{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = ['#83B8FE', '#FFA54C', '#94ED67', '#FF7FFF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv_train: (15, 52)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_type</th>\n",
       "      <th>fold</th>\n",
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       "      <th>val_unique_groups</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Pytorch</td>\n",
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       "      <td>Pytorch</td>\n",
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       "      <td>617</td>\n",
       "      <td>154</td>\n",
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       "      <td>0.486224</td>\n",
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       "      <td>0.519481</td>\n",
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       "      <td>Pytorch</td>\n",
       "      <td>2</td>\n",
       "      <td>617</td>\n",
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       "      <td>0.199741</td>\n",
       "      <td>0.515397</td>\n",
       "      <td>0.484603</td>\n",
       "      <td>0.376572</td>\n",
       "      <td>0.512987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.931139</td>\n",
       "      <td>0.611611</td>\n",
       "      <td>0.779070</td>\n",
       "      <td>0.781609</td>\n",
       "      <td>0.723404</td>\n",
       "      <td>0.85</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>Pytorch</td>\n",
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       "      <th>4</th>\n",
       "      <td>Pytorch</td>\n",
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       "      <td>617</td>\n",
       "      <td>154</td>\n",
       "      <td>0.800259</td>\n",
       "      <td>0.199741</td>\n",
       "      <td>0.515397</td>\n",
       "      <td>0.484603</td>\n",
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       "      <td>0.512987</td>\n",
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       "      <td>0.927257</td>\n",
       "      <td>0.561546</td>\n",
       "      <td>0.779070</td>\n",
       "      <td>0.781609</td>\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_type  fold  train_len  val_len  train_perc  val_perc  \\\n",
       "0    Pytorch     0        616      155    0.798962  0.201038   \n",
       "1    Pytorch     1        617      154    0.800259  0.199741   \n",
       "2    Pytorch     2        617      154    0.800259  0.199741   \n",
       "3    Pytorch     3        617      154    0.800259  0.199741   \n",
       "4    Pytorch     4        617      154    0.800259  0.199741   \n",
       "\n",
       "   train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
       "0           0.514610             0.485390                 0.376475   \n",
       "1           0.513776             0.486224                 0.377372   \n",
       "2           0.515397             0.484603                 0.376572   \n",
       "3           0.515397             0.484603                 0.376068   \n",
       "4           0.515397             0.484603                 0.377543   \n",
       "\n",
       "   val_active_perc  ...  val_roc_auc  test_loss  test_acc  test_f1_score  \\\n",
       "0         0.516129  ...     0.904333   0.661766  0.802326       0.817204   \n",
       "1         0.519481  ...     0.924493   0.658051  0.755814       0.764045   \n",
       "2         0.512987  ...     0.931139   0.611611  0.779070       0.781609   \n",
       "3         0.512987  ...     0.924557   0.693243  0.732558       0.747253   \n",
       "4         0.512987  ...     0.927257   0.561546  0.779070       0.781609   \n",
       "\n",
       "   test_precision  test_recall  test_roc_auc  split_type  train_unique_groups  \\\n",
       "0        0.716981         0.95      0.860326      random                  NaN   \n",
       "1        0.693878         0.85      0.858152      random                  NaN   \n",
       "2        0.723404         0.85      0.863587      random                  NaN   \n",
       "3        0.666667         0.85      0.847826      random                  NaN   \n",
       "4        0.723404         0.85      0.878804      random                  NaN   \n",
       "\n",
       "   val_unique_groups  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test: (9, 34)\n"
     ]
    },
    {
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       "      <td>0.809463</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>uniprot</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.631336</td>\n",
       "      <td>0.649270</td>\n",
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       "   train_loss  train_loss_step  train_loss_epoch  train_acc  train_acc_epoch  \\\n",
       "0    0.190532         0.307893          0.190532   0.939547         0.939547   \n",
       "1    0.188614         0.163636          0.188614   0.940806         0.940806   \n",
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       "0    0.543100         0.583436          0.543100   0.809463         0.809463   \n",
       "1    0.649270         0.631336          0.649270   0.767263         0.767263   \n",
       "\n",
       "   train_f1_score  train_f1_score_epoch  train_precision  \\\n",
       "0        0.940150              0.940150         0.930864   \n",
       "1        0.941760              0.941760         0.926829   \n",
       "2        0.942786              0.942786         0.931204   \n",
       "0        0.820265              0.820265         0.776256   \n",
       "1        0.795964              0.795964         0.708583   \n",
       "\n",
       "   train_precision_epoch  train_recall  ...  train_avg_tanimoto_dist  \\\n",
       "0               0.930864      0.949622  ...                 0.376806   \n",
       "1               0.926829      0.957179  ...                 0.376806   \n",
       "2               0.931204      0.954660  ...                 0.376806   \n",
       "0               0.776256      0.869565  ...                 0.375305   \n",
       "1               0.708583      0.907928  ...                 0.375305   \n",
       "\n",
       "   test_len  test_active_perc  test_inactive_perc  test_avg_tanimoto_dist  \\\n",
       "0        86          0.465116            0.534884                0.381147   \n",
       "1        86          0.465116            0.534884                0.381147   \n",
       "2        86          0.465116            0.534884                0.381147   \n",
       "0        85          0.541176            0.458824                0.394830   \n",
       "1        85          0.541176            0.458824                0.394830   \n",
       "\n",
       "   num_leaking_uniprot_train_test  num_leaking_smiles_train_test  \\\n",
       "0                              34                             44   \n",
       "1                              34                             44   \n",
       "2                              34                             44   \n",
       "0                               0                              6   \n",
       "1                               0                              6   \n",
       "\n",
       "   perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test split_type  \n",
       "0                         0.832685                        0.102464     random  \n",
       "1                         0.832685                        0.102464     random  \n",
       "2                         0.832685                        0.102464     random  \n",
       "0                         0.000000                        0.011658    uniprot  \n",
       "1                         0.000000                        0.011658    uniprot  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
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    },
    {
     "name": "stdout",
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     "text": [
      "ablation: (84, 23)\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>test_loss</th>\n",
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       "      <th>model_type</th>\n",
       "      <th>disabled_embeddings</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>split_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.726923</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.673077</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.717391</td>\n",
       "      <td>771</td>\n",
       "      <td>0.514916</td>\n",
       "      <td>0.485084</td>\n",
       "      <td>0.376806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381147</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>0.832685</td>\n",
       "      <td>0.102464</td>\n",
       "      <td>False</td>\n",
       "      <td>Pytorch</td>\n",
       "      <td>disabled e3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.697167</td>\n",
       "      <td>0.616279</td>\n",
       "      <td>0.535211</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.671739</td>\n",
       "      <td>771</td>\n",
       "      <td>0.514916</td>\n",
       "      <td>0.485084</td>\n",
       "      <td>0.376806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381147</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>0.832685</td>\n",
       "      <td>0.102464</td>\n",
       "      <td>False</td>\n",
       "      <td>Pytorch</td>\n",
       "      <td>disabled e3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.654254</td>\n",
       "      <td>0.639535</td>\n",
       "      <td>0.643678</td>\n",
       "      <td>0.595745</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.714131</td>\n",
       "      <td>771</td>\n",
       "      <td>0.514916</td>\n",
       "      <td>0.485084</td>\n",
       "      <td>0.376806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381147</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>0.832685</td>\n",
       "      <td>0.102464</td>\n",
       "      <td>False</td>\n",
       "      <td>Pytorch</td>\n",
       "      <td>disabled e3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.616279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.689674</td>\n",
       "      <td>771</td>\n",
       "      <td>0.514916</td>\n",
       "      <td>0.485084</td>\n",
       "      <td>0.376806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381147</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>0.832685</td>\n",
       "      <td>0.102464</td>\n",
       "      <td>True</td>\n",
       "      <td>Pytorch</td>\n",
       "      <td>disabled e3</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.744749</td>\n",
       "      <td>0.593023</td>\n",
       "      <td>0.653465</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.709239</td>\n",
       "      <td>771</td>\n",
       "      <td>0.514916</td>\n",
       "      <td>0.485084</td>\n",
       "      <td>0.376806</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381147</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>0.832685</td>\n",
       "      <td>0.102464</td>\n",
       "      <td>False</td>\n",
       "      <td>Pytorch</td>\n",
       "      <td>disabled poi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_loss  test_acc  test_f1_score  test_precision  test_recall  \\\n",
       "0   0.726923  0.604651       0.673077        0.546875        0.875   \n",
       "1   0.697167  0.616279       0.535211        0.612903        0.475   \n",
       "2   0.654254  0.639535       0.643678        0.595745        0.700   \n",
       "3        NaN  0.616279            NaN        0.629630        0.425   \n",
       "4   0.744749  0.593023       0.653465        0.540984        0.825   \n",
       "\n",
       "   test_roc_auc  train_len  train_active_perc  train_inactive_perc  \\\n",
       "0      0.717391        771           0.514916             0.485084   \n",
       "1      0.671739        771           0.514916             0.485084   \n",
       "2      0.714131        771           0.514916             0.485084   \n",
       "3      0.689674        771           0.514916             0.485084   \n",
       "4      0.709239        771           0.514916             0.485084   \n",
       "\n",
       "   train_avg_tanimoto_dist  ...  test_avg_tanimoto_dist  \\\n",
       "0                 0.376806  ...                0.381147   \n",
       "1                 0.376806  ...                0.381147   \n",
       "2                 0.376806  ...                0.381147   \n",
       "3                 0.376806  ...                0.381147   \n",
       "4                 0.376806  ...                0.381147   \n",
       "\n",
       "   num_leaking_uniprot_train_test  num_leaking_smiles_train_test  \\\n",
       "0                              34                             44   \n",
       "1                              34                             44   \n",
       "2                              34                             44   \n",
       "3                              34                             44   \n",
       "4                              34                             44   \n",
       "\n",
       "   perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test  \\\n",
       "0                         0.832685                        0.102464   \n",
       "1                         0.832685                        0.102464   \n",
       "2                         0.832685                        0.102464   \n",
       "3                         0.832685                        0.102464   \n",
       "4                         0.832685                        0.102464   \n",
       "\n",
       "   majority_vote  model_type  disabled_embeddings   test_f1 split_type  \n",
       "0          False     Pytorch          disabled e3       NaN     random  \n",
       "1          False     Pytorch          disabled e3       NaN     random  \n",
       "2          False     Pytorch          disabled e3       NaN     random  \n",
       "3           True     Pytorch          disabled e3  0.507463     random  \n",
       "4          False     Pytorch         disabled poi       NaN     random  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hparam: (3, 7)\n"
     ]
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       "      <th></th>\n",
       "      <th>hidden_dim</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>smote_k_neighbors</th>\n",
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       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>0.000925</td>\n",
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       "      <th>0</th>\n",
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       "      <td>True</td>\n",
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       "      <td>tanimoto</td>\n",
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       "   hidden_dim  learning_rate  smote_k_neighbors  use_smote   dropout  \\\n",
       "0         128       0.000925                 10       True  0.216350   \n",
       "0          32       0.000649                  4       True  0.092638   \n",
       "0          64       0.000771                 13      False  0.304535   \n",
       "\n",
       "   use_batch_norm split_type  \n",
       "0            True     random  \n",
       "0           False    uniprot  \n",
       "0            True   tanimoto  "
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     "text": [
      "majority_vote: (6, 28)\n"
     ]
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       "      <th>perc_leaking_uniprot_train_val</th>\n",
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       "      <td>0.868478</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>0.775000</td>\n",
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       "      <td>617</td>\n",
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       "      <td>86</td>\n",
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       "      <td>0.373853</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.910859</td>\n",
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       "      <td>0.675676</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>0.602410</td>\n",
       "      <td>772</td>\n",
       "      <td>0.506477</td>\n",
       "      <td>0.493523</td>\n",
       "      <td>0.375305</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.173913</td>\n",
       "      <td>0.242424</td>\n",
       "      <td>693</td>\n",
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       "      <td>0.515152</td>\n",
       "      <td>0.377092</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>0.696203</td>\n",
       "      <td>0.303797</td>\n",
       "      <td>0.359625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014430</td>\n",
       "      <td>True</td>\n",
       "      <td>Pytorch</td>\n",
       "      <td>uniprot</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.823761</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.576271</td>\n",
       "      <td>772</td>\n",
       "      <td>0.518135</td>\n",
       "      <td>0.481865</td>\n",
       "      <td>0.372540</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
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      ],
      "text/plain": [
       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
       "0  0.825581      0.847826        0.777778     0.875000       0.823529   \n",
       "1  0.813953      0.868478        0.815789     0.775000       0.794872   \n",
       "0  0.611765      0.614827        0.675676     0.543478       0.602410   \n",
       "1  0.411765      0.549610        0.400000     0.173913       0.242424   \n",
       "0  0.705882      0.823761        0.772727     0.459459       0.576271   \n",
       "\n",
       "   train_len  train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
       "0        771           0.514916             0.485084                 0.376806   \n",
       "1        617           0.515397             0.484603                 0.377543   \n",
       "0        772           0.506477             0.493523                 0.375305   \n",
       "1        693           0.484848             0.515152                 0.377092   \n",
       "0        772           0.518135             0.481865                 0.372540   \n",
       "\n",
       "   test_len  ...  val_active_perc  val_inactive_perc  val_avg_tanimoto_dist  \\\n",
       "0        86  ...              NaN                NaN                    NaN   \n",
       "1        86  ...         0.512987           0.487013               0.373853   \n",
       "0        85  ...              NaN                NaN                    NaN   \n",
       "1        85  ...         0.696203           0.303797               0.359625   \n",
       "0        85  ...              NaN                NaN                    NaN   \n",
       "\n",
       "   num_leaking_uniprot_train_val  num_leaking_smiles_train_val  \\\n",
       "0                            NaN                           NaN   \n",
       "1                           40.0                          70.0   \n",
       "0                            NaN                           NaN   \n",
       "1                            0.0                           8.0   \n",
       "0                            NaN                           NaN   \n",
       "\n",
       "   perc_leaking_uniprot_train_val  perc_leaking_smiles_train_val  cv_models  \\\n",
       "0                             NaN                            NaN        NaN   \n",
       "1                        0.910859                       0.176661       True   \n",
       "0                             NaN                            NaN        NaN   \n",
       "1                        0.000000                       0.014430       True   \n",
       "0                             NaN                            NaN        NaN   \n",
       "\n",
       "   model_type  split_type  \n",
       "0     Pytorch      random  \n",
       "1     Pytorch      random  \n",
       "0     Pytorch     uniprot  \n",
       "1     Pytorch     uniprot  \n",
       "0     Pytorch    tanimoto  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
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     "text": [
      "xgboost_cv_train: (15, 24)\n"
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       "      <th>...</th>\n",
       "      <th>perc_leaking_uniprot_train_val</th>\n",
       "      <th>perc_leaking_smiles_train_val</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_roc_auc</th>\n",
       "      <th>val_precision</th>\n",
       "      <th>val_recall</th>\n",
       "      <th>val_f1_score</th>\n",
       "      <th>split_type</th>\n",
       "      <th>train_unique_groups</th>\n",
       "      <th>val_unique_groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0</td>\n",
       "      <td>616</td>\n",
       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.376475</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>0.146104</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.917167</td>\n",
       "      <td>0.816092</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>0.850299</td>\n",
       "      <td>random</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>1</td>\n",
       "      <td>617</td>\n",
       "      <td>154</td>\n",
       "      <td>0.800259</td>\n",
       "      <td>0.199741</td>\n",
       "      <td>0.513776</td>\n",
       "      <td>0.486224</td>\n",
       "      <td>0.377372</td>\n",
       "      <td>0.519481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920583</td>\n",
       "      <td>0.188006</td>\n",
       "      <td>0.883117</td>\n",
       "      <td>0.959291</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.891566</td>\n",
       "      <td>random</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2</td>\n",
       "      <td>617</td>\n",
       "      <td>154</td>\n",
       "      <td>0.800259</td>\n",
       "      <td>0.199741</td>\n",
       "      <td>0.515397</td>\n",
       "      <td>0.484603</td>\n",
       "      <td>0.376572</td>\n",
       "      <td>0.512987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.914100</td>\n",
       "      <td>0.144246</td>\n",
       "      <td>0.876623</td>\n",
       "      <td>0.917975</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.877419</td>\n",
       "      <td>random</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>3</td>\n",
       "      <td>617</td>\n",
       "      <td>154</td>\n",
       "      <td>0.800259</td>\n",
       "      <td>0.199741</td>\n",
       "      <td>0.515397</td>\n",
       "      <td>0.484603</td>\n",
       "      <td>0.376068</td>\n",
       "      <td>0.512987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876823</td>\n",
