu2n6G6e9oLmY59RdBMpbxRRLc0q0/V0Rr/UR4wLM4pTW3rKxJaeT8JgVDVeap2aHV1aN791MzumtFZ63cJaE+vLNNdeANNHlE+vzfRYf/PgYfB7ea5ajyaH0GZ5ryu+esEgugzLTjtJd32N9vOxD3Z53QxdzNNelugm/dHoD+8UlFbq5PPNoGdhzxzxi2H0kIERFaTynGotuW611NdoVKq6EPaQRr3Kwc8vpG9ap62Sw5Cfrk7Q1CQSyw3LsOy0rSkupXyDlFK0+Dm4gOs+hZqfHcSLgf9YwLmWBo0i5blxZdJ10klkWKqRIogF7fJa4/pGjBB+c8dWlAkcSE/F+qvX6DYg2AidlmRqU/2q9ykhbxc51iby0HbVdbb15Yru6dqbu7w3i4RlyHvWs+CZEEI37OaBl6GEtyRlzUxYfewQ8BzwIarztP8UOF+P3m2FJQme1aM+8JIbV0yaiZHq1IxgIKZec227ujr5slHgphPWjmDQZvQn1cYQHenVQ+IHz2tzjcCaugnmdRM06lWgKkQQTYNnvRTsRif6T1LKPQ3mYyOEeCVqnvYO1Dzt35RSPtjJtZaFYEPriDC0j4w2M1M7FYK/fFHVT554VqW1dEpr5IXq+nr2VDtTeCGCF7y+hu7yqo9cL13keDWjqWD3rKRUStn0IvXzsf3Xvk+LKPiKQDDwoh/yINr5zM0CN50G5oJ+cmWYvaPyyNq0/tkOyzcXMse5m6DRCivJXOkIS0p7hV6WHHaaPgn6ydE+xdZpmKr0s1s/byEpm2789BVWkrnSETaB9AqnExmt96c1o0o7TTgvvXQeXPWphfmsC0nZdDNJcjEix8uXTXTRsSLph5eNj12PhaR2Gvm2mVNqnKye3RXpU4UfZzLQtBjBrdOlDG537rMvOHfmg2eLiWUr2AtBfVCpMFNlHjXs2hzumU73nEnBO9M4O4NzZz54tqg49XR1wPpKefCaoZ6Ib/qw30BRV7d98AGggWDvuRUe/rQqI42m4Mr3LHwDWII5zj1DGJyrwcoMnhnWsuig6Ql0UKkyQN6tvqfpiZs9oHtuhfv/1C94sdW/9/+pev1sQxicq8HKFGxY9tQ0HWPb1aqIZPL5KiWxhu6lbvaAPvxp0LOuDf9fhP/6WYZeVNKtIqxcwYalNbV60Vi/715VfZZYQ8MxOl65dfS4mAmQFvoQpnr9bMMyLOtcSqxMH1tjqUytXs1z0kUhosX+Kmj+gEZTSjMF/4zSVa+fjVjJMYIeY+Vq7KU0tXrFWKmLQoK81zUQiuWz2cN65XsAqVhCPf9fpP96iLMZK1Nje+7Ce4Z7gXYR2E4LJXRRiFvya7yDxAKGssxHGjBmaujod6+i4iFWDcI89kLQKmeqK6w6KZQItj26ZdViCSrCDao89Be+EJqXIZph2VEjrWy0isB2Y6brgM+a80D4NL5mFJBKqJcrsUCIZY9QsBeCVhHYbpspLrhOsZi+/Z9gy8vUKJ3lTiwQYtljZfrYywHNIrAL5b9aaEQ3bHwI0QChxm6GheapF7NQYhnyWYdYHggFuxFOR2AWs1BihQ2KC7F4CE3xRlgIm0gQi1UoETY+hGiCUGM3wjIkgG+IsPEhRBOEgt0IK0VgwsaHEE0QCnYjrBSBCRsfQjRBTyrPhBAbgNuAXcAG2g/l2wY8D2QDL/+7lPKNnVxvUSrPVjKbSIizBWecQUWP0f1j4P928bnNUsrpHq2htwg7hUKsYPREsKWUp1Dk/wjRdBMJESLEImGpfewfCyFOCiG+IYRo0cYUIkSIbtBWsIUQthAi1uJnISp6HDXf61xgJ7AfuFcI0ddiHUs7H3ulohdMLyFWHDrR2F9DDdpr9nNOtxeVUmaklD+UUpZ9H/tDgA1c1eIzt0spd0kpdw0PD3d7ybMTYcnpWYulGqNbf416wq8QvUBYcnrWomc+tjbN/V8j/u8Nzy+EeJkQ4kIhhCmESAkhbkUJ9kO9Wk8IVk4FXYieo5fBM22ag0p55YFrAIQQW4UQGX+cLsB24JvALCqffRHwGinlTA/XE2KlVNCF6DmWZIyulPJLwJd6de0QTbAYg/BCLEssdborxJlEWHJ61iJs21ztCCvozkqEGjtEiFWIULBDhFiFCAU7RIhViFCwQ4RYhQiDZ0GEVL4hVglCja0R1lWHWEUIBVsjrKsOsYoQCrZGWFcdYhUhFGyNsK46xCpCKNgaK4WZNESIDhAKtkZYVx1iFSFMdwUR1lWHWCUINXaIEKsQoWCHCLEKEQp2iBCrEKFghwixChEKdogQqxChYIcIsQpxdqS7wq6tEGcZVr/GDru2QpyF6IlgCyFeL4S4XwgxJYQYFULcIYTY3OYzrxBCPCmEyAkhnhBCvLwXa5mHsGsrxFmIXmnsfuBWYAtq0N4s8JVmBwshhlADA/4KGAQ+DXxTCDHQo/VUEXZthTgL0RPBllJ+UUr5LX/YXhb4S+BlQohmPvybgWNSyr+VUhallH8LnPRf7y3Crq0QZyHOlI/9M8AzUkqnyfuXAE/UvfaE/3pvEXZthTgL0TYqLoSwAbPFIUV/WqY+/jLgj4Cfb/GZFDBd99o0kG6xjhuBGwG2bu1C215wHfBxPyp+WGnqMCoeYpWjk3TX14DXt3j/XOAggBDiRcA9wG9JKVuFnTPAUN1r/UDTifZSytuB2wF27drV3cjdsGsrxFmGns3HFkJcDHwH+LCU8gttTvsU8OK6114M/EfX3yBEiBDz0Kt010XAd4Hfk1L+fQcf+RqwWQjxLiFERAjxLmCD/3qIECFOE70Knn0IGAb+wp+DnQnOw66fjy2lnATeCLwPmAHeC7xRSjnVo/WECHFWQwTiXisGu3btko888shSLyNEiKVG05n0q7+kNESIsxChYIcIsQqxIk1xIcQYcGiRLrcWGF+ka60mhPete3R7z8allNc3emNFCvZiQgjxiJRy11KvY6UhvG/do5f3LDTFQ4RYhQgFO0SIVYhQsNvj9qVewApFeN+6R8/uWehjhwixChFq7BAhViFCwQ4RYhUiFGxACBEVQvytEOJ5IcScEGKvEOK/BN63hRB/JYSY9H/+Vwt2mLMOQoi4EOJZIcR04LXwnrWAEOJNPtdfVghxXAjxG/7rPblvoWArWMAJ4NVAH/BrwJ8LIV7jv/8R4JXARf7P1cDvLv4yly0+Bhytey28Z00ghLge+AzwftTzdhGwx3+7N/dNShn+NPgBvgp8zP//EWB34L2fBw4t9RqXww9wOfA08HPAdOD18J41v2c/Am5s8l5P7luosRtACBEDXgo8JYQYBDZTy9H2BLBVCNG/+KtbPvBNxL8F3gMUA6+H96wJhBBJ4Aqgz3f5Tgoh/kkIsb6X9y0U7DoIIQTwWWA/Smun/LemA4fp/zflaDtL8EHgKSnlnrrXw3vWHIOodstfQVk55wNl4P/Qw/sWBjMC8IX6r4EXAK+WUnpCiIz/dj/VAn29e84t8hKXDYQQ56E09WUN3g7vWXPoe/MpKeUhACHEH6AUiee/d9r3LdTYPnyh/jTKBH+NlHIGQCpWl6PUcrS9GDiijzlLcTWKNedpIcRJlHXT5/9/B+E9awgp5TRwGGhUGTZDr+7bUgcSlssPSqifBNY0eO9jwGPAev/nMeD3l3rNS3y/4oH7sR54i/9grgfs8J61vHf/E+U7b/Lv4+eBe/33enLflvxLLocf4Bx/By2gTCX98zf++7Yv+FP+z18B1lKvezn9ANdSGxUP71nze2UCf44yt8eBfwbW9/K+hbXiIUKsQoQ+dogQqxChYIcIsQoRCnaIEKsQoWCHCLEKEQp2iBCrEKFghwixChEKdogQqxChYIdACPECIcS/CCHGhRCzftfRzXXHvFMIIYUQv9ng81IIkfNJKk4JIb4shBgJvH9tkIQhxJlHKNghAL6FKqfdiuo+eitwoO6YdwGT/r+NcJWUMg28EFVD/vEzs9QQnSAU7LMcQoi1wHnAbVLKnJTSlVI+LaX858Ax5wPXAP8FuFwIcWmz80kpJ1ANIVec4aWHaIFQsENMAHuBvxdC/IIQ4pwGx7wLeFxKeRfwAM21NkKIdSjWj31nYrEhOkMo2Gc5pGoW+E8oU/wPgANCiJ8IIa4DEEKYwK+iOpAA/gH4JSFEtO5UDwgh5oCTQAz4rcVYf4jGCAU7BFLKk1LKD0opL0L5x3cDXxNCDAGvQ02B/KJ/+D+jWg3fXHeaq30f++XAFmDjoiw+REOEgh2iBlLKSeCjQBI4F2V2G8B/+CQK+1CthQ3NcSnlw6jA2W0+eUWIJUAo2Gc5hBCDQog/FkLsFEKYQogE8AFUBHwceD3wThSTh/55I/CzQohtTU77NyhSvree0cWHaIpQsEOUUEwe30YxoBwGXgFcD7zN//3Lvrmuf+4BHkVFyedBSpkHPgF8VAgRPmNLgJBoIUSIVYhwNw0RYhUiFOwQIVYhQsEOEWIVIhTsECFWIULBDhFiFSIU7BAhViFCwQ4RYhUiFOwQIVYhQsEOEWIV4v8HvEscl0L7ajoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_name = {'bertscore_P': '$BERTScore_{Precision}$', 'bleu': 'BLEU', 'sari': 'SARI'}\n",
    "plt.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams.update({'font.size': 12.5})\n",
    "# fig, axes = plt.subplots(ncols=3, figsize=(15, 3), sharey=True)\n",
    "for metric in ['bertscore_P', 'bleu', 'sari']:\n",
    "    auto_scores = df_metrics_simplicity[df_metrics_simplicity['test_set']=='asset'][['sent_id','sys_name', metric]]\n",
    "    \n",
    "    df_human_scores = df_benchmark_simplicity[['sent_id','sys_name', 'simplicity_zscore']]\n",
    "    df_human_scores = df_human_scores.sort_values(by=['simplicity_zscore'])\n",
    "    df_human_scores['quality'] = 'low'\n",
    "    df_human_scores['quality'].iloc[int(len(df_human_scores) / 2):] = 'high'\n",
    "\n",
    "    df_aux = pd.merge(auto_scores, df_human_scores, on=['sent_id','sys_name'])\n",
    "\n",
    "    corr, pvalue = pearsonr(df_aux[metric], df_aux['simplicity_zscore'])\n",
    "    \n",
    "    graph = sns.lmplot(x=metric, y='simplicity_zscore', hue='quality', data=df_aux, fit_reg=False, legend_out=False, height=4, aspect=0.9)\n",
    "    \n",
    "    reg_plot = sns.regplot(x=metric, y='simplicity_zscore', data=df_aux, scatter=False, ax=graph.axes[0, 0])\n",
    "    \n",
    "    if metric == \"bertscore_P\":\n",
    "        reg_plot.set(xlabel=metric_name[metric], ylabel='Simplicity-DA (zscore)')\n",
    "    else:\n",
    "        reg_plot.set(xlabel=metric_name[metric], ylabel='')\n",
    "        graph._legend.remove()\n",
    "    \n",
    "    plt.savefig(f'{metric}_vs_simplicityDA.pdf', dpi=300, bbox_inches='tight')\n",
    "    \n",
    "#    ax.set_title(f\"r = {corr:.3f}\") # ax.set_title(f\"r = {corr:.3f} ({pvalue:.4f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(15, 3), sharey=True)\n",
    "for ax, metric in zip(axes, ['bertscore_P', 'bleu', 'sari']):\n",
    "    auto_scores = df_metrics_simplicity[df_metrics_simplicity['test_set']=='asset'][['sent_id','sys_name', metric]]\n",
    "    \n",
    "    df_human_scores = df_benchmark_simplicity[['sent_id','sys_name', 'simplicity_zscore']]\n",
    "#     df_human_scores = df_human_scores.sort_values(by=['simplicity_zscore'])\n",
    "#     df_human_scores['quality'] = 'low'\n",
    "#     df_human_scores['quality'].iloc[int(len(df_human_scores_sorted) / 2):] = 'high'\n",
    "\n",
    "    df_aux = pd.merge(auto_scores, df_human_scores, on=['sent_id','sys_name'])\n",
    "\n",
    "    corr, pvalue = pearsonr(df_aux[metric], df_aux['simplicity_zscore'])\n",
    "    \n",
    "    # graph = sns.lmplot(x=metric, y='simplicity_zscore', hue='quality', data=df_aux, fit_reg=False)\n",
    "    #Use regplot to plot the regression line for the whole points\n",
    "    # sns.regplot(x=metric, y='simplicity_zscore', data=df_aux, scatter=False, ax=graph.axes[0, 0])\n",
    "    sns.regplot(x=metric, y='simplicity_zscore', data=df_aux, scatter=True, ax=ax)\n",
    "    \n",
    "\n",
    "    ax.set_title(f\"r = {corr:.3f}\") # ax.set_title(f\"r = {corr:.3f} ({pvalue:.4f})\")\n",
    "#  plt.savefig('metrics_vs_simplicityDA.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simplicity Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_simplicity_gain = pd.read_csv(\"data/metrics_segment_benchmark_simplicity_gain.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_benchmark_simplicity_gain['simplicity_gain_zscore'] = df_benchmark_simplicity_gain['simplicity_gain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS_TO_ANALYSE = ['bleu', 'sari', 'samsa', 'fkgl', 'fkbleu', 'ibleu', \n",
    "                      'amean_bleu_sari', 'amean_sari_samsa', 'amean_bleu_samsa', 'amean_bleu_sari_samsa',\n",
    "                      'gmean_bleu_sari', 'gmean_sari_samsa', 'gmean_bleu_samsa', 'gmean_bleu_sari_samsa',\n",
    "                      'bertscore_P', 'bertscore_R', 'bertscore_F1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_simplicity_gain = df_metrics_simplicity_gain[['sent_id', 'sys_name', 'test_set'] + METRICS_TO_ANALYSE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simplicity_gain: High (186) - Low (186) - All (372)\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n"
     ]
    }
   ],
   "source": [
    "results_simplicity_gain = compute_correlations_per_quartile(df_benchmark_simplicity_gain, df_metrics_simplicity_gain, \n",
    "                                                           aspects=['simplicity_gain'], test_sets=['turk'], segment_id_cols=['sent_id','sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>metric</th>\n",
       "      <th>corr</th>\n",
       "      <th>p_value</th>\n",
       "      <th>is_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.331360</td>\n",
       "      <td>5.540003e-11</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.247146</td>\n",
       "      <td>1.394344e-06</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.240694</td>\n",
       "      <td>2.654025e-06</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.240657</td>\n",
       "      <td>2.663539e-06</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.214157</td>\n",
       "      <td>3.113099e-05</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.187296</td>\n",
       "      <td>2.807280e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gmean_bleu_sari_samsa</td>\n",
       "      <td>0.150767</td>\n",
       "      <td>3.559357e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.147268</td>\n",
       "      <td>4.422060e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.140655</td>\n",
       "      <td>6.582599e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amean_bleu_sari_samsa</td>\n",
       "      <td>0.128415</td>\n",
       "      <td>1.318583e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.127572</td>\n",
       "      <td>1.380425e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.122622</td>\n",
       "      <td>1.798017e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.099293</td>\n",
       "      <td>5.569902e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.091592</td>\n",
       "      <td>7.767818e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>amean_bleu_samsa</td>\n",
       "      <td>0.080616</td>\n",
       "      <td>1.206262e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gmean_bleu_samsa</td>\n",
       "      <td>0.075140</td>\n",
       "      <td>1.480600e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.013380</td>\n",
       "      <td>7.970156e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   metric      corr       p_value  is_winner\n",
       "0                    sari  0.331360  5.540003e-11       True\n",
       "1            bertscore_F1  0.247146  1.394344e-06      False\n",
       "2             bertscore_R  0.240694  2.654025e-06      False\n",
       "3             bertscore_P  0.240657  2.663539e-06      False\n",
       "4         gmean_bleu_sari  0.214157  3.113099e-05      False\n",
       "5         amean_bleu_sari  0.187296  2.807280e-04      False\n",
       "6   gmean_bleu_sari_samsa  0.150767  3.559357e-03      False\n",
       "7                    fkgl  0.147268  4.422060e-03      False\n",
       "8        gmean_sari_samsa  0.140655  6.582599e-03      False\n",
       "9   amean_bleu_sari_samsa  0.128415  1.318583e-02      False\n",
       "10                  ibleu  0.127572  1.380425e-02      False\n",
       "11                   bleu  0.122622  1.798017e-02      False\n",
       "12       amean_sari_samsa  0.099293  5.569902e-02      False\n",
       "13                 fkbleu  0.091592  7.767818e-02      False\n",
       "14       amean_bleu_samsa  0.080616  1.206262e-01      False\n",
       "15       gmean_bleu_samsa  0.075140  1.480600e-01      False\n",
       "16                  samsa  0.013380  7.970156e-01      False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_simplicity_gain[('all', 'simplicity_gain', 'turk')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_metrics_simplicity_gain = pprint_correlations(results_simplicity_gain, aspects=['simplicity_gain'], test_sets=['turk'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>metric</th>\n",
       "      <th>simplicity_gain_turk_low</th>\n",
       "      <th>simplicity_gain_turk_high</th>\n",
       "      <th>simplicity_gain_turk_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.291893</td>\n",
       "      <td>0.239562</td>\n",
       "      <td>0.331360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.245509</td>\n",
       "      <td>0.177108</td>\n",
       "      <td>0.214157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.222588</td>\n",
       "      <td>0.172219</td>\n",
       "      <td>0.187296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.220985</td>\n",
       "      <td>0.217286</td>\n",
       "      <td>0.240694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.215418</td>\n",
       "      <td>0.236093</td>\n",
       "      <td>0.247146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.208575</td>\n",
       "      <td>0.230697</td>\n",
       "      <td>0.240657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amean_bleu_sari_samsa</td>\n",
       "      <td>0.207139</td>\n",
       "      <td>0.139321</td>\n",
       "      <td>0.128415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gmean_bleu_sari_samsa</td>\n",
       "      <td>0.203616</td>\n",
       "      <td>0.144278</td>\n",
       "      <td>0.150767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.189218</td>\n",
       "      <td>0.117490</td>\n",
       "      <td>0.140655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.183490</td>\n",
       "      <td>0.103927</td>\n",
       "      <td>0.099293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.181394</td>\n",
       "      <td>0.135731</td>\n",
       "      <td>0.127572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.177613</td>\n",
       "      <td>0.132471</td>\n",
       "      <td>0.122622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>amean_bleu_samsa</td>\n",