       "      <td>0.162075</td>\n",
       "      <td>0.883117</td>\n",
       "      <td>0.937384</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.890244</td>\n",
       "      <td>random</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>4</td>\n",
       "      <td>617</td>\n",
       "      <td>154</td>\n",
       "      <td>0.800259</td>\n",
       "      <td>0.199741</td>\n",
       "      <td>0.515397</td>\n",
       "      <td>0.484603</td>\n",
       "      <td>0.377543</td>\n",
       "      <td>0.512987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.910859</td>\n",
       "      <td>0.176661</td>\n",
       "      <td>0.883117</td>\n",
       "      <td>0.939241</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.890244</td>\n",
       "      <td>random</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_type  fold  train_len  val_len  train_perc  val_perc  \\\n",
       "0    XGBoost     0        616      155    0.798962  0.201038   \n",
       "1    XGBoost     1        617      154    0.800259  0.199741   \n",
       "2    XGBoost     2        617      154    0.800259  0.199741   \n",
       "3    XGBoost     3        617      154    0.800259  0.199741   \n",
       "4    XGBoost     4        617      154    0.800259  0.199741   \n",
       "\n",
       "   train_active_perc  train_inactive_perc  train_avg_tanimoto_dist  \\\n",
       "0           0.514610             0.485390                 0.376475   \n",
       "1           0.513776             0.486224                 0.377372   \n",
       "2           0.515397             0.484603                 0.376572   \n",
       "3           0.515397             0.484603                 0.376068   \n",
       "4           0.515397             0.484603                 0.377543   \n",
       "\n",
       "   val_active_perc  ...  perc_leaking_uniprot_train_val  \\\n",
       "0         0.516129  ...                        0.886364   \n",
       "1         0.519481  ...                        0.920583   \n",
       "2         0.512987  ...                        0.914100   \n",
       "3         0.512987  ...                        0.876823   \n",
       "4         0.512987  ...                        0.910859   \n",
       "\n",
       "   perc_leaking_smiles_train_val   val_acc  val_roc_auc  val_precision  \\\n",
       "0                       0.146104  0.838710     0.917167       0.816092   \n",
       "1                       0.188006  0.883117     0.959291       0.860465   \n",
       "2                       0.144246  0.876623     0.917975       0.894737   \n",
       "3                       0.162075  0.883117     0.937384       0.858824   \n",
       "4                       0.176661  0.883117     0.939241       0.858824   \n",
       "\n",
       "   val_recall  val_f1_score  split_type  train_unique_groups  \\\n",
       "0    0.887500      0.850299      random                  NaN   \n",
       "1    0.925000      0.891566      random                  NaN   \n",
       "2    0.860759      0.877419      random                  NaN   \n",
       "3    0.924051      0.890244      random                  NaN   \n",
       "4    0.924051      0.890244      random                  NaN   \n",
       "\n",
       "   val_unique_groups  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
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     "text": [
      "xgboost_test: (9, 20)\n"
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       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
       "0  0.767442      0.879348        0.700000     0.875000       0.777778   \n",
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       "\n",
       "  model_type  test_model_id  train_len  train_active_perc  \\\n",
       "0    XGBoost              0        771           0.514916   \n",
       "1    XGBoost              1        771           0.514916   \n",
       "2    XGBoost              2        771           0.514916   \n",
       "0    XGBoost              0        772           0.506477   \n",
       "1    XGBoost              1        772           0.506477   \n",
       "\n",
       "   train_inactive_perc  train_avg_tanimoto_dist  test_len  test_active_perc  \\\n",
       "0             0.485084                 0.376806        86          0.465116   \n",
       "1             0.485084                 0.376806        86          0.465116   \n",
       "2             0.485084                 0.376806        86          0.465116   \n",
       "0             0.493523                 0.375305        85          0.541176   \n",
       "1             0.493523                 0.375305        85          0.541176   \n",
       "\n",
       "   test_inactive_perc  test_avg_tanimoto_dist  num_leaking_uniprot_train_test  \\\n",
       "0            0.534884                0.381147                              34   \n",
       "1            0.534884                0.381147                              34   \n",
       "2            0.534884                0.381147                              34   \n",
       "0            0.458824                0.394830                               0   \n",
       "1            0.458824                0.394830                               0   \n",
       "\n",
       "   num_leaking_smiles_train_test  perc_leaking_uniprot_train_test  \\\n",
       "0                             44                         0.832685   \n",
       "1                             44                         0.832685   \n",
       "2                             44                         0.832685   \n",
       "0                              6                         0.000000   \n",
       "1                              6                         0.000000   \n",
       "\n",
       "   perc_leaking_smiles_train_test split_type  \n",
       "0                        0.102464     random  \n",
       "1                        0.102464     random  \n",
       "2                        0.102464     random  \n",
       "0                        0.011658    uniprot  \n",
       "1                        0.011658    uniprot  "
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       "        eta  max_depth  min_child_weight     gamma  subsample  \\\n",
       "0  0.098975          9          0.013528  0.000218   0.851506   \n",
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       "\n",
       "   colsample_bytree split_type  \n",
       "0          0.630858     random  \n",
       "0          0.883731    uniprot  \n",
       "0          0.511964   tanimoto  "
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      "xgboost_majority_vote: (3, 7)\n"
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       "      <th></th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_roc_auc</th>\n",
       "      <th>test_precision</th>\n",
       "      <th>test_recall</th>\n",
       "      <th>test_f1_score</th>\n",
       "      <th>model_type</th>\n",
       "      <th>split_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.779070</td>\n",
       "      <td>0.880978</td>\n",
       "      <td>0.723404</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.781609</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.282609</td>\n",
       "      <td>0.356164</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>uniprot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.717647</td>\n",
       "      <td>0.831081</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>tanimoto</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_acc  test_roc_auc  test_precision  test_recall  test_f1_score  \\\n",
       "0  0.779070      0.880978        0.723404     0.850000       0.781609   \n",
       "0  0.447059      0.487179        0.481481     0.282609       0.356164   \n",
       "0  0.717647      0.831081        0.842105     0.432432       0.571429   \n",
       "\n",
       "  model_type split_type  \n",
       "0    XGBoost     random  \n",
       "0    XGBoost    uniprot  \n",
       "0    XGBoost   tanimoto  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "test_split = 0.1\n",
    "n_models_for_test = 3\n",
    "cv_n_folds = 5\n",
    "\n",
    "active_name = active_col.replace(' ', '_').replace('(', '').replace(')', '').replace(',', '')\n",
    "report_base_name = f'{active_name}_test_split_{test_split}'\n",
    "\n",
    "# Load the data\n",
    "reports = {\n",
    "    'cv_train': pd.concat([\n",
    "        pd.read_csv(f'reports/cv_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/cv_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/cv_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'test': pd.concat([\n",
    "        pd.read_csv(f'reports/test_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/test_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/test_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'ablation': pd.concat([\n",
    "        pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/ablation_zero_vectors_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'hparam': pd.concat([\n",
    "        pd.read_csv(f'reports/hparam_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/hparam_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/hparam_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'majority_vote': pd.concat([\n",
    "        pd.read_csv(f'reports/majority_vote_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/majority_vote_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/majority_vote_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'xgboost_cv_train': pd.concat([\n",
    "        pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_cv_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'xgboost_test': pd.concat([\n",
    "        pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_test_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'xgboost_hparam': pd.concat([\n",
    "        pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_hparam_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "    'xgboost_majority_vote': pd.concat([\n",
    "        pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_random.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_uniprot.csv'),\n",
    "        pd.read_csv(f'reports/xgboost_majority_vote_report_{report_base_name}_tanimoto.csv'),\n",
    "    ]),\n",
    "}\n",
    "for k, report in reports.items():\n",
    "    print(f'{k}: {report.shape}')\n",
    "    display(report.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{rlrrrllllll}\n",
      "\\toprule\n",
      " \\textbf{Fold} & \\textbf{Study split} &  \\textbf{Train size} &  \\textbf{Val size} &  \\textbf{Test size} & \\textbf{Train active \\%} & \\textbf{Val active \\%} & \\textbf{Test active \\%} & \\textbf{Leaking Uniprot \\%} & \\textbf{Leaking SMILES \\%} & \\textbf{Avg Tanimoto distance} \\\\\n",
      "\\midrule\n",
      "             0 &             Standard &                  616 &                155 &                  86 &                   51.5\\% &                 51.6\\% &                  46.5\\% &                      82.5\\% &                     11.2\\% &                          0.381 \\\\\n",
      "             1 &             Standard &                  617 &                154 &                  86 &                   51.4\\% &                 51.9\\% &                  46.5\\% &                      84.0\\% &                     10.2\\% &                          0.381 \\\\\n",
      "             2 &             Standard &                  617 &                154 &                  86 &                   51.5\\% &                 51.3\\% &                  46.5\\% &                      83.8\\% &                      9.4\\% &                          0.381 \\\\\n",
      "             3 &             Standard &                  617 &                154 &                  86 &                   51.5\\% &                 51.3\\% &                  46.5\\% &                      82.3\\% &                     10.4\\% &                          0.381 \\\\\n",
      "             4 &             Standard &                  617 &                154 &                  86 &                   51.5\\% &                 51.3\\% &                  46.5\\% &                      83.8\\% &                     10.0\\% &                          0.381 \\\\\n",
      "             0 &               Target &                  560 &                212 &                  85 &                   54.5\\% &                 40.6\\% &                  54.1\\% &                       0.0\\% &                      1.1\\% &                          0.395 \\\\\n",
      "             1 &               Target &                  627 &                145 &                  85 &                   51.7\\% &                 46.2\\% &                  54.1\\% &                       0.0\\% &                      0.8\\% &                          0.395 \\\\\n",
      "             2 &               Target &                  662 &                110 &                  85 &                   50.6\\% &                 50.9\\% &                  54.1\\% &                       0.0\\% &                      1.2\\% &                          0.395 \\\\\n",
      "             3 &               Target &                  546 &                226 &                  85 &                   48.4\\% &                 56.2\\% &                  54.1\\% &                       0.0\\% &                      1.5\\% &                          0.395 \\\\\n",
      "             4 &               Target &                  693 &                 79 &                  85 &                   48.5\\% &                 69.6\\% &                  54.1\\% &                       0.0\\% &                      1.3\\% &                          0.395 \\\\\n",
      "             0 &           Similarity &                  660 &                112 &                  85 &                   51.5\\% &                 53.6\\% &                  43.5\\% &                      57.7\\% &                      0.0\\% &                          0.420 \\\\\n",
      "             1 &           Similarity &                  589 &                183 &                  85 &                   49.7\\% &                 58.5\\% &                  43.5\\% &                      56.4\\% &                      0.0\\% &                          0.420 \\\\\n",
      "             2 &           Similarity &                  616 &                156 &                  85 &                   54.2\\% &                 42.3\\% &                  43.5\\% &                      57.3\\% &                      0.0\\% &                          0.420 \\\\\n",
      "             3 &           Similarity &                  598 &                174 &                  85 &                   52.8\\% &                 48.3\\% &                  43.5\\% &                      56.5\\% &                      0.0\\% &                          0.420 \\\\\n",
      "             4 &           Similarity &                  625 &                147 &                  85 &                   50.7\\% &                 56.5\\% &                  43.5\\% &                      57.0\\% &                      0.0\\% &                          0.420 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3069606/2599982738.py:35: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(tmp.to_latex(index=False, escape=False))\n"
     ]
    },
    {
     "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>train_active_perc</th>\n",
       "      <th>val_active_perc</th>\n",
       "      <th>test_active_perc</th>\n",
       "      <th>perc_leaking_uniprot_train_test</th>\n",
       "      <th>perc_leaking_smiles_train_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <td>51.491559</td>\n",
       "      <td>51.491412</td>\n",
       "      <td>46.511628</td>\n",
       "      <td>83.268223</td>\n",
       "      <td>10.246743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tanimoto</th>\n",
       "      <td>51.808814</td>\n",
       "      <td>51.817503</td>\n",
       "      <td>43.529412</td>\n",
       "      <td>56.976186</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniprot</th>\n",
       "      <td>50.715931</td>\n",
       "      <td>52.699394</td>\n",
       "      <td>54.117647</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.168248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            train_active_perc  val_active_perc  test_active_perc  \\\n",
       "split_type                                                         \n",
       "random              51.491559        51.491412         46.511628   \n",
       "tanimoto            51.808814        51.817503         43.529412   \n",
       "uniprot             50.715931        52.699394         54.117647   \n",
       "\n",
       "            perc_leaking_uniprot_train_test  perc_leaking_smiles_train_test  \n",
       "split_type                                                                   \n",
       "random                            83.268223                       10.246743  \n",
       "tanimoto                          56.976186                        0.000000  \n",
       "uniprot                            0.000000                        1.168248  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_show = {\n",
    "    'fold': 'Fold',\n",
    "    'split_type': 'Study split',\n",
    "    'train_len': 'Train size',\n",
    "    'val_len': 'Val size',\n",
    "    'test_len': 'Test size',\n",
    "    'train_active_perc': 'Train active %',\n",
    "    'val_active_perc': 'Val active %',\n",
    "    'test_active_perc': 'Test active %',\n",
    "    # 'train_unique_groups': '',\n",
    "    # 'val_unique_groups': '',\n",
    "    'perc_leaking_uniprot_train_test': 'Leaking Uniprot %',\n",
    "    'perc_leaking_smiles_train_test': 'Leaking SMILES %',\n",
    "    'test_avg_tanimoto_dist': 'Avg Tanimoto distance',\n",
    "}\n",
    "# print(reports['cv_train'][cols_to_show].to_markdown(index=False))\n",
    "# Print a subset of columns (that contain the string \"perc_\") as percentages in format: .1%\n",
    "tmp = reports['cv_train'][list(cols_to_show.keys())].copy()\n",
    "for col in tmp.columns:\n",
    "    if 'perc' in col:\n",
    "        tmp[col] = tmp[col].apply(lambda x: f'{x*100:.1f}\\\\%')\n",
    "    if 'dist' in col:\n",
    "        tmp[col] = tmp[col].apply(lambda x: f'{x:.3f}')\n",
    "# Rename columns\n",
    "tmp.rename(columns=cols_to_show, inplace=True)\n",
    "# Rename studies\n",
    "tmp['Study split'] = tmp['Study split'].replace({\n",
    "    'random': 'Standard',\n",
    "    'uniprot': 'Target',\n",
    "    'tanimoto': 'Similarity',\n",
    "})\n",
    "tmp = tmp[list(cols_to_show.values())]\n",
    "tmp.columns = [f\"\\\\textbf{{{col}}}\".replace('%', '\\\\%') for col in tmp.columns]\n",
    "# Print to LaTeX\n",
    "print(tmp.to_latex(index=False, escape=False))\n",
    "\n",
    "# Print the average active % for each study split (for train val and test sets)\n",
    "tmp = reports['cv_train'].groupby(['split_type'])[['train_active_perc', 'val_active_perc', 'test_active_perc', 'perc_leaking_uniprot_train_test', 'perc_leaking_smiles_train_test']].mean()\n",
    "tmp = tmp * 100\n",
    "tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot (Raw) Datasets Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROTAC-DB: (5388, 89)\n",
      "PROTAC-Pedia: (1203, 43)\n"
     ]
    }
   ],
   "source": [
    "data_dir = 'data'\n",
    "\n",
    "protac_db_df = pd.read_csv(os.path.join(data_dir, 'PROTAC-DB.csv'))\n",
    "protac_pedia_df = pd.read_csv(os.path.join(data_dir, 'PROTAC-Pedia.csv'))\n",
    "print(f'PROTAC-DB: {protac_db_df.shape}')\n",
    "print(f'PROTAC-Pedia: {protac_pedia_df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PROTACDB ID', 'PROTAC SMILES', 'Active/Inactive', 'Best PROTAC',\n",
       "       'Cells', 'cLogP', 'Comments', 'Curator', 'Dc50', 'Dmax',\n",
       "       'E3 Binder SMILES', 'E3 Ligase', 'Ec50 of Ligand Cells',\n",
       "       'Ec50 of PROTAC Cells', 'exit_vector', 'Hbond acceptors',\n",
       "       'Hbond donors', 'Ic50 of Ligand', 'Ic50 of PROTAC', 'Ligand Name',\n",
       "       'Ligand SMILES', 'Linker', 'Linker Type', 'linker_ha', 'linker_no',\n",
       "       'linker_rb', 'MW', 'Off Targets Reported', 'PATENT', 'Ligand PDB',\n",
       "       'Ligand ID', 'Pubmed', 'PROTAC Name', 'Proteomics Data Available',\n",
       "       'Secondary Pubmed', 'Status', 'Target',\n",
       "       'Tested A Non Binding E3 Control', 'Tested Competition With Ligand',\n",
       "       'Tested Engagement In Cells', 'Tested Proteaseome Inhibitor', 'Time',\n",
       "       'TPSA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_pedia_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f59e5eefc7d84b288934d5170ec10909",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PROTAC-DB:   0%|          | 0/5388 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77a299637d3c407595f99eadbb22e71b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PROTAC-Pedia:   0%|          | 0/1203 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canonize SMILES column in both datasets\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "# Import tqdm for jupyter\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "def canonize_smiles(smiles):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    if mol is None:\n",
    "        return None\n",
    "    return Chem.MolToSmiles(mol)\n",
    "\n",
    "tqdm.pandas(desc='PROTAC-DB')\n",
    "protac_db_df['Smiles'] = protac_db_df['Smiles'].progress_apply(canonize_smiles)\n",
    "tqdm.pandas(desc='PROTAC-Pedia')\n",
    "protac_pedia_df['PROTAC SMILES'] = protac_pedia_df['PROTAC SMILES'].progress_apply(canonize_smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROTAC-DB unique SMILES: 3270\n",
      "PROTAC-Pedia unique SMILES: 1178\n",
      "PROTAC-DB unique targets: 323\n",
      "PROTAC-Pedia unique targets: 79\n",
      "PROTAC-DB single SMILES: 2451 (45.5%)\n",
      "PROTAC-Pedia single SMILES: 1153 (95.8%)\n",
      "PROTAC-DB single targets: 78 (1.4%)\n",
      "PROTAC-Pedia single targets: 15 (1.2%)\n",
      "Shared SMILES: 807 (24.7%) of PROTAC-DB, (68.5%) of PROTAC-Pedia\n",
      "Shared targets: 0 (0.0%) of PROTAC-DB, (0.0%) of PROTAC-Pedia\n",
      "\\begin{tabular}{lrrrrlrlrl}\n",
      "\\toprule\n",
      "\\textbf{Dataset} &  \\textbf{Length} &  \\textbf{Unique SMILES} &  \\textbf{Unique Targets} &  \\textbf{Shared SMILES} & \\textbf{Shared SMILES \\%} &  \\textbf{Single SMILES} & \\textbf{Single SMILES \\%} &  \\textbf{Single Targets} & \\textbf{Single Targets \\%} \\\\\n",
      "\\midrule\n",
      "       PROTAC-DB &             5388 &                    3270 &                      323 &                    1222 &                    22.7\\% &                    2451 &                    45.5\\% &                       78 &                      1.4\\% \\\\\n",
      "    PROTAC-Pedia &             1203 &                    1178 &                       79 &                     832 &                    69.2\\% &                    1153 &                    95.8\\% &                       15 &                      1.2\\% \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3069606/285611913.py:62: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(shared_stats.to_latex(index=False, escape=False))\n"
     ]
    }
   ],
   "source": [
    "# Count the number of unique SMILES in both datasets (and as percentage)\n",
    "protac_db_unique_smiles = protac_db_df['Smiles'].nunique()\n",
    "protac_pedia_unique_smiles = protac_pedia_df['PROTAC SMILES'].nunique()\n",
    "print(f'PROTAC-DB unique SMILES: {protac_db_unique_smiles}')\n",
    "print(f'PROTAC-Pedia unique SMILES: {protac_pedia_unique_smiles}')\n",
    "\n",
    "# Count the number of unique targets in both datasets (and as a percentage)\n",
    "protac_db_unique_targets = protac_db_df['Target'].nunique()\n",
    "protac_pedia_unique_targets = protac_pedia_df['Target'].nunique()\n",
    "print(f'PROTAC-DB unique targets: {protac_db_unique_targets}')\n",
    "print(f'PROTAC-Pedia unique targets: {protac_pedia_unique_targets}')\n",
    "\n",
    "# Count the number of entries in both datasets with a SMILES that appears only once (and show the percentage compare to the total)\n",
    "protac_db_single_smiles = protac_db_df['Smiles'].value_counts().value_counts().get(1, 0)\n",
    "protac_pedia_single_smiles = protac_pedia_df['PROTAC SMILES'].value_counts().value_counts().get(1, 0)\n",
    "print(f'PROTAC-DB single SMILES: {protac_db_single_smiles} ({protac_db_single_smiles / protac_db_df.shape[0]:.1%})')\n",
    "print(f'PROTAC-Pedia single SMILES: {protac_pedia_single_smiles} ({protac_pedia_single_smiles / protac_pedia_df.shape[0]:.1%})')\n",
    "\n",
    "# Count the number of entries in both datasets with a target that appears only once (and show the percentage compare to the total)\n",
    "protac_db_single_targets = protac_db_df['Target'].value_counts().value_counts().get(1, 0)\n",
    "protac_pedia_single_targets = protac_pedia_df['Target'].value_counts().value_counts().get(1, 0)\n",
    "print(f'PROTAC-DB single targets: {protac_db_single_targets} ({protac_db_single_targets / protac_db_df.shape[0]:.1%})')\n",
    "print(f'PROTAC-Pedia single targets: {protac_pedia_single_targets} ({protac_pedia_single_targets / protac_pedia_df.shape[0]:.1%})')\n",
    "\n",
    "# Count the number of shared SMILES between the two datasets\n",
    "protac_db_unique_smiles = set(protac_db_df['Smiles'].unique())\n",
    "protac_pedia_unique_smiles = set(protac_pedia_df['PROTAC SMILES'].unique())\n",
    "shared_smiles = protac_db_unique_smiles.intersection(protac_pedia_unique_smiles)\n",
    "print(f'Shared SMILES: {len(shared_smiles)} ({len(shared_smiles) / len(protac_db_unique_smiles):.1%}) of PROTAC-DB, ({len(shared_smiles) / len(protac_pedia_unique_smiles):.1%}) of PROTAC-Pedia')\n",
    "\n",
    "# Count the number of shared targets between the two datasets\n",
    "protac_db_unique_targets = set(protac_db_df['Target'].unique())\n",
    "protac_pedia_unique_targets = set(protac_pedia_df['Target'].unique())\n",
    "shared_targets = protac_db_unique_targets.intersection(protac_pedia_unique_targets)\n",
    "print(f'Shared targets: {len(shared_targets)} ({len(shared_targets) / len(protac_db_unique_targets):.1%}) of PROTAC-DB, ({len(shared_targets) / len(protac_pedia_unique_targets):.1%}) of PROTAC-Pedia')\n",
    "\n",
    "# Assemble a dataframe of these statistics (and corresponding percentages)\n",
    "shared_stats = pd.DataFrame({\n",
    "    'Dataset': ['PROTAC-DB', 'PROTAC-Pedia'],\n",
    "    'Length': [protac_db_df.shape[0], protac_pedia_df.shape[0]],\n",
    "    'Unique SMILES': [len(protac_db_unique_smiles), len(protac_pedia_unique_smiles)],\n",
    "    'Unique Targets': [len(protac_db_unique_targets), len(protac_pedia_unique_targets)],\n",
    "})\n",
    "# Count the number of entries with a SMILES that is shared\n",
    "protac_db_shared_smiles = protac_db_df['Smiles'].isin(shared_smiles).sum()\n",
    "protac_pedia_shared_smiles = protac_pedia_df['PROTAC SMILES'].isin(shared_smiles).sum()\n",
    "shared_stats['Shared SMILES'] = [protac_db_shared_smiles, protac_pedia_shared_smiles]\n",
    "shared_stats['Shared SMILES %'] = shared_stats['Shared SMILES'] / shared_stats['Length']\n",
    "# Count the number of entries with a SMILES/target that is single\n",
    "shared_stats['Single SMILES'] = [protac_db_single_smiles, protac_pedia_single_smiles]\n",
    "shared_stats['Single SMILES %'] = shared_stats['Single SMILES'] / shared_stats['Length']\n",
    "shared_stats['Single Targets'] = [protac_db_single_targets, protac_pedia_single_targets]\n",
    "shared_stats['Single Targets %'] = shared_stats['Single Targets'] / shared_stats['Length']\n",
    "\n",
    "# Convert all percentages to percentages in a for loop\n",
    "for col in shared_stats.columns:\n",
    "    if '%' in col:\n",
    "        shared_stats[col] = shared_stats[col].apply(lambda x: f'{x*100:.1f}\\\\%')\n",
    "\n",
    "# Print bold headers in Latex\n",
    "shared_stats.columns = [f\"\\\\textbf{{{col}}}\".replace('%', '\\\\%') for col in shared_stats.columns]\n",
    "print(shared_stats.to_latex(index=False, escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff34d95ccf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff34d95e730>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff3368051c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff336806c70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G7I3LMpddU1yjZZQQQjIzM0UiEYfDuX79Oothhw0bBqBTp0700De9FBUVdejQ4Z2Fksvl2tnZffLJJ05OTl5eXj4+Pl26dBkwYMDQoUNbtmwJwNra+tmzZ6b+BJTegoKCwN7boZkzZ5bFrNpL7cuWCS1xG3Mz5haRonhlfKo6VaPTZGuzjQ9e4cvod999VxbzcS9fvmTaRO3atUu/kWo12baNhIWRJKMODqm4tFrt9u3bz507Fxsbm5yc/PTp09zc3A9+S/j7+wPo2bNn+SRJsSU5OdnCwsLMzCwxMZGVgAUFBcysWlhYGCsBCSHZ2myPeA/chssDl3QN+4ePVewympaWxuPxuFxuXFwc68F37twJoHr16syCjNIKCiJXrpAXL0j//uTvv1nPqrLKzMxkJrgPsngmNVX2Bg8eDGAsKwc3/r+LFy9yOByBQMDKrFqWNss93h234frQNUOTYXzAt1XsBU+BgYHR0dEjRoxgSh7runXrdu7cuc8//3zw4MESiYRZSCyRSNRq9TSRyCMlBWo1ZDIoFFCrIZFg7VqcOoXDhwHgyBFoNBg8uCwSq5S2bNkyfvz42rVrx8fHMyWV+sg9ePDA09PT0tIyOTm5Xr16LEZm+v926tSJeVFhcJwsbVbnR50fqh425Te94HzBwcKBxST/URa1uXw8fPjQ3NzcwsKiLF4EhYWFPX36NDk5mc/n16hR4+2/t+Nt276jM/y6daRfv9chYmLIoUOsJ1aJ6XQ6X19fAMunTjV1LlSpfPXVVwCmlsG/18uXL5ntTDt37jQ4SGZmpm+Ab7279TzjPVmZA/0vFfhutGXLlvfu3WMai5w9e7ZNmzbMJgrjnTt3rlu3bnXq1FmwYMGECRNq1KjRr18/Zo9w8XbSLtWq1bO0BI8HsRgCAfh82NlBIMC8eWjXDs7OCAnBzp0Qi1lJqYpITEzMmDSp85UrOHMGn39u6nSo97l165a3t7dQKHz8+LG9vT3r8Xfv3j1ixAg7O7sjR474+Pjo23X32bNnX3zxxePHj7sHdt8Ttae6eXXWM/xH2VXosubt7c3hcPz8/FasWAEgMDCQxcgAQkNDmeeUAwcO6DFYpyOnTpGdO0lWFrlyhU6P6m3RIgIQFxdixJHUVDn44osvAMybN4/1yOfPn//tt98IIW5ubsyOOHNzcycnp969e8+ePXvnzp2xsbEFBQXviZCWlsa8p2rVqtXLly9Zz7CEClxGL1++zLxf2rdvH5/P53A459k4Xu3IkSMAHBwcwsLCALi7uxu4ST8yknC5ZNAg41OqWtRq4uZGADJ/vqlTof7T/v37mbXVeXl52dnZV65cYSuyVqt1cXEBsH//fmaKvH79+m+vO7awsHBzcwsICFi0aNGhQ4cSEhK0Wi0T4cmTJw0bNgTQunXrV69esZXYe1TgMkoIWbBgAQBXV9eFCxcCcHZ2fv/PqA8qKiry8PAAEB4ezjynnDp1ysBYT58SKysCkOPHjUmpKrp+nXC5xNyc3L1r6lSod9u3bx9TRu/cuVOjRo1atWqxddO3detW5nt53rx5ADp06EAIUalU9+7di4mJCQ4O7tu3b+PGjc3MzEoUVktLSw8Pjz59+jAvMz777LP8/HxWUvqgil1G1Wp1s2bNACxYsKBFixYAgoODjQm4a9cuAA0aNGDqcvv27Y3Kb+1aApD69UlZ7hGunCZOJABp25YUFpo6FeodtFots3Fz/PjxXbp0ATB69Gjjw6rVauZGcuvWrcwm/T///POdX6nRaB48eHDw4MGQkJCAgIBmzZoVF1ZHR0cPD49yq6GkopdRQsi1a9e4XK6lpeX+/fvNzMzMzc3v3LljWCiNRsPMp2zevJl5mrh48aJRyRUVkXbtCEDoq2d9SaWkbl1Sty55/NjUqVDvlpiYyEym7dq1SyAQsDKrFh4ezsykTZs2DYCfn1/pxyoUilu3bjH9cWbPnm1kJnqp8GWUEDJu3DgAn376KbOjqU2bNoUG3cJs3ryZmSJgNul/+eWXLCQXF0csLAiXS65eZSFalXL3LpFKyenTZOVK+rf3cQoJCQHg4uLCtFhr2LAh0/reMHK5nJlJ27lzJ1OX7+ozq/PXX3/t379/27ZtAAICAgxOwwAfaBhRIaxateqXX365fv36oEGD6tWrd+vWrY0bN06ePFmvICqVatmyZQBmz57NtKth/s8wlocHpk9XnDq1eNmypT//XKLfPvU+LVogMhKFhRgxAitWQKVCrVp49QoqFaRSKJVQqSCR7LWwuJ+VlZ+fr1arZTKZXC5Xq9UqlWrVqlXt27c39Weo5IKDgw8dOhQfH6/Valu2bHn37t2lS5cuX77csGgRERFZWVlt27a9evWqUqkcNGgQM1NXSmPHjr1x4wYztZqcnGxYDgYqz5pddg4fPgzAxsaGmdwUiUSpqal6RWB6HXp5eTH1t1/xKnqjFRYUNHVxAbB8+XK2YlYVAwa8/kV6Opk0iXh6vr3loa+399v/V3M4HEdHR8MeSii9sDWrlpeXx6z73rt3r6WlpZmZWUJCgl4Rhg8fDoDpMSYQCAw+BcMAlaSMkjd6BgcEBADo0aNH6cfKZLLiLRPMIirjW3696dKlS8we4UePHrEYtvLz9yfMN0NaGpk2jYweTTp0IF26kIAAMmwYGTeOzJhxaNWqFStWbNiwYdu2bQcPHjx16tThw4dr164NYN26dab+AFXChAkTAHh7e0+aNAmGzqoFBwcD6NKly9ChQwGMGTNG3wjM4+OcOXOYf/3y7BZWecpoRkYG0zM4OjqaeUFU+mXzzHt5Hx+fwMBAAMOGDWM9vZEjRwLo2LEj7bynh927ydKlJDmZjB9P9GmEePr0aQDMBpuyy45iSKVSZpF8eHg48549PDxcrwjFzfP37t3L3Nvq+zRJCNm/fz9zI8VsKWYW8JePylNGCSEbNmwA4ODgwLzvs7e3z83N/eCogoIC5mkiJiam7DbpF+8Rjo6OZj14ZXb1KomMJA8e6Dtu4MCBALp3714WSVElMJtWimfVhEKhXnWQeRvRt29f5ply8uTJBuRw584dAM2bN2f6n5bn6YqVqowWFRX5+PgAmDhx4ueff/7JJ5/cunWrNAMTExOXL1/OfOONHz++jNLbs2cPgFq1ahm5R4AqjZycHGYZ9r59+0ydS5VQPKvGfB+VflatuHl+TEwMh8MRiUSZmZkGJCCXyzkcDo/HY95xTZs2zYAghqlUZZQQkpCQwExuHj16VCqVvv+Lc3NzMzMzHz9+fOfOndDQUC6Xy+fzy3RKZdq0abGxsWUXn3rT9u3bAdSoUSMnJ8fUuXz05HIikRgToPgkrp9++omZVSvlDzBmRnXIkCFdu3YFMHfuXINzYOYWoqKiAPTq1cvgOPqqbGWUEPLDDz8AqFOnztixY0eNGhUQENCtW7eOHTt6eXm5uro2aNCA6dX0zte7vXv3NnX6FGt0Ol3nzp3B0gabymzVKjJtGgkOJrNmGROGeUvu4ODw448/cjicUm4pzM/PX7x48b59+wCIxWJjdsF36tQJQPF2UoPj6KsSllGFQmFra1uaJrK2trb29vZOTk6enp4NGzbs3bv3ixcvTJ0+xabk5GS2NthUWjIZKT5Gado0YsSLgeJZtW+++aY0y54kEklWVtbjx4/v3bvXtm1bAEuXLjX46oSQ8ePHMy+4mKVXarXamGilVxmW35dw4MABiUQiEAjWr18vEomsrKysrKz4fL6NjY1QKOTz+ba2tnw+XyAQmDpTqsw5OzvPnz9/3rx5EydOjIuLo//o75Cbi+JuofXqITMTjRoZFonL5UZFRbVq1SoqKio/P9/GxubNPRFSqbSgoECtVufl5anV6oKCgjfH2tvb8/n8MWPGGPNRnJ2dAaSmpjo6OqampqampjZp0sSYgKVVPtW63Gg0GicnJwD03HOKwWywgXGTbpWZVkv69CFaLdHpyJAhxOjOct27d69Tp05pio+1tXXNmjWdnJzc3d0dHBwAdOjQwZjtpCdOnADQvXv37t27AzheXs3VKtvdaFRUVGpqavPmzQfTQ5AoAIC5uXlUVFS7du1WrVoVEBDAlNQKR6fTvXk3p1KpcnJyXrx4MX78+LdbxunH3ByzZ2PqVHC5GDoUxh0hIZfL79y5k52dHRgY2KpVq3c+Bdra2vJ4PJFI9ObAZ8+eff7553/88Uf37t3PnDlT4k9LiWlUmpyc3Lt37zNnzpTfltDyqdblo6CggHlVd+zYMVPnQn1cDNtgk5pKduwghJCnT8nVq2TPHsJsnvjlF+Nv2v5FLpePHj26a9euAQEBXbt29fX19fLycnZ2rl+/vp2d3XtaMbDwIkWhILNnk++/JwcOkIAAotdZD29hdrJ89tlnBox99OgR8/3bpUsXwxYFajQaKyurFi1a/PjjjwDGjRtnQBADVKoyypwm0rp1a7pTiCpBLpc3aNAAQERERCmH6HTk0iXSpAm5e5dcvUo2bCBDhrzemzpnDtF/l837fP6hg6c4HI6dnZ2Dg4OTk1OrVq3atWv36aefMveh+h1y8za5nABEKCSLFxt56EBubi6z5un33383LEJiYiKzlbN79+4qlSEviJjvfaZbW6NGjQxLQ1+V56FeKpWuWrUKwPLly405kZWqlEQiUbdu3aKior7//vuEhAR7e3uVSlX8roPLrff332vkcshkUKuRnw+FAhoNoqMxcSJCQzFlyus4ixYBwJUrGDeOtdxevHhx8+ZNDocTGBjYunVrpjjqdDoul6vVapm6UFBQUFBQoFKpig/6XrBgwbVr18aNGzdlypSuXbsafiS1UAgOB0olhEIAUCgM/iDLly+XSCQ9evT44E+F/+Lq6vr777937NhRoajXr5/5zz+Dx9MvQnp6+urVqyMjIy0sLJiKXB7Kp1qXA2a5KHPkAEWVUNzL8p0/YmvX9nr7tGyAbNlCtm0jR4+SMWNe343K5UShIDNmsHk3OnbsWACDBw8mhDBn2JTG2bNndTodc65cUFCQURkIhQQgEREEIIY+CL948UIoFHI4nBs3bhiVDCEPHjyvWVMHkL59iUZT2lFpaWkTJkxgDhDlcrlff/21wR3c9VVJ7kZfvnwZEREBgHmup6gSmF6WrVq1UqlU8fHxbm5uw4cPF4vFfD5fJBKJxTUsLWFtDZEIfP4/Z2ZfvoxHj9CvH3766XUcgQBcLt46YM1wKSkpO3bsMDMzYw4Wc3Z21mg0IpHI2tqaz+dbW1uLRCI+ny8WiwUCQfGLGqFQ6OnpyeFwNm/e7OnpuX379kGDBjF7DQwhEqGg4PWnMvRudO1aXps24+vUyWFWgBrDza3u+fPo3BnHjmHQIBw48IG/8LQ0rFo1fdu2jRqNxszMbOjQofPnzy+npU6M8qnWZW369OkA6B4k6p0kEkmJXpbx8fGlGZiXRzIyCCEkK4ucPk3Cwl7//qNHrB3/PGTIEABjx441OALTIM7Z2VlpaE46dzdSs3rR7q1FzV0KpxlyY5uWRng8YmZGHj40LIV3uHuXVKtGAOLvT/7/0M93XHfyZMLjEV/ffVwuNyAgQN8upayoDGU0IyODeZoot3t4qmJhzpjs0qXLsGHDAAQGBuobISmJmJsTkYjlN0v379/ncrk8Hs+YTg5ardbT0xPAfEPfDj186BYbi+zsqNhYJCd3NSDCyJEEICNHGnb9/3TjBhGLCUDenrRITSVjxhALCwIQc3MycaIsKSmJ5cuXWmUoo8wOsIEDB5o6Eepj9GYvSzMzM8N6WRJChgwhAOnWjc3c+vTpA2Cq0SceXr9+ndn+qNfhRcUSEtrGxiInZ0tsLBIT9T4Nl/kZY2FRJscPXrtG7O3JgQPk8GFCCPn7b/LLLyQw8J8COnIkSU5m/7p6qfBl9MmTJ3o9plFVTXZIyPctW3711Vf9+vUDMGnSJMPi5OSQmjUJQNjaH8e8nReJRH///bfx0b799lsAbdu2NaDzfFJSx+Iy+vChp77D/f0JQCZO1HdcaSkU5EZ6xKwAABQJSURBVMYN0rAh+esvcvs2mT+fWFoSLpcEBBDT3YD+S4Uvo8xjmgFHDlBVwrNnhM8nXK704MGg5s0N7mXJiI4mAKlenWRns5Aa80Zo3rx5LMQiJD8/v27dugDWr1+v79hHj3rFxiInJyo2FvfvN9ZrbFwc4XIJn0+eP9f3snq4cYOEhZGBA0lsLFmzhkRHG9NBhX0V4009s/tNqVRKpVKVSqVQKGQymVqtTkxMjImJsbS0ZCa/KKqkxYuhUmHIEJtt27Y+eLBk+XJj1hKOGoWYGJw/j5kzsWOHUXldvnz5woULtra2zNtR41lbW0dGRvbu3Ts4OLhPnz6l6XBWjMtldl7qAOh0+r2pt7REz55o0gR16+o1Tm9WVhgwALt2wdERo0aV7bX0Zuo6/o/bt2+7ubm1bdv2zcagvA+tvnV0dPT29jZ17tRHKTmZWFgQMzOydy8BiFhs/BbOJ0+Ii0tq69bdz507Z0ycdu3aAQgNDTUynxIGDBgAoGfPnnqNkkh+ycmJUijuvnq1Pz//UmmGqNWkUycil5OiIhISQvbsIX/9RQghFy+Ss2f1z/tDbtwgGzcSQkivXmTNGvbjG+ljuRstKioaOHDgs2fPNBrN239a3MvAxsaGx+MVL6YrLCw8duxYenr67du3vby8yj9t6qMWEgKtFmPHYssWAJgxw8i+GwAaNEBg4KE5c858882jv/76S8js/NHTyZMnr127VrNmze+++87IfErYsGHDxYsXT58+ffjwYX9//1KOEot75ubGZGdHmJvXdHD44e0vyM+HSgW5HHI51GpIpWjcGEVFWLYMS5YgLQ12dq/Xm0okeNd3sLHq1EG7dgCwYQMyM9mPbyQOIcTUOQDA7t27R4wYYW9vv379eolEAkCj0XA4HEKIXC4vfqJXq9VyuZx5os/Pz79+/frcuXNXr17t4eERGxv7ng4OVJXz4AE8PWFhgagojBqFGjWQmgpra+MDFxYWtm3b9u7du7NnzzZguwchpFWrVvfu3YuIiJg8ebLx+ZQQFRU1YcKE2rVrx8fHW1lZyeVy5jsoPz+/oKCgSROtufkrnU5FiLKwUEKIWqdT8PlNFIqb1auPFIm8tdqMlJQ+Op2SEFVhYR4hap2uYMAA7dOn/7rl2rgRf/0FGxsMGYLwcLRsiZs34eCAlBQMHoyBA1n+UOfP4/lzBAYiORl7977ekvsRMfHdMCGEEI1G06hRIwA7d+4s0cz1/fLz8xUKBTN21apVpv4c1MckOJgAZMoU0rIlAQirZ9bfvHmTWWB0+/Ztfccyp2XUr19fpVKxmBIhRKFQSCQSnU7n7e3N4XDeue01NrZZbCxK/JeS0l+rzXr+fPrjxwOVyqS3v8DbW1GjBnFyIs2bk9atSZcu5MABMn48kUrJoEFk5EgSHk6uXSOEkKNHyf797H4sQgg5d46sXEnS0silS2TBAvbjG+mjeKjfvn3748ePXV1dhwwZYmZm9vnnn1taWr7dnfDN7XE8Hs/W1lYgEJibm2/ZsqVLly4hISH9+vVrZGjjbqqyWbYMHTogNxcREahbFxMmsBi7TZs2kyZNCg8PHz9+PLNms/iP3m4M+uarUalUyuxaXrBgAY/HU6vVH5z9L72IiIjVq1evX7++YcOGd+7c0Wq15ubmb36/8Pl8LreRWNyMw+FzuUIzMxsul8/lWgkEzczNa9WtuyYnZ5Na/bhp01gOh8/lCszMxFwun8sVXb9e8loqFS5ehI0NvvoKZ8+y9QneJy4OAgFycsrjWnozdR0nSqWSWahxmFlfaxBm2VOnTp1oizyKEEIiI8mMGWTWLJKdTQ4fJkeOsH4FmUzm6OgIoHr16s7OzvXq1Xt/Y9Bi9erVs7W1zc7ODgkJcXJy+uD5taWUl5dXYsPr/fv3Sz88J2fLs2dT0tKCiooMaT7/5rddWXwLnjtHtm8nhJCkJHo3+i6bNkWlp6d7eXn179/f4CDh4eFnz569dOnS3r17mZJKVV0XLiAvD6tWIS0Nc+Zg+/ayuIiVlVVYWNiYMWNevXr16tWr4t/ncDhvP0i9+Wr0yJEjEokkNDT01q1bqampwcHBGzZsMD6fVatW5ebmdunS5cyZMxqNJjAwsHnz5qUcq1LFq9VPRCJvjebJ06dBdeos5fH0e6p7c/6gLLpU1q4NS0sAsLGBuzv78Y1l2iqen08aNCjq2PF/Z878ZmSoHTt2AKhevXpWVhYruVEV1dKlpLi7woABZXqplJSUkydPJiUlpaWl5ebmlma6My4uzsLCgsvlMreNXC73ypUrRqZh5IbXvLyjsbFISemXmPhZbCxksstG5lPVcE1bxNeuRVoat7DQt1s3Q3t8/b+RI0d27dr11atXM2fOZCU3qqKqVev1opiiIpTxQpRGjRr17t3bxcXF0dGxNMucAXh4eEydOlWn04WFhc2YMUOn0wUFBanVamPSWLZsmUwm69Onz+HDh4uKisaPH9+wYcPSD+dyhQB0OgWzDl/fFfiUKe9Gc3OJrS0BiKEnDpT05MkT5iSss2WxApiqKPLzib8/2byZjBtHfvnF1Nm8Q/HykmXLljVt2hTA4sWLDY6Wnp4uEAi4XG5MTAyHwxEIBOnp6XpFkMn+iI1FYqJPSkr/2Fjk5hr+lqJqMmUZnTWLAKRHDzZjMuv4GjRoYMwxrVSFp9WS5GTC0tubsnDx4kWm5O3bt4/D4fB4PIN76wQFBQEYMmTIl19+CWDOnDn6RlAo7sTGIj6+xZMnw2Nj8fLlTsMyqbJMVkZfvCBCIeFwiNEnDvyLVqtt1aoVgFmzZrEZl6LYNnz4cACdOnVi6qCvr68B60ySk5MtLCzMzMz27t0LQCwWv9J/w6tSmfTXHzbJ11pnPvj+8VGXvKRt+kao4kxWRr/9lgBl8gLg1q1bBi+Npqhy8/Lly1q1agGIjIysU6cOgC1btugbZPDgwQDGjh3LnCK3ZMkSQ1JJTycAqVOHfP89AQjdyaInk20GTUnBokUIDkbTpuwHnz59+rp169zd3U+dOlVYWMgcplhQUJCfn69SqUpsjys+bTEzM1Mul69Zs6Znz57s50RRb2H2QFevXn358uXjxo0Ti8UPHz5kzmovjQcPHnh6elpaWkZGRo4aNapGjRqpqanWBmx4lUhgZwexGFOmYPFiLFyIkBC9g1Rl5Vy2f/6Z+PoSnY6cPEn27Surq8jl8tq1a1tZWRnwFyIWi2UyWVllRlH/xkxoDhs2jOmEHxAQUPqxX331FYCpU6cyE1lr1641MAmN5nUr+RUrCEDohJieWF5+X7z7LT+/UKl0USggk0GlgkwGhQIqFRo0QKNG+Okn2Nuze+V/sbS0tLCwUKlUNWrUsLGxEYvFPB7PysrK2tqax+PZ2Ni8uT2OECIQCJhV0z/88ENycvKCBQvWrl1bhvlR1P/bsmVL8+bN9+zZEx0dffHixUOHDh0/fpwpqe9369atkydPikQiNze38PDwOnXqTDB4w6uFBSwsoNWCzweMOqq+ijK+EoeFhb29Yq5p0/88+Pvnn8mwYWTHjjK8G42KigLg4uKi1Wpnzpw5YsSIgICAL7/80tfX18vLq3iVH3OkdbG4uLjipdFXr14tq+Qo6t9WrlwJwNHRkfmFq6trUVHRB0fduXOnQ4cOwcHBzJKpqKgoo5Jg1h6uX08AMmqUUaGqHhbuRrVabV5eHvPr4t1v9evXt7GBtTWsrMDjQSyGUAge7/VPu5AQ9O+P4GDjL/4OKpVq6dKlAJYuXWpubn7kyJHU1NT3fL2dnR2zdY8Q4unpOW3atJUrV06YMIF23qPKx7Rp0w4cOHD79u2srKxp06Z99913XO6H98W0bNny8uXLW7duTUhIaNy48ejRo41KIiIChYVo1QqnT0OfzvkUWOk3KpVKdTodU4k++MW//w4AHTtizRq4u8PX93VhZdG6deumT5/u4eFx9+5dLpd76NAhhULx5r5mKysrHo8nFosFAgH/rcsrlUp3d/fHjx+vWLFi9uzZLCdHUe8SFxfXpk0bnU4XHh7erl07hUKhVqslEolSqSx+Bcq8I1Wr1TKZTC6Xq9VqqVR69+5dnU4XHR09ysiDNZ4+xdy5aNIE8fEIDYWTEzsfrIow1W1wRgbx8yP9+7McViaT2dvbA/jFiO0rxUujHz16xGJuFPUeo0ePfmeH0PfjcDjM5JWxl58yhTDb8J8+Jd9+a/zHqVJM2eHpjz8gkeDYMfTty1rM8PDwrKwsHx8fYxYtderUafjw4bt27Ro7dixTUlnLj6L+w8qVK9PT069fv+7i4sI8JzGvHIRC4dvvSIVCIZ/PZxrudevWrTSTAB+QmYn69QGgXj1kZRn/caoUUx4ismEDJk2CgwPi42Fry0JAiUTi5OSUl5d38eLFTp06GRPq1atXzZo1y87O3rFjx8iRI1lIjqI+ZsuX47PP0KEDrl3DhQuYP9/UCVUkpiyjOh18ffHnn/j2W7DRcRFz585dsWJFt27dzpw5Y3y0PXv2DB8+vFq1agkJCcxuE4qqtBQKLFkCjQYWFliwACKRqROqSEx8pN2DB/DyQmEhLl+Gj49RoXJycpycnORy+fXr1729vVlJz8/P79SpU0OHDt2zZw8rASmKqnxM3G+0eXPMnAmdDitXXn/n0cqlt2TJErlc3r9/f7ZqKIANGzZYWVnt3bv31KlTbMWkKKqSMVu4cKFpM2jfHomJS48fH2Zpaenr62tYkGfPno0aNYoQsm/fPnv2Nkgxy2DPnTt39erVoKCgEsv1KYqiYPK7UQB8PiZN6gBg6dKlCQkJhgVZtGiRWq0eOnSoh4cHq9lh6tSpXl5eT58+NfnPG4qiPk4mnhstFhQUtH37dl9f399//13fBUbJyclubm4cDichIaEsDlhmlkYXFRXt2rVr6NChrMenKKpCM/3dKGPNmjV16tS5fPnydv3Pcfzhhx8KCwvHjBnDeg29fPnyxo0bmzZtypTps+VzIDdFURWK6Q9YZojF4qCgoMWLF0+dOlWlUjk4OKjV6v9qDKpUKiUSiVqtVigUUqm0oKCAx+PNnz8/Ozv70qVLAwcOZCUlQsjMmTNv3rypVqtzc3OLiopat27NSmSKoiqTj6WMAjh//jwAjUYzadIkvQZaW1srlcr09HQ/P7+8vDxXV9cWLVoYn8/x48dv3rzp4OBQVFT07Nkzd3f37777zviwFEVVMh/L3OiJEyf69OljZ2dXq1atpKQkW1vbHj16WFtbFzcGZbbHMR1ChUKhjY0Nn8+3srKysrIKDg7evn27t7e3t7f3jz/+2KZNm2vXrpmZmRmTj06na9WqVVxc3Nq1a8PCwrKysk6cOOHn58fW56UoqvIw4X7+Yjqdjrl/XL16NXOCwrFjx0o/XCqVMqPCw8OZ47nXrVtnZErMentHR8dFixYBaNu2rQHHjVEUVRV8FGU0JiYGQP369Zk+oa1bt9a3Zh05cgSAjY3Nrl27AAiFwsePHxucj0ZDfHw6A9i0aVO1atUAXLhwweBoFEVVbqYvo4WFhU2aNAGwYcOG6tWrAzh//rwBcfr16wegd+/ezCum7t27G5xSVBSxtFQHBEQvWZLh4zOia9cvDQ5FUVSlZ/oymrF3b2dHRxcXl/nz5wPo0KGDYXFevHhha2sLYMeOHTVq1ACwz6BTSpRKUq8eAcjWrcTKinA45Pp1o5s5UhRVeZm6jKpUpH59YmHxfOfOjo0bA/jzzz8NDrZx40YADg4O69evB1CjRo2cnBx9g6xZQwDi4UEmTyYA6dPH4HQoiqoSTL38PjISz56hSZO6cXGX0tNjv/22ffv2BgebMGHCZ599lpmZGR8f37lz55cvX86aNUuvCHI5wsIAYMYMbN0KLheLFhmcDkVRVYMpa7hcTuztCUB27iRCIeFwyJ07RoZMTEzk8XhcLnffvn3Mscl6zbQuXkwA4uNDgoIIQIYMMTIdiqIqP5PejUZEICsLbdvi6lUUFODrr9GypZEhXV1dZ8+erdPpFi5cOGfOHELIxIkTlUplKYd3744uXfDNN9i5E2ZmWLDAyHQoiqr8TFdGJRKsWQMAU6YgOhpmZggJYSXwvHnzmjVrlpSUVFRU1LJly0ePHi1ZsqSUY9u0wfnzaN4cXl4IDISrKysZURRVmZmujG7ejNxcdO6MX3+FRoNRo9C0KSuBLS0tIyMjORzOihUrZs2aZW5urlAoSjNQp0P//lCp4OmJPn0QEcFKOhRFVXKm2wyq0WDLFtSqhcGDYW6OxEQ0bMhi+PHjx2/ZsuXTTz+NiYlp+N+RpVKo1ZDLIZOhdm34+aFXL4SEYPhw7N7NYjoURVVaJmpNcvs2jh5FrVr44gssW4aCAnZrKICwsLCTJ09ev369R48eHh4eCoVCpVJJpVKlUqlSqTic5MxMs4KCfw3ZsAEeHsjLQ1ISu7lQFFWZmaKMPnmCdeuwdSsePsScOdi2rSwuYmtrO3bs2MWLFyclJSW9VRfr1SNMDbW2Bp8Pa2uIRLC2BoAFCzBtWllkRFFU5WSKMnrxIoYNg0CA1q0hkZTddRYtWuTj4/Pw4cO6desKhUIej2dnZ8fn8wUCga0teLySh8gWFeHyZVSrho4dceFC2eVFUVSlYoq50b17wedjwAAAGDAAR46UdwL/raAAQiEIQUEBPamboqhSMcXdaK9eGD0a9va4fx+tWpkggf8mFAIAh0NrKEVRpWWiN/V5efj9dzg44NNPTXB1iqIo9nws3e8piqIqKFO3JqEoiqrgaBmlKIoyCi2jFEVRRqFllKIoyii0jFIURRmFllGKoiij0DJKURRlFFpGKYqijELLKEVRlFFoGaUoijIKLaMURVFG+T8oPoPsAk/tzQAAA9l6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wOS41AAB4nIWTa2wUVRTH79zZnd2672232wdtp6XdR8tu2132zWMuFNQFRVAeFRpvS2smFg0mpiQGImW1RZCUENAPkEAVS8W0BBOCsbSdm2gTNYGgNSoqiUE/aG1IiEkTWht3zqYtboPc5Ob+zn/O/M+5ZzJ3RwZuo/SyoIW1Ir3XpPdBTkA0fXK8HnnSJ48FCU6NIMnqyXMLQtYDXUbg5xzmEh7qNP/CgvWiGlnCvFdWjUyMsQ6JajwXPkLWg4wfKf+3tg5BU5ibT1jcbVbbVnAU5hwWO/2Pw0PGbgNL7oFhRlV4YCSLLbLuaUAc4rCIeRnzGqrRilpBxoKO6vRIl0NzHvPgHAM1GGVsNFGTWcZmC7VYZayxydhmp/ZcMTcP2R0yduSjfCdyFqCCQlRYhIqKUfEStKQElZSi0jJUJiKxHJVXoIqlHlxRKVZWoSoXcrll7PZQj1fG3mpUXaPH1cvS24d8ftFfK+PaOlpXL+P6AA0EaXA5CoZkHArTcIR6ozQak3FRHMUTyIFpwomsWhpzUauBRvw0EpBxnk6dNnbYbRpeEHQ5BqtWMJrMFqtBKCgsiiecgtvjjcZcQm1dfSDi1wYDkXDIeQun5zn/Q8zEbyvad5Ir1WD2/pRibOpfrfKfU7ls1ncO+Fl7Gfvw9LlhlUeMEbZyzDaistu9hh3ce21U5RPlz7NjZz8Hnk60sQ/aI4rKL4YPsOpUAvj+zh1sJvImcGvHbjbxxSDw8WaJxZYdAu7pDLLWwYvAld3FrPf9fuChPVbW5t0PfG1jCbNU1QC7Hg+xd1fUAo/+tZb9dqMHet76TZRd50sklbd9so5V6l8BnnwvztCJXuCJ70W2tbgcuHPYweojHcD+rmnl06dfB64Z+1VpSXozPjcvK380+4BTZ04rd1Z1Au/atF3pS6aAd9xYqkwdD2X0r7tHD/THgDf6UiObnrgJvXH6s6ub9v0IrNXw0thnv6xS+dJkizTTMQt8K3xU+uG7BpinWfOStGWzAHe8VDMkDe9bDvrliXHpSMMM8Nq9mOx3/A3c8IyF5Fk2ZPKTbnJsaj1wQVuANF+9AzmpF5LkucM/Abe2bSDjA09BTm/jk2T6QhN838nGFrK+8VvI2f7ayyTcdR70bScpSVx/FfofHWwn7T02uOOp8U5yr8+R4YbDJLrubci5V3iScF9uAQ58fIb0jd8F/ufnU+S8yQv+u0uPkjeEi8DNVw8R7qsU1HK+VUX2NPGgDxwxke6hXOCuXVcUM/sIau3c/LuSvHIBOP9fcwQn5KOscokAAAUmelRYdE1PTCByZGtpdCAyMDIzLjA5LjUAAHicfVfbbt02EHz3V+gHLHCvJB8TOyiKIjbQpP2HAn3s/6MzlMOjoIuey0KS51Cj3dnh+ung6/fX3/7659gvfX16Oo72P5855/Gntdaevh48OD5/+eXXt+Pl+6fPP668vP/x9v3bke3IxG/w/hn76fv71x9X5Hg5nvOc7r0BeDafHutgvR4/1eP9eO5n92ljHnKahHkFNK44T/XRNAHUEG9eAP0CTpfZ/XgGoA9XLZBBpMjp3lKEUHG1GAU0F1RPMY+YODy9qwwroH1B7UwzmUloGJafBXQcb/i7n6ZdHFz1HBlIVAGda9U4h0YfQujUOXD0X6i0Dwah6gEGDtrgWiULj/2+KKgk0/kcp6dkVhUQ/UhCUxt4Hqzbso9Z5UvsI7cqU3COdc2sjxLrTMM8u0IlRqjmHFFVTOIqLiQQSNOzocyNeiigeUGHidrKbXYX6xW0f5A1pLSv5HYbrVfVlfGRBKRWJxWL5cNKsvNj2QixkENPD7OoGGhbZdDTIpvNw84uU72EyseqmdqmHMHear0ioFfB8IegwEAgpOusHks/2gvq7+huO4f3WrO6qtWh1CZKplOkRSVZXcXquHu48/HTQ7JErloFEpWjkWfHk7VK29p5d8fddUDaUCBso6y/rkLZaenQIJDSFL+qkKtOSE7M4Ckkk7Msvl1lguzbQBrRg63NVmXJVpUaXApVCqgkLVqZT2OR0CeujkMA55hlOs1wczQfCqNsTzdrWT250QHR/R59ic0nPLh6cGOB4D7R3JNse45Rtqgl7g1Li8RizFVjo1SGYiwQbAqaHLrWhDzLXjYWKLGmdfacnKAJo66QLNA4JdTbWMiW+FTe3xZyBgSUrD703meVTWeB0Bqw847MEtrGiKpCviqERveZYzHtfbbSTt2Ob/BJKB22s6h2iVY6JPz+nVBUU8NJQOFOVnKNCxrD7Uoq3Tqr/kAd32Co4JpUEQhkiJSyd7oeoMOhYeOqBgZWpnVc0GapQobY1yOqDvF5Ibs1SXaIdrdeZTVYKslTdYzGrjNDzqqkxioVfMTMoU60X0e3VImKVakBm9NM3nQoVq32MpB/+RsOjZxaTPbd6Ja9Mtzwa9GO3QGtjDxhe4SrVNBg9vsJTxpohGfuKMO0SlTk9fhmM6DaZRXdS8+LfknV3OdchcJ+wnmhgI6l/+Gwm9XR0dFUZQLmZbmZHe6MjrXmQ6riZ7tsHFOBDO437MJyzS9vrz8NgddY+Pn97fUxFvKtj+EPJ4c9Rjzn9zHI8R2PaQ03P/IxkTm+/TF1Ob7jMVkJvvMxPTkv3UckZRC5DUK+rmxyGHecQTY/IUEE2RSFDBFks0R78BSlvQ0iziCbq5AsIJstrMIZZBPGViEMeh8RhEE3ZV2UgXnkcyVUsZvf9nVh0E0Z+7cw6KaszCyCbsocsBl0U8a+Kwy6OStTjKCbs5IzR4L7finH2g1v+6Iw2ObMTY/BHjIgZwTbnI2cEWxzhk8qg23ORs68sjkbOSPY5mzkjGCbs1EZDPctxFfYnJ15ZticnZwRfHOG8fMUrn7z9+vK5uzMM69szk7OCL45O6XBsDk7tcGwOTs5I8TdS50hNucgZ4TYnIOcEWJzDsqZVzbnWA2Hz6PpKGeE2Jw5dfHZY3OOJecO87nZ0FJLbM5BbSDk3VacWt10ktLIdpNqrg7st55fjO/1S78gj7bNxfie08zryiM7NKq7LfH8x3+6OH76F0Oq0Xw+ZfCiAAACf3pUWHRTTUlMRVMgcmRraXQgMjAyMy4wOS41AAB4nC2TOY4bQQxFr+JQAroL3MmC4KgTR6PAoeGoU2NOMIf3Z9UICqRfXB/J633zfT/OW+7Px/W8Hz/fz1vvz/u+RZ94eUPk64Ov6+NxXe/1baOPCy54/91/lgA3fOzWP5+//uLH9Q/+8rwu/vH1OGNMszxokOHHnMfrzJE2dR48lF0NyhxiRQpFnLcwjWceJ/yyTLj9mIcZRYtsol6tyWA1aHhL4SXpCFVpyTVYWrKhknycMip8dgb2UYIQkKbM+nZ0ET9OQ0wu3Y7CYcfpw4IjdkYSrbaiyO+6UCC3kaouaY4UNNySxCxfEkk4oit6I9IllbJo1xBpq3HEUgElSKlFuROKecOBres28uYkA7p+F6Ueh47kKb5NIoQPb+47DFC6JJycU3aRYJgJr7JcnBJ4iBXKZCZfCrlbpwqDHxRH7igISej5BQwVUnEAh9bs8nRomAgUpn55oRn3idKBX2OuagCPakKZtOMQZh+CpKhcnVAOsJpYNw6EE3FAfhVKw1Spp4GJmWe0MpPaCZN2Mu8lyajqwWIfPLyR0ixswQujB5RaNsDjcAvYKFigMAQqjKIGuxgvhaJLrDG9l0gHACIuiJp7LoGqGwV1trmiZM6eMTc4rFIr7LyEXDNXLI3mNvEC4iaAXYs2AjpbYcKZYwllNNtG4aZbIo2Ggbvy3gsoqWCAfUnTLjCGSPWsVLFpEDBO1UZMCbbLQnUuWuJptG0wx+rkKqVtVIgrK3kEctqSnKXPukRXqokmUBaPSo0NR/cYHMdkC2hZ6DpfTzBeuxSRbthSJau9gbgynDHuxsD0eH79B8Gk9EuZ9Et5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff336807450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff336805460>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff336805700>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff336806960>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff336805540>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ff336807ca0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw some PROTAC molecules using RDKit\n",