       "      <td>0.174894</td>\n",
       "      <td>0.107075</td>\n",
       "      <td>0.080616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gmean_bleu_samsa</td>\n",
       "      <td>0.166117</td>\n",
       "      <td>0.097530</td>\n",
       "      <td>0.075140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.119883</td>\n",
       "      <td>0.041868</td>\n",
       "      <td>0.013380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.045494</td>\n",
       "      <td>0.101147</td>\n",
       "      <td>0.147268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.041182</td>\n",
       "      <td>0.006821</td>\n",
       "      <td>0.091592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   metric  simplicity_gain_turk_low  \\\n",
       "0                    sari                  0.291893   \n",
       "1         gmean_bleu_sari                  0.245509   \n",
       "2         amean_bleu_sari                  0.222588   \n",
       "3             bertscore_R                  0.220985   \n",
       "4            bertscore_F1                  0.215418   \n",
       "5             bertscore_P                  0.208575   \n",
       "6   amean_bleu_sari_samsa                  0.207139   \n",
       "7   gmean_bleu_sari_samsa                  0.203616   \n",
       "8        gmean_sari_samsa                  0.189218   \n",
       "9        amean_sari_samsa                  0.183490   \n",
       "10                  ibleu                  0.181394   \n",
       "11                   bleu                  0.177613   \n",
       "12       amean_bleu_samsa                  0.174894   \n",
       "13       gmean_bleu_samsa                  0.166117   \n",
       "14                  samsa                  0.119883   \n",
       "15                   fkgl                  0.045494   \n",
       "16                 fkbleu                  0.041182   \n",
       "\n",
       "    simplicity_gain_turk_high  simplicity_gain_turk_all  \n",
       "0                    0.239562                  0.331360  \n",
       "1                    0.177108                  0.214157  \n",
       "2                    0.172219                  0.187296  \n",
       "3                    0.217286                  0.240694  \n",
       "4                    0.236093                  0.247146  \n",
       "5                    0.230697                  0.240657  \n",
       "6                    0.139321                  0.128415  \n",
       "7                    0.144278                  0.150767  \n",
       "8                    0.117490                  0.140655  \n",
       "9                    0.103927                  0.099293  \n",
       "10                   0.135731                  0.127572  \n",
       "11                   0.132471                  0.122622  \n",
       "12                   0.107075                  0.080616  \n",
       "13                   0.097530                  0.075140  \n",
       "14                   0.041868                  0.013380  \n",
       "15                   0.101147                  0.147268  \n",
       "16                   0.006821                  0.091592  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_metrics_simplicity_gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrr}\n",
      "\\toprule\n",
      "                metric &  simplicity\\_gain\\_turk\\_low &  simplicity\\_gain\\_turk\\_high &  simplicity\\_gain\\_turk\\_all \\\\\n",
      "\\midrule\n",
      "                  sari &                     0.292 &                      0.240 &                     0.331 \\\\\n",
      "       gmean\\_bleu\\_sari &                     0.246 &                      0.177 &                     0.214 \\\\\n",
      "       amean\\_bleu\\_sari &                     0.223 &                      0.172 &                     0.187 \\\\\n",
      "           bertscore\\_R &                     0.221 &                      0.217 &                     0.241 \\\\\n",
      "          bertscore\\_F1 &                     0.215 &                      0.236 &                     0.247 \\\\\n",
      "           bertscore\\_P &                     0.209 &                      0.231 &                     0.241 \\\\\n",
      " amean\\_bleu\\_sari\\_samsa &                     0.207 &                      0.139 &                     0.128 \\\\\n",
      " gmean\\_bleu\\_sari\\_samsa &                     0.204 &                      0.144 &                     0.151 \\\\\n",
      "      gmean\\_sari\\_samsa &                     0.189 &                      0.117 &                     0.141 \\\\\n",
      "      amean\\_sari\\_samsa &                     0.183 &                      0.104 &                     0.099 \\\\\n",
      "                 ibleu &                     0.181 &                      0.136 &                     0.128 \\\\\n",
      "                  bleu &                     0.178 &                      0.132 &                     0.123 \\\\\n",
      "      amean\\_bleu\\_samsa &                     0.175 &                      0.107 &                     0.081 \\\\\n",
      "      gmean\\_bleu\\_samsa &                     0.166 &                      0.098 &                     0.075 \\\\\n",
      "                 samsa &                     0.120 &                      0.042 &                     0.013 \\\\\n",
      "                  fkgl &                     0.045 &                      0.101 &                     0.147 \\\\\n",
      "                fkbleu &                     0.041 &                      0.007 &                     0.092 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df_corr_metrics_simplicity_gain.to_latex(float_format=\"%.3f\", index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aspect_to_plot = 'simplicity_gain'\n",
    "test_set_to_plot = 'turk'\n",
    "fig, axes = plt.subplots(1, 3, figsize=(20, 4))\n",
    "i = 0\n",
    "for (split, aspect, test_set), (_, df_sig) in results_simplicity_gain.items():\n",
    "    if aspect == aspect_to_plot and test_set == test_set_to_plot:\n",
    "        ax = axes[i]\n",
    "        sns.heatmap(df_sig, cmap=\"Greens\", center=0.0, linewidth=0.05, cbar=False, robust=True, square=True, ax=ax)\n",
    "        ax.set_title(split)\n",
    "        i+=1 \n",
    "plt.savefig('significance_quality_simplicity_gain_complete.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>sent_id</th>\n",
       "      <th>sys_name</th>\n",
       "      <th>test_set</th>\n",
       "      <th>bleu</th>\n",
       "      <th>sari</th>\n",
       "      <th>samsa</th>\n",
       "      <th>fkgl</th>\n",
       "      <th>fkbleu</th>\n",
       "      <th>ibleu</th>\n",
       "      <th>amean_bleu_sari</th>\n",
       "      <th>amean_sari_samsa</th>\n",
       "      <th>amean_bleu_samsa</th>\n",
       "      <th>amean_bleu_sari_samsa</th>\n",
       "      <th>gmean_bleu_sari</th>\n",
       "      <th>gmean_sari_samsa</th>\n",
       "      <th>gmean_bleu_samsa</th>\n",
       "      <th>gmean_bleu_sari_samsa</th>\n",
       "      <th>bertscore_P</th>\n",
       "      <th>bertscore_R</th>\n",
       "      <th>bertscore_F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PBMT-R</td>\n",
       "      <td>asset</td>\n",
       "      <td>66.585664</td>\n",
       "      <td>39.817534</td>\n",
       "      <td>37.50</td>\n",
       "      <td>18.734545</td>\n",
       "      <td>18.203832</td>\n",
       "      <td>54.614596</td>\n",
       "      <td>53.201599</td>\n",
       "      <td>38.658767</td>\n",
       "      <td>52.042832</td>\n",
       "      <td>47.967733</td>\n",
       "      <td>51.490552</td>\n",
       "      <td>38.641397</td>\n",
       "      <td>49.969615</td>\n",
       "      <td>46.326425</td>\n",
       "      <td>0.723299</td>\n",
       "      <td>0.729734</td>\n",
       "      <td>0.724048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PBMT-R</td>\n",
       "      <td>turk</td>\n",
       "      <td>58.180890</td>\n",
       "      <td>29.002769</td>\n",
       "      <td>37.50</td>\n",
       "      <td>18.734545</td>\n",
       "      <td>15.682543</td>\n",
       "      <td>47.050300</td>\n",
       "      <td>43.591830</td>\n",
       "      <td>33.251384</td>\n",
       "      <td>47.840445</td>\n",
       "      <td>41.561220</td>\n",
       "      <td>41.078059</td>\n",
       "      <td>32.978839</td>\n",
       "      <td>46.709564</td>\n",
       "      <td>39.848964</td>\n",
       "      <td>0.780699</td>\n",
       "      <td>0.738619</td>\n",
       "      <td>0.759971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>PBMT-R</td>\n",
       "      <td>hsplit</td>\n",
       "      <td>54.396930</td>\n",
       "      <td>60.948920</td>\n",
       "      <td>37.50</td>\n",
       "      <td>18.734545</td>\n",
       "      <td>15.441445</td>\n",
       "      <td>46.326964</td>\n",
       "      <td>57.672925</td>\n",
       "      <td>49.224460</td>\n",
       "      <td>45.948465</td>\n",
       "      <td>50.948616</td>\n",
       "      <td>57.579806</td>\n",
       "      <td>47.807787</td>\n",
       "      <td>45.165084</td>\n",
       "      <td>49.910343</td>\n",
       "      <td>0.728504</td>\n",
       "      <td>0.563011</td>\n",
       "      <td>0.639868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>SBMT-BLEU</td>\n",
       "      <td>asset</td>\n",
       "      <td>52.752476</td>\n",
       "      <td>37.042616</td>\n",
       "      <td>18.75</td>\n",
       "      <td>16.477778</td>\n",
       "      <td>2.177284</td>\n",
       "      <td>43.776369</td>\n",
       "      <td>44.897546</td>\n",
       "      <td>27.896308</td>\n",
       "      <td>35.751238</td>\n",
       "      <td>36.181697</td>\n",
       "      <td>44.205087</td>\n",
       "      <td>26.354298</td>\n",
       "      <td>31.450102</td>\n",
       "      <td>33.213547</td>\n",
       "      <td>0.742001</td>\n",
       "      <td>0.781063</td>\n",
       "      <td>0.761853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>SBMT-BLEU</td>\n",
       "      <td>turk</td>\n",
       "      <td>39.698403</td>\n",
       "      <td>33.052623</td>\n",
       "      <td>18.75</td>\n",
       "      <td>16.477778</td>\n",
       "      <td>1.592946</td>\n",
       "      <td>32.027704</td>\n",
       "      <td>36.375513</td>\n",
       "      <td>25.901312</td>\n",
       "      <td>29.224202</td>\n",
       "      <td>30.500342</td>\n",
       "      <td>36.223423</td>\n",
       "      <td>24.894511</td>\n",
       "      <td>27.282688</td>\n",
       "      <td>29.084398</td>\n",
       "      <td>0.789708</td>\n",
       "      <td>0.769728</td>\n",
       "      <td>0.780058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>100</td>\n",
       "      <td>SBMT-FKBLEU</td>\n",
       "      <td>turk</td>\n",
       "      <td>80.705573</td>\n",
       "      <td>46.792417</td>\n",
       "      <td>43.75</td>\n",
       "      <td>8.009091</td>\n",
       "      <td>0.872106</td>\n",
       "      <td>64.564458</td>\n",
       "      <td>63.748995</td>\n",
       "      <td>45.271209</td>\n",
       "      <td>62.227786</td>\n",
       "      <td>57.082663</td>\n",
       "      <td>61.452492</td>\n",
       "      <td>45.245643</td>\n",
       "      <td>59.421114</td>\n",
       "      <td>54.872198</td>\n",
       "      <td>0.964293</td>\n",
       "      <td>0.964296</td>\n",
       "      <td>0.964353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>100</td>\n",
       "      <td>SBMT-FKBLEU</td>\n",
       "      <td>hsplit</td>\n",
       "      <td>36.791347</td>\n",
       "      <td>22.211538</td>\n",
       "      <td>43.75</td>\n",
       "      <td>8.009091</td>\n",
       "      <td>0.352856</td>\n",
       "      <td>26.122905</td>\n",
       "      <td>29.501443</td>\n",
       "      <td>32.980769</td>\n",
       "      <td>40.270674</td>\n",
       "      <td>34.250962</td>\n",
       "      <td>28.586578</td>\n",
       "      <td>31.172982</td>\n",
       "      <td>40.120088</td>\n",
       "      <td>32.943327</td>\n",
       "      <td>0.870593</td>\n",
       "      <td>0.720448</td>\n",
       "      <td>0.794868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>100</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>asset</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>44.411410</td>\n",
       "      <td>43.75</td>\n",
       "      <td>8.009091</td>\n",
       "      <td>1.106664</td>\n",
       "      <td>81.929443</td>\n",
       "      <td>72.205705</td>\n",
       "      <td>44.080705</td>\n",
       "      <td>71.875000</td>\n",
       "      <td>62.720470</td>\n",
       "      <td>66.641886</td>\n",
       "      <td>44.079464</td>\n",
       "      <td>66.143783</td>\n",
       "      <td>57.919420</td>\n",
       "      <td>0.913790</td>\n",
       "      <td>0.950957</td>\n",
       "      <td>0.932426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>100</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>turk</td>\n",
       "      <td>80.705573</td>\n",
       "      <td>46.792417</td>\n",
       "      <td>43.75</td>\n",
       "      <td>8.009091</td>\n",
       "      <td>0.872106</td>\n",
       "      <td>64.564458</td>\n",
       "      <td>63.748995</td>\n",
       "      <td>45.271209</td>\n",
       "      <td>62.227786</td>\n",
       "      <td>57.082663</td>\n",
       "      <td>61.452492</td>\n",
       "      <td>45.245643</td>\n",
       "      <td>59.421114</td>\n",
       "      <td>54.872198</td>\n",
       "      <td>0.964293</td>\n",
       "      <td>0.964296</td>\n",
       "      <td>0.964353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>100</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>hsplit</td>\n",
       "      <td>36.791347</td>\n",
       "      <td>22.211538</td>\n",
       "      <td>43.75</td>\n",
       "      <td>8.009091</td>\n",
       "      <td>0.352856</td>\n",
       "      <td>26.122905</td>\n",
       "      <td>29.501443</td>\n",
       "      <td>32.980769</td>\n",
       "      <td>40.270674</td>\n",
       "      <td>34.250962</td>\n",
       "      <td>28.586578</td>\n",
       "      <td>31.172982</td>\n",
       "      <td>40.120088</td>\n",
       "      <td>32.943327</td>\n",
       "      <td>0.870593</td>\n",
       "      <td>0.720448</td>\n",
       "      <td>0.794868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1116 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sent_id     sys_name test_set        bleu       sari  samsa       fkgl  \\\n",
       "0           1       PBMT-R    asset   66.585664  39.817534  37.50  18.734545   \n",
       "1           1       PBMT-R     turk   58.180890  29.002769  37.50  18.734545   \n",
       "2           1       PBMT-R   hsplit   54.396930  60.948920  37.50  18.734545   \n",
       "3           1    SBMT-BLEU    asset   52.752476  37.042616  18.75  16.477778   \n",
       "4           1    SBMT-BLEU     turk   39.698403  33.052623  18.75  16.477778   \n",
       "...       ...          ...      ...         ...        ...    ...        ...   \n",
       "1111      100  SBMT-FKBLEU     turk   80.705573  46.792417  43.75   8.009091   \n",
       "1112      100  SBMT-FKBLEU   hsplit   36.791347  22.211538  43.75   8.009091   \n",
       "1113      100    SBMT-SARI    asset  100.000000  44.411410  43.75   8.009091   \n",
       "1114      100    SBMT-SARI     turk   80.705573  46.792417  43.75   8.009091   \n",
       "1115      100    SBMT-SARI   hsplit   36.791347  22.211538  43.75   8.009091   \n",
       "\n",
       "         fkbleu      ibleu  amean_bleu_sari  amean_sari_samsa  \\\n",
       "0     18.203832  54.614596        53.201599         38.658767   \n",
       "1     15.682543  47.050300        43.591830         33.251384   \n",
       "2     15.441445  46.326964        57.672925         49.224460   \n",
       "3      2.177284  43.776369        44.897546         27.896308   \n",
       "4      1.592946  32.027704        36.375513         25.901312   \n",
       "...         ...        ...              ...               ...   \n",
       "1111   0.872106  64.564458        63.748995         45.271209   \n",
       "1112   0.352856  26.122905        29.501443         32.980769   \n",
       "1113   1.106664  81.929443        72.205705         44.080705   \n",
       "1114   0.872106  64.564458        63.748995         45.271209   \n",
       "1115   0.352856  26.122905        29.501443         32.980769   \n",
       "\n",
       "      amean_bleu_samsa  amean_bleu_sari_samsa  gmean_bleu_sari  \\\n",
       "0            52.042832              47.967733        51.490552   \n",
       "1            47.840445              41.561220        41.078059   \n",
       "2            45.948465              50.948616        57.579806   \n",
       "3            35.751238              36.181697        44.205087   \n",
       "4            29.224202              30.500342        36.223423   \n",
       "...                ...                    ...              ...   \n",
       "1111         62.227786              57.082663        61.452492   \n",
       "1112         40.270674              34.250962        28.586578   \n",
       "1113         71.875000              62.720470        66.641886   \n",
       "1114         62.227786              57.082663        61.452492   \n",
       "1115         40.270674              34.250962        28.586578   \n",
       "\n",
       "      gmean_sari_samsa  gmean_bleu_samsa  gmean_bleu_sari_samsa  bertscore_P  \\\n",
       "0            38.641397         49.969615              46.326425     0.723299   \n",
       "1            32.978839         46.709564              39.848964     0.780699   \n",
       "2            47.807787         45.165084              49.910343     0.728504   \n",
       "3            26.354298         31.450102              33.213547     0.742001   \n",
       "4            24.894511         27.282688              29.084398     0.789708   \n",
       "...                ...               ...                    ...          ...   \n",
       "1111         45.245643         59.421114              54.872198     0.964293   \n",
       "1112         31.172982         40.120088              32.943327     0.870593   \n",
       "1113         44.079464         66.143783              57.919420     0.913790   \n",
       "1114         45.245643         59.421114              54.872198     0.964293   \n",
       "1115         31.172982         40.120088              32.943327     0.870593   \n",
       "\n",
       "      bertscore_R  bertscore_F1  \n",
       "0        0.729734      0.724048  \n",
       "1        0.738619      0.759971  \n",
       "2        0.563011      0.639868  \n",
       "3        0.781063      0.761853  \n",
       "4        0.769728      0.780058  \n",
       "...           ...           ...  \n",
       "1111     0.964296      0.964353  \n",
       "1112     0.720448      0.794868  \n",
       "1113     0.950957      0.932426  \n",
       "1114     0.964296      0.964353  \n",
       "1115     0.720448      0.794868  \n",
       "\n",
       "[1116 rows x 20 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics_simplicity_gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n",
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n",
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_name = {'bertscore_P': '$BERTScore_{Precision}$', 'bleu': 'BLEU', 'sari': 'SARI'}\n",
    "plt.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams.update({'font.size': 12.5})\n",
    "for metric in ['bertscore_P', 'bleu', 'sari']:\n",
    "    auto_scores = df_metrics_simplicity_gain[df_metrics_simplicity_gain['test_set']=='turk'][['sent_id','sys_name', metric]]\n",
    "    \n",
    "    df_human_scores = df_benchmark_simplicity_gain[['sent_id','sys_name', 'simplicity_gain']]\n",
    "    df_human_scores = df_human_scores.sort_values(by=['simplicity_gain'])\n",
    "    df_human_scores['quality'] = 'low'\n",
    "    df_human_scores['quality'].iloc[int(len(df_human_scores) / 2):] = 'high'\n",
    "\n",
    "    df_aux = pd.merge(auto_scores, df_human_scores, on=['sent_id','sys_name'])\n",
    "\n",
    "    corr, pvalue = pearsonr(df_aux[metric], df_aux['simplicity_gain'])\n",
    "    \n",
    "    graph = sns.lmplot(x=metric, y='simplicity_gain', hue='quality', data=df_aux, fit_reg=False, legend_out=False, height=4, aspect=0.9)\n",
    "    reg_plot = sns.regplot(x=metric, y='simplicity_gain', data=df_aux, scatter=False, ax=graph.axes[0, 0])\n",
    "    \n",
    "    if metric == \"bertscore_P\":\n",
    "        reg_plot.set(xlabel=metric_name[metric], ylabel='Simplicity Gain')\n",
    "    else:\n",
    "        reg_plot.set(xlabel=metric_name[metric], ylabel='')\n",
    "        graph._legend.remove()\n",
    "    \n",
    "    # plt.title(f\"r = {corr:.3f}\")\n",
    "    plt.savefig(f'{metric}_vs_simplicity_gain.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structural Simplicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_struct_simplicity = pd.read_csv(\"data/metrics_segment_benchmark_structural_simplicity.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_benchmark_struct_simplicity['structural_simplicity_zscore'] = df_benchmark_struct_simplicity['structural_simplicity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS_TO_ANALYSE = ['bleu', 'sari', 'samsa', 'fkgl', 'fkbleu', 'ibleu', \n",
    "                      'amean_bleu_sari', 'amean_sari_samsa', 'amean_bleu_samsa', 'amean_bleu_sari_samsa',\n",
    "                      'gmean_bleu_sari', 'gmean_sari_samsa', 'gmean_bleu_samsa', 'gmean_bleu_sari_samsa',\n",
    "                      'bertscore_P', 'bertscore_R', 'bertscore_F1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_struct_simplicity = df_metrics_struct_simplicity[['sent_id', 'sys_name', 'test_set'] + METRICS_TO_ANALYSE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "structural_simplicity: High (875) - Low (875) - All (1750)\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n"
     ]
    }
   ],
   "source": [
    "results_struct_simplicity = compute_correlations_per_quartile(df_benchmark_struct_simplicity, df_metrics_struct_simplicity, \n",
    "                                                              aspects=['structural_simplicity'], test_sets=['hsplit'], segment_id_cols=['sent_id','sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>metric</th>\n",
       "      <th>corr</th>\n",
       "      <th>p_value</th>\n",
       "      <th>is_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.642701</td>\n",
       "      <td>4.095064e-103</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.634623</td>\n",
       "      <td>8.344831e-100</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.608230</td>\n",
       "      <td>1.190281e-89</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amean_bleu_samsa</td>\n",
       "      <td>0.607590</td>\n",
       "      <td>2.041297e-89</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.601000</td>\n",
       "      <td>4.930421e-87</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>amean_bleu_sari_samsa</td>\n",
       "      <td>0.600780</td>\n",
       "      <td>5.910201e-87</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.598914</td>\n",
       "      <td>2.729308e-86</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.589115</td>\n",
       "      <td>7.134608e-83</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gmean_bleu_samsa</td>\n",
       "      <td>0.568942</td>\n",
       "      <td>3.408111e-76</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gmean_bleu_sari_samsa</td>\n",
       "      <td>0.553312</td>\n",
       "      <td>2.502640e-71</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.529174</td>\n",
       "      <td>2.662840e-64</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.494925</td>\n",
       "      <td>2.960362e-55</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.467751</td>\n",
       "      <td>8.961067e-49</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.431499</td>\n",
       "      <td>5.460363e-41</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.418008</td>\n",
       "      <td>2.522912e-38</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.310326</td>\n",
       "      <td>5.483277e-21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.164816</td>\n",
       "      <td>9.491382e-07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   metric      corr        p_value  is_winner\n",
       "0                    bleu  0.642701  4.095064e-103       True\n",
       "1                   ibleu  0.634623  8.344831e-100      False\n",
       "2                  fkbleu  0.608230   1.190281e-89      False\n",
       "3        amean_bleu_samsa  0.607590   2.041297e-89      False\n",
       "4             bertscore_R  0.601000   4.930421e-87      False\n",
       "5   amean_bleu_sari_samsa  0.600780   5.910201e-87      False\n",
       "6         amean_bleu_sari  0.598914   2.729308e-86      False\n",
       "7         gmean_bleu_sari  0.589115   7.134608e-83      False\n",
       "8        gmean_bleu_samsa  0.568942   3.408111e-76      False\n",
       "9   gmean_bleu_sari_samsa  0.553312   2.502640e-71      False\n",
       "10           bertscore_F1  0.529174   2.662840e-64      False\n",
       "11       amean_sari_samsa  0.494925   2.960362e-55      False\n",
       "12       gmean_sari_samsa  0.467751   8.961067e-49      False\n",
       "13                  samsa  0.431499   5.460363e-41      False\n",
       "14                   sari  0.418008   2.522912e-38      False\n",
       "15            bertscore_P  0.310326   5.483277e-21      False\n",
       "16                   fkgl  0.164816   9.491382e-07      False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_struct_simplicity[('high', 'structural_simplicity', 'hsplit')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_metrics_struct_simplicity = pprint_correlations(results_struct_simplicity, aspects=['structural_simplicity'], test_sets=['hsplit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>metric</th>\n",
       "      <th>structural_simplicity_hsplit_low</th>\n",
       "      <th>structural_simplicity_hsplit_high</th>\n",
       "      <th>structural_simplicity_hsplit_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.552367</td>\n",
       "      <td>0.310326</td>\n",
       "      <td>0.090183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.482993</td>\n",
       "      <td>0.529174</td>\n",
       "      <td>0.325369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.420684</td>\n",
       "      <td>0.642701</td>\n",
       "      <td>0.443223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.410703</td>\n",
       "      <td>0.601000</td>\n",
       "      <td>0.429968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.407928</td>\n",
       "      <td>0.634623</td>\n",
       "      <td>0.436215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.395093</td>\n",
       "      <td>0.608230</td>\n",
       "      <td>0.364153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.345771</td>\n",
       "      <td>0.598914</td>\n",
       "      <td>0.430551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.329143</td>\n",
       "      <td>0.589115</td>\n",
       "      <td>0.437741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gmean_bleu_samsa</td>\n",
       "      <td>0.293120</td>\n",
       "      <td>0.568942</td>\n",
       "      <td>0.370390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gmean_bleu_sari_samsa</td>\n",
       "      <td>0.290611</td>\n",
       "      <td>0.553312</td>\n",
       "      <td>0.365994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>amean_bleu_samsa</td>\n",
       "      <td>0.288968</td>\n",
       "      <td>0.607590</td>\n",
       "      <td>0.420313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>amean_bleu_sari_samsa</td>\n",
       "      <td>0.280984</td>\n",
       "      <td>0.600780</td>\n",
       "      <td>0.428136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.171286</td>\n",
       "      <td>0.467751</td>\n",
       "      <td>0.301883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.140192</td>\n",
       "      <td>0.494925</td>\n",
       "      <td>0.347414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.136915</td>\n",
       "      <td>0.418008</td>\n",
       "      <td>0.313386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.103485</td>\n",
       "      <td>0.431499</td>\n",
       "      <td>0.283872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.070202</td>\n",
       "      <td>0.164816</td>\n",
       "      <td>0.227701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   metric  structural_simplicity_hsplit_low  \\\n",
       "0             bertscore_P                          0.552367   \n",
       "1            bertscore_F1                          0.482993   \n",
       "2                    bleu                          0.420684   \n",