    "from rdkit.Chem import Draw\n",
    "\n",
    "# Draw the first 10 PROTACs from PROTAC-DB\n",
    "protac_db_mols = [Chem.MolFromSmiles(smiles) for smiles in protac_db_df['Smiles'].unique()[:10]]\n",
    "protac_db_mols = [mol for mol in protac_db_mols if mol is not None]\n",
    "protac_db_mols = protac_db_mols[:10]\n",
    "for mol in protac_db_mols:\n",
    "    display(mol)\n",
    "# img = Draw.MolsToGridImage(protac_db_mols, molsPerRow=5, subImgSize=(200, 200))\n",
    "# img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plots of Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Optional, List, Dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['train_loss', 'train_loss_step', 'train_loss_epoch', 'train_acc',\n",
       "       'train_acc_epoch', 'train_f1_score', 'train_f1_score_epoch',\n",
       "       'train_precision', 'train_precision_epoch', 'train_recall',\n",
       "       'train_recall_epoch', 'train_roc_auc', 'train_roc_auc_epoch',\n",
       "       'test_loss', 'test_acc', 'test_f1_score', 'test_precision',\n",
       "       'test_recall', 'test_roc_auc', 'model_type', 'test_model_id',\n",
       "       'train_len', 'train_active_perc', 'train_inactive_perc',\n",
       "       'train_avg_tanimoto_dist', 'test_len', 'test_active_perc',\n",
       "       'test_inactive_perc', 'test_avg_tanimoto_dist',\n",
       "       'num_leaking_uniprot_train_test', 'num_leaking_smiles_train_test',\n",
       "       'perc_leaking_uniprot_train_test', 'perc_leaking_smiles_train_test',\n",
       "       'split_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reports['test'].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pytorch performances:\n",
      "Metrics: ['Accuracy', 'ROC AUC']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric              |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:--------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation Accuracy | 0.845161 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Accuracy | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Accuracy | 0.844156 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Accuracy | 0.857143 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Accuracy | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Accuracy | 0.650943 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Accuracy | 0.896552 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Accuracy | 0.654545 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Accuracy | 0.526549 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Accuracy | 0.797468 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Accuracy | 0.767857 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Accuracy | 0.84153  | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Accuracy | 0.865385 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Accuracy | 0.758621 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Accuracy | 0.721088 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      0 | random       | Validation ROC AUC  | 0.904333 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation ROC AUC  | 0.924493 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation ROC AUC  | 0.931139 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation ROC AUC  | 0.924557 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation ROC AUC  | 0.927257 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation ROC AUC  | 0.735142 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation ROC AUC  | 0.911787 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation ROC AUC  | 0.679398 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation ROC AUC  | 0.545852 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation ROC AUC  | 0.858333 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation ROC AUC  | 0.846955 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation ROC AUC  | 0.905189 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation ROC AUC  | 0.937542 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation ROC AUC  | 0.852513 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation ROC AUC  | 0.792357 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.825581 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.755814 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.790698 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.635294 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.552941 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.576471 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.788235 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC        | 0.840761 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC        | 0.842935 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC        | 0.838044 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC        | 0.632664 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC        | 0.56689  | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC        | 0.612598 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC        | 0.804617 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC        | 0.849662 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC        | 0.811937 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.825581 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.611765 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.705882 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC        | 0.847826 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC        | 0.614827 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC        | 0.823761 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.514914 | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC  | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test ROC AUC        | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC        | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.526994 | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC  | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test ROC AUC        | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC        | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.518175 | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC  | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test ROC AUC        | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC        | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "Metric: Accuracy\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric              |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:--------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation Accuracy | 0.845161 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Accuracy | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Accuracy | 0.844156 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Accuracy | 0.857143 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Accuracy | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Accuracy | 0.650943 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Accuracy | 0.896552 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Accuracy | 0.654545 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Accuracy | 0.526549 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Accuracy | 0.797468 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Accuracy | 0.767857 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Accuracy | 0.84153  | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Accuracy | 0.865385 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Accuracy | 0.758621 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Accuracy | 0.721088 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.825581 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.755814 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.790698 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.635294 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.552941 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.576471 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.788235 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.825581 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.611765 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.705882 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.514914 | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.526994 | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.518175 | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "Metric: ROC AUC\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric             |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:-------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation ROC AUC | 0.904333 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation ROC AUC | 0.924493 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation ROC AUC | 0.931139 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation ROC AUC | 0.924557 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation ROC AUC | 0.927257 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation ROC AUC | 0.735142 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation ROC AUC | 0.911787 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation ROC AUC | 0.679398 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation ROC AUC | 0.545852 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation ROC AUC | 0.858333 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation ROC AUC | 0.846955 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation ROC AUC | 0.905189 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation ROC AUC | 0.937542 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation ROC AUC | 0.852513 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation ROC AUC | 0.792357 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.840761 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.842935 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.838044 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.632664 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.56689  | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.612598 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.804617 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.849662 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.811937 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.847826 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.614827 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.823761 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "Plotting performance for main part of the paper...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_combined_data(combined_data: pd.DataFrame, title: str, show_plot: bool = False, metrics: List = ['Accuracy']) -> None:\n",
    "    num_metrics = len(metrics)\n",
    "    plt.figure(figsize=(6 * num_metrics, 4)) # 12,6\n",
    "    sns.barplot(\n",
    "        data=combined_data,\n",
    "        x='Metric',\n",
    "        y='Score',\n",
    "        hue='Split Type',\n",
    "        errorbar=('sd', 1),\n",
    "        palette=palette)\n",
    "    plt.title('')\n",
    "    plt.ylabel('')\n",
    "    plt.xlabel('')\n",
    "    plt.ylim(0, 1.0)  # Assuming scores are normalized between 0 and 1\n",
    "    plt.grid(axis='y', alpha=0.5, linewidth=0.5)\n",
    "\n",
    "    # Make the y-axis as percentage\n",
    "    if 'Accuracy' in metrics:\n",
    "        plt.gca().yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    # Plot the legend below the x-axis, outside the plot, and divided in two columns\n",
    "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=4)\n",
    "\n",
    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
    "    for i, p in enumerate(plt.gca().patches):\n",
    "        # TODO: For some reasons, there are 4 additional rectangles being\n",
    "        # plotted... I suspect it's because the dummy_df doesn't have the same\n",
    "        # shape as the df containing all the evaluation data...\n",
    "        if p.get_height() < 0.01:\n",
    "            continue\n",
    "\n",
    "        if num_metrics == 1:\n",
    "            if 'Accuracy' in metrics:\n",
    "                value = f'{p.get_height():.1%}'\n",
    "            else:\n",
    "                value = f'{p.get_height():.3f}'\n",
    "        else:\n",
    "            if i % 2 == 0:\n",
    "                value = f'{p.get_height():.1%}'\n",
    "            else:\n",
    "                value = f'{p.get_height():.3f}'\n",
    "        \n",
    "        # print(f'Plotting value: {p.get_height():.5f} -> {value}')\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = 0.3 # p.get_height() - p.get_height() / 2\n",
    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
    "\n",
    "    plt.savefig(f'plots/{title}.pdf', bbox_inches='tight')\n",
    "    plt.savefig(f'plots/{title}.png', bbox_inches='tight')\n",
    "    if show_plot:\n",
    "        plt.show()\n",
    "    \n",
    "    print(combined_data.to_markdown(index=False))\n",
    "\n",
    "\n",
    "def plot_performance_metrics(\n",
    "        df_cv: pd.DataFrame,\n",
    "        df_test: pd.DataFrame,\n",
    "        df_test_majority: Optional[pd.DataFrame] = None,\n",
    "        title: Optional[str] = None,\n",
    "        show_plot: bool = False,\n",
    "        metrics_to_plot: Dict[str, str] = {\n",
    "            'val_acc': 'Validation Accuracy',\n",
    "            'val_roc_auc': 'Validation ROC AUC',\n",
    "            'test_acc': 'Test Accuracy',\n",
    "            'test_roc_auc': 'Test ROC AUC',\n",
    "        },\n",
    ") -> None:\n",
    "    # Extract and prepare CV data\n",
    "    val_metrics = [k for k in metrics_to_plot.keys() if 'val' in k]\n",
    "    cv_data = df_cv[['model_type', 'fold', 'split_type'] + val_metrics]\n",
    "    cv_data = cv_data.melt(id_vars=['model_type', 'fold', 'split_type'], var_name='Metric', value_name='Score')\n",
    "    cv_data['Metric'] = cv_data['Metric'].replace(metrics_to_plot)\n",
    "    cv_data['Stage'] = cv_data['Metric'].apply(lambda x: 'Validation' if 'Val' in x else 'Test')\n",
    "    # Remove test data from CV data\n",
    "    cv_data = cv_data[cv_data['Stage'] == 'Validation']\n",
    "\n",
    "    # Extract and prepare test data\n",
    "    test_metrics = [k for k in metrics_to_plot.keys() if 'test' in k]\n",
    "    test_data = df_test[['model_type', 'split_type'] + test_metrics]\n",
    "    test_data = test_data.melt(id_vars=['model_type', 'split_type'], var_name='Metric', value_name='Score')\n",
    "    # Add a suffix to the metric name to differentiate from the majority score\n",
    "    test_data['Metric'] = test_data['Metric'].replace({k: f'{v}\\n(Average Score)' for k, v in metrics_to_plot.items()})\n",
    "    test_data['Stage'] = '(Average Score)'\n",
    "\n",
    "    # Combine CV and test data\n",
    "    combined_data = pd.concat([cv_data, test_data], ignore_index=True)\n",
    "\n",
    "    if df_test_majority is not None:\n",
    "        # Extract and prepare test data\n",
    "        test_data_majority = df_test_majority[['model_type', 'split_type'] + test_metrics]\n",
    "        test_data_majority = test_data_majority.melt(id_vars=['model_type', 'split_type'], var_name='Metric', value_name='Score')\n",
    "        # Add a suffix to the metric name to differentiate from the average score\n",
    "        test_data_majority['Metric'] = test_data_majority['Metric'].replace({k: f'{v}\\n(Majority Vote)' for k, v in metrics_to_plot.items()})\n",
    "        test_data_majority['Stage'] = '(Majority Vote)'\n",
    "        combined_data = pd.concat([combined_data, test_data_majority], ignore_index=True)\n",
    "\n",
    "    # Rename 'split_type' values according to a predefined map for clarity\n",
    "    group2name = {\n",
    "        'random': 'Standard Split',\n",
    "        'uniprot': 'Target Split',\n",
    "        'tanimoto': 'Similarity Split',\n",
    "    }\n",
    "    combined_data['Split Type'] = combined_data['split_type'].map(group2name)\n",
    "\n",
    "    # Add dummy model data\n",
    "    dummy_val_acc = []\n",
    "    dummy_test_acc = []\n",
    "    for i, group in enumerate(group2name.keys()):\n",
    "        # Get the majority class in group_df\n",
    "        group_df = df_cv[df_cv['split_type'] == group]\n",
    "        major_col = 'inactive' if group_df['val_inactive_perc'].mean() > 0.5 else 'active'\n",
    "        dummy_val_acc.append(group_df[f'val_{major_col}_perc'].mean())\n",
    "\n",
    "        group_df = df_test[df_test['split_type'] == group]\n",
    "        major_col = 'inactive' if group_df['test_inactive_perc'].mean() > 0.5 else 'active'\n",
    "        dummy_test_acc.append(group_df[f'test_{major_col}_perc'].mean())\n",
    "\n",
    "    dummy_scores = []\n",
    "    for i in range(len(dummy_val_acc)):\n",
    "        metrics = {\n",
    "            'Validation Accuracy': dummy_val_acc[i],\n",
    "            'Test Accuracy\\n(Average Score)': dummy_test_acc[i],\n",
    "        }\n",
    "        # All other metrics are set to 0.5 (i.e., random guessing)\n",
    "        for k, v in metrics_to_plot.items():\n",
    "            if 'acc' not in k:\n",
    "                if 'val' not in k:\n",
    "                    metrics[f'{v}\\n(Average Score)'] = 0.5\n",
    "                    metrics[f'{v}\\n(Majority Vote)'] = 0.5\n",
    "                else:\n",
    "                    metrics[v] = 0.5\n",
    "\n",
    "        if df_test_majority is not None:\n",
    "            metrics['Test Accuracy\\n(Majority Vote)'] = dummy_test_acc[i]\n",
    "\n",
    "        for metric, score in metrics.items():\n",
    "            dummy_scores.append({\n",
    "                'Experiment': i,\n",
    "                'Metric': metric,\n",
    "                'Score': score,\n",
    "                'Split Type': 'Dummy model',\n",
    "            })\n",
    "    dummy_model = pd.DataFrame(dummy_scores)\n",
    "    combined_data = pd.concat([combined_data, dummy_model], ignore_index=True)\n",
    "\n",
    "    # Plotting\n",
    "    metrics = list({k.replace('Test ', '').replace('Validation ', '') for k in metrics_to_plot.values()})\n",
    "    print(f'Metrics: {metrics}')\n",
    "    num_metrics = len(metrics)\n",
    "\n",
    "    plot_combined_data(combined_data, title, show_plot, metrics)\n",
    "\n",
    "    for metric in metrics:\n",
    "        print(f'Metric: {metric}')\n",
    "        # Plot the data for the current metric\n",
    "        metric_data = combined_data[combined_data['Metric'].str.contains(metric)]\n",
    "        plot_combined_data(metric_data, f'{title}_{metric}', show_plot, [metric])\n",
    "\n",
    "    # plt.figure(figsize=(6 * num_metrics, 6)) # 12,6\n",
    "    # sns.barplot(\n",
    "    #     data=combined_data,\n",
    "    #     x='Metric',\n",
    "    #     y='Score',\n",
    "    #     hue='Split Type',\n",
    "    #     errorbar=('sd', 1),\n",
    "    #     palette=palette)\n",
    "    # plt.title('')\n",
    "    # plt.ylabel('')\n",
    "    # plt.xlabel('')\n",
    "    # plt.ylim(0, 1.0)  # Assuming scores are normalized between 0 and 1\n",
    "    # plt.grid(axis='y', alpha=0.5, linewidth=0.5)\n",
    "\n",
    "    # # Make the y-axis as percentage\n",
    "    # plt.gca().yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    # # Plot the legend below the x-axis, outside the plot, and divided in two columns\n",
    "    # plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=4)\n",
    "\n",
    "    # # For each bar, add the rotated value (as percentage), inside the bar\n",
    "    # for i, p in enumerate(plt.gca().patches):\n",
    "    #     # TODO: For some reasons, there are 4 additional rectangles being\n",
    "    #     # plotted... I suspect it's because the dummy_df doesn't have the same\n",
    "    #     # shape as the df containing all the evaluation data...\n",
    "    #     if p.get_height() < 0.01:\n",
    "    #         continue\n",
    "    #     if i % 2 == 0:\n",
    "    #         value = f'{p.get_height():.1%}'\n",
    "    #     else:\n",
    "    #         value = f'{p.get_height():.3f}'\n",
    "        \n",
    "    #     # print(f'Plotting value: {p.get_height():.5f} -> {value}')\n",
    "    #     x = p.get_x() + p.get_width() / 2\n",
    "    #     y = 0.4 # p.get_height() - p.get_height() / 2\n",
    "    #     plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
    "\n",
    "    # # plt.savefig(f'plots/{title}.pdf', bbox_inches='tight')\n",
    "    # if show_plot:\n",
    "    #     plt.show()\n",
    "\n",
    "    print('Plotting performance for main part of the paper...')\n",
    "\n",
    "    # Plot in the same above the accuracy and the ROC AUC in two different subplots\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(11, 5))\n",
    "    sns.barplot(\n",
    "        data=combined_data[combined_data['Metric'].str.contains('Accuracy')],\n",
    "        x='Metric',\n",
    "        y='Score',\n",
    "        hue='Split Type',\n",
    "        errorbar=('sd', 1),\n",
    "        palette=palette,\n",
    "        ax=axes[0])\n",
    "    sns.barplot(\n",
    "        data=combined_data[combined_data['Metric'].str.contains('ROC AUC')],\n",
    "        x='Metric',\n",
    "        y='Score',\n",
    "        hue='Split Type',\n",
    "        errorbar=('sd', 1),\n",
    "        palette=palette,\n",
    "        ax=axes[1])\n",
    "    # axes[0].set_title('Accuracy')\n",
    "    axes[0].set_ylabel('')\n",
    "    axes[0].set_xlabel('')\n",
    "    axes[0].set_ylim(0, 1.0)\n",
    "    axes[0].grid(axis='y', alpha=0.5, linewidth=0.5)\n",
    "    axes[0].yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    axes[0].legend().remove()\n",
    "\n",
    "    # axes[1].set_title('ROC AUC')\n",
    "    axes[1].set_ylabel('')\n",
    "    axes[1].set_xlabel('')\n",
    "    axes[1].set_ylim(0, 1.0)\n",
    "    axes[1].grid(axis='y', alpha=0.5, linewidth=0.5)\n",
    "    # axes[1].yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    axes[1].legend().remove()\n",
    "\n",
    "    # For each bar in both subplots, add the rotated value (as percentage), inside the bar\n",
    "    for i, ax in enumerate(axes):\n",
    "        for p in ax.patches:\n",
    "            if p.get_height() < 0.01:\n",