       "3             bertscore_R                          0.410703   \n",
       "4                   ibleu                          0.407928   \n",
       "5                  fkbleu                          0.395093   \n",
       "6         amean_bleu_sari                          0.345771   \n",
       "7         gmean_bleu_sari                          0.329143   \n",
       "8        gmean_bleu_samsa                          0.293120   \n",
       "9   gmean_bleu_sari_samsa                          0.290611   \n",
       "10       amean_bleu_samsa                          0.288968   \n",
       "11  amean_bleu_sari_samsa                          0.280984   \n",
       "12       gmean_sari_samsa                          0.171286   \n",
       "13       amean_sari_samsa                          0.140192   \n",
       "14                   sari                          0.136915   \n",
       "15                  samsa                          0.103485   \n",
       "16                   fkgl                          0.070202   \n",
       "\n",
       "    structural_simplicity_hsplit_high  structural_simplicity_hsplit_all  \n",
       "0                            0.310326                          0.090183  \n",
       "1                            0.529174                          0.325369  \n",
       "2                            0.642701                          0.443223  \n",
       "3                            0.601000                          0.429968  \n",
       "4                            0.634623                          0.436215  \n",
       "5                            0.608230                          0.364153  \n",
       "6                            0.598914                          0.430551  \n",
       "7                            0.589115                          0.437741  \n",
       "8                            0.568942                          0.370390  \n",
       "9                            0.553312                          0.365994  \n",
       "10                           0.607590                          0.420313  \n",
       "11                           0.600780                          0.428136  \n",
       "12                           0.467751                          0.301883  \n",
       "13                           0.494925                          0.347414  \n",
       "14                           0.418008                          0.313386  \n",
       "15                           0.431499                          0.283872  \n",
       "16                           0.164816                          0.227701  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_metrics_struct_simplicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrr}\n",
      "\\toprule\n",
      "                metric &  structural\\_simplicity\\_hsplit\\_low &  structural\\_simplicity\\_hsplit\\_high &  structural\\_simplicity\\_hsplit\\_all \\\\\n",
      "\\midrule\n",
      "           bertscore\\_P &                             0.552 &                              0.310 &                             0.090 \\\\\n",
      "          bertscore\\_F1 &                             0.483 &                              0.529 &                             0.325 \\\\\n",
      "                  bleu &                             0.421 &                              0.643 &                             0.443 \\\\\n",
      "           bertscore\\_R &                             0.411 &                              0.601 &                             0.430 \\\\\n",
      "                 ibleu &                             0.408 &                              0.635 &                             0.436 \\\\\n",
      "                fkbleu &                             0.395 &                              0.608 &                             0.364 \\\\\n",
      "       amean\\_bleu\\_sari &                             0.346 &                              0.599 &                             0.431 \\\\\n",
      "       gmean\\_bleu\\_sari &                             0.329 &                              0.589 &                             0.438 \\\\\n",
      "      gmean\\_bleu\\_samsa &                             0.293 &                              0.569 &                             0.370 \\\\\n",
      " gmean\\_bleu\\_sari\\_samsa &                             0.291 &                              0.553 &                             0.366 \\\\\n",
      "      amean\\_bleu\\_samsa &                             0.289 &                              0.608 &                             0.420 \\\\\n",
      " amean\\_bleu\\_sari\\_samsa &                             0.281 &                              0.601 &                             0.428 \\\\\n",
      "      gmean\\_sari\\_samsa &                             0.171 &                              0.468 &                             0.302 \\\\\n",
      "      amean\\_sari\\_samsa &                             0.140 &                              0.495 &                             0.347 \\\\\n",
      "                  sari &                             0.137 &                              0.418 &                             0.313 \\\\\n",
      "                 samsa &                             0.103 &                              0.431 &                             0.284 \\\\\n",
      "                  fkgl &                             0.070 &                              0.165 &                             0.228 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df_corr_metrics_struct_simplicity.to_latex(float_format=\"%.3f\", index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aspect_to_plot = 'structural_simplicity'\n",
    "test_set_to_plot = 'hsplit'\n",
    "fig, axes = plt.subplots(1, 3, figsize=(20, 4))\n",
    "i = 0\n",
    "for (split, aspect, test_set), (_, df_sig) in results_struct_simplicity.items():\n",
    "    if aspect == aspect_to_plot and test_set == test_set_to_plot:\n",
    "        ax = axes[i]\n",
    "        sns.heatmap(df_sig, cmap=\"Greens\", center=0.0, linewidth=0.05, cbar=False, robust=True, square=True, ax=ax)\n",
    "        ax.set_title(split)\n",
    "        i+=1 \n",
    "plt.savefig('significance_quality_structural_simplicity_complete.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>sent_id</th>\n",
       "      <th>sys_name</th>\n",
       "      <th>samsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NTS-h1_default_model</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>NTS-h1_default_model</td>\n",
       "      <td>30.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>NTS-h1_default_model</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>NTS-h1_default_model</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>NTS-h1_default_model</td>\n",
       "      <td>7.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>66</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>21.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5240</th>\n",
       "      <td>67</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5243</th>\n",
       "      <td>68</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5246</th>\n",
       "      <td>69</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>18.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5249</th>\n",
       "      <td>70</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1750 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sent_id              sys_name       samsa\n",
       "2           1  NTS-h1_default_model   50.000000\n",
       "5           2  NTS-h1_default_model   30.555556\n",
       "8           3  NTS-h1_default_model  100.000000\n",
       "11          4  NTS-h1_default_model   33.333333\n",
       "14          5  NTS-h1_default_model    7.812500\n",
       "...       ...                   ...         ...\n",
       "5237       66             SBMT-SARI   21.875000\n",
       "5240       67             SBMT-SARI   25.000000\n",
       "5243       68             SBMT-SARI   50.000000\n",
       "5246       69             SBMT-SARI   18.750000\n",
       "5249       70             SBMT-SARI   12.500000\n",
       "\n",
       "[1750 rows x 3 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics_struct_simplicity[df_metrics_struct_simplicity['test_set']=='hsplit'][['sent_id', 'sys_name', 'samsa']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aux = df_benchmark_struct_simplicity.merge(right=df_metrics_struct_simplicity[df_metrics_struct_simplicity['test_set']=='hsplit'][['sent_id', 'sys_name', 'samsa']],\n",
    "                                    on = ['sent_id', 'sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aux.to_csv('analysis_quantiles_samsa.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n",
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n",
      "/home/falva/miniconda3/envs/benchmet/lib/python3.6/site-packages/pandas/core/indexing.py:670: 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",
      "  iloc._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 259.2x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_name = {'bertscore_P': '$BERTScore_{Precision}$', 'bleu': 'BLEU', 'samsa': 'SAMSA'}\n",
    "plt.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams.update({'font.size': 12.5})\n",
    "for metric in ['bertscore_P', 'bleu', 'samsa']:\n",
    "    auto_scores = df_metrics_struct_simplicity[df_metrics_struct_simplicity['test_set']=='hsplit'][['sent_id','sys_name', metric]]\n",
    "    \n",
    "    df_human_scores = df_benchmark_struct_simplicity[['sent_id','sys_name', 'structural_simplicity']]\n",
    "    df_human_scores = df_human_scores.sort_values(by=['structural_simplicity'])\n",
    "    df_human_scores['quality'] = 'low'\n",
    "    df_human_scores['quality'].iloc[int(len(df_human_scores) / 2):] = 'high'\n",
    "\n",
    "    df_aux = pd.merge(auto_scores, df_human_scores, on=['sent_id','sys_name'])\n",
    "\n",
    "    corr, pvalue = pearsonr(df_aux[metric], df_aux['structural_simplicity'])\n",
    "    \n",
    "    graph = sns.lmplot(x=metric, y='structural_simplicity', hue='quality', data=df_aux, fit_reg=False, legend_out=False, height=4, aspect=0.9)\n",
    "    reg_plot = sns.regplot(x=metric, y='structural_simplicity', data=df_aux, scatter=False, ax=graph.axes[0, 0])\n",
    "    \n",
    "    if metric == \"bertscore_P\":\n",
    "        reg_plot.set(xlabel=metric_name[metric], ylabel='Structural Simplicity')\n",
    "    else:\n",
    "        reg_plot.set(xlabel=metric_name[metric], ylabel='')\n",
    "        graph._legend.remove()\n",
    "    \n",
    "    # plt.title(f\"r = {corr:.3f}\")\n",
    "    plt.savefig(f'{metric}_vs_structural_simplicity.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics across Types of Systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_correlations_per_approach(\n",
    "    df_benchmark, df_segment_metrics, aspects, test_sets, segment_id_cols\n",
    "):\n",
    "    results = {}\n",
    "    for sys_type, df_scores_sys_type in df_benchmark.groupby([\"sys_type\"]):\n",
    "        print(f\"{sys_type}: {len(df_scores_sys_type)}\")\n",
    "        for aspect in aspects:\n",
    "            for test_set in test_sets:\n",
    "                df_metrics = df_segment_metrics[\n",
    "                    (df_segment_metrics['test_set'] == test_set)\n",
    "                    & (df_segment_metrics['sys_name'].isin(df_scores_sys_type[\"sys_name\"]))\n",
    "                ].drop(columns=[\"test_set\"])\n",
    "                results[\n",
    "                    (sys_type, aspect, test_set)\n",
    "                ] = compute_direct_assessment_correlations(\n",
    "                    df_scores_sys_type,\n",
    "                    df_metrics,\n",
    "                    aspect,\n",
    "                    segment_id_cols=segment_id_cols,\n",
    "                )\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pprint_correlations_per_approach(results_correlations, aspects, test_sets, system_types):\n",
    "    correlations = []\n",
    "    for aspect in aspects:\n",
    "        for sys_type in system_types:\n",
    "            for test_set in test_sets:\n",
    "                df_corr, _ = results_correlations[(sys_type, aspect, test_set)]\n",
    "                df_corr = df_corr[['metric', 'corr']]\n",
    "                df_corr.columns = [\"metric\", f\"{aspect}_{test_set}_{sys_type}\"]\n",
    "                correlations.append(df_corr)\n",
    "\n",
    "    df_corr_metrics = reduce(\n",
    "        lambda left, right: pd.merge(left, right, on=[\"metric\"], how=\"outer\"), correlations\n",
    "    )\n",
    "    \n",
    "    return df_corr_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simplicity-DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_simplicity = compute_correlations_per_approach(df_benchmark_simplicity, df_metrics_simplicity, \n",
    "                                                       aspects=['simplicity'], test_sets=['asset'], segment_id_cols=['sent_id','sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_simplicity[('NeuralSeq2Seq', 'simplicity', 'asset')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_types = list(set(df_benchmark_simplicity['sys_type'].values))\n",
    "df_corr_metrics_simplicity = pprint_correlations_per_approach(results_simplicity, aspects=['simplicity'], test_sets=['asset'], system_types=system_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_metrics_simplicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df_corr_metrics_simplicity.to_latex(float_format=\"%.3f\", index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aspect_to_plot = 'simplicity'\n",
    "test_set_to_plot = 'asset'\n",
    "sys_type_to_plot = ['PBMT', 'Semantics+PBMT']\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n",
    "i = 0\n",
    "for (sys_type, aspect, test_set), (_, df_sig) in results_simplicity.items():\n",
    "    if aspect == aspect_to_plot and test_set == test_set_to_plot and sys_type in sys_type_to_plot:\n",
    "        ax = axes[i]\n",
    "        sns.heatmap(df_sig, cmap=\"Greens\", center=0.0, linewidth=0.05, cbar=False, robust=True, square=True, ax=ax)\n",
    "        ax.set_title(sys_type)\n",
    "        i+=1\n",
    "plt.savefig('significance_systype_simplicity_complete_part2.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simplicity Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_simplicity_gain = compute_correlations_per_approach(df_benchmark_simplicity_gain, df_metrics_simplicity_gain, \n",
    "                                                            aspects=['simplicity_gain'], test_sets=EVAL_DATASETS, segment_id_cols=['sent_id','sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_types = list(set(df_benchmark_simplicity_gain['sys_type'].values))\n",
    "df_corr_metrics_simplicity_gain = pprint_correlations_per_approach(results_simplicity_gain, aspects=['simplicity_gain'], test_sets=['turk'], system_types=system_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_metrics_simplicity_gain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structural Simplicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NeuralSeq2Seq: 280\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "PBMT: 70\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "SBMT: 70\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Semantics: 140\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Semantics+NeuralSeq2Seq: 560\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Semantics+PBMT: 630\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n"
     ]
    }
   ],
   "source": [
    "results_struct_simplicity = compute_correlations_per_approach(df_benchmark_struct_simplicity, df_metrics_struct_simplicity, \n",
    "                                                              aspects=['structural_simplicity'], test_sets=EVAL_DATASETS, segment_id_cols=['sent_id','sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_types = list(set(df_benchmark_struct_simplicity['sys_type'].values))\n",
    "df_corr_metrics_struct_simplicity = pprint_correlations_per_approach(results_struct_simplicity, aspects=['structural_simplicity'], test_sets=['hsplit'], \n",
    "                                                                     system_types=system_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['metric', 'structural_simplicity_hsplit_PBMT',\n",
       "       'structural_simplicity_hsplit_Semantics+NeuralSeq2Seq',\n",
       "       'structural_simplicity_hsplit_SBMT',\n",
       "       'structural_simplicity_hsplit_Semantics',\n",
       "       'structural_simplicity_hsplit_Semantics+PBMT',\n",
       "       'structural_simplicity_hsplit_NeuralSeq2Seq'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_metrics_struct_simplicity.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrr}\n",
      "\\toprule\n",
      "                metric &  structural\\_simplicity\\_hsplit\\_PBMT &  structural\\_simplicity\\_hsplit\\_SBMT &  structural\\_simplicity\\_hsplit\\_NeuralSeq2Seq &  structural\\_simplicity\\_hsplit\\_Semantics &  structural\\_simplicity\\_hsplit\\_Semantics+PBMT &  structural\\_simplicity\\_hsplit\\_Semantics+NeuralSeq2Seq \\\\\n",
      "\\midrule\n",
      "           bertscore\\_P &                              0.501 &                              0.571 &                                       0.292 &                                   0.330 &                                        0.096 &                                              0.111 \\\\\n",
      "          bertscore\\_F1 &                              0.405 &                              0.497 &                                       0.553 &                                   0.180 &                                        0.049 &                                              0.362 \\\\\n",
      "           bertscore\\_R &                              0.339 &                              0.418 &                                       0.635 &                                   0.066 &                                        0.134 &                                              0.480 \\\\\n",
      "                  bleu &                              0.284 &                              0.380 &                                       0.661 &                                   0.130 &                                        0.147 &                                              0.540 \\\\\n",
      "                 ibleu &                              0.252 &                              0.380 &                                       0.642 &                                   0.130 &                                        0.145 &                                              0.536 \\\\\n",
      "      amean\\_bleu\\_samsa &                              0.240 &                              0.334 &                                       0.603 &                                   0.095 &                                        0.072 &                                              0.573 \\\\\n",
      "      gmean\\_bleu\\_samsa &                              0.216 &                              0.279 &                                       0.563 &                                   0.109 &                                        0.075 &                                              0.561 \\\\\n",
      "                fkbleu &                              0.215 &                              0.344 &                                       0.617 &                                   0.009 &                                        0.119 &                                              0.539 \\\\\n",
      "                  fkgl &                              0.205 &                              0.016 &                                       0.251 &                                   0.083 &                                        0.155 &                                              0.242 \\\\\n",
      " amean\\_bleu\\_sari\\_samsa &                              0.204 &                              0.351 &                                       0.591 &                                   0.092 &                                        0.111 &                                              0.554 \\\\\n",
      " gmean\\_bleu\\_sari\\_samsa &                              0.185 &                              0.292 &                                       0.567 &                                   0.111 &                                        0.087 &                                              0.551 \\\\\n",
      "       amean\\_bleu\\_sari &                              0.184 &                              0.364 &                                       0.603 &                                   0.100 &                                        0.175 &                                              0.507 \\\\\n",
      "       gmean\\_bleu\\_sari &                              0.157 &                              0.341 &                                       0.589 &                                   0.097 &                                        0.185 &                                              0.515 \\\\\n",
      "                 samsa &                              0.141 &                              0.177 &                                       0.368 &                                   0.052 &                                        0.009 &                                              0.497 \\\\\n",
      "      gmean\\_sari\\_samsa &                              0.123 &                              0.253 &                                       0.446 &                                   0.081 &                                        0.025 &                                              0.505 \\\\\n",
      "      amean\\_sari\\_samsa &                              0.121 &                              0.271 &                                       0.425 &                                   0.056 &                                        0.063 &                                              0.502 \\\\\n",
      "                  sari &                              0.015 &                              0.286 &                                       0.330 &                                   0.028 &                                        0.166 &                                              0.355 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_aux = df_corr_metrics_struct_simplicity[['metric', \n",
    "                                            'structural_simplicity_hsplit_PBMT', 'structural_simplicity_hsplit_SBMT', 'structural_simplicity_hsplit_NeuralSeq2Seq',\n",
    "                                           'structural_simplicity_hsplit_Semantics', 'structural_simplicity_hsplit_Semantics+PBMT', 'structural_simplicity_hsplit_Semantics+NeuralSeq2Seq']]\n",
    "print(df_aux.to_latex(float_format=\"%.3f\", index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>metric</th>\n",
       "      <th>corr</th>\n",
       "      <th>p_value</th>\n",
       "      <th>is_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.184678</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.174812</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.166370</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.154854</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.147007</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.144544</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.134204</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.119115</td>\n",
       "      <td>0.002749</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amean_bleu_sari_samsa</td>\n",
       "      <td>0.111225</td>\n",
       "      <td>0.005192</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.096045</td>\n",
       "      <td>0.015887</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gmean_bleu_sari_samsa</td>\n",
       "      <td>0.086525</td>\n",
       "      <td>0.029893</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gmean_bleu_samsa</td>\n",
       "      <td>0.075154</td>\n",
       "      <td>0.059394</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>amean_bleu_samsa</td>\n",
       "      <td>0.072271</td>\n",
       "      <td>0.069868</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.063431</td>\n",
       "      <td>0.111712</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.048950</td>\n",
       "      <td>0.219850</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.024837</td>\n",
       "      <td>0.533767</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.008592</td>\n",
       "      <td>0.829584</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   metric      corr   p_value  is_winner\n",
       "0         gmean_bleu_sari  0.184678  0.000003      False\n",
       "1         amean_bleu_sari  0.174812  0.000010      False\n",
       "2                    sari  0.166370  0.000027      False\n",
       "3                    fkgl  0.154854  0.000095      False\n",
       "4                    bleu  0.147007  0.000213      False\n",
       "5                   ibleu  0.144544  0.000273      False\n",
       "6             bertscore_R  0.134204  0.000733      False\n",
       "7                  fkbleu  0.119115  0.002749      False\n",
       "8   amean_bleu_sari_samsa  0.111225  0.005192      False\n",
       "9             bertscore_P  0.096045  0.015887      False\n",
       "10  gmean_bleu_sari_samsa  0.086525  0.029893      False\n",
       "11       gmean_bleu_samsa  0.075154  0.059394      False\n",
       "12       amean_bleu_samsa  0.072271  0.069868      False\n",
       "13       amean_sari_samsa  0.063431  0.111712      False\n",
       "14           bertscore_F1  0.048950  0.219850      False\n",
       "15       gmean_sari_samsa  0.024837  0.533767      False\n",
       "16                  samsa  0.008592  0.829584      False"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_struct_simplicity[('Semantics+PBMT', 'structural_simplicity', 'hsplit')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aspect_to_plot = 'structural_simplicity'\n",
    "test_set_to_plot = 'hsplit'\n",
    "sys_type_to_plot = ['Semantics', 'Semantics+PBMT', 'Semantics+NeuralSeq2Seq']\n",
    "fig, axes = plt.subplots(1, 3, figsize=(20, 4))\n",
    "i = 0\n",
    "for (sys_type, aspect, test_set), (_, df_sig) in results_struct_simplicity.items():\n",
    "    if aspect == aspect_to_plot and test_set == test_set_to_plot and sys_type in sys_type_to_plot:\n",
    "        ax = axes[i]\n",
    "        sns.heatmap(df_sig, cmap=\"Greens\", center=0.0, linewidth=0.05, cbar=False, robust=True, square=True, ax=ax)\n",
    "        ax.set_title(sys_type)\n",
    "        i+=1\n",
    "plt.savefig('significance_systype_structural_simplicity_complete_part2.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tseval.feature_extraction import count_sentence_splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_benchmark_struct_simplicity['with splitting'] = df_benchmark_struct_simplicity.apply(lambda row: count_sentence_splits(row['orig_sent'], row['simp_sent']) > 1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_benchmark_struct_simplicity[(df_benchmark_struct_simplicity['with splitting'] == False)]['structural_simplicity'], \n",
    "    edgecolor='black', color=['orange'], label = 'no splitting', hatch=\"X\")\n",
    "plt.hist(df_benchmark_struct_simplicity[(df_benchmark_struct_simplicity['with splitting'] == True)]['structural_simplicity'], \n",
    "        edgecolor='black', color=['#8da0cb'], label = 'with splitting')\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig('scores_distribution_structural_simplicity_withsplit_vs_nosplit.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=4, figsize=(15, 3), sharey=True)\n",
    "for ax, (sys_type, sys_type_name) in zip(axes, [('Semantics', 'Sem'), ('Semantics+PBMT', 'Sem+PBMT'), ('NeuralSeq2Seq', 'S2S'), ('Semantics+NeuralSeq2Seq', 'Sem+S2S')]):\n",
    "    ax.hist(df_benchmark_struct_simplicity[(df_benchmark_struct_simplicity['sys_type'] == sys_type) \n",
    "                                       & (df_benchmark_struct_simplicity['with splitting'] == False)]['structural_simplicity'], \n",
    "        edgecolor='black', color=['orange'], label = 'no splitting', hatch='x')\n",
    "    ax.hist(df_benchmark_struct_simplicity[(df_benchmark_struct_simplicity['sys_type'] == sys_type) \n",
    "                                           & (df_benchmark_struct_simplicity['with splitting'] == True)]['structural_simplicity'], \n",
    "            edgecolor='black', color=['#8da0cb'], label = 'with splitting')\n",
    "    ax.set_title(sys_type_name)\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig('scores_distribution_structural_simplicity_approaches_withsplit_vs_nosplit.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of References"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_correlations(df_benchmark, df_segment_metrics, aspects, test_sets, segment_id_cols):\n",
    "    results = {}\n",
    "    for aspect in aspects: \n",
    "        for test_set in test_sets:\n",