    "                continue\n",
    "            if i % 2 == 0:\n",
    "                value = f'{p.get_height():.1%}'\n",
    "            else:\n",
    "                value = f'{p.get_height():.3f}'\n",
    "            \n",
    "            x = p.get_x() + p.get_width() / 2\n",
    "            y = 0.3 # p.get_height() - p.get_height() / 2\n",
    "            ax.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
    "\n",
    "    plt.legend(loc='upper center', bbox_to_anchor=(-0.1, -0.15), ncol=4)\n",
    "    plt.savefig(f'plots/{title}.pdf', bbox_inches='tight')\n",
    "    if show_plot:\n",
    "        plt.show()\n",
    "\n",
    "print('Pytorch performances:')\n",
    "plot_performance_metrics(\n",
    "    df_cv=reports['cv_train'],\n",
    "    df_test=reports['test'],\n",
    "    df_test_majority=reports['majority_vote'][reports['majority_vote']['cv_models'].isna()],\n",
    "    title=f'summary_performance-best_models_as_test',\n",
    "    show_plot=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pytorch performances:\n",
      "Metrics: ['Recall', 'Precision', 'ROC AUC', 'F1 Score', 'Accuracy']\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric               |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:---------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation Accuracy  | 0.845161 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Accuracy  | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Accuracy  | 0.844156 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Accuracy  | 0.857143 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Accuracy  | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Accuracy  | 0.650943 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Accuracy  | 0.896552 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Accuracy  | 0.654545 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Accuracy  | 0.526549 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Accuracy  | 0.797468 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Accuracy  | 0.767857 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Accuracy  | 0.84153  | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Accuracy  | 0.865385 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Accuracy  | 0.758621 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Accuracy  | 0.721088 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      0 | random       | Validation ROC AUC   | 0.904333 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation ROC AUC   | 0.924493 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation ROC AUC   | 0.931139 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation ROC AUC   | 0.924557 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation ROC AUC   | 0.927257 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation ROC AUC   | 0.735142 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation ROC AUC   | 0.911787 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation ROC AUC   | 0.679398 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation ROC AUC   | 0.545852 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation ROC AUC   | 0.858333 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation ROC AUC   | 0.846955 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation ROC AUC   | 0.905189 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation ROC AUC   | 0.937542 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation ROC AUC   | 0.852513 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation ROC AUC   | 0.792357 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      0 | random       | Validation F1 Score  | 0.853659 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation F1 Score  | 0.878049 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation F1 Score  | 0.837838 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation F1 Score  | 0.865854 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation F1 Score  | 0.875    | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation F1 Score  | 0.663636 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation F1 Score  | 0.890511 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation F1 Score  | 0.693548 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation F1 Score  | 0.467662 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation F1 Score  | 0.851852 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation F1 Score  | 0.790323 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation F1 Score  | 0.857143 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation F1 Score  | 0.848921 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation F1 Score  | 0.764045 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation F1 Score  | 0.738854 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      0 | random       | Validation Precision | 0.833333 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Precision | 0.857143 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Precision | 0.898551 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Precision | 0.835294 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Precision | 0.864198 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Precision | 0.544776 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Precision | 0.871429 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Precision | 0.632353 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Precision | 0.635135 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Precision | 0.867925 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Precision | 0.765625 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Precision | 0.90625  | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Precision | 0.808219 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Precision | 0.723404 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Precision | 0.783784 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      0 | random       | Validation Recall    | 0.875    | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Recall    | 0.9      | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Recall    | 0.78481  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Recall    | 0.898734 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Recall    | 0.886076 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Recall    | 0.848837 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Recall    | 0.910448 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Recall    | 0.767857 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Recall    | 0.370079 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Recall    | 0.836364 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Recall    | 0.816667 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Recall    | 0.813084 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Recall    | 0.893939 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Recall    | 0.809524 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Recall    | 0.698795 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test Accuracy        | 0.825581 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy        | 0.755814 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy        | 0.790698 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy        | 0.635294 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy        | 0.552941 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy        | 0.576471 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy        | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy        | 0.788235 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy        | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC         | 0.840761 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC         | 0.842935 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC         | 0.838044 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC         | 0.632664 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC         | 0.56689  | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC         | 0.612598 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC         | 0.804617 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC         | 0.849662 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC         | 0.811937 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test F1 Score        | 0.831461 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test F1 Score        | 0.769231 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test F1 Score        | 0.804348 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score        | 0.651685 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score        | 0.641509 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score        | 0.617021 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score        | 0.646154 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score        | 0.742857 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score        | 0.646154 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.755102 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.686275 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.711538 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.674419 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.566667 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.604167 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.75     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.787879 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.75     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Recall          | 0.925    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Recall          | 0.875    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Recall          | 0.925    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall          | 0.630435 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall          | 0.73913  | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall          | 0.630435 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall          | 0.567568 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall          | 0.702703 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall          | 0.567568 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy        | 0.825581 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy        | 0.611765 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy        | 0.705882 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC         | 0.847826 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC         | 0.614827 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC         | 0.823761 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test F1 Score        | 0.823529 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score        | 0.60241  | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score        | 0.576271 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.777778 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.675676 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.772727 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Recall          | 0.875    | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall          | 0.543478 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall          | 0.459459 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy  | 0.514914 | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC   | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Validation F1 Score  | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Validation Recall    | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy  | 0.526994 | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC   | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Validation F1 Score  | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Validation Recall    | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy  | 0.518175 | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC   | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Validation F1 Score  | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Validation Recall    | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "Metric: Recall\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric            |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation Recall | 0.875    | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Recall | 0.9      | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Recall | 0.78481  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Recall | 0.898734 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Recall | 0.886076 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Recall | 0.848837 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Recall | 0.910448 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Recall | 0.767857 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Recall | 0.370079 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Recall | 0.836364 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Recall | 0.816667 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Recall | 0.813084 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Recall | 0.893939 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Recall | 0.809524 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Recall | 0.698795 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test Recall       | 0.925    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Recall       | 0.875    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Recall       | 0.925    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall       | 0.630435 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall       | 0.73913  | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall       | 0.630435 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall       | 0.567568 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall       | 0.702703 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall       | 0.567568 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Recall       | 0.875    | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Recall       | 0.543478 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Recall       | 0.459459 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Recall | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Recall | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Recall | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "Metric: Precision\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric               |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:---------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation Precision | 0.833333 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Precision | 0.857143 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Precision | 0.898551 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Precision | 0.835294 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Precision | 0.864198 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Precision | 0.544776 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Precision | 0.871429 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Precision | 0.632353 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Precision | 0.635135 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Precision | 0.867925 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Precision | 0.765625 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Precision | 0.90625  | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Precision | 0.808219 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Precision | 0.723404 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Precision | 0.783784 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.755102 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.686275 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.711538 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.674419 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.566667 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.604167 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.75     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.787879 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.75     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Precision       | 0.777778 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Precision       | 0.675676 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Precision       | 0.772727 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "Metric: ROC AUC\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric             |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:-------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation ROC AUC | 0.904333 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation ROC AUC | 0.924493 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation ROC AUC | 0.931139 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation ROC AUC | 0.924557 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation ROC AUC | 0.927257 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation ROC AUC | 0.735142 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation ROC AUC | 0.911787 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation ROC AUC | 0.679398 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation ROC AUC | 0.545852 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation ROC AUC | 0.858333 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation ROC AUC | 0.846955 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation ROC AUC | 0.905189 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation ROC AUC | 0.937542 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation ROC AUC | 0.852513 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation ROC AUC | 0.792357 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.840761 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.842935 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.838044 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.632664 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.56689  | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.612598 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.804617 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.849662 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.811937 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test ROC AUC       | 0.847826 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test ROC AUC       | 0.614827 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test ROC AUC       | 0.823761 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "Metric: F1 Score\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric              |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:--------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation F1 Score | 0.853659 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation F1 Score | 0.878049 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation F1 Score | 0.837838 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation F1 Score | 0.865854 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation F1 Score | 0.875    | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation F1 Score | 0.663636 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation F1 Score | 0.890511 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation F1 Score | 0.693548 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation F1 Score | 0.467662 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation F1 Score | 0.851852 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation F1 Score | 0.790323 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation F1 Score | 0.857143 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation F1 Score | 0.848921 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation F1 Score | 0.764045 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation F1 Score | 0.738854 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test F1 Score       | 0.831461 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test F1 Score       | 0.769231 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test F1 Score       | 0.804348 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score       | 0.651685 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score       | 0.641509 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score       | 0.617021 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score       | 0.646154 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score       | 0.742857 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score       | 0.646154 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test F1 Score       | 0.823529 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test F1 Score       | 0.60241  | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test F1 Score       | 0.576271 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation F1 Score | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation F1 Score | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation F1 Score | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "Metric: Accuracy\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric              |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:--------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| Pytorch      |      0 | random       | Validation Accuracy | 0.845161 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      1 | random       | Validation Accuracy | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      2 | random       | Validation Accuracy | 0.844156 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      3 | random       | Validation Accuracy | 0.857143 | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      4 | random       | Validation Accuracy | 0.87013  | Validation      | Standard Split   |          nan |\n",
      "| Pytorch      |      0 | uniprot      | Validation Accuracy | 0.650943 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      1 | uniprot      | Validation Accuracy | 0.896552 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      2 | uniprot      | Validation Accuracy | 0.654545 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      3 | uniprot      | Validation Accuracy | 0.526549 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      4 | uniprot      | Validation Accuracy | 0.797468 | Validation      | Target Split     |          nan |\n",
      "| Pytorch      |      0 | tanimoto     | Validation Accuracy | 0.767857 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      1 | tanimoto     | Validation Accuracy | 0.84153  | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      2 | tanimoto     | Validation Accuracy | 0.865385 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      3 | tanimoto     | Validation Accuracy | 0.758621 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |      4 | tanimoto     | Validation Accuracy | 0.721088 | Validation      | Similarity Split |          nan |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.825581 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.755814 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.790698 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.635294 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.552941 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.576471 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.788235 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.729412 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | random       | Test Accuracy       | 0.825581 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | uniprot      | Test Accuracy       | 0.611765 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| Pytorch      |    nan | tanimoto     | Test Accuracy       | 0.705882 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.514914 | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.526994 | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.518175 | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "Plotting performance for main part of the paper...\n"
     ]
    },
    {
     "data": {
      "image/png": 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P8XuACu9TOyt37NgxTJs2DfPnz4ePjw/u3r2L4cOHY+rUqRg/fnyuz2Efmru0tBcFaVahlSlTRi1x1OFjzAExCpID6dqZqs81iYWgn7+f3syBgvsYc0CMjzUH8tuHSl5kjho1Cj/88AO6d+8OAKhWrRoePXqE6dOno3fv3rC1tQUAREVFwc7OTvm8qKgoVK9ePdd9Hj16FDdv3sTSpUsxatQotGrVCgYGBujatSv+/vvvXJ+jo6MDHR0daV8cERFRMbK0tIRcLkdUVJTK8qioKGX/+q7x48fjyy+/xNdffw0gu19OTk7GgAED8NNPP0FDI+edM+xDiYhIDMnvyUxJScnRYcnlcigUCgCAs7MzbG1tcfjwYeX6hIQEnDt3Dr6+vjn29+rVKwwePBiLFi1SXn6bkZF9+UFGRgaysrKkfglEREQfJW1tbXh5ean0oQqFAocPH861DwXe3y8DUJlejIik9Xrwp9d/HPyJPiWSn8ls27YtfvnlF5QtWxbu7u64cuUKZs+eja+++gpA9o3wI0aMwM8//4yKFSsqpzCxt7dHhw4dcuxv6tSpaNWqFWrUqAEA8PPzw6hRo9C3b1/8/fff8PPzk/ollGpL7o3N97bpKaqXeax6MBna+bzMo7/L9AK162M292BcvrdNf6V6r8+io/HQ1s18z9aqhjYzLUCriOhTFRgYiN69e8Pb2xu1a9fGnDlzkJycrBxttlevXnBwcMD06dnfw23btsXs2bNRo0YN5eWy48ePR9u2bZXFJhFJ7/XgT0SfIskzf+7cuRg/fjy+/fZbREdHw97eHt988w0mTJig3Gb06NHKS3Xi4uJQr1497Nu3D7q6uir7unHjBjZu3Ijg4GDlss8++wzHjh1D/fr1UblyZaxbt07ql0BE/xEEQWWQDAMDA46QSFTMunXrhufPn2PChAmIjIxE9erVsW/fPuVgQOHh4SpnLseNGweZTIZx48YhIiICVlZWygPCRERERUHyItPIyAhz5szBnDlz3ruNTCbDlClTMGXKlDz3VbVqVYSFhaks09DQwPz58zF//nwpmktEeUhOTlbOZwsA27dv50BGRB+BIUOGYMiQIbmuO3bsmMpjTU1NTJw4ERMnTlRDy4iIiIrgnkwiIiIiIiL6dLHIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJsMgkIiIiIiIiybDIJCIiIiIiIsmwyCQiIiIiIiLJaBZ3A4hKEy0dfbQasVrlMRERERHRp4RFJpGEZDIZtHUNirsZeVpyb2y+t01PyVR5vOrBZGjr5+9ro7/L9AK1i4iIiIhKBxaZREREVKJFr+uX722T07JUHj/fNAQpOvL8PdnHuiDNIiL6ZPGeTCIiIiIiIpIMi0wiIiIiIiKSDItMIiIiIiIikgyLTCIiIiIiIpIMi0wiIiIiIiKSDItMIiIiIiIikgyLTCIiIiIiIpIM58mk99LSk6PTnJoqj4mIiIiIiPLCIpPeSyaTQVufKUJERERERPnHy2WJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMhzVhYjeiyMMExEREVFBscgkovfiCMOl05J7Y9USp7/LdLXEISIioo8Lfz1+JKLX9cv3tslpWSqPn28aghSdfJ5h8rEuSLOIiIiIiIgKhPdkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkNIu7AURERERE6iIIApKTk5WPDQwMIJPJirFFRKUPi0wiIiIi+mQkJyejffv2ysfbt2+HoaFhMbaIqPRhkUlUAvEoLBERERF9rFhkEpVAPApLRERERB8rDvxDREREREREkmGRSURERERERJJhkUlERERERESSYZFJREREREREkmGRSURERERERJJhkUlERERERESS4RQmRERERERUokWv66eeQD7W6olTwvFMJhEREREREUmmSM5kRkREYMyYMdi7dy9SUlJQoUIFrFixAt7e3gAAQRAwceJELFmyBHFxcfDz88OCBQtQsWJFAEBaWhq+/vprbN++Hba2tpg/fz78/f2V+581axbCw8Mxd+7comg+EVGx4FFYyq958+Zh1qxZiIyMhKenJ+bOnYvatWu/d/u4uDj89NNP2LJlC2JjY1GuXDnMmTMHrVq1UmOriYjoUyF5kfny5Uv4+fmhcePG2Lt3L6ysrBAWFgYzMzPlNjNnzsRff/2FVatWwdnZGePHj0dAQABu3boFXV1dLF68GJcuXUJQUBD27t2Lzz//HFFRUZDJZHjw4AGWLFmCixcvSt10IiKij96GDRsQGBiIhQsXwsfHB3PmzEFAQABCQ0NhbZ3zAEJ6ejqaNWsGa2tr/O9//4ODgwMePXoEU1NT9TeeiIg+CZIXmTNmzICjoyNWrFihXObs7Kz8f0EQMGfOHIwbNw7t27cHAKxevRo2NjbYtm0bunfvjpCQELRr1w7u7u4oX748Ro0ahZiYGFhZWWHQoEGYMWMGjI2NpW46ERHRR2/27Nno378/+vbtCwBYuHAhdu/ejeXLl+OHH37Isf3y5csRGxuLM2fOQEtLCwDg5OSkziYTEdEnRvIic8eOHQgICECXLl1w/PhxODg44Ntvv0X//v0BAA8ePEBkZKTK5a8mJibw8fFBUFAQunfvDk9PT6xZswapqanYv38/7OzsYGlpibVr10JXVxcdO3b8YDvS0tKQlpamfJyQkCD1SyUiIlKr9PR0XLp0CWPHjlUu09DQgL+/P4KCgnJ9zo4dO+Dr64vBgwdj+/btsLKywueff44xY8ZALpfn+hz2oVTSFOR2g+S0LJXHzzcNQYpO7p+FHHi7AVG+SF5k3r9/HwsWLEBgYCB+/PFHXLhwAcOGDYO2tjZ69+6NyMhIAICNjY3K82xsbJTrvvrqK1y7dg1ubm6wtLTExo0b8fLlS0yYMAHHjh3DuHHjsH79eri4uGD58uVwcHDI0Y7p06dj8uTJOZZHREQUqLPUz0wpyMsvtBh5ztfwPinyTACPlY9fyO2RKs/fP6VOnHrOAD958kQtcdRBXTnw5ElSvrdNTk5WeRwREQEDA4N8PVcnzqJA7Sos5kDBFeR7QIxP/XsgMTGxuJtQaDExMcjKysq1D719+3auz7l//z6OHDmCnj17Ys+ePbh79y6+/fZbZGRkYOLEibk+h31o7j71z05hMAcKhzlQcOxD1SO/fajkRaZCoYC3tzemTZsGAKhRowZu3LiBhQsXonfv3vnah5aWFubNm6eyrG/fvhg2bBiuXLmCbdu24erVq5g5cyaGDRuGzZs359jH2LFjERgYqHyckJAAR0dHODg4FOhS25SQuHxvK4ZlVkS+t03OUj0CZ5H1FAZZ+TsCl2aaUaB2FVaZMmXUEkcd1JUDZcqY5nvbpCTVgtTBwQGGhob5em5a2ouCNKvQmAMFV5DvATE+9e+BT+2snEKhgLW1NRYvXgy5XA4vLy9ERERg1qxZ7y0y2Yfm7lP/7BQGc6BwmAMFxz5UPfLbh0peZNrZ2cHNzU1lWZUqVZSFoK2tLQAgKioKdnZ2ym2ioqJQvXr1XPd59OhR3Lx5E0uXLsWoUaPQqlUrGBgYoGvXrvj7779zfY6Ojg50dHQkeEVEREQfB0tLS8jlckRFRaksj4qKUvav77Kzs4OWlpbKpbFVqlRBZGQk0tPToa2tneM5pbkP1dfWwNIvHFUeExGRtCT/ZvXz80NoaKjKsjt37qBcuXIAsgcBsrW1xeHDh5XrExIScO7cOfj6+ubY36tXrzB48GAsWrQIcrkcWVlZyMjIPoKQkZGBrHeORhEREZVW2tra8PLyUulDFQoFDh8+nGsfCmT3y3fv3oVCoVAuu3PnDuzs7HItMEs7mUwGAx258k8mkxV3k4iISh3Ji8zvvvsOZ8+exbRp03D37l2sW7cOixcvxuDBgwFkf7mPGDECP//8M3bs2IHr16+jV69esLe3R4cOHXLsb+rUqWjVqhVq1KgBILuz3LJlC65du4a///4bfn5+Ur8EIiKij1ZgYCCWLFmCVatWISQkBIMGDUJycrJytNlevXqpDAw0aNAgxMbGYvjw4bhz5w52796NadOmKftlIiIiqUl+uWytWrWwdetWjB07FlOmTIGzszPmzJmDnj17KrcZPXo0kpOTMWDAAMTFxaFevXrYt28fdHV1VfZ148YNbNy4EcHBwcpln332GY4dO4b69eujcuXKWLdundQvgahYcGQ8IsqPbt264fnz55gwYQIiIyNRvXp17Nu3TzkYUHh4ODQ03hxDdnR0xP79+/Hdd9/Bw8MDDg4OGD58OMaMGVNcL4GIiEo5yYtMAGjTpg3atGnz3vUymQxTpkzBlClT8txP1apVERYWprJMQ0MD8+fPx/z58yVpKxERUUkzZMgQDBkyJNd1x44dy7HM19cXZ8+eLeJWERERZePd7kRERERERCQZFplEREREREQkGRaZREREREREJBkWmURERERERCQZFplEREREREQkGRaZREREREREJJkimcKEiKggBEFAcnKy8rGBgQFkMlkxtojUjTlARERUerDIJKJil5ycjPbt2ysfb9++HYaGhsXYIlI35gARqYu+tgaWfuGo8piIpMUik4iIiIg+GTKZDAY68uJuBlGpxkM3REREREREJBmeySyBeJkHERERERF9rFhklkC8zIOIiIiIiD5WPAVGREREREREkmGRSURERERERJLh5bJEVCTi5sTle9vkjGSVx/EL4pGplZmv55qOMC1Aq0idmANERESfJp7JJCIiIiIiIsnwTCZRCcQRhomIiIjoY8Uik6gE4gjDRERERPSx4ukPIiIiIiIikgyLTCIiIiIiIpIML5elYicIApKT34wsaWBgAJlMVowtIiIiIiKiwmKRScUuOTkZ7du3Vz7evn07DA0Ni7FFRERERERUWCwyiajY6WvqY3WL1SqP6dPCHCAiIio9WGQSUbGTyWQw0DIo7mZQMWIOEBERlR4sMqlIxM2Jy/e2yRnJKo/jF8QjUyvzg88zHWFawFYREREREVFR4+iyREREREREJBkWmURERERERCQZFplEREREREQkGd6TScWOo0oSEREREZUeLDKp2HFUSSIiIiKi0oOXyxIREREREZFkWGQSERERERGRZHi5LBEREdFHpCBzTRcW55r+uKkjBwDmwcespOcAz2QSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkWGQSERERERGRZFhkEhERERERkWRYZBIREREREZFkirzI/PXXXyGTyTBixAjlslevXmHw4MGwsLCAoaEhOnfujKioKOX62NhYtG3bFoaGhqhRowauXLmiss/Bgwfj999/L+qmExERfZTmzZsHJycn6OrqwsfHB+fPn8/X89avXw+ZTIYOHToUbQOJiOiTVqRF5oULF7Bo0SJ4eHioLP/uu++wc+dObNq0CcePH8fTp0/RqVMn5fpffvkFiYmJuHz5Mho1aoT+/fsr1509exbnzp1TKVqJiIg+FRs2bEBgYCAmTpyIy5cvw9PTEwEBAYiOjs7zeQ8fPsT333+P+vXrq6mlRET0qSqyIjMpKQk9e/bEkiVLYGZmplweHx+PZcuWYfbs2WjSpAm8vLywYsUKnDlzBmfPngUAhISEoHv37qhUqRIGDBiAkJAQAEBGRgYGDhyIhQsXQi6XF1XTiYiIPlqzZ89G//790bdvX7i5uWHhwoXQ19fH8uXL3/ucrKws9OzZE5MnT0b58uXV2FoiIvoUFVmROXjwYLRu3Rr+/v4qyy9duoSMjAyV5a6urihbtiyCgoIAAJ6enjhy5AgyMzOxf/9+5ZnQmTNnolGjRvD29v5g/LS0NCQkJKj8ERERlWTp6em4dOmSSh+qoaEBf39/ZR+amylTpsDa2hr9+vXLVxz2oUREJIZmUex0/fr1uHz5Mi5cuJBjXWRkJLS1tWFqaqqy3MbGBpGRkQCAH374AYMGDYKLiwucnJywbNkyhIWFYdWqVQgKCsLAgQNx4MABeHt7Y8mSJTAxMckRZ/r06Zg8eXKO5REREQXqLPUzU/K9rRgxcge1xNGJM1ZLnGiDvC/bkkLSk6QijwEwBwpLHTkAqCcPmAOF87HmQGJiYhG1pOjFxMQgKysLNjY2KsttbGxw+/btXJ9z6tQpLFu2DMHBwfmOwz40d6Xps8M+tHBKUw4A7EML41PPgfz2oZIXmY8fP8bw4cNx8OBB6OrqFmofJiYmWLduncqyJk2aYNasWVi7di3u37+P0NBQ9O/fH1OmTMl1EKCxY8ciMDBQ+TghIQGOjo5wcHCAsXH+kyMlJK5Qr6GgLLMi1BInzTRDLXGsk62LPIZpGdMijwEwBwpLHTkAqCcPmAOF87HmwKd0Vi4xMRFffvkllixZAktLy3w/j31o7krTZ4d9aOGUphwA2IcWxqeeA/ntQyUvMi9duoTo6GjUrFlTuSwrKwsnTpzA33//jf379yM9PR1xcXEqZzOjoqJga2ub6z5XrFgBU1NTtG/fHp06dUKHDh2gpaWFLl26YMKECbk+R0dHBzo6OpK+NiIiouJkaWkJuVyuMiI78P4+9N69e3j48CHatm2rXKZQKAAAmpqaCA0NhYuLS47nsQ8lIiIxJC8ymzZtiuvXr6ss69u3L1xdXTFmzBg4OjpCS0sLhw8fRufOnQEAoaGhCA8Ph6+vb479PX/+HFOmTMGpU6cAZBesGRnZRxAyMjKQlZUl9UsgIiL6KGlra8PLywuHDx9WTkOiUChw+PBhDBkyJMf2rq6uOfrkcePGITExEX/++SccHR3V0WwiIvrESF5kGhkZoWrVqirLDAwMYGFhoVzer18/BAYGwtzcHMbGxhg6dCh8fX1Rp06dHPsbMWIERo4cCQeH7Ous/fz8sGbNGjRv3hyLFy+Gn5+f1C+BiIjooxUYGIjevXvD29sbtWvXxpw5c5CcnIy+ffsCAHr16gUHBwdMnz4durq6Ofrk11cRvbuciIhIKkUy8M+H/PHHH9DQ0EDnzp2RlpaGgIAAzJ8/P8d2+/fvx927d7FmzRrlsiFDhuDixYvw8fFB7dq1MXHiRHU2nYiIqFh169YNz58/x4QJExAZGYnq1atj3759ysGAwsPDoaFRpNNgExER5UktReaxY8dUHuvq6mLevHmYN29ens8LCAhAQECAyjJ9fX1s3LhR6iYSERGVGEOGDMn18lggZ5/7rpUrV0rfICIiorfwUCcRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUlGs7gbUBqlp6VCUCigo2dQ3E0homKQlZWJ2KjHEAQFzK0doamlXSRxMrMUeBKTBIUgoIylIbQ15UUSh4iIiKggWGRKKObZQ+xZPR3Rj8MAmQyWtuXQ4ssxsC1bubibVmhZmQq8iIiHIAiwsDeBpjZ/xH5IaSww0l9lQFAI0NEvmtdSmjy+ew27lk+BIisLCkUWNDTkaNnrB5R395E0zvWHLzBtw0VkKgRkKQTINWQY3bkGalWykTTOa8wBIiIiyi8WmRI68O9s1GjQAa5ejZGVmYFLR/6HPat/xVfjVkga50XiK8zfdR1XH8RAIQDuZc3xbeuqsDOX9sxp+M1IbP39BBRZCiiyFNCQa6Dd8HpwqVlG0jgvkl7gr6N/4erjq1AIClS1r4ohjYfA3tRe0jjqUNoKjOfhcdjx50lE3n8BmUwGS0dTtB3qB7sKlpLGKck5oFAooKHx5s6Do//7G637jEPZStUBAFdP7cShDXMwYMq/IuMI0NCQKR8v2HMDP3T1gqdz9r/F7gsP8dfOa1gzspmoOO9iDhAREVFBscgUYeuicfDvOhxGZlYAgNSkeFTwqAstbV1oaevC2d0HV05ulzzu71uC4epoii+bVkZmpoAd5x5g+sZL+GtgA1H7ffdH7MFlF9AhsAHKVbUFAFzeH4q9C89iyOLPRMV518wDM1HFtgr6+vZFhiID24K34ec9P2P+5/MljVMUSnuBsXfBGXi3coVbPWdkZWTh3M5b2PHnKXwzt4OkcUpyDqz97Vs07x4Im7KVAACKrEwYm1sr1xuZWSMzM0N0nGGLTmB4e09UtDcFkH0m29pET7ne2kQPGZkK0XHexRygovbu9+jby5PinsPYXNqDZ0mpGThxIwLR8amwNtVHw6r2MNDVEr3fs9tuwLWuE0ytDSVoZd5ik2MR8iwEsSmxAABzfXNUsasCcwPzIo+tTqnJCbh/IwjuPgFFsv9Ry07j+041YGOmL+l+3+2z316e+CIZJlbS5EhpyYPoJ3cR9fgOHCtWh6mlPZ4/fYDgE9sgCAIqetaDs1ttyWMKgoCrD17g6YskmBvpwruiNTTlRTdUzT/j96HN0HqSfz98zDnAIlMEt1r+2Dh3JKrXb4+ajTqhRsMOWPFLPzhW8IAiKxOP7lxBrSZdRceZv/s6+jarAj3t7H+up7FJmPh5LehoZV8e2dG3PAKXnhIdZ+XoXWg5qC7sXCwAAFlZChhbvjk7amJlgKyMLNFx5h6di6/9voaedvYP5Ii4CExpNwU6mjoAgM41OmP4xuGi46hDaSswNk07jIBv6sDYIvvfPSUhDRVrO0JLRxNaOpqo4FUGl/beFh2nNOVA0y7DsH/dbyhTwRP12n4F31a9sXrGNzC3doQiKxOxUeFo2nWY6DiD23jgj21X4eFkgd7+rviicWV8O/84HC0NkakQ8Ph5Iga38RAdhzlA6pKWmoz962bh3vUgaOsawLNeG9Rt2Rsa8uy+LTUpDosnfo7v5x4WFWfyuvNo7FEGDara42FUAr5fdhoymQx2ZvqIikvF6sO3MaNvXZSzNhIV5/Cqiziy5hKcqtrB078iKtcpC00taW9jSE1PxexDs3E09ChkMhmMdLLbnJiWCEEQ0MS1CQL9A6GrpStp3OKS+DIae/+ZKbrIDAqJzHX59UcvcDY0EtYm2UWmbxVbUXHSUtKx++8zCLv4GNp6WqgZUBn1u3lC47/iJSX+FeZ9sxk/buktKk5pyoM7V05g5/Ip0NE3RFZGOjoMmIrtyybBtmxlaGhoYMuCH9Gy1w9wq+UvKs5Pq8/ix65eMNDVQkJKOsatPovQiDgY62sjISUdZSwM8Xt/P5ga6Ih7PefDc10efisKdy88hrFVdt9aqXZZUXFKQg6wyBShcs1GcKrijePbFmPtb4PRrPt36DJ4Jh6HBUOhyELtZj1g51RFdBxLY10MmX8CXwe4wbeKLRpVc8DQhSdQu5INMhUKnL71DE09xV/CGjCgDvbMO4Oy7jZo2LMG6nfzxLKRO2HhYAxFpoCYiHgE9Bd/2aeVoRUGrh2IAQ0GwM/FD40rN8agdYNQx6kOMhWZOHn3JPxdxX2ZqEtpKzDcG7pg7YT98Grpilqtq8C7lSuWDNuOsu62yMpS4OG1Z6jT3l10nNKUA/bObvhi1AKcP7Qea2YMRMMOA9Bv/Go8e3gLgiDAtmxl5dUOYlRxNMPcgfWx8eRd5ffB8hFNcfvxSygEAZUcTGH11oGHwmIOkLqc2rUczyPuo1XvH5GWkoSgfWsQ9TgMHfpPgVzzvzOLgiA6ztUHL9C3WXZfvGT/LXhVsMb3nWpAS1MDmVkK/LXjGhbuuYHpfXxFx2r9bV3cOReOHXNOQsdAC1UblEf1ZpVgXc5M9L4B4O9jf+N25G1M6zgNXmW9INfILmKzFFm4HH4Zc4/Oxd9H/8b3zb+XJF5RS0tNznN9elqqJHEmrTv/3nXzd99Q/v/+qe1ExTm+7gqiHsWi3Yj6eJWcjtMbryLy/gt0HtNYecBBkCCnS1MenN3/D+q27gPfFl8g5OIRbF82Cd5NuqBuy14AgAuHNuDCoQ2ii8yLYdFIz1TAAMDKQyFITc/Eyu+aws7cANHxqZi89jxWHbqN4e09RcXZNP0IZDJZrv/O+5eeAwDIZDLRBxpKQg6wyBRJR88QzXsE4sm969i7ejrKuXqjXtuvoKUt3ZGDrvUror67PebuvI4DV8LxbetqqOxghqsPYyAoBHwd4IYG7uLvW3KoZIW+s1ojaOsNLB+5C016e2HQvI6IuPMcgkKAXUVL5dkNMbrX6o4GFRvgzyN/Yt/NfRjWeBhcbV2V92J9U/8bNKzUUHQcdShtBYabnxPKV7fH0dWXsHLMbrQc6IseE5vj0c1IKLIUqNuxKuwriX89pSkHAEBDLkedgJ6oXLMRDq7/Azrn9qNJl2EwMpX2vkW5hgZ6NKyEhlUd8NeOqzh4RQvftqkKS2Px//avMQdIXe5eO4WWX45V3l5QwbMetiwYiy0Lf0THb37J3kiW85LDgsrIzILmf5fj3nsWj5+/rAMtzezHmnINdKlXAcMWnRAdBwAqeJWBZ9OKSI5LxbWjd3H10F1c3HMbdi4WqN6sItzqOYsaPOtE2AlM6zAN1RyqqSyXa8hRy6kWRjUfhR+3/VgiigsAmDuqbd7/xoIgSQ54VbCGhgYwsmMNmBm+OVPVcsJOLBjSEE7WxqJjAEDouXC0G15feZtRZZ+y2PDzIWz85TC6/tgUQHaBIVZpyoPY6Mdwq5X93rh6Ncae1dNR0aOecn3F6vVxZs8qSWNevf8CXwe4KccysTbRQ78AN8zZdlX0vl1qOECmIUObIX4wMH3TN0/vvBpf/9EOVmVNRccASkYOsMgUKTU5HvEvImFl74wvf1iMs/v+wepfB6Bx529R3r2OZHHszA0wrXcdHA5+jO+XnUZH3/L4poW7JF9Wb9OQa8DvMw+4+Tlh78KzuHb0HgK+9oGRhbT3K9ib2mNGpxk4GHIQIzaOQOeanTGo4SDJX486lKYCAwB0DbTRcpAvHt+Kwo4/T8HZ0w6NetaElo60XxelKQeeP32Al1GPYWnvhK5Df8ONs/uw/o/h8G7aBTUadJAszsOoBDyOSYKTtTF+7VsXBy6HY+TS0+js54J2Ps6SxWEOkDqkJsWr3F6gb2iCLkNm4X/zx2Dzgh8Q8Lk0P46cbYwRfP857C0MYG6ki6i4FFSwN1Guj45PUd5+IhUDUz34dqwG347VEH4zEsGHwnBw+QUcXH4Bo9d/Uej9CoIALfn77x/V1NCU5EyZumjr6qNOwBewLeea6/q45xE4sH626DjTetfB5tP3MGTBCQxtWw11XMVdFvs+KQlpKrcZ6Rvr4vNJzfHvlIPY8PMhtPq2riRxSlMeaOsaIDU5ASYWdniVkghBkYXU5ATl+tSkeGjpSvMb9HXXkpiaDjtz1X06mBvgReIr0TG6T2iGcztuYvmoXWgxoA4q1nIUvc/clIQcKLo7XD8Bty4cwqJx3bBlwY9YNL47Htw8B7/WfdBhwM84f3A9diybhOSEWMniJaSko2l1R/w9sAHuPYvH8EUncT8yXrL9A0D0o5e4HfQQCoWAzyc3R6Vajlj9015c3CP+Hqx3xafGo1mVZljQcwHuRt/F4H8H497ze5LHKWrPnz7AnSsnICiy0HXob3CpVhfr/xiOKye2SRrnYVQCTt58iiyFgF/71kUdVxuMXHoaO849kDROSuIrPLsbA6tyZuj3e1vo6GtjaeAO3L30RNI4QOnIgQuHN2LtrEE4f2g91v0+FFdP7ULVOi3Qc9R8PHsYgrW/DcbziPui4/zv9D0MXXgCm07exYglJ7HnwkM0r1kWf31TH7cfv8TwRSfxIDLhwzvKB+YAqYORmQ1io1TvX9LRM0CXwTORmZGO7UsmSBKnZ+NKWHYwBPsvh6N9HWcs3HsDey8+ws1HL7DvUjhmb70qyS0n7zs4UtbdFu2G18fw5V3R7KtaomLUKV8Hsw/NRlh0WI51YdFhmHN4DnzLi7/sV12sy1QAAJStVD3XP9tylSW5ZBoAOvu5YHLP2lh24BbmbL+KV+mZkuz3bSaWBngRofq7TEdfGz0mNkdGeiY2zzgqSZzSlAflKtfEoQ1/4tb5g9i7Zgacqnjj5I4leBEZjtiocBzftggO5cXfogEAszZfweR155GpEBD5MkVlXWxSGgwlGAAMAHzauaPL2CY4svoS9sw/g4w06XOtJOQAz2SKcHLHEgT0HI0q3k0QGR6Kff/MRAUPP1jYlkX3EXNw9dQurP19CAZMXicqzuV7z/HrxkuIT0mHhZEufurujZGdaiD4fgymb7yE2pVs0Kupq+gjsee238SxdVdgXc4UL58lovGXNVGjeWVU8C6DQ8svYOXxe2g1qC6sncTdW3Lp0SX8sucXxKXGwcLQAhPbTMTogNG4En4FP+/+GXXK10Gfun2UA4B8zC4c3ojTu5bD0r484p5HoH67/vCs1wblq/ri2Jb5uHX+IJr3GAkrh/Ki4vzv9D2sOhQCZxtjRMQmo1+zKmhVywk+lW2waO9NHA5+ghHtPeFsK+6SnxvH72P3/DPQ0dNCZnom2o2ojwbdq8Ot3n9ntg/fRfP+PjA0E3f2tFTlwKEN6DRoOspWqoH4F5H437wx8KzXBvqGJmjVaywehlzEjmWT0W+CuMt9Np28i6lf1kH18paIepmCH1edRataTjAx0MHoz2ri0t1o/Lz+IpaNaCIqDnOA1MXJ1QvXg/bmuOpHW1cfn307A5v+HiVJHJ/KthjR3hML99xATEL2mYo527Mvi9OSa6BNbSd81Vz8+AkfOmugo6+NGs3FzZs9rMkw/LLnF3zzzzcw0jWCqZ4pACAuNQ5JaUmoVa4WhjURPw6AulTx9kdmRtp71xsYm8P3v3vzpFDB3gR/D2qIhXtuYNC84xAg7Zke5+r2uHo4DBW8VA9a6OhpoceEZlg36YAkcUpTHjTqOBC7V03DwfV/wMGlKtp+NQGndi7Hip/7ADIZTC3tEdBT/HdBs+pvzijWdbVF2jsDWZ66+RTl7aS5bBoAbMtb4Kvf2uDQ8gtY+t0OyXOtJOQAi0wRMtJewdwmO2lNLe2Rma76RelZrw0qePiJjjNv53V0rV8BbX2ccTEsGov23MBfAxugenlLzPu2IdYevYNB845h+YimouIEbb2BbuOawqmaHeKik7B+8kHUaF4Z+sa6aDeiPu4HP8WWWccwcF5HUXH+OvIXutXqhg7VO+DCwwuYf2w+5n8+HzXK1sCiLxZh9dnV6L+mP1b3XS0qjjqUtgLj6D+X0GaIH9zrO+PZ3Rjs+vs0KtUuC8sypvjy5xa4ciAUq37YjcGLxE1jU5pyIPuHZfYZDFku0zA4VfFGrx8WSxLn9YkSWS5D43tVsMb8weLvYWQOkLrUbd0HSfEvcl2no2eALkNmIepxzqP0hVHf3R51q9gi7Gk8ImNTIECAuZEuKtiZSDJ9CQD8tLWPJPvJi7GuMWZ0moFHLx7h5rObeJn8EgBgZmCGqvZVUdZc3IiV6uZZr02e6w2MzeHXuo+kMXW05Bje3hNBIZG4+iAGJvrSHchq0L06EmNTcl2no6+Nzyc1R+R98Ve4laY8MDA2R9ehv6ksa9p1GGo27ozMjHRY2JRVjjgtxveda+S5/osmlaEh8a0aWjqaaDnIF3fOh+PR9UjoG0uXayUhB1hkiuDuE4DNC8aibEVPRIbfgVvtnHMUGhiLH1EuNukVale2gY6WHN4VrbFo703lOm1NOfo2q4LGHg6i42T/iM3+gOU2v1P56vboN7ut6Dgvkl/At7wvdDR1UMupFuYffzMPnramNr6u9zWauoormNWltBUYGa8yYeGQfSTPzNYoxyUeNZpXFj3sNlC6cqCWf1dsWfADrMpUwMvoJ6jftl+ObbS0xXcsXepVwLjVZ1HezgQRMUnK0TLfJsV9ZcwBUhc9A2PoGbz/zIGOnoFyUCApyDU04FrGDK5lpBnptTiVsyiHchblirsZRebt3yNFxbeKregpS96lZ6QDPaP3f9/r6GsrBwWSQmnOA1NLe7Xen/96msCiUKl2WUn6zdx8zDnAIlOExp2/hWNFT8RGPYZ7nRZwriLuXov3qeNqi6n/XoSvqw1uPIpF7UrWObZxshF/ir9Oh6pYP/UQbJzNEPs0AY161syxjRQDf9R1qYtJOyfB18UXNyJuwMcp57QozpbSDWJSlEpbgVGtsQs2TD2EslVtEXnvBao1dMmxzdujpRVWacqB2v7d4VylNmKjwmFpXx4WtkXTkXSpXwHeFa0RHpMIZxtjlLUSN6ff+zAHSJ0y0tMQ9fgOdPWNYGnnlGPdnSvHRM+R+D69fj+Eab3roIylNJOjP7v3AroG2jCzzf5sXj92D5f3hSI+JhmmVgbwalUF7vXF53RGVgZO3T2FW89uITb5vwnYDczhbucOvwp+eQ4GUlLMHt4cfcYuhYWddD+e45PTsP9yOG6Fv8TLpOwrz8wMdeBW1gzNa5YVPT/iaxlpmYi89wK6hjo5RhLNSMtEyJmH8GhcQXycUp4HRZEDeYmOT8Waw7cxslPeZzzzgzmQjUWmSFJcDvshgR2qY/eFh3gck4Sm1cughVfR/Ij17VgVLjUcEBMRD+typrAsY1okcUY1H4Wd13YiPDYc/lX80apqqyKJow7qLDC8KlrhcUxSkRYYzb6qjXJVbfEiIh6eTSqgfA3xZ8hzU5pyAACsHMqLvu82P5xtjUXfd/shzAFSl9iocGz6ezQSX0YDMhnKlK+GNl+Nh6GJBQAg/VUy9v4zU3SRuTUo94G3ouNTcOByOMyMsqcc6+gr7jO8a+4p+PetBTNbI1w5eAcHlp5HjWYVUbVRecRGJGDP/NPISMtEdf+KhY7x5OUTjNkyBjFJMahiVwVm+tlnZcOiw7Dj6g5YGVnh146/ooyZ+IGM1OHo5vm5LhcEBc4dXAc9g+xRgBt3/lZUnNtPXuLHVWehqyVHDRdL5YGFl0mvsP3sA2w4eRfTetVBZZFnuV9ExOPfSQcQH5MMmUwGxyrW6DCyIYz+G8k0LSUDu+aeFl1glKY8UFcOfEhiSjoOXHksushkDrzBIlOEwxv/QqWajeBYwaNI42hpaqCDyM4vv6ydzEQP7PMhWnItdKrRqUhjqJO6CozytiYob2vy4Q1FKqpLOt5WmnIgKvwOdPQNYWqZPVftzfMHcPXkTiS8jIKxuS1qNOiAKt7i7pV97UXiK1y59xzGetqo4WKlnOsPAFLTM/G/U/fwZRNxA4sAzAFSjxPbl8DS3hlfjlmEtJREHNk8D+tmD0X34X/A2NxGsjgL99yApbFujttABAE4FPwEcrkMMshEF5mxzxJh9t/AIZf3hqJ5v1oqA/3YVbDE6f9dE1Vkzjk8B86Wzlj8xWIY6KjOW52clozp+6bjzyN/YlbnWYWOoU6Xjv4PVg4u0NF7Zw5uQUBsZDg0tXUkuWRy/q7raFDVHsPbeeTYnyAI+HPHNczffQN/flNfVJwjqy/BqpwZvvq9LV4lpePg8vNYPXYPvvi5BUyspDljDpSuPFBXDgSFROa5/mlssugYAHPgbSwyRbhyYhuunNwOU0t7VPNthap1AmBgbF7kcQVBwNUHL/D0RRLMjXThXdEamvKin40mISYZJ/69gjZD63144w/42E/xSyXhZTRO716Jll+MFr2v7WfvI/RJHGpXskEjDwccCn6M9cfDIAiAn5stevu7Qp7LfaEFlZGWiZsnH+BJSBSSXqYCMsDM1hiVajvC2dNe9P4BYMPFDWhYsSFsTYpmrjJ12vvPDDTu9C1MLe1x7fRuHPnfXFSr2xputZshNuoxDqz7DZkZr1DNV9yZuttPXmLsyiAAQGaWAhbGupj0eW3lpfKpaZn452io6CIzISYZmtpy6Btnn90JvxmJy/vvIOF5EkysDeHV0hVlXHNesl9QpSkHqHAi7t9E16G/Qd/QBPqGJug0MHuEyX//GIZuw/+AlrauJHFaeZfD7Scv8UMXL5SzfnMVSMsJOzGtTx04WUtzdYCWjhypCa9gam2IhBfJsK9opbLeoZIl4qKTRMW48fQGFny+IMePSgAw0DHAV3W/wrf/Fu0ZHynVb/c1rp7ehUadBqFc5Te36Pw+rBlafDkmxyXUhXU/MgHfd66Ra7Eik8nQqW55fDvvuOg4T25Ho+fkAOgb60LfWBddf2qKfQvPYvWPe/HF1BaSzTVcmvJAXTkwad15SfbzIcyBN1hkitRl8EzcuxGEC4c34NSu5Sjv7gOPuq3g7F4HGhL84AeAn1afxY9dvWCgq4WElHSMW30WoRFxMNbXRkJKOspYGOL3/n6S3U/wPqmJabh29J7oIrMknOKXyqvkBNw8t190kbn22B1sOnkXXhWssHDvDUTFpWDTqbvoVNcFMhmw5cx9aMo10Ktp7hNa51fs0wSsm7gfGelZ0NSWIyEmGRW8yuBp2HNc2ncbrj7l0GFkA2iIPKix6MQiLDm5BNUdq6NV1VaoX7F+iT2wEPc8AqZW2ZeUBp/cjsadh6iMmGhbrjLO7l8rushceTAE9dzs8F2H6khNz8SyA7fw/bLT+LVPXZWJ5cXaPPMo6nXxRMVajgg9F47NM46ioncZlKlijdinCVjz0z589kNj0RNMl6YcoMLJzEiDhsabe8llMhma9wjEoY1/Yv2cEWjdZ5wkcYa398Spm8/w46qz6FrfBe3rFM2VJy41HXBpXyjaDLFEOXdbhJx5CBvnNweeb51+qLxfs7AMdQzxLP7Ze+9XfpbwDIY60p0tKWo+zT9H2Uo1sGf1dJSv6osG7ftDLpf+p6mZkQ5Cn8S991aT0CdxMDUU/xsqMz0LGvI3haxMJkPLQb7Yt/gs1ozbhw7fiTtT+lppygN15YC5oQ6GtvNA3Sp2ua6/+zQegxeIP9DAHHiDRaZIlvblUc7VCw07DkRY8EncOLsX2xZPgL6RKarWaYGqdVrAzFpcsXQxLBrpmQoYAFh5KASp6ZlY+V1T2JkbIDo+FZPXnseqQ7cxvL2nqDh3zofnuf5lZKKo/b9WEk7x59fda6fzXB8X81SSOAcuh2Nkp+qo726Pe8+yvwhHdaqBpv/N++RoaYSlB26KLjIPLDsHl5pl0GJgHchkMpzZch3hNyPRd2YbvHgaj38nHcSpTdfQoHt10a9pZLOROH3vNKbvm46/jvwF/yr+aF2tdYkb7EVTWxepyfEwsbBFYlwM7JxU/w3snKog4cUz0XHuPI3H4DYe0NCQwUBXC8PaecLaRB+jV5zBtN51YG0ifjAeAHgeHgdLR1MAwJnN19Doi5qo26macv3F3SE48W+w6CITKD05QIVjYVMWkY9Dcwzs4d91OABg66KfJItVz90OlcuYYtbmKzgXGo3vO1WXbN+vNfnSG6vG7sGan/bC1sUC53bcQviNSFg4miI2Ih4Roc/x2Vhxl863qtoKv+77FV/W+RI1y9ZUHqR9mfISl8Mv459z/6BjdXHTjKmbnVMVfDl6IQ5t/BNrZnwj2cGFt33m54I5268i7GkcapS3UhaUcUlpuHL/OfZefIT+LdxFx7EsY4Jn914ov0NfazEgey7YjdOOiI4BlL48UEcOVHQwRVhE/HuLTKkGsmUOvMEiUyJyuSZcvRrD1asxEmKjcD1oL26c3YdzB//F93MPSxbn6v0X+DrADXbm2cWZtYke+gW4Yc62q6L3vWn6EchksjwnlJbiuviScIo/v7YtHp/9zZTXJNwSvGexia9Q2cEUAOBiZwIZZHCxe3P2qqK9CV78N8m4GI9uROHrP2or/51rt3XD8bVXkJL4Chb2JmjWrzYOLjsvSZFZx7kOWlZtiZfJL7H/1n7svbEXW4O3opJ1JbSu1hqNKzfONUc+Ns5utRF8YgdafDEKjhU9EHrlOKzLvLmhP/TyMeX9mmJlZKlOHt29YUXINWQYuzIIIzuKHxEPyJ6+KP1VBgAgLioJLjVVB/5xqemAw6svSRKrtOQAFU4Fz3q4ffEI3Gs3z7HOv+twCAoFrp7aKVk8KxM9zOjri/UnwvDt/OOST45uZKGPr2e3xZkt1xF24QkgCHgaFoOEmGSUqWKDXtNrwb6ipagYX/l9BT0tPWy4uAELji9QflcLggBzA3P0qNUD3Wt1l+LlqJW2rj5a9RqLkItHsGnu9xAEhaT7b1+nPEz0dbDlzD3sPPcQiv/6bA2ZDBXtTfB9pxpoWE38IGeVfMri5sn7qNYo56jcLQbUgaAQcHl/qOg4pTEPijoHutSrgFfpme9db29hgJlf1RUdhznwBovMImBsbgO/1n1Qt1VvPLotzY+x13VKYmo67P4boeo1B3MDvEgUX2AYmumjxTd1UNkn90E/Iu+/wPLvd4mPUwJO8eeXgbE5/Lt/h4rvGWU46nEY1swcKDqOmaEuHkUnwtpUH09ikqAQBDx6nqi8H+9RdKIkl/roGmgjPfXNl3BmWiYUCgXk/10ea13OLPs+TQmZGZihe63u6F6rO649uYY9N/Zg3rF5mHdsHvYO2ytprKLQoP0A/Dt7KNb/MQI2ZSvh0pFNeBx2FRa2ZfEy6gmePryFDgOmiI7jZG2EW+Evcwz+1KV+BQgQMH2TNN815ara4uaJB7BxModteXM8uhEJG6c3l/w9vBEJYwv9PPZQcCU9B6hw6gT0zHN9s+7foVn37ySNKZPJ0KNhJXhVsMaNRy9gYSjNfZ+v6RrqoEkvbzTp5S3pft/Wo3YP9KjdA0/jniI25b8xDfTNYW8qzcGs4lTFuwkcXKoi6nEYjM3E3/v9tkYeDmjk4YDMLAXiU9IBACb62pKOaeH3Wd4DQbYc6IuWA30liVVa86CocqCak0We6/W0NeHpLO4gEMAceBuLTBGMzW3yvDdNJpPBqYo0Hc2szVegramBTIWAyJcpKvNixialwVBX/L1Mdi4WiLz34r1F5ofOcuZXSTjFn182ZSsjKjz0vUWm7ENnOfOpiacDZm6+Al9XWwTfj0HX+hWweO8tJKZkQCYD1h0LQ/2quV8CUhDOnnY4tOICWg6sA7mmHMf+uQxbZwvo6GsDyB4UxsBU/I+y950R9yjjAY8yHhjaeCiOhh4VHUcdjEwt0euHxTh34F/cuxEEQRAQ+eg2El9Gw6F8VfQI/At25cRdxgwA/jUccf3hC7Sp7ZRjXdf6FSEIwK4LD0XHafylF1b/tBdJsSlwrGKD42sv49ndGFg4mCA2IgG3Tj9Ey4F1RMcpTTlAJU8lB1NU+u/qEHUQBEHyieXtTe0/mh+TUjI2s4axmbUkvzdyoynXgMV/09ZkKaQ9W5aXosgBoHTmQVHnwNuyFApJBk3Mj08tB1hkijBgyr9qidOs+pt7n+q62iItQ/WSuVM3n6K8nfjR8ep0cEdG2vsvJTCzM8IXU1uIjlMSTvHnVy3/bshIe/+ZPVMrB3QdNlt0nF5NXKGtKUfI45do6V0W3RtUhIutCZbsv4W0jEzUqWyL3iLvxwSApr29sWn6ESwaug0ymQzGFvoq9xClJLxCnQ5VRcf5UMdhoGOANh5t8tzmY6Krb4SGHQagYYcBRRajlXc5tPJ+/6TU3RpURLcGhZ8a4TVLR1P0ndEax9ZdQdDWG0h/lYkbx+9DQ0MD9hUt0XFkA1SuI35y7NKWA1Q40U/u4uKRTXhy7zqS419AJpPB1NIeLh5+qO3fPee0BoUwfs05NKxmj/ru9tDRkn/4CYWUmZ6FY2sv42lYDCp4l0HdTtVwauNVnNlyHQBQsZYjWg3yVR60k0JMUgx2XtuJiLgIWBhYoHW11ihrXvRTEEklMyMdJ3cuQ+Sj2yjvXgc+zXsgaO8anDu4DgBQoVpdNOseKDoPLtyJgqWxHpxtjaFQCFh3/A52n3+I2KQ0WBjpop2PM7o1qCC6ACiOHABKdh4wB6TxMeYAi8wS4PvOed9r9UWTytCQ4MhIWfe8pxLQ1tVCuarSTDfwsZ/iz68PzZGqraOHspWqi46joSHD540qqSx7femPlAxM9dBnRmu8eBqPrAwFLMuYqJytr1LXSZI4RwKlufGdioaZnTE6jmwIQRCQHPcKgiBA31gXck3pjvYyB+jBrfPYvmQCnN194FC+KsKCT6Cabytoausi9NJR3L54BJ+PnCt6arDzd6JwMSwa83ZdR6NqDmjpXa5IzmIe/ecSbp16CPf6zrh25C4Snicj7OJjtBzkCw2ZDMf/vYJja68goL9PoWO0+KsF1n+9Hqb6pngY8xBD1g+Bqb4pKlhVwNn7Z7H96nbM6zEPLlY57wf7GJ3csRS3Lx2Bq3dT3Dy3Hwkvo3D/RhCadw+ETEMDp3etwKmdy9C06zBRcRbuuYkRHbIHR9xwMgzbgu6jR8NKKGtliMcxSdhw4i5kMog+UKeOHABKVx4wBwqnJOQAi8widOXENqQmxaNuq95FGkdPu+T+M36sp/g/dRYSTonxqVPX98DyA7fwMikNIztJMwAQkH1Jq6GZNKPWEr3rxPYlaNTpW1Sv3w4A8LB2cxzeNBf9JqxCvbZfYfO8MTixfQlafjlGdKwFQxriUthz7L8cjj0XH8HZxhgtvcuiiUcZGEl0RuF20CO0G14Pzp728GrpigXfbkHnMY2Vt6DoGetiz7zTon5cpmemK68CWHp6KTzLeGJKuymQa8ihUCjwy95fsOzUMkzrOE2S11TU7gQfR6teY1HO1Qs1GrTH0slfon3/KcpbUPQMTLB/3W+iC4zIuBTYmGZ/lx29FoGh7TzQsGr2QdpalWzgYGGABXtuiC4w1JEDQOnKA+ZA4ZSEHFDPRcifqDvBJ3Dj3P4ij3Mm5BkOXnlc5HEu7rmNk+uDizzO6bunsf9m0b9v6nDlxDac2bOqyOPsOPcAa46IH63sQ0LPhePa0btFHqc05YC6vgdiEl4h8mVKkcdhDpBUYqPC4exWS/m4nKsX4mKeIin+BeRyTfi26o37N85KEstEXwed/VyweGhj/PlNfbg6mmHlodv4fNYBTNt4EVfuPRcdIyUhDeb22beumNkaQSaTqcyLaW5nhOSENNFxXguLDkM3726Q/zfXqIaGBrrX6o470Xcki1HUUpPildO8mVraQybTUBmN28zaAalJcaLjGOlpIea/Edjjk9PhYK46uKCDhaEkI7SrOweAkp8HzAHxPtYcYJFZhLoNm40Bk9cVeZxl+0Pw25YrRR4n9Owjtfy4XHxyMWYemFnkcdRBXQXGqZvqOdBwdPUl7Jqb99ygUihNOaCu74HRn9XErH65D0AlJeYAScXQxBKxUW++t+KeRwCCAD2D7B