    "            df_metrics = df_segment_metrics[df_segment_metrics.test_set==test_set].drop(columns=['test_set'])\n",
    "            results[(aspect, test_set)] = compute_direct_assessment_correlations(df_benchmark, \n",
    "                                                                                 df_metrics, \n",
    "                                                                                 aspect, \n",
    "                                                                                 segment_id_cols=segment_id_cols)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pprint_correlations(results_correlations, aspects, test_sets):\n",
    "    correlations = []\n",
    "    for aspect in aspects:\n",
    "        for test_set in test_sets:\n",
    "            df_corr, _ = results_correlations[(aspect, test_set)]\n",
    "            df_corr = df_corr[['metric', 'corr']]\n",
    "            df_corr.columns = [\"metric\", f\"{aspect}_{test_set}\"]\n",
    "            correlations.append(df_corr)\n",
    "\n",
    "    df_corr_metrics = reduce(\n",
    "        lambda left, right: pd.merge(left, right, on=[\"metric\"], how=\"outer\"), correlations\n",
    "    )\n",
    "    \n",
    "    return df_corr_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_simplicity = pd.read_csv(\"data/metrics_segment_benchmark_simplicity_with_all.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS_TO_ANALYSE = ['bleu', 'sari', 'samsa', 'fkgl', 'fkbleu', 'ibleu', \n",
    "                      'amean_bleu_sari', 'amean_sari_samsa', \n",
    "                      'gmean_bleu_sari', 'gmean_sari_samsa',\n",
    "                      'bertscore_P', 'bertscore_R', 'bertscore_F1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_simplicity = df_metrics_simplicity[['sent_id', 'sys_name', 'test_set'] + METRICS_TO_ANALYSE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n"
     ]
    }
   ],
   "source": [
    "results_simplicity = compute_correlations(df_benchmark_simplicity, df_metrics_simplicity, \n",
    "                                          aspects=['simplicity'], test_sets=EVAL_DATASETS + ['all'], segment_id_cols=['sent_id','sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>metric</th>\n",
       "      <th>corr</th>\n",
       "      <th>p_value</th>\n",
       "      <th>is_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.593975</td>\n",
       "      <td>1.728493e-58</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.497769</td>\n",
       "      <td>6.954998e-39</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.382100</td>\n",
       "      <td>2.709435e-22</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.285119</td>\n",
       "      <td>1.097735e-12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.284966</td>\n",
       "      <td>1.129938e-12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.284349</td>\n",
       "      <td>1.269478e-12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.275022</td>\n",
       "      <td>7.120458e-12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.192199</td>\n",
       "      <td>2.112533e-06</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.143658</td>\n",
       "      <td>4.157672e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.119361</td>\n",
       "      <td>3.410329e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.117057</td>\n",
       "      <td>4.089161e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.057665</td>\n",
       "      <td>1.583272e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.052016</td>\n",
       "      <td>2.032555e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              metric      corr       p_value  is_winner\n",
       "0        bertscore_P  0.593975  1.728493e-58       True\n",
       "1       bertscore_F1  0.497769  6.954998e-39      False\n",
       "2        bertscore_R  0.382100  2.709435e-22      False\n",
       "3    gmean_bleu_sari  0.285119  1.097735e-12      False\n",
       "4               bleu  0.284966  1.129938e-12      False\n",
       "5              ibleu  0.284349  1.269478e-12      False\n",
       "6    amean_bleu_sari  0.275022  7.120458e-12      False\n",
       "7               sari  0.192199  2.112533e-06      False\n",
       "8   amean_sari_samsa  0.143658  4.157672e-04      False\n",
       "9   gmean_sari_samsa  0.119361  3.410329e-03      False\n",
       "10              fkgl  0.117057  4.089161e-03      False\n",
       "11             samsa  0.057665  1.583272e-01      False\n",
       "12            fkbleu  0.052016  2.032555e-01      False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_simplicity[('simplicity', 'hsplit')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_metrics_simplicity = pprint_correlations(results_simplicity, aspects=['simplicity'], test_sets=EVAL_DATASETS + ['all'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>metric</th>\n",
       "      <th>simplicity_asset</th>\n",
       "      <th>simplicity_turk</th>\n",
       "      <th>simplicity_hsplit</th>\n",
       "      <th>simplicity_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.617463</td>\n",
       "      <td>0.612741</td>\n",
       "      <td>0.593975</td>\n",
       "      <td>0.629430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.572982</td>\n",
       "      <td>0.537376</td>\n",
       "      <td>0.497769</td>\n",
       "      <td>0.575879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.503749</td>\n",
       "      <td>0.417733</td>\n",
       "      <td>0.284349</td>\n",
       "      <td>0.537022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.496336</td>\n",
       "      <td>0.411836</td>\n",
       "      <td>0.284966</td>\n",
       "      <td>0.526417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.502735</td>\n",
       "      <td>0.418024</td>\n",
       "      <td>0.275022</td>\n",
       "      <td>0.519141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.499577</td>\n",
       "      <td>0.440770</td>\n",
       "      <td>0.382100</td>\n",
       "      <td>0.505658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.475908</td>\n",
       "      <td>0.409892</td>\n",
       "      <td>0.285119</td>\n",
       "      <td>0.489579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.358708</td>\n",
       "      <td>0.340571</td>\n",
       "      <td>0.192199</td>\n",
       "      <td>0.352978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amean_sari_samsa</td>\n",
       "      <td>0.165872</td>\n",
       "      <td>0.166622</td>\n",
       "      <td>0.143658</td>\n",
       "      <td>0.155622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gmean_sari_samsa</td>\n",
       "      <td>0.155818</td>\n",
       "      <td>0.163818</td>\n",
       "      <td>0.119361</td>\n",
       "      <td>0.151746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.117057</td>\n",
       "      <td>0.117057</td>\n",
       "      <td>0.117057</td>\n",
       "      <td>0.117057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>0.069299</td>\n",
       "      <td>0.052016</td>\n",
       "      <td>0.087721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>samsa</td>\n",
       "      <td>0.057665</td>\n",
       "      <td>0.057665</td>\n",
       "      <td>0.057665</td>\n",
       "      <td>0.057665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              metric  simplicity_asset  simplicity_turk  simplicity_hsplit  \\\n",
       "0        bertscore_P          0.617463         0.612741           0.593975   \n",
       "1       bertscore_F1          0.572982         0.537376           0.497769   \n",
       "2              ibleu          0.503749         0.417733           0.284349   \n",
       "5               bleu          0.496336         0.411836           0.284966   \n",
       "3    amean_bleu_sari          0.502735         0.418024           0.275022   \n",
       "4        bertscore_R          0.499577         0.440770           0.382100   \n",
       "6    gmean_bleu_sari          0.475908         0.409892           0.285119   \n",
       "7               sari          0.358708         0.340571           0.192199   \n",
       "8   amean_sari_samsa          0.165872         0.166622           0.143658   \n",
       "9   gmean_sari_samsa          0.155818         0.163818           0.119361   \n",
       "10              fkgl          0.117057         0.117057           0.117057   \n",
       "11            fkbleu          0.098361         0.069299           0.052016   \n",
       "12             samsa          0.057665         0.057665           0.057665   \n",
       "\n",
       "    simplicity_all  \n",
       "0         0.629430  \n",
       "1         0.575879  \n",
       "2         0.537022  \n",
       "5         0.526417  \n",
       "3         0.519141  \n",
       "4         0.505658  \n",
       "6         0.489579  \n",
       "7         0.352978  \n",
       "8         0.155622  \n",
       "9         0.151746  \n",
       "10        0.117057  \n",
       "11        0.087721  \n",
       "12        0.057665  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_metrics_simplicity.sort_values(by=['simplicity_all'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "           metric &  simplicity\\_asset &  simplicity\\_turk &  simplicity\\_hsplit &  simplicity\\_all \\\\\n",
      "\\midrule\n",
      "      bertscore\\_P &             0.617 &            0.613 &              0.594 &           0.629 \\\\\n",
      "     bertscore\\_F1 &             0.573 &            0.537 &              0.498 &           0.576 \\\\\n",
      "            ibleu &             0.504 &            0.418 &              0.284 &           0.537 \\\\\n",
      "             bleu &             0.496 &            0.412 &              0.285 &           0.526 \\\\\n",
      "  amean\\_bleu\\_sari &             0.503 &            0.418 &              0.275 &           0.519 \\\\\n",
      "      bertscore\\_R &             0.500 &            0.441 &              0.382 &           0.506 \\\\\n",
      "  gmean\\_bleu\\_sari &             0.476 &            0.410 &              0.285 &           0.490 \\\\\n",
      "             sari &             0.359 &            0.341 &              0.192 &           0.353 \\\\\n",
      " amean\\_sari\\_samsa &             0.166 &            0.167 &              0.144 &           0.156 \\\\\n",
      " gmean\\_sari\\_samsa &             0.156 &            0.164 &              0.119 &           0.152 \\\\\n",
      "             fkgl &             0.117 &            0.117 &              0.117 &           0.117 \\\\\n",
      "           fkbleu &             0.098 &            0.069 &              0.052 &           0.088 \\\\\n",
      "            samsa &             0.058 &            0.058 &              0.058 &           0.058 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df_corr_metrics_simplicity.sort_values(by=['simplicity_all'], ascending=False).to_latex(float_format=\"%.3f\", index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All References vs Operation-Specific per Sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "asset_orig, asset_refs = read_test_set(\"asset_test\", as_lists=True)\n",
    "turk_orig, turk_refs = read_test_set(\"turkcorpus_test\", as_lists=True)\n",
    "hsplit_orig, hsplit_refs = read_test_set(\"hsplit_test\", as_lists=True)\n",
    "\n",
    "# We create a dataset composed of all references together\n",
    "all_orig = asset_orig\n",
    "all_refs = asset_refs + turk_refs + hsplit_refs\n",
    "\n",
    "asset_turk_orig = asset_orig\n",
    "asset_turk_refs = asset_refs + turk_refs\n",
    "\n",
    "EVAL_DATASETS = {\n",
    "    \"asset\": (asset_orig, asset_refs, 10),\n",
    "    \"turk\": (turk_orig, turk_refs, 8),\n",
    "    \"hsplit\": (hsplit_orig, hsplit_refs, 4),\n",
    "    \"all\": (all_orig, all_refs, 22),\n",
    "    \"asset+turk\": (asset_turk_orig, asset_turk_refs, 18)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "lowercase = False  # case-insensitive\n",
    "tokenizer = \"moses\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "30it [00:00, 2813.14it/s]\n",
      "30it [00:00, 3431.58it/s]\n",
      "30it [00:00, 3312.34it/s]\n",
      "30it [00:00, 3303.21it/s]\n"
     ]
    }
   ],
   "source": [
    "sents_to_eval = [\n",
    "    read_sentences_to_evaluate(f\"../human_ratings/benchmark_simplert/batch_{i}.csv\")\n",
    "    for i in range(1, 5)\n",
    "]\n",
    "df_sents_to_eval = pd.concat(sents_to_eval, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>sent_id</th>\n",
       "      <th>sys_name</th>\n",
       "      <th>orig_sent</th>\n",
       "      <th>simp_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>268</td>\n",
       "      <td>ACCESS</td>\n",
       "      <td>Prunk is a member of Institute of European His...</td>\n",
       "      <td>Prunk is a member of Institute of European His...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>In return, Rollo swore fealty to Charles, conv...</td>\n",
       "      <td>In return, Rollo swore fealty to Charles, conv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>341</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>From 1900 to 1920 many new facilities were con...</td>\n",
       "      <td>From 1900 to 1920 many new plants were built o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>278</td>\n",
       "      <td>PBMT-R</td>\n",
       "      <td>Mercury is similar in appearance to the Moon: ...</td>\n",
       "      <td>Mercury is similar in appearance to the Moon: ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>He settled in London, devoting himself chiefly...</td>\n",
       "      <td>He settled in London, devoting himself.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>210</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>Orchestration Stravinsky first conceived of wr...</td>\n",
       "      <td>Orchestration Stravinsky conceived of writing ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>202</td>\n",
       "      <td>Dress-Ls</td>\n",
       "      <td>The album, however, was banned from many recor...</td>\n",
       "      <td>The album was banned from many record stores n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>194</td>\n",
       "      <td>PBMT-R</td>\n",
       "      <td>ISBN 1-876429-14-3 is an historic township loc...</td>\n",
       "      <td>Gunnedah is a historic town near Cowra in the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>148</td>\n",
       "      <td>Dress-Ls</td>\n",
       "      <td>Later, Esperanto speakers began to see the lan...</td>\n",
       "      <td>Esperanto speakers began to see the language a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>112</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>The New York City Housing Authority Police Dep...</td>\n",
       "      <td>The new city York Housing Authority Police Dep...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sent_id   sys_name                                          orig_sent  \\\n",
       "0        268     ACCESS  Prunk is a member of Institute of European His...   \n",
       "1         67  SBMT-SARI  In return, Rollo swore fealty to Charles, conv...   \n",
       "2        341  SBMT-SARI  From 1900 to 1920 many new facilities were con...   \n",
       "3        278     PBMT-R  Mercury is similar in appearance to the Moon: ...   \n",
       "4        107     Hybrid  He settled in London, devoting himself chiefly...   \n",
       "..       ...        ...                                                ...   \n",
       "595      210     Hybrid  Orchestration Stravinsky first conceived of wr...   \n",
       "596      202   Dress-Ls  The album, however, was banned from many recor...   \n",
       "597      194     PBMT-R  ISBN 1-876429-14-3 is an historic township loc...   \n",
       "598      148   Dress-Ls  Later, Esperanto speakers began to see the lan...   \n",
       "599      112     Hybrid  The New York City Housing Authority Police Dep...   \n",
       "\n",
       "                                             simp_sent  \n",
       "0    Prunk is a member of Institute of European His...  \n",
       "1    In return, Rollo swore fealty to Charles, conv...  \n",
       "2    From 1900 to 1920 many new plants were built o...  \n",
       "3    Mercury is similar in appearance to the Moon: ...  \n",
       "4              He settled in London, devoting himself.  \n",
       "..                                                 ...  \n",
       "595  Orchestration Stravinsky conceived of writing ...  \n",
       "596  The album was banned from many record stores n...  \n",
       "597  Gunnedah is a historic town near Cowra in the ...  \n",
       "598  Esperanto speakers began to see the language a...  \n",
       "599  The new city York Housing Authority Police Dep...  \n",
       "\n",
       "[600 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sents_to_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-10 17:39:45,900 - simalign.simalign - INFO - Initialized the EmbeddingLoader with model: bert-base-multilingual-cased\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting server with command: java -Xmx5G -cp /experiments/falva/easse/easse/resources/tools/stanford-corenlp-full-2018-10-05/* edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 60000 -threads 40 -maxCharLength 100000 -quiet True -serverProperties corenlp_server-d6c16498b4ff473e.props -preload tokenize,ssplit,pos,lemma,ner,depparse\n"
     ]
    }
   ],
   "source": [
    "df_sents_to_eval = easse_labelling_with_simalign(df_sents_to_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bertscore_rescale = BERTScorer(lang=\"en\", rescale_with_baseline=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20it [00:02, 10.19it/s]/home/falva/anaconda3/envs/mtl4ts/lib/python3.6/site-packages/scipy/stats/stats.py:330: RuntimeWarning: divide by zero encountered in log\n",
      "  log_a = np.log(np.array(a, dtype=dtype))\n",
      "600it [00:53, 11.28it/s]\n"
     ]
    }
   ],
   "source": [
    "metrics = []\n",
    "for _, row in tqdm(df_sents_to_eval.iterrows()):\n",
    "    if row['splitting'] and not row['compression'] and not row['paraphrasing']: \n",
    "        test_set = 'hsplit'\n",
    "    elif (row['compression'] or row['paraphrasing']) and not row['splitting']:\n",
    "        test_set = 'asset+turk'\n",
    "    else:\n",
    "        test_set = 'asset'\n",
    "        \n",
    "    test_set_orig, test_set_refs, num_refs = EVAL_DATASETS[test_set]\n",
    "    orig_sents, ref_sents = collect_references(\n",
    "            [row[\"sent_id\"]], test_set_orig, test_set_refs, num_refs\n",
    "        )\n",
    "    # BLEU\n",
    "    bleu_sys_refs = corpus_bleu(\n",
    "        [row[\"simp_sent\"]],\n",
    "        ref_sents,\n",
    "        smooth_method=\"floor\",\n",
    "        tokenizer=tokenizer,\n",
    "        lowercase=lowercase,\n",
    "        use_effective_order=True,\n",
    "    )\n",
    "\n",
    "    # SARI\n",
    "    sari_score, add_score, keep_score, del_score = corpus_sari(\n",
    "        orig_sents,\n",
    "        [row[\"simp_sent\"]],\n",
    "        ref_sents,\n",
    "        tokenizer=tokenizer,\n",
    "        lowercase=lowercase,\n",
    "        use_f1_for_deletion=False,\n",
    "        return_operation_scores=True\n",
    "    )\n",
    "\n",
    "    # iBLEU (alpha = 0.9)\n",
    "    bleu_sys_orig = corpus_bleu(\n",
    "        [row[\"simp_sent\"]],\n",
    "        [orig_sents],\n",
    "        force=True,\n",
    "        tokenizer=tokenizer,\n",
    "        lowercase=lowercase,\n",
    "    )\n",
    "    ibleu_score = 0.9 * bleu_sys_refs - (1 - 0.9) * bleu_sys_orig\n",
    "\n",
    "    # Avg. of BLEU and SARI\n",
    "    amean_bleu_sari = np.mean([bleu_sys_refs, sari_score])\n",
    "    gmean_bleu_sari = scipy.stats.gmean([bleu_sys_refs, sari_score])\n",
    "\n",
    "    # Flesch\n",
    "    fkgl_sys = corpus_fkgl([row[\"simp_sent\"]], tokenizer=tokenizer)\n",
    "\n",
    "    # FKBLEU\n",
    "    fkgl_orig = corpus_fkgl(orig_sents, tokenizer=tokenizer)\n",
    "    fk_diff = sigmoid(fkgl_sys - fkgl_orig)\n",
    "    fkbleu_score = ibleu_score * fk_diff\n",
    "\n",
    "    # BERTScore\n",
    "    ref_sents = [ref for [ref] in ref_sents]\n",
    "    bertscore_rescale_scores = bertscore_rescale.score([row[\"simp_sent\"]], [ref_sents])\n",
    "\n",
    "    metrics.append(\n",
    "        {\n",
    "            \"sent_id\": row[\"sent_id\"],\n",
    "            \"sys_name\": row[\"sys_name\"],\n",
    "            \"test_set\": test_set,\n",
    "            \"bleu\": bleu_sys_refs,\n",
    "            \"sari\": sari_score,\n",
    "            \"sari_add\": add_score,\n",
    "            \"sari_keep\": keep_score,\n",
    "            \"sari_del\": del_score,\n",
    "            \"ibleu\": ibleu_score,\n",
    "            \"amean_bleu_sari\": amean_bleu_sari,\n",
    "            \"gmean_bleu_sari\": gmean_bleu_sari,\n",
    "            \"fkgl\": fkgl_sys,\n",
    "            \"fkbleu\": fkbleu_score,\n",
    "            \"bertscore_P\": bertscore_rescale_scores[0].cpu().item(),\n",
    "            \"bertscore_R\": bertscore_rescale_scores[1].cpu().item(),\n",
    "            \"bertscore_F1\": bertscore_rescale_scores[2].cpu().item(),\n",
    "        }\n",
    "    )\n",
    "\n",
    "df_metrics_segment = pd.DataFrame(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>sent_id</th>\n",
       "      <th>sys_name</th>\n",
       "      <th>test_set</th>\n",
       "      <th>bleu</th>\n",
       "      <th>sari</th>\n",
       "      <th>sari_add</th>\n",
       "      <th>sari_keep</th>\n",
       "      <th>sari_del</th>\n",
       "      <th>ibleu</th>\n",
       "      <th>amean_bleu_sari</th>\n",
       "      <th>gmean_bleu_sari</th>\n",
       "      <th>fkgl</th>\n",
       "      <th>fkbleu</th>\n",
       "      <th>bertscore_P</th>\n",
       "      <th>bertscore_R</th>\n",
       "      <th>bertscore_F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>268</td>\n",
       "      <td>ACCESS</td>\n",
       "      <td>asset</td>\n",
       "      <td>82.802644</td>\n",
       "      <td>47.579917</td>\n",
       "      <td>12.077973</td>\n",
       "      <td>81.405824</td>\n",
       "      <td>49.255952</td>\n",
       "      <td>67.429755</td>\n",
       "      <td>65.191280</td>\n",
       "      <td>62.767371</td>\n",
       "      <td>7.633846</td>\n",
       "      <td>0.268007</td>\n",
       "      <td>0.904156</td>\n",
       "      <td>0.862443</td>\n",
       "      <td>0.883413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>67.801658</td>\n",
       "      <td>39.083659</td>\n",
       "      <td>3.304267</td>\n",
       "      <td>48.806719</td>\n",
       "      <td>65.139991</td>\n",
       "      <td>54.715578</td>\n",
       "      <td>53.442659</td>\n",
       "      <td>51.477538</td>\n",
       "      <td>11.056667</td>\n",
       "      <td>3.277390</td>\n",
       "      <td>0.921957</td>\n",
       "      <td>0.924982</td>\n",
       "      <td>0.920372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>341</td>\n",
       "      <td>SBMT-SARI</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>61.436117</td>\n",
       "      <td>39.423251</td>\n",
       "      <td>5.158791</td>\n",
       "      <td>62.629901</td>\n",
       "      <td>50.481061</td>\n",
       "      <td>49.763566</td>\n",
       "      <td>50.429684</td>\n",
       "      <td>49.213935</td>\n",
       "      <td>17.142500</td>\n",
       "      <td>1.227814</td>\n",
       "      <td>0.767044</td>\n",
       "      <td>0.799759</td>\n",
       "      <td>0.773052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>278</td>\n",
       "      <td>PBMT-R</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>86.506155</td>\n",
       "      <td>40.449427</td>\n",
       "      <td>2.322645</td>\n",
       "      <td>41.177313</td>\n",
       "      <td>77.848325</td>\n",
       "      <td>70.206586</td>\n",
       "      <td>63.477791</td>\n",
       "      <td>59.153397</td>\n",
       "      <td>11.547407</td>\n",
       "      <td>4.230873</td>\n",
       "      <td>0.780502</td>\n",
       "      <td>0.751494</td>\n",
       "      <td>0.756557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>86.944174</td>\n",
       "      <td>40.612840</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.755187</td>\n",
       "      <td>72.083333</td>\n",
       "      <td>72.976326</td>\n",
       "      <td>63.778507</td>\n",
       "      <td>59.422638</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>0.793896</td>\n",
       "      <td>0.917693</td>\n",
       "      <td>0.747387</td>\n",
       "      <td>0.831554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>210</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>83.070185</td>\n",
       "      <td>46.106396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.356226</td>\n",
       "      <td>62.962963</td>\n",
       "      <td>67.246665</td>\n",
       "      <td>64.588290</td>\n",
       "      <td>61.887534</td>\n",
       "      <td>8.370000</td>\n",
       "      <td>39.625802</td>\n",
       "      <td>0.949457</td>\n",
       "      <td>0.930342</td>\n",
       "      <td>0.916086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>202</td>\n",
       "      <td>Dress-Ls</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.640409</td>\n",
       "      <td>2.846264</td>\n",
       "      <td>63.658297</td>\n",
       "      <td>85.416667</td>\n",
       "      <td>83.846009</td>\n",
       "      <td>75.320205</td>\n",
       "      <td>71.162075</td>\n",
       "      <td>7.190000</td>\n",
       "      <td>51.475261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>194</td>\n",
       "      <td>PBMT-R</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>47.947318</td>\n",
       "      <td>31.788174</td>\n",
       "      <td>8.007386</td>\n",
       "      <td>54.562060</td>\n",
       "      <td>32.795075</td>\n",
       "      <td>39.357168</td>\n",
       "      <td>39.867746</td>\n",
       "      <td>39.040462</td>\n",
       "      <td>10.310435</td>\n",
       "      <td>24.618682</td>\n",
       "      <td>0.209967</td>\n",
       "      <td>0.172067</td>\n",
       "      <td>0.192260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>148</td>\n",
       "      <td>Dress-Ls</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>31.153518</td>\n",
       "      <td>0.968625</td>\n",
       "      <td>53.843599</td>\n",
       "      <td>38.648330</td>\n",
       "      <td>85.726507</td>\n",
       "      <td>65.576759</td>\n",
       "      <td>55.815337</td>\n",
       "      <td>8.895238</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>0.894883</td>\n",
       "      <td>0.555689</td>\n",
       "      <td>0.720652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>112</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>asset+turk</td>\n",
       "      <td>69.309773</td>\n",
       "      <td>27.329631</td>\n",
       "      <td>4.191919</td>\n",
       "      <td>49.628984</td>\n",
       "      <td>28.167989</td>\n",
       "      <td>58.800411</td>\n",
       "      <td>48.319702</td>\n",
       "      <td>43.522529</td>\n",
       "      <td>12.775000</td>\n",
       "      <td>40.738063</td>\n",
       "      <td>0.628087</td>\n",
       "      <td>0.469003</td>\n",
       "      <td>0.548123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sent_id   sys_name    test_set        bleu       sari   sari_add  \\\n",
       "0        268     ACCESS       asset   82.802644  47.579917  12.077973   \n",
       "1         67  SBMT-SARI  asset+turk   67.801658  39.083659   3.304267   \n",
       "2        341  SBMT-SARI  asset+turk   61.436117  39.423251   5.158791   \n",
       "3        278     PBMT-R  asset+turk   86.506155  40.449427   2.322645   \n",
       "4        107     Hybrid  asset+turk   86.944174  40.612840   0.000000   \n",
       "..       ...        ...         ...         ...        ...        ...   \n",
       "595      210     Hybrid  asset+turk   83.070185  46.106396   0.000000   \n",
       "596      202   Dress-Ls  asset+turk  100.000000  50.640409   2.846264   \n",
       "597      194     PBMT-R  asset+turk   47.947318  31.788174   8.007386   \n",
       "598      148   Dress-Ls  asset+turk  100.000000  31.153518   0.968625   \n",
       "599      112     Hybrid  asset+turk   69.309773  27.329631   4.191919   \n",
       "\n",
       "     sari_keep   sari_del      ibleu  amean_bleu_sari  gmean_bleu_sari  \\\n",
       "0    81.405824  49.255952  67.429755        65.191280        62.767371   \n",
       "1    48.806719  65.139991  54.715578        53.442659        51.477538   \n",
       "2    62.629901  50.481061  49.763566        50.429684        49.213935   \n",
       "3    41.177313  77.848325  70.206586        63.477791        59.153397   \n",
       "4    49.755187  72.083333  72.976326        63.778507        59.422638   \n",
       "..         ...        ...        ...              ...              ...   \n",
       "595  75.356226  62.962963  67.246665        64.588290        61.887534   \n",
       "596  63.658297  85.416667  83.846009        75.320205        71.162075   \n",
       "597  54.562060  32.795075  39.357168        39.867746        39.040462   \n",
       "598  53.843599  38.648330  85.726507        65.576759        55.815337   \n",
       "599  49.628984  28.167989  58.800411        48.319702        43.522529   \n",
       "\n",
       "          fkgl     fkbleu  bertscore_P  bertscore_R  bertscore_F1  \n",
       "0     7.633846   0.268007     0.904156     0.862443      0.883413  \n",
       "1    11.056667   3.277390     0.921957     0.924982      0.920372  \n",
       "2    17.142500   1.227814     0.767044     0.799759      0.773052  \n",
       "3    11.547407   4.230873     0.780502     0.751494      0.756557  \n",
       "4     2.280000   0.793896     0.917693     0.747387      0.831554  \n",
       "..         ...        ...          ...          ...           ...  \n",
       "595   8.370000  39.625802     0.949457     0.930342      0.916086  \n",
       "596   7.190000  51.475261     1.000000     1.000000      1.000000  \n",
       "597  10.310435  24.618682     0.209967     0.172067      0.192260  \n",
       "598   8.895238   0.003160     0.894883     0.555689      0.720652  \n",
       "599  12.775000  40.738063     0.628087     0.469003      0.548123  \n",
       "\n",
       "[600 rows x 16 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics_segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sent_id</th>\n",
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       "      <th>asset</th>\n",
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       "      <td>58</td>\n",
       "      <td>58</td>\n",
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       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
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       "      <td>58</td>\n",
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       "      <th>asset+turk</th>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
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      ],
      "text/plain": [
       "            sent_id  sys_name  bleu  sari  sari_add  sari_keep  sari_del  \\\n",
       "test_set                                                                   \n",
       "asset            58        58    58    58        58         58        58   \n",
       "asset+turk      542       542   542   542       542        542       542   \n",
       "\n",
       "            ibleu  amean_bleu_sari  gmean_bleu_sari  fkgl  fkbleu  \\\n",
       "test_set                                                            \n",
       "asset          58               58               58    58      58   \n",
       "asset+turk    542              542              542   542     542   \n",
       "\n",
       "            bertscore_P  bertscore_R  bertscore_F1  \n",
       "test_set                                            \n",
       "asset                58           58            58  \n",
       "asset+turk          542          542           542  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics_segment.groupby('test_set').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_correlations_per_quality(df_benchmark, df_segment_metrics, aspects, segment_id_cols):\n",
    "    results = {}\n",
    "    for aspect in aspects: \n",
    "        df_benchmark_sorted = df_benchmark.sort_values(by=[f'{aspect}_zscore'])\n",
    "        df_scores_low = df_benchmark_sorted.head(int(len(df_benchmark_sorted) / 2))\n",
    "        df_scores_high = df_benchmark_sorted.tail(int(len(df_benchmark_sorted) / 2))\n",
    "        print(f\"{aspect}: High ({len(df_scores_high)}) - Low ({len(df_scores_low)}) - All ({len(df_benchmark)})\")\n",
    "        for quality, df_scores in {'low': df_scores_low, 'high': df_scores_high, 'all': df_benchmark}.items():\n",
    "            df_metrics = df_segment_metrics.drop(columns=['test_set'])\n",
    "            results[(quality, aspect)] = compute_direct_assessment_correlations(df_scores, \n",
    "                                                                                df_metrics, \n",
    "                                                                                aspect, \n",
    "                                                                                segment_id_cols=segment_id_cols)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pprint_correlations(results_correlations, aspects):\n",
    "    correlations = []\n",
    "    for aspect in aspects:\n",
    "        for quality in ['low', 'high', 'all']:\n",
    "            df_corr, _ = results_correlations[(quality, aspect)]\n",
    "            df_corr = df_corr[['metric', 'corr']]\n",
    "            df_corr.columns = [\"metric\", f\"{aspect}_{quality}\"]\n",
    "            correlations.append(df_corr)\n",
    "\n",
    "    df_corr_metrics = reduce(\n",
    "        lambda left, right: pd.merge(left, right, on=[\"metric\"], how=\"outer\"), correlations\n",
    "    )\n",
    "    \n",
    "    return df_corr_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS_TO_ANALYSE = ['bleu', 'sari', 'fkgl', 'fkbleu', 'ibleu', \n",
    "                      'amean_bleu_sari', 'gmean_bleu_sari',\n",
    "                      'bertscore_P', 'bertscore_R', 'bertscore_F1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics_segment = df_metrics_segment[['sent_id', 'sys_name', 'test_set'] + METRICS_TO_ANALYSE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simplicity: High (300) - Low (300) - All (600)\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n",
      "Computing correlations...\n",
      "Determining if the difference in performance is significant...\n"
     ]
    }
   ],
   "source": [
    "results_simplicity = compute_correlations_per_quality(df_benchmark_simplicity, df_metrics_segment, \n",
    "                                                      aspects=['simplicity'], segment_id_cols=['sent_id','sys_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>metric</th>\n",
       "      <th>corr</th>\n",
       "      <th>p_value</th>\n",
       "      <th>is_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.276053</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.243521</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.223346</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.221199</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.205090</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.201613</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.165235</td>\n",
       "      <td>0.004108</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.115462</td>\n",
       "      <td>0.045695</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.093468</td>\n",
       "      <td>0.106160</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.727474</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            metric      corr   p_value  is_winner\n",
       "0      bertscore_P  0.276053  0.000001      False\n",
       "1            ibleu  0.243521  0.000020      False\n",
       "2             bleu  0.223346  0.000096      False\n",
       "3  amean_bleu_sari  0.221199  0.000112      False\n",
       "4  gmean_bleu_sari  0.205090  0.000350      False\n",
       "5     bertscore_F1  0.201613  0.000442      False\n",
       "6      bertscore_R  0.165235  0.004108      False\n",
       "7             sari  0.115462  0.045695      False\n",
       "8             fkgl  0.093468  0.106160      False\n",
       "9           fkbleu  0.020202  0.727474      False"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_simplicity[('high', 'simplicity')][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr_metrics_simplicity = pprint_correlations(results_simplicity, aspects=['simplicity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>simplicity_low</th>\n",
       "      <th>simplicity_high</th>\n",
       "      <th>simplicity_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.543140</td>\n",
       "      <td>0.276053</td>\n",
       "      <td>0.634898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.534331</td>\n",
       "      <td>0.201613</td>\n",
       "      <td>0.583902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.478600</td>\n",
       "      <td>0.165235</td>\n",
       "      <td>0.510859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.418015</td>\n",
       "      <td>0.221199</td>\n",
       "      <td>0.522921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.409683</td>\n",
       "      <td>0.205090</td>\n",
       "      <td>0.495546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.401937</td>\n",
       "      <td>0.223346</td>\n",
       "      <td>0.525337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.395613</td>\n",
       "      <td>0.243521</td>\n",
       "      <td>0.536045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>0.115462</td>\n",
       "      <td>0.350300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.271669</td>\n",
       "      <td>0.093468</td>\n",
       "      <td>0.117057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.126251</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.098137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            metric  simplicity_low  simplicity_high  simplicity_all\n",
       "0      bertscore_P        0.543140         0.276053        0.634898\n",
       "1     bertscore_F1        0.534331         0.201613        0.583902\n",
       "2      bertscore_R        0.478600         0.165235        0.510859\n",
       "3  amean_bleu_sari        0.418015         0.221199        0.522921\n",
       "4  gmean_bleu_sari        0.409683         0.205090        0.495546\n",
       "5             bleu        0.401937         0.223346        0.525337\n",
       "6            ibleu        0.395613         0.243521        0.536045\n",
       "7             sari        0.352113         0.115462        0.350300\n",
       "8             fkgl        0.271669         0.093468        0.117057\n",
       "9           fkbleu        0.126251         0.020202        0.098137"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_metrics_simplicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>simplicity_low</th>\n",
       "      <th>simplicity_high</th>\n",
       "      <th>simplicity_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bertscore_P</td>\n",
       "      <td>0.543140</td>\n",
       "      <td>0.276053</td>\n",
       "      <td>0.634898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bertscore_F1</td>\n",
       "      <td>0.534331</td>\n",
       "      <td>0.201613</td>\n",
       "      <td>0.583902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ibleu</td>\n",
       "      <td>0.395613</td>\n",
       "      <td>0.243521</td>\n",
       "      <td>0.536045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bleu</td>\n",
       "      <td>0.401937</td>\n",
       "      <td>0.223346</td>\n",
       "      <td>0.525337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amean_bleu_sari</td>\n",
       "      <td>0.418015</td>\n",
       "      <td>0.221199</td>\n",
       "      <td>0.522921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bertscore_R</td>\n",
       "      <td>0.478600</td>\n",
       "      <td>0.165235</td>\n",
       "      <td>0.510859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gmean_bleu_sari</td>\n",
       "      <td>0.409683</td>\n",
       "      <td>0.205090</td>\n",
       "      <td>0.495546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sari</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>0.115462</td>\n",
       "      <td>0.350300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fkgl</td>\n",
       "      <td>0.271669</td>\n",
       "      <td>0.093468</td>\n",
       "      <td>0.117057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fkbleu</td>\n",
       "      <td>0.126251</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.098137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            metric  simplicity_low  simplicity_high  simplicity_all\n",
       "0      bertscore_P        0.543140         0.276053        0.634898\n",
       "1     bertscore_F1        0.534331         0.201613        0.583902\n",
       "6            ibleu        0.395613         0.243521        0.536045\n",
       "5             bleu        0.401937         0.223346        0.525337\n",
       "3  amean_bleu_sari        0.418015         0.221199        0.522921\n",
       "2      bertscore_R        0.478600         0.165235        0.510859\n",
       "4  gmean_bleu_sari        0.409683         0.205090        0.495546\n",
       "7             sari        0.352113         0.115462        0.350300\n",
       "8             fkgl        0.271669         0.093468        0.117057\n",
       "9           fkbleu        0.126251         0.020202        0.098137"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_metrics_simplicity.sort_values(by=['simplicity_all'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrr}\n",
      "\\toprule\n",
      "          metric &  simplicity\\_low &  simplicity\\_high &  simplicity\\_all \\\\\n",
      "\\midrule\n",
      "     bertscore\\_P &           0.543 &            0.276 &           0.635 \\\\\n",
      "    bertscore\\_F1 &           0.534 &            0.202 &           0.584 \\\\\n",
      "           ibleu &           0.396 &            0.244 &           0.536 \\\\\n",
      "            bleu &           0.402 &            0.223 &           0.525 \\\\\n",
      " amean\\_bleu\\_sari &           0.418 &            0.221 &           0.523 \\\\\n",
      "     bertscore\\_R &           0.479 &            0.165 &           0.511 \\\\\n",
      " gmean\\_bleu\\_sari &           0.410 &            0.205 &           0.496 \\\\\n",
      "            sari &           0.352 &            0.115 &           0.350 \\\\\n",
      "            fkgl &           0.272 &            0.093 &           0.117 \\\\\n",
      "          fkbleu &           0.126 &            0.020 &           0.098 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df_corr_metrics_simplicity.sort_values(by=['simplicity_all'], ascending=False).to_latex(float_format=\"%.3f\", index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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