9oRqaWyEiXdjRrAHAtY4YR7T3x7+jmGNLWA8/jX+GHlUGi92tiaYAnt7OL1ad3nkMmA56GxSjXR9yJET0ys0wmU94zJoMsxxQ/BtoGSHwlzZzW6mBkZoOI+zcBAM8ehkAmkyHyUYhy/dMHITA0tRIdx8/NDv8eD0OWQgHfKrbYce6Byn3h288+QHk78VfwqCMHgNKVB8yBwikJOVByr7MkpWUjxE3unF89pwSoJc6qvkV/5k9dukkw6E9+SDG3U34MnKeeUX9LUw6UNswBkoq7T3PsX/cb6gR8AbmmJi4d+R9cqvlCrpk9Wnr0k3swthA/avb76GprIqBmWQTULIvHz5NE769GQCXsmnsKVw+F4dm9GDTtWwvH115G7NN4ADJc3ncbPu3FDZwmCAK+XPElZJAhNSMV957fU7nn6mn8U5gbiLuHVZ0867XBvn9m4HrQHkSF30GjjgNxcudyxEY9gUwGBJ/cAe+mXUXH+apZFYxZcQb9/jwCN0dznLjxFJfvPUcZCwM8jU1BYmo6pvUWP62EOnIAKF15wBwonJKQAywyJSQIAh6HBeNldAQMTczh5FYbcjnfYqJPQcLLaOjqGUJbV/UIZVZWJp7evwnHip5FGj828RV2X3iEL5tULtI4RFKpE/AFMtLTELR3NbKyMuHk6o0mXYYq1xuZWqJZtxGi41RzsoDWB+ZCdLQSPzezTzt3GJjoIeLOc3g2rQD3BuVhXc4Mx9ddQUZaJmq3c4dfl7wHi/uQ0QGjVR47mKoO/nbr2S3Ur1BfVAx18m7SBfpGZnj24Baq+bZEFe+msLQvj9O7VyAj/RW8Gn+GOgFfiI5joKuFOQPqY9+lcATdjoSNqT4ECMhUCGjs4YDWtZ1gbSL+/nN15ABQuvKAOVA4JSEHWAGJsHn+D2jTdxx09AyRmhyPzfPHIvLRbegZmCA1JQFmVmXQ47s/oW9kKkm8209e4lZ4LF4mZV/LbWaoA7ey5nAtYybJ/l97eO0ZHt+KQtLLVMg0ZDC1MUTF2o6SDwajUCigkcvcRAqFAs+TnsPG2EbSeEXpUehlRNy7jqT4F5BpaMDUwg4uHnVhbu344Sfn071n8Qh7Gg9PZwvYmRvgYVTCf5d7AHXd7FCrorQTV7/tn/H70GZoPZhai/8hlpfvNn6HMQFjYGsizSjG6pAU/wLbFo1D5OM7kEGGKrWawr/rcGWx+So5ARv+CsT3cw8XaTteJqXhn6OhkhWZT+88x5PQ50h6mX25oqGZHspUtoJ9JfGXLeWlJOYAFY6GXJ7n1D92TlUkifObGi4jf61qw/Ko2rC88nG5qrboNa2lZPtv4Z73NGK96vSSLJa6uNXyh1stf+XjspWqo2ylPyWPoynXQJvaTrnONSylos4BoPTlAXOg4EpCDrDIFOHBrfPIzMiAjh5waudypKel4utJ/8DU0h4JL6OxbfF4nNq1HM17BIqK8zIpDVP+vYBb4bGwMtGDuaEOACA2KQ2L9t6EW1lzTOhRC2b/LS+s5LhUbJx2GM/uZs9VJggCbJzNEXr2EY6suQSfdu5o2ttbVAwASE5LxqwDsxB0PwgG2tlz4fX27a28YTkuNQ6fL/sch78r2h/lUkhOeImti35CZHgoZDINCIIC1mUqIOzqKZzYvgTeTbqgYcdvRMc5efMpftlwEYa6WsjIVGBiz9qY+u8FVHYwhUxDhvFrzmJ055po4llGVJw758NzXR5+Kwp3LzyGsVX2Nf+Vaoube+n0vdzv6bsecR1B94NgbZxdMPu5qO/HYWGd2L4YkMnQ8/t5SE9NxvHti7Hhr0B8Nnim8t4ySDCh9P3I+DzXP4kRf7kfkP09sHnGUTy+HQ0TSwMY/De6bPLLVByMSYajqzU6j2kMA1NxR3xLUw4QERGRKhaZEgm/E4yGHQYoR8QyNrNGw/YDsP/f30Xv+++d16AQBCwd1iTHJT2Pnyfh961X8PfOaxjfo9Z79pA/B5aeh6GZPkb+0xxyTTkOr7qItOR09Pu9LR5ee4Ytvx2Dkbk+ard1ExVn+ZnluB9zHz+2/BFJaUlYc3YNwqLDMKXdFGjJs+/F+dBE7R+LI/+bC0MTCwyduQNyTS0c37oQaa+S0WvMIjwKvYydy6fA0NQCXo0/ExXn3+Nh6NXEFZ83qoRj1yIw9d8L6Ozngi8aZ5+1+t+pu9h06q7oInPT9CPKAwzv2r/0HIDsm81/3CJuOo7x28e/N87co3OVcUrCgYZHty+hw4CpsCvnCgD43GUudiybjI1/jUTXYb9lbyTBPLaD5h0XvY/82Lf4LBSCgIF/d4SFg+rVCy8i4rFr7mnsW3wWnUc3FhWnNOUAFY0TO5YgOeElWn4x+sMbi1AU0//k5uiaS0iOS0WbofWKLMaSU0vwMvlljkvpSirmQOGUpjxgDhTOx5ADLDJFej2y06uURJhYqs73aGrlgKT4mNyeViAXw6Lx+9f1cr1nxNHKEN+2roZRy8SP9njv8hP0mt4KOv/NGdb4y5r4vee/CBjgAycPOzT7qjZOb7omusg8dfcUxrYYi+qO1QEA9SrUw9itY/Hjth/xS/tfALx5Xz92D26dR4/AudDRyz7DV799f8wd1Q5NuwxDuco10bjztzi7b63oIvNJTJKygGxYzR4z/ncZflXeDIjh52YnyRQmLjUcINOQoc0QP5UzVdM7r8bXf7SDVVlT0TEAoJZTLWjINDC6+WiYGby53LvZnGZY8sUSOFk6SRJHHdJeJUNH/81nU1NLGx36T8GOZZOw4c9AtO79kyRxjPS08HWAO2qUt8x1/aPoRIz/55zoOPevRODLX1rmKDABwMLBBM2/ro1/xu8THac05QAVjaS4GCS+jC7yODEJr/A8XvpRbN+V+CIFCS+SizRGTGIMohOL/j1TF+ZA4ZSmPGAOFM7HkAMsMkXau+ZXyDW1oMjKRMKLZ7Cyd1auS06Iha6eUR7Pzh8tTQ0kp2W8d31qeia0NMXPRiPXkqsUd6/PMmRlKQAAZVytERct/pK8+NR4WBu9uX/QRM8EszrPwpgtY/DD1h/wfbPvRcdQF7lc6533LPuSWYUiEwDgUL4q4mOfiY6jr6OJhJR02JrpIyk1AwpBQEJqunJ9fEo69HTEf5y7T2iGcztuYvmoXWgxoA4q1pLuntK3zeg0A5subcLAdQMxvMlw1HVRz+i4RcHUwg4xEQ9U7r/VkMvRrt8k7Fg2CVsWjpUkTkV7U7xIfAUbs9yHPk969f7viIKQa8mRlvL+faW/yoRcSy46TmnKASoarXpJ89n5kNGf1VRLnHYjin4QjrEt1fOeqQtzoHBKUx4wBwrnY8gBFpkiuPu8mdKjgocfMtJVJ1cNCz4J6zIu7z6twBpWc8CszVcwsGVV1HCxhIFu9iWlya8ycOVeDBbtu4HGHuIukwQAxyrWOLH+CtoOqwe5phzH/rkMUxtD6BvpAgBSEl5Bz1BbdBwbIxuEx4bD3vTNmV8DHQPM7DwTozePxoSdE0THUBcHl6o4vXsFWn75A+SaWji5YylMLeygZ5B9FiglMQ66+sai49RwscLfO6+hfR1nHL/xFDUrWGH5gRCM7FQdMsiwdP8tuJeVZqhqn3buKFfVFtv/OImwC4/RrF9tSfb7ri5eXVDdsTqm7ZmGoPtBGNxocJHEKWrO7nVw9fROVKrRQGX560Jz+9KJSIwTf0VD61pOeJWR+d711iZ6GNmxuug4bn7O2PnnSfh/VRvOnnbKKxvSUtLx4OozHFpxAe71y39gL/lTWnKACi8lKR43gvbi6YObSE6IBQAYGJvD3tkdVeu0kGzgvPjkNOy/HI5b4S/fGTzPDM1rloWpgbgxDV5LSXiFq4fD8OT2cyTHZZ8VMTDVQxlXK3g0qQgDE13RMeJT47H3xl7cfHoTsSnZ75m5vjnc7d3Rwr0FTPVNRcdQJ+ZA4ZSmPGAOFM7HngMsMkVo+eWYPNf7tuoFmUz8GcZvWrpDoRAwbeNFZCkE5VDsGVkKyDVkaOFVFv1biLuEFQCa9qmFdZMO4Pee/wIAtHQ10Xl0I+X6mMdxqNa4gug4XuW8sPfmXtQpX0dlub62PmZ0moFRm0eJjqEujToNwqa5ozB3VDsAgJaOLtr3m6Rc/yLyEar6NBcdp3+AG2b+7zL+2nENbuXMMa6bN1Yeuo3+fx0FANibGyCwQ3XRcV6zLW+Br35rg0PLL2DpdzsgoGjuka1oXRELv1iIecfmof+a/iXmXty31W/bDxnpr3JdpyGXo/3Xk5EY91x0nHruec8XaKSvjeY1xQ3IBAD+fWtBUAjY9vtxKBQC5P9dJZGVqYCGhgye/hUlGQDstdKQA1Q4zx6G4H/zxkBLWwdlK3vB7L+rAZITYnH5+BacP/gvOg+eobzfubBuP3mJH1edha6WHDVcLFHGMvvy9pdJr7D97ANsOHkX03rVQWWRI7U/vfMc/045CC0dTTh52MHCIfsAY9LLVFzYfRtBW26g+4RmsK+Y+yXv+RHyLARjtoyBjpYOvMp6wdEs+z2LTYnFlitb8O+FfzGj0wy42op7z9SFOVA4pSkPmAOFUxJygEVmEdLWET/fDgBoa8oxvL0nvg5ww52IOMQlvz76oouK9ibKM5timdkaYcCf7fH4VhSyMhVwqGwFfeM3R1s8m1aUJE6fun3wIulFrusMdAwwq/MshEWHSRKrqJla2qPPT8vw5N51KDIzYOfsDn3DN/eyVfOVZshqcyNd/NpX9XLCwW2qoVPd8kjLyIKjlSHkuUwHI4aWjiZaDvLFnfPheHQ9EvrG0hzhe5eOpg4C/QNx+t5pBD8OhometFPlFDUNuVx5T+771ptYlJzpODS15Wg5yBdNenvh2d0Xb47CmunBzsVCeWZTSiU9B6hwDm+ai8o1G6JZ98Ac9+ELgoCD62fjyKa56Pn9PFFx5u+6jgZV7TG8nUeucf7ccQ3zd9/An9+Iu4xt/9JzqFLXCS0H+eYaZ++CIBxYeg59ZrQudIy5R+eiYaWGCPTP/T2bfWg25h6di3k9xL1n6sIcKJzSlAfMgcIpCTkg7a/ST9Dl41uxZ/V0hFw8AgC4ef4Alk/tg2VTeuPEjiVQZGWJjjFv13Vcf/gCBrpaqOFihcYeZdDYowyql7eUrMAEgP1LzuHZ3RiUr+GAirUcVQpMKRnrGsPZ8s29q6npqdhzYw+WnlqKrVe2IlORqRwU6GN3eONfiAy/A+cqteBSra5KgVmUUtMzse9SOPZdCsfVBzFIfvX+yygLYv+Scwi/GamyrFLtsmjWr7boKSvykpqeivjUeOho6uDYnWOIT817uo6PjTq+B8KexuFZ7JvBAg4FP8aIxSfRc9YBfLfkFI5dixAdA3iTAzr62nDysIN7g/Jwb1AeTtXsiqTAfK2k5wAV3POIe/Bq3CXXgd5kMhm8GndB9JO7ouPcj0xAp7rl3xunU93yuPdMfL5FP3yJ2u3c3hundjs3RD2IFRXj3vN76OL1/vesi1cX3I0W/56pC3OgcEpTHjAHCqck5ADPZIoQtHcNzh9aD6cq3ji6ZT4SYqNw4fAGeDX+DDKZDJeO/A9yDU34tekrKs6Ocw+w49wD2JsboIVXWTSr4QhzI+kLwIt7QnBp722Y2RrB078iPBpXgKGZ9IVFn5V98Ge3P2GiZ4LoxGgM3zAcia8S4WjmiKfxT7Hm7Br83eNvlXs2P1ZXTmzDlZPbYWppj2q+rVC1TgAMjKW5N/JtX/95BLP714Oxvjai41MRuOQUkl9loIylIZ7GJmPt0Tv485v6sDN//xm1/CiuHBi2fhiS0pJKZA6o63vgty1X8E3LqrAzN8Cei4+wYPd1tPQuB//qjngSk4Q/tgXjVUYWWniJu2SWOUDqYmBsjshHIbCwzT1nIx+FQN9I3KVrAGBmpIPQJ3Eoa5X7QHyhT+JgKnKeaSD7nqunYTGwLGOa6/qnYTGi78UyNzBHyLMQlDXP/T0LeRYCM33x75m6MAcKpzTlAXOgcEpCDrDIFOHG2X1o+cUYVKrRANFP7mLNjIFo+eUYuNVuBgAwtymLE9sWif5xCQDT+/ji3O1IbDp1FysP3UbtStZo6V0OtSvZQENDuuk+ekxshrALj3F22w0cX3sFFbwcUL1ZJbh4lZEsTnhsOBSK7BFrl5xcAktDSyz5cgkMdQyRkp6CCTsmYNnpZRjferwk8Ypal8Ezce9GEC4c3oBTu5ajvLsPPOq2grN7HWhIdAnr45gkZCmy71VbfuAWLI11sWhIIxjoaiElLRNT1p3HikMh+LGr+HvliiMHrIyssLTX0hKZA+r6Hnj6IhkO/x1E2HX+IQa1qopWtZyU6ys5mOLf43dEF5kAc4DUw7tpV+z/93dEht9Buco1lT8kUxJf4lHoZVw/sxsNOw4UHeczPxfM2X4VYU/jUKO8lfKHZFxSGq7cf469Fx+hfwt30XHqtHfHnvlBiLz3Ak4edsqrP5LjUvHw2jNcORgm+n7mrl5d8fuh33En+g5qlq2p/BH5MuUlLodfxu7ruzGwgfj3TF2YA4VTmvKAOVA4JSEHWGSKkJzwArblKgMArMtUAGQyWJV5MzCOjWNFSebJBABnG2PUdLFC/xbuOH3rGfZdDsekdedhZqCD5jXLonlNRzhY5JxHs6Csy5nB2dMeTfvUQujZR7h6OAybfj0CAxM9eDapAI8mFWBuL3601NduPbuF7/y/g6FOdtv1tfXR27c3ft7zs2QxipqlfXmUc/VCw44DERZ8EjfO7sW2xROgb2SKqnVaoGqdFjCzFj/672shj19iWDsP5aXS+jqa+LJJZUzbeFmS/TMHCkZd3wM6WnLEp6TDxkwfMfGpOQYncC1jhsiXKaLjAMwBUo+aDTtCz8AEl47+D8End0BQZF9WLtOQw8axIlp8MQauXo1Fx2lfpzxM9HWw5cw97Dz3EIr/BpfSkMlQ0d4E33eqgYbVHETH8W5dBXrGuji/8yYu7Q1VHkTR0NCArYs52g71g1s95w/sJW8da3SEiZ4J/nf5f9hxdQey/nvP5BpyVLSuiDEBY9C4svj3TF2YA4VTmvKAOVA4JSEHWGSKoG9sjphnD2FsboPY6McQBAViIx8p58p8EflQklP8b9OUa6BhNQc0rOaA6LgU7LscjgOXH2P9iTDsn9pOsjhyTQ241XOGWz1nxD9PwtXDd3HtcBjObLmOH7f0Fr3/19eQp2WmwcLAQmWdlaEV4lLiRMdQN7lcE65ejeHq1RgJsVG4HrQXN87uw7mD/+L7uYdF7//1ZffpGVmweOdyaUtjPcQnp+XyrMJjDuSPur4HalWywa7zDxHYsTqqOVvg5M2ncLF7cw/w8RsRoi+XfhdzgIpaFe8mqOLdBFlZmUhNyr4fSs/QBHK5tD9PGnk4oJGHAzKzFIhPyZ5j2ERfG5pyaYemcK/vDPf6zsjKVCAlIXvUaX1jXeUozVJo4toETVybIDMrU3nvsomeCTQlfs/UhTlQOKUpD5gDhfOx58DH0YoSqop3U+xd/SsqePjh0Z3LqO3fDce2LkBqcgJkMhnO7v8Hlao3LLL41qb66NXEFV82rozL98RPkfA+JlaGaNC9Oup388SDq88k2Wfg/wKhqaGJlPQUPH75WGUgoKiEKBjrSXeWpDgYm9vAr3Uf1G3VG49uX5Jkn6OXn4GmXAMpaZl4HJMEJ5s371FUXAqMi3BQFubA+6nre6Bf8yr4bskpjFx6ChUdTLH59D1cffACZa0M8SQmCSGPX2LS50UzpynAHKCiJZdrwtDE4sMbiqQp18hxkK4oyDU1YGSuX6QxNOWasDAs+vdMXZgDhVOa8oA5UDgfaw6wyBTBr3VfaGrp4NmDW/Co2xo+zT+HlUMFnNi+CBnpaXCp6ivJ/ZjWpnqQ53EflEwmg1cFa9FxTKwMIZPnHad8dfGDcPSq00vlsa6W6gc96H4QPBw8RMdRB2NzG2jkcQRMJpPBqYr4a+97Nq701iMb6GqrfnTPhkahqpP4AYeYAwWnru8BS2M9LPi2EdafCMO50EgIAhD65CWex6fCvaw5/ujvLnp+L4A5QEREROKxyBRBQ0MDvi2+UFn2+pS/lNaMbCbp/t5nyOLP1BKnT90+ea4f2LBk3KwOAAOm/KuWOL2a5D2Z7gAJblYHmAOFoa7vAQAw1NPC1wFu+DrATfJ9v8YcICIiIrE4TyYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJhkUmERERERERSYZFJhEREREREUmGRSYRERERERFJRvIic/r06ahVqxaMjIxgbW2NDh06IDQ0VGWbV69eYfDgwbCwsIChoSE6d+6MqKgo5frY2Fi0bdsWhoaGqFGjBq5cuaLy/MGDB+P333+XuulEREQlwrx58+Dk5ARdXV34+Pjg/Pnz7912yZIlqF+/PszMzGBmZgZ/f/88tyciIhJL8iLz+PHjGDx4MM6ePYuDBw8iIyMDzZs3R3JysnKb7777Djt37sSmTZtw/PhxPH36FJ06dVKu/+WXX5CYmIjLly+jUaNG6N+/v3Ld2bNnce7cOYwYMULqphMREX30NmzYgMDAQEycOBGXL1+Gp6cnAgICEB0dnev2x44dQ48ePXD06FEEBQXB0dERzZs3R0REhJpbTkREnwrJi8x9+/ahT58+cHd3h6enJ1auXInw8HBcunQJABAfH49ly5Zh9uzZaNKkCby8vLBixQqcOXMGZ8+eBQCEhISge/fuqFSpEgYMGICQkBAAQEZGBgYOHIiFCxdCLpdL3XQiIqKP3uzZs9G/f3/07dsXbm5uWLhwIfT19bF8+fJct1+7di2+/fZbVK9eHa6urli6dCkUCgUOHz6s5pYTEdGnosjvyYyPjwcAmJubAwAuXbqEjIwM+Pv7K7dxdXVF2bJlERQUBADw9PTEkSNHkJmZif3798PDwwMAMHPmTDRq1Aje3t4fjJuWloaEhASVPyIiopIsPT0dly5dUulDNTQ04O/vr+xDPyQlJQUZGRnKfjk37EOJiEgMzaLcuUKhwIgRI+Dn54eqVasCACIjI6GtrQ1TU1OVbW1sbBAZGQkA+OGHHzBo0CC4uLjAyckJy5YtQ1hYGFatWoWgoCAMHDgQBw4cgLe3N5YsWQITE5McsadPn47JkyfnWB4REVGgzlI/M6UAr7jwYuQOaomjE2esljjRBrlftiWlpCdJRR4DYA4UljpyAFBPHjAHCudjzYHExMQiaknRi4mJQVZWFmxsbFSW29jY4Pbt2/nax5gxY2Bvb69SqL6LfWjuStNnh31o4ZSmHADYhxbGp54D+e1Di7TIHDx4MG7cuIFTp04V6HkmJiZYt26dyrImTZpg1qxZWLt2Le7fv4/Q0FD0798fU6ZMyXUQoLFjxyIwMFD5OCEhAY6OjnBwcICxcf6TIyUkrkBtLyzLLPXcG5NmmqGWONbJ1kUew7SMaZHHAJgDhaWOHADUkwfMgcL5WHPgUz4r9+uvv2L9+vU4duwYdHV137sd+9DclabPDvvQwilNOQCwDy2MTz0H8tuHFlmROWTIEOzatQsnTpxAmTJllMttbW2Rnp6OuLg4lbOZUVFRsLW1zXVfK1asgKmpKdq3b49OnTqhQ4cO0NLSQpcuXTBhwoRcn6OjowMdHR1JXxMREVFxsrS0hFwuVxmRHci7D33tt99+w6+//opDhw4pb0N5H/ahREQkhuT3ZAqCgCFDhmDr1q04cuQInJ2dVdZ7eXlBS0tLZcCB0NBQhIeHw9fXN8f+nj9/jilTpmDu3LkAgKysLGRkZB9ByMjIQFZWltQvgYiI6KOkra0NLy8vlT709SA+ufWhr82cORNTp07Fvn378jWuARERkRiSn8kcPHgw1q1bh+3bt8PIyEh5n6WJiQn09PRgYmKCfv36ITAwEObm5jA2NsbQoUPh6+uLOnXq5NjfiBEjMHLkSDg4ZF9n7efnhzVr1qB58+ZYvHgx/Pz8pH4JREREH63AwED07t0b3t7eqF27NubMmYPk5GT07dsXANCrVy84ODhg+vTpAIAZM2ZgwoQJWLduHZycnJT9sqGhIQwNDYvtdRARUekleZG5YMECAECjRo1Ulq9YsQJ9+vQBAPzxxx/Q0NBA586dkZaWhoCAAMyfPz/Hvvbv34+7d+9izZo1ymVDhgzBxYsX4ePjg9q1a2PixIlSvwQiIqKPVrdu3fD8+XNMmDABkZGRqF69Ovbt26ccDCg8PBwaGm8uVFqwYAHS09Px2Wefqexn4sSJmDRpkjqbTkREnwjJi0xBED64ja6uLubNm4d58+bluV1AQAACAgJUlunr62Pjxo2i2khERFSSDRkyBEOGDMl13bFjx1QeP3z4sOgbRERE9JYinyeTiIiIiIiIPh0sMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMiwyiYiIiIiISDIsMomIiIiIiEgyLDKJiIiIiIhIMsVWZM6bNw9OTk7Q1dWFj48Pzp8/r1wXGBgIc3NzODo6Yu3atSrP27RpE9q2bavu5hIREX008upDc7Np0ya4urpCV1cX1apVw549e9TUUiIi+hQVS5G5YcMGBAYGYuLEibh8+TI8PT0REBCA6Oho7Ny5E+vWrcOBAwcwc+ZMfP3114iJiQEAxMfH46effsK8efOKo9lERETFLq8+NDdnzpxBjx490K9fP1y5cgUdOnRAhw4dcOPGDTW3nIiIPhXFUmTOnj0b/fv3R9++feHm5oaFCxdCX18fy5cvR0hICBo1agRvb2/06NEDxsbGePDgAQBg9OjRGDRoEMqWLVsczSYiIip2efWhufnzzz/RokULjBo1ClWqVMHUqVNRs2ZN/P3332puORERfSo01R0wPT0dly5dwtixY5XLNDQ04O/vj6CgIHz77bdYvHgxXr58ifv37yM1NRUVKlTAqVOncPnyZcyfPz9fcdLS0pCWlqZ8HB8fDwBISEgoUHtTkwu2fWElpqSrJU5qYtqHN5JAwquif980EtRzjIQ5UDjqyAFAPXnAHCicjzUHXvcDgiAURXOK1If60NwEBQUhMDBQZVlAQAC2bdv23jjsQ3NXmj477EMLpzTlAMA+tDA+9RzIdx8qqFlERIQAQDhz5ozK8lGjRgm1a9cWBEEQJk6cKLi4uAhVq1YVtmzZIqSlpQlVq1YVLl68KMydO1eoVKmSULduXeHGjRvvjTNx4kQBAP/4xz/+8Y9/uf49fvy4SPu7opCfPvRdWlpawrp161SWzZs3T7C2tn5vHPah/OMf//jHv7z+PtSHqv1MZn5MmjQJkyZNUj6ePHky/P39oaWlhZ9//hnXr1/Hrl270KtXL1y6dCnXfYwdO1blyK1CoUBsbCwsLCwgk8mK+iV8lBISEuDo6IjHjx/D2Ni4uJtDxYA5QMwBQBAEJCYmwt7evrib8tFiH5oTPzvEHCDmQP77ULUXmZaWlpDL5YiKilJZHhUVBVtb2xzb3759G//88w+uXLmC5cuXo0GDBrCyskLXrl3x1VdfITExEUZGRjmep6OjAx0dHZVlpqamkr6WksrY2PiT/WBQNuYAfeo5YGJiUtxNKJSC9qEAYGtrW6DtAfahefnUPzvEHCDmQH76ULUP/KOtrQ0vLy8cPnxYuUyhUODw4cPw9fVV2VYQBHzzzTeYPXs2DA0NkZWVhYyMDABQ/jcrK0t9jSciIipGBelDX/P19VXZHgAOHjz43u2JiIjEKpbLZQMDA9G7d294e3ujdu3amDNnDpKTk9G3b1+V7ZYuXQorKyvlvJh+fn6YNGkSzp49i71798LNzY1HVomI6JPyoT60V69ecHBwwPTp0wEAw4cPR8OGDfH777+jdevWWL9+PS5evIjFixcX58sgIqJSrFiKzG7duuH58+eYMGECIiMjUb16dezbtw82NjbKbaKiovDLL7/gzJkzymW1a9fGyJEj0bp1a1hbW2PVqlXF0fwSS0dHBxMnTsxxCRR9OpgDxBwo+T7Uh4aHh0ND482FSnXr1sW6deswbtw4/Pjjj6hYsSK2bduGqlWrFtdLKJH42SHmADEH8k8mCCVwDHciIiIiIiL6KKn9nkwiIiIiIiIqvVhkEhERERERkWRYZBIREREREZFkWGTmQ6NGjTBixAjlYycnJ8yZMyfP58hkMmzbtk10bKn2Q0REVBzYhxIRfXpKdZHZtm1btGjRItd1J0+ehEwmw7Vr1wq83wsXLmDAgAFim6di0qRJqF69eo7lz549Q8uWLSWN9T6pqakwNzeHpaUl0tLS1BKzJJLJZHn+TZo0SdS+C/KD6JtvvoFcLsemTZsKHZMKh3lApR370PxZuXKl8nOvoaEBOzs7dOvWDeHh4Tm2vXnzJrp27QorKyvo6OigUqVKmDBhAlJSUnJse+XKFXTp0gU2NjbQ1dVFxYoV0b9/f9y5c+eDbfr3338hl8sxePDgXNv7vunfcvvu2bx5Mxo1agQTExMYGhrCw8MDU6ZMQWxs7Afb8b4Yxf3d+XY8Y2Nj1KpVC9u3b8+xXWpqKiZOnIhKlSpBR0cHlpaW6NKlC27evJlj24SEBPz0009wdXWFrq4ubG1t4e/vjy1btuBDY2x+6PfX+15Xnz590KFDB5Vld+/eRd++fVGmTBno6OjA2dkZPXr0wMWLF/N+U9SIOZBTacyBUl1k9uvXDwcPHsSTJ09yrFuxYgW8vb3h4eFR4P1aWVlBX19fiiZ+kK2trdqGSd68eTPc3d3h6upa7Ed+BUFAZmZmsbbhfZ49e6b8mzNnDoyNjVWWff/992ppR0pKCtavX4/Ro0dj+fLlaomZl/T09OJugloxD3L3qeVBacY+NP9ef/4jIiKwefNmhIaGokuXLirbnD17Fj4+PkhPT8fu3btx584d/PLLL1i5ciWaNWum8tnZtWsX6tSpg7S0NKxduxYhISH4559/YGJigvHjx3+wPcuWLcPo0aPx77//4tWrV4V+XT/99BO6deuGWrVqYe/evbhx4wZ+//13XL16FWvWrCnUPj+W784VK1bg2bNnuHjxIvz8/PDZZ5/h+vXryvVpaWnw9/fH8uXL8fPPP+POnTvYs2cPMjMz4ePjg7Nnzyq3jYuLQ926dbF69WqMHTsWly9fxokTJ9CtWzeMHj0a8fHxebZFqt9fFy9ehJeXF+7cuYNFixbh1q1b2Lp1K1xdXTFy5MhC71dqzIGcSmUOCKVYRkaGYGNjI0ydOlVleWJiomBoaCgsWLBAiImJEbp37y7Y29sLenp6QtWqVYV169apbN+wYUNh+PDhysflypUT/vjjD+XjO3fuCPXr1xd0dHSEKlWqCAcOHBAACFu3blVuM3r0aKFixYqCnp6e4OzsLIwbN05IT08XBEEQVqxYIQBQ+VuxYoUgCEKO/Vy7dk1o3LixoKurK5ibmwv9+/cXEhMTlet79+4ttG/fXpg1a5Zga2srmJubC99++60yVl4aNWokLFy4UFiwYIHQrFmzHOtv3LghtG7dWjAyMhIMDQ2FevXqCXfv3lWuX7ZsmeDm5iZoa2sLtra2wuDBgwVBEIQHDx4IAIQrV64ot3358qUAQDh69KggCIJw9OhRAYCwZ88eoWbNmoKWlpZw9OhR4e7du0K7du0Ea2trwcDAQPD29hYOHjyo0q5Xr14Jo0ePFsqUKSNoa2sLLi4uwtKlSwWFQiG4uLgIs2bNUtn+ypUrAgAhLCzsg+/Jh6xYsUIwMTFRWbZkyRLB1dVV0NHRESpXrizMmzdPuS4tLU0YPHiwYGtrK+jo6Ahly5YVpk2bJghCdl69nQPlypXLM/bKlSuFOnXqCHFxcYK+vr4QHh6usv5978tref17vpvzgiAI7du3F3r37q18XK5cOWHKlCnCl19+KRgZGSnX5ZXrr+3YsUPw9vYWdHR0BAsLC6FDhw6CIAjC5MmTBXd39xyv1dPTUxg3blye70dxYh4wD0oj9qH560Nz+/z/9ddfAgAhPj5eEARBUCgUgpubm+Dt7S1kZWWpbBscHCzIZDLh119/FQRBEJKTkwVLS0vl5+FdL1++fG9bBEEQ7t+/L+jp6QlxcXGCj4+PsHbt2g+297W3369z584JAIQ5c+YUqh35UVzfne/mRUJCggBA+PPPP5XLfv31V0EmkwnBwcEqz83KyhK8vb0FNzc3QaFQCIIgCIMGDRIMDAyEiIiIHLESExOFjIyMPN+HD/3+ere9r73OV0HIzjF3d3fBy8srR44JgjT/XkWBOZCtNOZAqS4yBUEQRo0aJbi4uCiTQBAEYfny5cov4CdPngizZs0Srly5Ity7d0/466+/BLlcLpw7d065fV4dZFZWllC1alWhadOmQnBwsHD8+HGhRo0aOZJh6tSpwunTp4UHDx4IO3bsEGxsbIQZM2YIgiAIKSkpwsiRIwV3d3fh2bNnwrNnz4SUlBRBEFSTKikpSbCzsxM6deokXL9+XTh8+LDg7Oys8oOvd+/egrGxsTBw4EAhJCRE2Llzp6Cvry8sXrw4z/fp7t27go6OjhAbGyu8ePFC0NXVFR4+fKhc/+TJE8Hc3Fzo1KmTcOHCBSE0NFRYvny5cPv2bUEQBGH+/PmCrq6uMGfOHCE0NFQ4f/688j0qSJHp4eEhHDhwQLh7967w4sULITg4WFi4cKFw/fp14c6dO8K4ceMEXV1d4dGjR8p9de3aVXB0dBS2bNki3Lt3Tzh06JCwfv16QRAE4ZdffhHc3NxUXuuwYcOEBg0a5Pl+5Ne7X47//POPYGdnJ2zevFm4f/++sHnzZsHc3FxYuXKlIAiCMGvWLMHR0VE4ceKE8PDhQ+HkyZPKH2TR0dHKH0fPnj0ToqOj84xdv3594e+//xYEQRA6d+4sTJkyRWV9Xu/Lh/4981tcGBsbC7/99ptw9+5dZWGSV64LgiDs2rVLkMvlwoQJE4Rbt24JwcHByg7i8ePHgoaGhnD+/Hnl9pcvXxZkMplw7969PN+P4sQ8YB6UVuxDP9yHvvv5j4qKEho3bizI5XIhKSlJEITs/AWQowB/rVmzZoKnp6cgCIKwZcsWAYBw5syZ98bMy/jx44XPPvtMEARBmDt3rtCkSZM82/u2t9+vYcOGCYaGhvk6SF1YxfXd+fbrzMjIEP744w8BgLBgwQLlNh4eHkLz5s1zff7atWuVv2uysrIEMzMzYcCAAYV6Dz70++vd9r7t7QLjQzn2sWIOlN4cKPVFZkhIiEoxIwjZP8q++OKL9z6ndevWwsiRI5WP8+og9+/fL2hqaqocudi7d+97k+G1WbNmCV5eXsrHEydOVHYwb3t7P4sXLxbMzMyUnZYgCMLu3bsFDQ0NITIyUhCE7GQrV66ckJmZqdymS5cuQrdu3d7bFkEQhB9//FHlqGn79u2FiRMnKh+PHTtWcHZ2fm9nY29vL/z000+5ritIkblt27Y82ykIguDu7i7MnTtXEARBCA0NFQDkOLv5WkREhMoPnvT0dMHS0lL5ZSXWu1+OLi4uOT7cU6dOFXx9fQVBEIShQ4cKTZo0UfnB9rYP5c1rd+7cEbS0tITnz58LgiAIW7duFZydnZX7/dD78qF/z/wWF+870v62d3Pd19dX6Nmz53u3b9mypTBo0CDl46FDhwqNGjX6YJzixDxgHpRW7EM/3Ie+PpNqYGAg6OvrK8+iDBs2TLnN+vXrc/SDbxs2bJigp6cnCIIgzJgxQwAgxMbGvjfm+2RlZQmOjo7KvvT58+eCtra2cP/+fZX25qfIbNmypeDh4VHgNhREcX13AhB0dXUFAwMDQUNDQwAgODk5CS9evFBuo6urm+P777XXP+Y3bNggREVFCQCE2bNnfzBubj70++t1ez9UYGzYsEEAIFy+fLlQ7SguzIHSmwOl+p5MAHB1dUXdunWV9yrdvXsXJ0+eRL9+/QAAWVlZmDp1KqpVqwZzc3MYGhpi//79ud6wn5uQkBA4OjrC3t5euczX1zfHdhs2bICfnx9sbW1haGiIcePG5TvG27E8PT1hYGCgXObn5weFQoHQ0FDlMnd3d8jlcuVjOzs7REdHv3e/WVlZWLVqFb744gvlsi+++AIrV66EQqEAAAQHB6N+/frQ0tLK8fzo6Gg8ffoUTZs2LdDryY23t7fK46SkJHz//feoUqUKTE1NYWhoiJCQEOV7FxwcDLlcjoYNG+a6P3t7e7Ru3Vr5779z506kpaXluFdGCsnJybh37x769esHQ0ND5d/PP/+Me/fuAci+QTs4OBiVK1fGsGHDcODAgULFWr58OQICAmBpaQkAaNWqFeLj43HkyBEAH35f8vr3LIh3/72AD+d6cHBwnrnSv39/5X1E6enpWLduHb766itR7VQn5kG2Tz0PSgv2oR/uQwHAyMgIwcHBuHjxIn7//XfUrFkTv/zyS47thA8M/pHfbd7n4MGDSE5ORqtWrQAAlpaWaNasWaHu1xbTjsJQ53cnAPzxxx8IDg7G3r174ebmhqVLl8Lc3Fxlm6L+98rP76/8Uve/V1FgDmQrLTlQ6otMIHvwgs2bNyMxMRErVqyAi4uL8kfXrFmz8Oeff2LMmDE4evQogoODERAQIOngFUFBQejZsydatWqFXbt24cqVK/jpp5+KbICMd38wymSyPBN1//79iIiIQLdu3aCpqQlNTU10794djx49wuHDhwEAenp6731+XusAQEMjO83eTv6MjIxct3278weA77//Hlu3bsW0adNw8uRJBAcHo1q1asr37kOxAeDrr7/G+vXrkZqaihUrVqBbt25FMuhEUlISAGDJkiUIDg5W/t24cUN5c3jNmjXx4MEDTJ06FampqejatSs+++yzAsV5/YW0e/du5b+Xvr4+YmNjlT8kPvS+5Off7N0vq9z+zd7998pPrn8odtu2baGjo4OtW7di586dyMjIKPB7VJyYB8yD0oZ9aN59KJD9WalQoQKqVKmCwMBA1KlTB4MGDVKur1SpEoDsQjc3ISEhym1e//f27dsFbvuyZcsQGxsLPT095ffCnj17sGrVKuVrMDY2RnJyco7XFBcXBwAwMTFRtuP+/fvv7a+lpq7vztdsbW1RoUIFNG/eXPnb4O2DCZUqVcrz3+v1NlZWVjA1NS3Uv1d+fn8B2Qcxchs4Ji4uTuXfCyhc3nwsmAOlKwc+iSKza9eu0NDQwLp167B69Wp89dVXkMlkAIDTp0+jffv2+OKLL+Dp6Yny5cvna3jw16pUqYLHjx/j2bNnymVvjzYFAGfOnEG5cuXw008/wdvbGxUrVsSjR49UttHW1kZWVtYHY129ehXJycnKZadPn4aGhgYqV66c7za/a9myZejevbvKBzo4OBjdu3fHsmXLAAAeHh44efJkrp2NkZERnJycVD4Mb7OysgIAlfcoODg4X207ffo0+vTpg44dO6JatWqwtbXFw4cPleurVasGhUKB48ePv3cfrVq1goGBARYsWIB9+/YV2dkQGxsb2Nvb4/79+6hQoYLKn7Ozs3I7Y2NjdOvWDUuWLMGGDRuwefNm5VDwWlpaH8yDPXv2IDExEf9v787DqqjeOIB/2a5c1ssqCCSyyKIGCJpLqGGoWWSGu6UIWoIRkrhkLuCuibhVqMlWLrmSvwpzyQUxFRVwASERUhM3lAwRRXh/f/AwMlyWK7K4vJ/n4XmY7ZwzM+fOmTNzzpmUlBTR+dq0aRN27NiBgoKCOo9LbecTKD9nlc9XaWkpzp07V+cxUCSvv/766zXmFQBQVVXF6NGjER0djejoaAwbNkyhhwnPC84HnA9eNlyGPr1p06bhp59+wunTpwEAzs7OsLe3R0REhFzlLi0tDfv27cPw4cMBAH369IGhoSGWLFlSbdgVlcGq8vPz8fPPP2Pz5s2ia0JKSgru3r0rvPGxs7PD48eP5crhirRW3KiOGDEChYWF+Pbbb58qHfXVVNfO6nTu3Bmurq6it8/Dhg3Dvn37kJaWJlq3rKwMERERcHR0hJOTE5SVlTFs2DBs2LAB165dkwu7sLCwxtHyFbn/AsrP2alTp0TblpaWIi0tTThfzs7OcHR0RHh4eLUPRRr6fDUGzgMvWR5onla6Tc/Pz4/09PRIRUVF1PcjODiYLCwsKCkpidLT02ns2LGko6MjtG8mqnvQAkdHR/L09KTU1FQ6fPgwubq6itpO//zzz6SqqkqbNm2iixcv0ooVK0hfX1/UBn3Dhg2kqalJKSkpdOvWLSouLiYicRvs+/fvk6mpKXl7e9PZs2fpjz/+ICsrK7lBCyqnnYgoKCiIevbsWe1xuXnzJqmpqVFCQoLcst9++41atGhB+fn5dPv2bTIwMBAGCMnKyqK4uDhhgJCYmBhSV1enFStWUFZWFp06dYpWrlwphNWlSxdyd3en9PR0OnjwIHXu3LnaPplVR74aOHAgOTs7U0pKCqWmppKXlxdpa2uLzoePjw9ZWFjQzp076dKlS3TgwAH66aefROFMnz6dJBIJOTg4VHsc6qtqX4J169aRVCqlFStWUGZmJp05c4aioqIoPDyciIjCw8Np48aNlJGRQZmZmeTn50cmJibCKGC2trbk7+9PeXl5NfbHGTBgQLX9g0pLS8nExEQYBKa241LX+YyMjCQNDQ365ZdfKCMjg8aNG0c6OjpyffEqjxBJpFheP3DgACkrKwsDvpw5c0YYVbFCVlYWqaiokIqKCh07dqzuE9HMOB9EiNLwquaDlxmXoT1rPDY19XEcMmQIvfvuu8J0UlISaWho0AcffEDHjx+nv//+m7Zs2UIWFhbUrVs3Ic1ERPHx8aSmpkZeXl60d+9eysnJoeTkZJo8eXKN/UMjIiLI1NS02r5qQ4YMEQYDIiLq06cPOTk50b59++jSpUuUkJBAdnZ2cmFPmTKFVFRUaPLkyXT06FHKzc2lffv20aBBg2ocdfZpNMe1k6j6/m0V9zxXr14lIqIHDx7QG2+8QRYWFrRlyxb6+++/6cSJE/TBBx+QpqYm/fnnn8K2+fn5ZG9vT+bm5hQbG0vnz5+nrKwsWr9+PdnY2FQ7qqei919ERBs3biSpVErffPMNZWVlUUpKCvn6+pKurq7Qn5iofERgbW1t6tatG/3666+UnZ1NaWlpNG/evAYb8LChcR54efPAK1PJPHr0KAGg/v37i+bn5+fTgAEDSEtLi4yNjWnGjBk0atQohQtIovLBNd58802SSCTUtm1b2r17t1zmnTx5MhkYGJCWlhYNHTqUIiIiRD+q4uJi8vb2JplM1iDDr1dWWwG5dOlSkslk1Q788fDhQ5LJZMJwzmlpadSnTx/S0NAgbW1tcnd3F430GBkZSXZ2dqSmpkampqYUGBgoLEtPT6euXbuSVColZ2dnYYj6uiqZOTk59NZbb5FUKiULCwtavXq13Pl48OABBQcHk6mpKUkkErKxsaGoqChRONnZ2QSAlixZUu1xqK/qbi42bNhAzs7OJJFISE9Pj3r06EE7duwgovKBJ5ydnUlTU5N0dHSod+/eog7au3btIhsbG1JVVa126O3r16+Tqqoqbdmypdr0+Pv7k4uLCxHVfVxqO5+PHj0if39/0tfXJ2NjY1q4cGG1A75UrVwQ1Z3XiYi2b98uHCNDQ0P68MMP5cJxd3ev9jMWzyPOBxFyaXgV88HLjMvQnjUem5oqmX/++ScBEI20e+bMGfL29iZ9fX1SU1Mja2trmjFjBt2/f19u++TkZPrwww/JyMiIWrRoQTY2NvTJJ5/U+PmtDh06UEBAQLXLfvrpJ5JIJMIgYXfv3qXPP/+crK2tSSqVkq2tLU2ZMkV0LCpv26NHD9LW1iZNTU16/fXXac6cOY32CZPGvHZWqK6CUVZWRvb29qIBx+7fv09fffUV2djYkJqaGunr6wsPKaoqKCigadOmka2tLUkkEmrZsiW9/fbbtHPnzmor/k9z/1VxXFxdXUlbW5tatmxJ/fv3p7S0NLltMzMzadSoUdSqVSuSSCTUunVrGj58+HMzGExVnAde3jygRPSc9RJlrBEkJiaid+/euHLlClq2bNncyWF1ICLY2toiICAAX3zxRXMnhzUTzgeMMcbYi0m1uRPAWGN6+PAhbt26hdDQUAwePJgrmC+AW7duYfPmzbh+/TrGjBnT3MlhzYTzAWOMMfbi4kome6lt2rQJfn5+cHZ2RlxcXHMnhynA2NgYhoaGWLt2LfT09Jo7OayZcD5gjDHGXlzcXJYxxhhjjDHGWIN5JT5hwhhjjDHGGGOsaXAlk7Ea5Ofnw9jYWPRdTvZ8GTZsGMLDw5s0zqbIF6GhoXB2dn7mcGJiYiCTyZ45nOdJZGQkvLy8mjsZjLE6cBn6/OMytHZchj6jJh3LlrEXSHBwMI0dO7baZX369CFlZWU6ceJEE6eq6d2/f5+mTZtGVlZW1KJFCzI0NKQePXpQfHx8cyeNzp49S3p6elRQUNBkcVbNFzk5OQSAlJWVhW9rVbh27RqpqKgQAMrJyVE4jv/++49u3779zGktKiqiGzduCNOzZ88mJyenZwpz27Zt1e5rBRsbGwoODq4znPqm5eHDh9SqVSs6fPjwU2/LGGs6XIaW4zJUjMvQV6cM5TeZjFWjqKgI69evh5+fn9yyy5cv4+jRo/jss88QFRXV6Gl59OhRo8dRm/Hjx2PHjh1YtWoVLly4gN27d2PQoEHIz89vtDgV3ef27dvD2toaP/74Y6OlpbLa8oWZmZnc4FKxsbEwMzN76ni0tLRgYGBQ73QCQElJCaRSKYyNjZ8pnKref/99GBgYIDY2Vm7Z4cOHcfHixWqPT0ORSCQYMWIEVq5c2WhxMMaeDZehT3AZ+gSXoa9YGdro1VjGXkBbt24lIyOjapeFhobSsGHDKCMjg3R1damoqIiIyj9+C4AyMjJE6y9btoysrKyE6bNnz1K/fv1IU1OTjI2N6aOPPhI+kE1U/uHyCRMmUFBQEBkYGFCvXr2IiCg8PJzat29PGhoaZG5uTv7+/nIfzl67di2Zm5uTVCqlDz74gMLDw+U+chwfH08uLi7UokULatOmDYWGhlJJSUmNx0JXV5diYmJqPV7FxcU0ZcoUMjc3J4lEQtbW1vT9998Lyw8ePEidOnUiiURCJiYmNHXqVFGcNe1zXceKiCgsLIzefPPNWtPXUKrLFxVPYWfMmEG2traiZW3btqWZM2eKnsI+fvyYfH19ydLSktTV1alt27a0fPly0XZVn1CWlpZSWFgYmZmZkUQiIScnJ0pISJBLw+bNm6lHjx7UokULio6OFn3kOjo6mgCI/qKjo2nMmDH07rvviuJ/9OgRGRkZic5hZV988YXcvhKVf8j+jTfeICKiv//+m95//33S1NQkbW1tGjx4MF2/fr3WtBCVfyTez8+PDA0NSVtbm9566y1KTU0VxXPo0CGSSCTCb48x9nzhMvQJLkOf4DK03KtShnIlk7FqfP7559SvXz+5+WVlZdS6dWv65ZdfiIjI1dWV4uLihOVubm40Y8YM0Taurq7CvLt375KRkRF9+eWXlJGRQadPnyZPT0966623hPV79uxJWlpaNHnyZLpw4QJduHCBiIgiIiLojz/+oJycHNq/fz/Z2dmRv7+/sN2RI0dIWVmZvv76a8rMzKRvvvmG9PX1RQXk4cOHSUdHh2JiYig7O5v27NlDlpaWFBoaWuOxsLOzoyFDhtC9e/dqXGfIkCFkYWFBO3bsoOzsbNq3bx9t3ryZiIiuXr1KGhoaFBAQQBkZGbRz504yNDSk2bNn17rPihwrIqKEhASSSCRUXFxcY/oaSnX5oqJwOnHiBBkaGlJiYiIRESUmJpKRkRGdOHFCVEA+evSIZs2aRcnJyXTp0iX68ccfSUNDg3766SchzKoF5LJly0hHR4c2bdpEFy5coClTppCamhplZWWJ0mBpaUnbt2+nS5cu0bVr10QFZFFREU2aNInatWtHeXl5lJeXR0VFRZSUlEQqKip07do1Ib4dO3aQpqam3A1YhfPnzxMAOnTokDDvv//+I01NTVq7di2VlpaSs7Mzvfnmm3Ty5Ek6duwYubq6Us+ePWtNCxHR22+/TV5eXpScnExZWVk0adIkMjAwoPz8fCGu+/fvk7KyMh04cEDxk8cYazJchj7BZegTXIaWe1XKUK5kMlaNAQMGkK+vr9z8PXv2kJGRkfAEMSIiQvjRV0xbW1sL01WfzM6dO5f69OkjCvPKlSsEgDIzM4movLBwcXGpM41bt24lAwMDYXro0KFyT9NGjhwpKiB79+5NCxYsEK3zww8/kKmpaY3xHDp0iMzNzUlNTY3c3Nxo4sSJdOTIEbl93Lt3b7XbT58+nezs7KisrEyY980335CWlhaVlpbWuM+KHCsiorS0NAJAubm5Ne5DQ6kuX1QUTikpKTRx4kQaM2YMERGNGTOGgoODKSUlpc7+JBMmTCBvb29humoB2apVK5o/f75om06dOlFAQIAoDVWf5lYuIKsLt4KjoyMtXrxYmPby8iIfH58a00tE1KVLFxo9erQwvX79etLQ0KB79+7Rnj17SEVFhS5fviwsryhUK/pgVZeWxMRE0tHRkbvZsba2pjVr1ojm6enp1fl2gDHWPLgMfYLL0Ce4DH3iVShDuU8mY9V48OAB1NXV5eZHRUVh6NChUFVVBQAMHz4cSUlJyM7OBlA+Ultubi6OHTsGANiwYQM6duwIe3t7AEBaWhoOHDgALS0t4a9iWUUYAODq6ioX9759+9C7d2+YmZlBW1sbH3/8MfLz81FUVAQAyMzMROfOnUXbVJ1OS0vDnDlzRPGPGzcOeXl5QjhV9ejRA5cuXcL+/fsxaNAgnD9/Hu7u7pg7dy4AIDU1FSoqKujZs2e122dkZKBr165QUlIS5nXv3h2FhYW4evVqjfus6LGSSqUAUGP6G1JN+aKCr68vtm7diuvXr2Pr1q3w9fWtdr1vvvkGrq6uMDIygpaWFtauXYvLly9Xu+69e/dw7do1dO/eXTS/e/fuyMjIEM1zc3N7yj0qN3bsWERHRwMAbty4gYSEhBrTXsHX1xfbtm3Df//9B6D8tzF48GBoa2sjIyMDFhYWsLCwENZ3dHSETCaTS3NlaWlpKCwshIGBgei85+TkiM45UH7em+KcM8aeHpehT3AZ+gSXoU+8CmWoaqOGztgLytDQEHfv3hXNu3PnDnbu3ImSkhJ89913wvzS0lJERUVh/vz5MDExgYeHBzZu3IguXbpg48aN8Pf3F9YtLCyEl5cXFi9eLBenqamp8L+mpqZoWW5uLt577z34+/tj/vz50NfXx5EjR+Dn54dHjx5BQ0NDof0qLCxEWFgYPvzwQ7lltV341dTU4O7uDnd3d0ydOhXz5s3DnDlzMHXqVKGAelZV91nRY3Xnzh0AgJGRUYOkozbV5YvKOnToAHt7ewwfPhwODg5o3749UlNTRets3rwZISEhCA8PR9euXaGtrY2vv/4ax48ff+b0VT2Giho1ahSmTZuGP//8E0ePHkWbNm3g7u5e6zbDhg1DcHAwtmzZgh49eiApKQkLFy6sV/wVCgsLYWpqioMHD8otqzqM/J07d5rknDPGnh6XoWJchpbjMvSJV6EM5UomY9VwcXGRG21tw4YNMDc3R3x8vGj+nj17EB4ejjlz5kBFRQUjR47ElClTMHz4cFy6dAnDhg0T1u3YsSO2b98OS0tL4UmuIk6dOoWysjKEh4dDWbm8AcKWLVtE69jZ2SE5OVk0r+p0x44dkZmZCRsbG4Xjro6joyMeP36M4uJidOjQAWVlZTh06BDefvttuXUdHBywfft2EJHwJDYpKQna2towNzevMQ5Fj9W5c+dgbm4OQ0PDZ9onRVSXL6ry9fVFQECA6CaqsqSkJHTr1g0BAQHCvKpPGCvT0dFBq1atkJSUJHrSnZSUJPeUvS4SiQSlpaVy8w0MDPDBBx8gOjoaf/75J8aMGVNnWNra2hg8eDCioqKQnZ2Ntm3bCoWqg4MDrly5gitXrghPYtPT01FQUABHR8ca09KxY0dcv34dqqqqsLS0rDHu7OxsFBcXw8XFRdFdZ4w1IS5Da8dlaM24DH2JytBGbYzL2AvqzJkzpKqqSnfu3BHmOTk50dSpU+XWLSgoIIlEIgxkcO/ePZJKpeTk5ES9e/cWrfvPP/+QkZERDRo0iE6cOEEXL16k3bt3k4+PDz1+/JiIyvtWBAUFibZLTU0V+gtkZ2dTXFwcmZmZEQC6e/cuET0ZtCA8PJyysrIoMjKSDAwMSCaTCeHs3r2bVFVVKTQ0lM6dO0fp6em0adMm+uqrr2o8Fj179qTIyEg6efIk5eTk0K+//kp2dnbk4eEhrOPj40MWFha0c+dOunTpEh04cEDohF8xaMGECRMoIyOD4uPjqx20oOo+K3KsiMpHY6uu709jqC5fVO5PQkRUUlJCt27dEvocVe1PsmLFCtLR0aHdu3dTZmYmzZgxg3R0dER9K6r2tYiIiCAdHR3avHkzXbhwgaZOnVrtoAUVaahQtT/Jhg0bSFNTk1JSUujWrVuifht79uwhiURCKioq9M8//yh0PBITEwkA6enp0aJFi4T5ZWVl5OzsTO7u7nTq1Ck6fvy4aNCCmtJSVlZGb775Jjk5OdHvv/9OOTk5lJSURNOnT6fk5GTRflUebZIx9nzhMvQJLkOf4DJU7GUvQ7mSyVgNOnfuTJGRkUREdPLkSVGH66reeecdGjhwoDA9ZMgQAkBRUVFy62ZlZdHAgQNJJpORVCole3t7mjhxotCpv7rCgqh8dDRTU1OSSqXUt29fiouLExWQROXDr5uZmQnDr8+bN49MTExE4ezevZu6detGUqmUdHR0qHPnzrR27doaj8OCBQuoa9eupK+vT+rq6mRlZUWff/656EPHDx48oODgYDI1NSWJREI2NjaifVdk+PXq9rmuY/XgwQPS1dWlP//8s8b0N7TK+YKo5sKpQtUCsri4mHx8fEhXV5dkMhn5+/vTtGnTai0gS0tLKTQ0lMzMzEhNTa3G4dfrKiCLi4vJ29ubZDKZaMhzoiejPvbv3/+pjoednZ3cyHpEtQ+/Xlta7t27R4GBgdSqVStSU1MjCwsLGjlypGgAhD59+tDChQufKp2MsabFZWg5LkPFuAwVe5nLUK5kMlaDX375hRwcHITR215EY8eObbLvXzWHb7/9ljw9PZs0zqbIF9OmTaPu3bs3WvjV+e+//0hHR4e2b9/epPE+rXPnzpGxsTEVFBQ0d1IYY7XgMvT5x2Vow+EyVB73yWSsBu+++y7++usv/PPPP6IRvp5nS5cuhaenJzQ1NZGQkIDY2Fh8++23zZ2sRqOmpoZVq1Y1aZyNmS+ISBiFsKn6G5aVleH27dsIDw+HTCbD+++/3yTx1ldeXh7i4uKgq6vb3ElhjNWCy9DnH5ehz47L0JopERE1eiyMsSYxZMgQHDx4EP/99x+srKwQGBiI8ePHN3eymIIKCgrQsmVLdOrUCRs2bEDr1q0bPc7c3Fy0adMG5ubmiImJQe/evRs9TsYYex5xGfpi4zL0+cKVTMYYY4wxxhhjDUa5uRPAGGOMMcYYY+zlwZVMxhhjjDHGGGMNhiuZjDHGGGOMMcYaDFcyGWOMMcYYY4w1GK5kMsYYY4wxxhhrMFzJZIwxxhhjjDHWYLiSyRhjjDHGGGOswXAlkzHGGGOMMcZYg+FKJmOMMcYYY4yxBsOVTMYYY4wxxhhjDYYrmYwxxhhjjDHGGgxXMhljjDHGGGOMNRiuZDLGGGOMMcYYazBcyWSMMcYYY4wx1mC4kskYY4wxxhhjrMFwJZMxxhhjjDHGWIPhSiZjjDHGGGOMsQbDlUzGGGOMMcYYYw2GK5mMMcYYY4wxxhoMVzIZY4wxxhhjjDUYrmQyxhhjjDHGGGswXMlkjDHGGGOMMdZguJLJGGOMMcYYY6zBqDZ3Ahh7GqWlpSgpKWnuZDDGGGOMsXpQU1ODiopKcyeDNTKuZLIXAhHh+vXrKCgoaO6kMMYYY4yxZyCTyWBiYgIlJaXmTgprJFzJZC+EigqmsbExNDQ0+KLEGGOMMfaCISIUFRXh5s2bAABTU9NmThFrLFzJZM+90tJSoYJpYGDQ3MlhjDHGGGP1JJVKAQA3b96EsbExN519SfHAP+y5V9EHU0NDo5lTwhhjjDHGnlXFPR2Ps/Hy4kome2FwE1nGGGOMsRcf39O9/LiSyRhjjDHGGGOswXAlk7FXSG5uLpSUlJCamvpChV3ZwYMHoaSkJIw0HBMTA5lM1qhxspdLaGgonJ2dhWkfHx988MEHzZael5GSkhLi4+OfKYyq56VXr16YOHHiM4UJyJ//542lpSWWL18uTDfEsWTsWT3tdbJqWc1ePTzwD3uhrdpb0GRxBXrKnnqbW7duYdasWfj1119x48YN6OnpwcnJCbNmzUL37t0BlN9A7Ny585W4yc3JycFXX32FgwcP4s6dOzA0NISrqysWL14Me3v7eoU5dOhQ9O/fX5gODQ1FfHx8o1d2a3Jzo1+Txmc8Yr3C69bVPGn27NkIDQ19xhTVj6K/g0OHDiEsLAypqakoLi6GmZkZunXrhnXr1kEikdQr7hUrVoCIhOlevXrB2dlZdKPflNZlf9mk8Y2zXvhU6ytyXcvLy4Oent4zpavqeWkoISEhCAwMFKZ9fHxQUFDwzBW50tJSfP3114iJicHff/8NqVQKW1tbjBs3DmPHjq13uJWPZW5uLtq0aYOUlJRmqygXLC9o0vhkE2VPtb6Pjw9iY2MBAKqqqtDX18frr7+O4cOHw8fHB8rK/H6FsabAlUzGGpG3tzcePXqE2NhYWFlZ4caNG9i/fz/y8/ObO2n19ujRo3rdzJeUlMDT0xN2dnbYsWMHTE1NcfXqVSQkJDzTk06pVCqMVMdql5eXJ/z/008/YdasWcjMzBTmaWlpPVV49c0L9ZWeno5+/fohMDAQK1euhFQqxV9//YXt27ejtLS03uHq6uo2YCpffopc10xMTJ45noY+L0SE0tJSaGlpPXVeV0RYWBjWrFmD1atXw83NDffu3cPJkydx9+7dZwq3IY7lq6Zfv36Ijo5GaWkpbty4gd27dyMoKAjbtm3Drl27oKrKt7+MNTZ+nMNYIykoKEBiYiIWL16Mt956C61bt0bnzp3x5Zdf4v333wdQ3iwKAAYOHAglJSVhOjs7GwMGDEDLli2hpaWFTp06Yd++faLwLS0tsWDBAvj6+kJbWxuvvfYa1q5dK1rnxIkTcHFxgbq6Otzc3JCSkiJaXlpaCj8/P7Rp0wZSqRR2dnZYsWKFaJ2KJjLz589Hq1atYGdnp1DYVZ0/fx7Z2dn49ttv0aVLF7Ru3Rrdu3fHvHnz0KVLFwBPmtxu3rwZ3bp1g7q6Otq3b49Dhw7VGG7l5rIxMTEICwtDWloalJSUoKSkhJiYmFrT9SoxMTER/nR1daGkpCRM379/HyNHjqwzz82dOxejRo2Cjo4OPvnkEwDAunXrYGFhAQ0NDQwcOBDLli2Ta8L8888/o2PHjlBXV4eVlRXCwsLw+PFjIVxA/ndQ1Z49e2BiYoIlS5agffv2sLa2Rr9+/bBu3TrhQUNFfoiPj4etrS3U1dXRt29fXLlypcbjUrkZmI+PDw4dOoQVK1YIeSg3N/fpDvRLTJHrGiBu4lnxu96yZQvc3d0hlUrRqVMnZGVlITk5GW5ubtDS0sI777yDW7duCWHU1Tzvhx9+gJubG7S1tWFiYoIRI0YI394DnjTXS0hIgKurK1q0aIEjR46ImsuGhoYiNjYWP//8s3C+Dx48CA8PD3z22Wei+G7dugWJRIL9+/dXm55du3YhICAAgwcPRps2beDk5AQ/Pz+EhIQI6/Tq1QufffYZPvvsM+jq6sLQ0BAzZ86s9Y1t5WPZpk0bAICLiwuUlJTQq1evGrd7lbVo0QImJiYwMzNDx44dMX36dPz8889ISEgQyoTqungUFBQIeQB4kod+//13uLi4QCqVwsPDAzdv3kRCQgIcHBygo6ODESNGoKioSAinV69eCAwMxMSJE6Gnp4eWLVti3bp1uH//PsaMGQNtbW3Y2NggISEBQPkDEBsbGyxdulS0H6mpqVBSUsLFixer3c+K38iCBQvQsmVLyGQyzJkzB48fP8bkyZOhr68Pc3NzREdHi7Y7e/YsPDw8IJVKYWBggE8++QSFhYXC8tLSUnzxxReQyWQwMDDAlClT5PJoWVkZFi5cKNw/ODk5Ydu2bU91ntjLjSuZjDWSiqfl8fHxePjwYbXrJCcnAwCio6ORl5cnTBcWFqJ///7Yv38/UlJS0K9fP3h5eeHy5cui7cPDw4UKXkBAAPz9/YU3U4WFhXjvvffg6OiIU6dOITQ0VHSzA5QXEubm5ti6dSvS09Mxa9YsTJ8+HVu2bBGtt3//fmRmZmLv3r345ZdfFAq7KiMjIygrK2Pbtm11vnWaPHkyJk2ahJSUFHTt2hVeXl4Kvf0dOnQoJk2ahHbt2iEvLw95eXkYOnRondsxxfPc0qVL4eTkhJSUFMycORNJSUkYP348goKCkJqaCk9PT8yfP1+0TWJiIkaNGoWgoCCkp6djzZo1iImJEdar6XdQlYmJCfLy8nD48OFa96WoqAjz589HXFwckpKSUFBQgGHDhil0HFasWIGuXbti3LhxQh6ysLBQaNtXgSLXtZrMnj0bM2bMwOnTp6GqqooRI0ZgypQpWLFiBRITE3Hx4kXMmjVL4fBKSkowd+5cpKWlIT4+Hrm5ufDx8ZFbb9q0aVi0aBEyMjLw+uuvi5aFhIRgyJAh6Nevn3C+u3XrhrFjx2Ljxo2iffzxxx9hZmYGDw+PatNjYmKCP/74Q1RRrk5sbCxUVVVx4sQJrFixAsuWLcP333+v0D6fOHECALBv3z7k5eVhx44dCm3HAA8PDzg5OdXrmIWGhmL16tU4evQorly5giFDhmD58uXYuHEjfv31V+zZswerVq0SbRMbGwtDQ0OcOHECgYGB8Pf3x+DBg9GtWzecPn0affr0wccff4yioiIoKSnB19dXrjIYHR2NHj16wMbGpsa0/fHHH7h27RoOHz6MZcuWYfbs2Xjvvfegp6eH48ePY/z48fj0009x9epVAMD9+/fRt29f6OnpITk5GVu3bsW+fftED1XCw8MRExODqKgoHDlyBHfu3MHOnTtF8S5cuBBxcXGIjIzE+fPnERwcjI8++qjWh8Ls1cKVTMYaiaqqKmJiYhAbGwuZTIbu3btj+vTpOHPmjLCOkZERAEAmk8HExESYdnJywqeffor27dvD1tYWc+fOhbW1NXbt2iWKo3///ggICICNjQ2mTp0KQ0NDHDhwAACwceNGlJWVYf369WjXrh3ee+89TJ48WbS9mpoawsLC4ObmhjZt2mDkyJEYM2aMXCVTU1MT33//Pdq1a4d27dopFHZVZmZmWLlyJWbNmgU9PT14eHhg7ty5uHTpkty6n332Gby9veHg4IDvvvsOurq6WL++7r6HUqkUWlpaUFVVFd7QcVNaxSia5zw8PDBp0iRYW1vD2toaq1atwjvvvIOQkBC0bdsWAQEBeOedd0TbhIWFYdq0aRg9ejSsrKzg6emJuXPnYs2aNQBq/h1UNXjwYAwfPhw9e/aEqakpBg4ciNWrV+PevXui9UpKSrB69Wp07doVrq6uiI2NxdGjR4Ub9Nro6upCIpFAQ0NDyEP8ofAnFLmu1SQkJAR9+/aFg4MDgoKCcOrUKcycORPdu3eHi4sL/Pz8hOuXInx9ffHOO+/AysoKXbp0wcqVK5GQkCB6IwMAc+bMgaenJ6ytraGvry9apqWlBalUKrz5MjExgUQiwYcffgig/A18hZiYGPj4+NTYt3nZsmW4desWTExM8Prrr2P8+PHCm6rKLCwsEBERATs7O4wcORKBgYGIiIhQaJ8rfhsGBgYwMTGR2x9WO3t7+3q1TJg3b54onx46dAjfffcdXFxc4O7ujkGDBsnlXScnJ8yYMQO2trb48ssvoa6uDkNDQ4wbNw62traYNWsW8vPzhd+Oj48PMjMzhetUSUkJNm7cCF9f31rTpq+vj5UrV8LOzg6+vr6ws7NDUVERpk+fLsQtkUhw5MgRAOX3BsXFxYiLi0P79u3h4eGB1atX44cffsCNGzcAAMuXL8eXX36JDz/8EA4ODoiMjBQ1X3/48CEWLFiAqKgo9O3bF1ZWVvDx8cFHH30kXNcZ40omY43I29sb165dw65du9CvXz8cPHgQHTt2rLMJZ2FhIUJCQuDg4ACZTAYtLS1kZGTIvVWq/FS+ouljRXOxiqf26urqwjpdu3aVi+ubb76Bq6srjIyMoKWlhbVr18rF06FDB1HfO0XDrmrChAm4fv06NmzYgK5du2Lr1q1o164d9u7dK1qvcliqqqpwc3NDRkZGneGz+lM0z7m5uYmmMzMz0blzZ9G8qtNpaWmYM2eO8BZMS0tLeFNYuYlZXVRUVBAdHY2rV69iyZIlMDMzw4IFC4Q31xVUVVXRqVMnYdre3h4ymYzzUAOp73Wt8vWqZcuWAMqvLZXnVW7uWpdTp07By8sLr732GrS1tdGzZ08AqDPPKkJdXR0ff/wxoqKiAACnT5/GuXPnqn1TWsHR0RHnzp3DsWPH4Ovri5s3b8LLy0tu0J8uXbqIKqpdu3bFX3/99Uz9ipliiKhe32esmnc1NDRgZWUlmlc171beRkVFBQYGBnL5HYCwXatWrfDuu+8Kee5///sfHj58iMGDB9eatnbt2okGM2rZsqUonoq4K98bODk5QVNTU1ine/fuKCsrQ2ZmJv7991/k5eXhjTfeEJZXlMMVLl68iKKiInh6eoqu63FxccjOzq41vezVwZVMxhqZuro6PD09MXPmTBw9ehQ+Pj6YPXt2rduEhIRg586dWLBgARITE5GamooOHTrg0aNHovXU1NRE00pKSigrK1M4bZs3b0ZISAj8/PywZ88epKamYsyYMXLxVC6MnpW2tja8vLwwf/58pKWlwd3dHfPmzWuw8Fn9KJrn6pMXCgsLhRFhK/7Onj2Lv/76S/SgQlFmZmb4+OOPsXr1apw/fx7FxcWIjIx86nBY/dXnulb5elVxo191nqLXr4omfzo6OtiwYQOSk5OF5nwNdf0aO3Ys9u7di6tXryI6OhoeHh5o3bp1rdsoKyujU6dOmDhxInbs2IGYmBisX78eOTk59UoDa1gZGRlCv9aKilnlvoYlJSXVblc1nypS9la3TnW/gcrbjR07Fps3b8aDBw8QHR2NoUOHQkNDo9Z9qiuemtL3LCpaC/z666+i63p6ejr3y2QCrmQy1sQcHR1x//59YVpNTU3uCXZSUhJ8fHwwcOBAdOjQASYmJk/dxMfBwQFnzpxBcXGxMO/YsWNy8XTr1g0BAQFwcXGBjY2NQk8hFQlbEUpKSrC3txcdj6phPX78GKdOnYKDg4NCYUokEn4jUA/1zXN2dnZyfSirTnfs2BGZmZmwsbGR+6u40avud6AIPT09mJqaivLQ48ePcfLkSWE6MzMTBQUFnIcaUdXrWmO7cOEC8vPzsWjRIri7u8Pe3v6p3oJWVtP57tChA9zc3LBu3TqFmi1Wx9HREQBEx+b48eOidY4dOwZbW1uFmmVXtCjh/Pn0/vjjD5w9exbe3t4AnjQ9rtwKork+fVWhf//+0NTUxHfffYfdu3fXK8/VxcHBAWlpaaI8mZSUBGVlZdjZ2UFXVxempqaifFpRDldwdHREixYtcPnyZblrOvdhZxW4kslYI8nPz4eHhwd+/PFHnDlzBjk5Odi6dSuWLFmCAQMGCOtZWlpi//79uH79ujDUva2tLXbs2IHU1FSkpaVhxIgRT/0UcsSIEVBSUsK4ceOQnp6O3377TW7kOltbW5w8eRK///47srKyMHPmzBoHXXnasKtKTU3FgAEDsG3bNqSnp+PixYtYv349oqKiRMcDKG/Cu3PnTly4cAETJkzA3bt3FS5sLS0tkZOTg9TUVNy+ffupByd5VdU3zwUGBuK3337DsmXL8Ndff2HNmjVISEgQNUmbNWsW4uLiEBYWhvPnzyMjIwObN2/GjBkzhHWq+x1UtWbNGvj7+2PPnj3Izs7G+fPnMXXqVJw/fx5eXl7CempqaggMDMTx48dx6tQp+Pj4oEuXLnLNeGtiaWmJ48ePIzc3F7dv327QNwAvOkWva43ttddeg0QiwapVq3Dp0iXs2rULc+fOrVdYlpaWOHPmDDIzM3H79m3R26yxY8di0aJFICIMHDiw1nAGDRqEiIgIHD9+HH///TcOHjyICRMmoG3btqLvAF++fBlffPEFMjMzsWnTJqxatQpBQUEKpdXY2BhSqRS7d+/GjRs38O+//9Zrn192Dx8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      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost performances:\n",
      "Metrics: ['Recall', 'Precision', 'ROC AUC', 'F1 Score', 'Accuracy']\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric               |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:---------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| XGBoost      |      0 | random       | Validation Accuracy  | 0.83871  | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation Accuracy  | 0.883117 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation Accuracy  | 0.876623 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation Accuracy  | 0.883117 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation Accuracy  | 0.883117 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation Accuracy  | 0.650943 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation Accuracy  | 0.834483 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation Accuracy  | 0.554545 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation Accuracy  | 0.539823 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation Accuracy  | 0.797468 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation Accuracy  | 0.821429 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation Accuracy  | 0.868852 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation Accuracy  | 0.839744 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation Accuracy  | 0.781609 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation Accuracy  | 0.680272 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      0 | random       | Validation ROC AUC   | 0.917167 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation ROC AUC   | 0.959291 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation ROC AUC   | 0.917975 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation ROC AUC   | 0.937384 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation ROC AUC   | 0.939241 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation ROC AUC   | 0.727944 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation ROC AUC   | 0.857252 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation ROC AUC   | 0.63211  | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation ROC AUC   | 0.622604 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation ROC AUC   | 0.80303  | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation ROC AUC   | 0.875    | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation ROC AUC   | 0.907403 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation ROC AUC   | 0.926599 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation ROC AUC   | 0.888095 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation ROC AUC   | 0.775226 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      0 | random       | Validation F1 Score  | 0.850299 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation F1 Score  | 0.891566 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation F1 Score  | 0.877419 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation F1 Score  | 0.890244 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation F1 Score  | 0.890244 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation F1 Score  | 0.519481 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation F1 Score  | 0.823529 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation F1 Score  | 0.409639 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation F1 Score  | 0.555556 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation F1 Score  | 0.868852 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation F1 Score  | 0.83871  | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation F1 Score  | 0.886792 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation F1 Score  | 0.822695 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation F1 Score  | 0.786517 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation F1 Score  | 0.708075 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      0 | random       | Validation Precision | 0.816092 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation Precision | 0.860465 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation Precision | 0.894737 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation Precision | 0.858824 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation Precision | 0.858824 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation Precision | 0.588235 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation Precision | 0.811594 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation Precision | 0.62963  | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation Precision | 0.607477 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation Precision | 0.791045 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation Precision | 0.8125   | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation Precision | 0.895238 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation Precision | 0.773333 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation Precision | 0.744681 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation Precision | 0.730769 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      0 | random       | Validation Recall    | 0.8875   | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation Recall    | 0.925    | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation Recall    | 0.860759 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation Recall    | 0.924051 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation Recall    | 0.924051 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation Recall    | 0.465116 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation Recall    | 0.835821 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation Recall    | 0.303571 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation Recall    | 0.511811 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation Recall    | 0.963636 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation Recall    | 0.866667 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation Recall    | 0.878505 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation Recall    | 0.878788 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation Recall    | 0.833333 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation Recall    | 0.686747 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |    nan | random       | Test Accuracy        | 0.767442 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Accuracy        | 0.790698 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Accuracy        | 0.767442 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy        | 0.447059 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy        | 0.517647 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy        | 0.411765 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy        | 0.752941 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy        | 0.764706 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy        | 0.694118 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC         | 0.879348 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC         | 0.884239 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC         | 0.882065 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC         | 0.499443 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC         | 0.486065 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC         | 0.5      | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC         | 0.835586 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC         | 0.827703 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC         | 0.759572 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test F1 Score        | 0.777778 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test F1 Score        | 0.804348 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test F1 Score        | 0.782609 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score        | 0.373333 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score        | 0.506024 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score        | 0.375    | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score        | 0.655738 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score        | 0.677419 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score        | 0.580645 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.7      | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.711538 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.692308 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.482759 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.567568 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.441176 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.833333 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.84     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.72     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Recall          | 0.875    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Recall          | 0.925    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Recall          | 0.9      | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall          | 0.304348 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall          | 0.456522 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall          | 0.326087 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall          | 0.540541 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall          | 0.567568 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall          | 0.486486 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Accuracy        | 0.77907  | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy        | 0.447059 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy        | 0.717647 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC         | 0.880978 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC         | 0.487179 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC         | 0.831081 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test F1 Score        | 0.781609 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score        | 0.356164 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score        | 0.571429 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.723404 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.481481 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.842105 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Recall          | 0.85     | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall          | 0.282609 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall          | 0.432432 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy  | 0.514914 | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC   | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Validation F1 Score  | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Validation Recall    | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy  | 0.526994 | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC   | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Validation F1 Score  | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Validation Recall    | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy  | 0.518175 | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC   | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Validation F1 Score  | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Validation Recall    | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC         | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score        | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall          | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy        | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "Metric: Recall\n"
     ]
    },
    {
     "data": {
      "image/png": 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0FttyoqXWYlPFY65RRWGuUUmxR5GIiIiIJLFQJCIiIiJJLBSJiIiISBILRSIiIiKSxIdZiIjKgZ6RHO8t8VSbJiJ63bFQJCIqBzKZDPrGPKUSUeXCS89EREREJImFIhERERFJYqFIRERERJJYKBIRERGRJBaKRERERCSJhSIRERERSWKhSERERESSWCgSERERkSSODktElYYQAhkZGappExMTyGQyLbaIiOj1xkKRiCqNjIwM9OrVSzW9e/dumJqaarFFRESvN156JiIiIiJJLBSJiIiISBILRSIiIiKSxEKRiIiIiCTxYRYiIiItWBM1XWux+2Gq1mLT64U9ikREREQkiYUiEREREUlioUhEREREklgoEhEREZEkFopEREREJKnKF4orV66Eq6srDA0N0apVK5w9e7ZE223ZsgUymQy9e/fWbAOJiIiItKRKF4pbt26Fv78/Zs+ejdDQUHh4eMDX1xeJiYkv3C4mJgaTJ09Gu3btKqilRERERBWvSheKixYtwqhRozB8+HA0bNgQq1atgrGxMdatW1fsNgqFAoMGDUJAQABq165dga0lIiIiqlhVtlDMzc3FhQsX0KVLF9U8HR0ddOnSBadPny52u6+//hrVqlXDyJEjSxQnJycHqampav+IiIiIXgdV9s0sycnJUCgUsLe3V5tvb2+PiIgIyW1OnDiBX3/9FWFhYSWOs2DBAgQEBBSZHxsbW6ai0Tg/s9TblJdkuXOZtsuU5wO4p5p+KHdClrx0qWeQYl6m2OUh0eTFtyJoUvr9dK3F1mau3S/j587IyFCbjo2NhYmJSan2YZBiU6bY5YG5VvHKmmvlgblWemlpaeXYEiqJKlsollZaWhqGDBmCNWvWwNbWtsTbTZ8+Hf7+/qrp1NRUuLi4wNnZGebmpS9+MsNTSr1NebFVxJZpuwyFQm3aRvEAJgp5qfaRY5lXptjloVpGNa3FtqxuqbXY2sy16mX83Onp6l9Azs7OMDU1LdU+cnIelil2eWCuVbyy5lp5YK6VHq/KVbwqWyja2tpCLpcjISFBbX5CQgIcHByKrB8VFYWYmBj06NFDNU+pVAIAdHV1ERkZiTp16hTZzsDAAAYGBuXceiIiIiLNq7L3KOrr68PLywvBwcGqeUqlEsHBwfDx8SmyfoMGDXDlyhWEhYWp/vXs2RMdO3ZEWFgYXFxcKrL5RERERBpXZXsUAcDf3x/Dhg2Dt7c3WrZsiSVLliAjIwPDhw8HAAwdOhTOzs5YsGABDA0N0bhxY7XtLS0tAaDIfCIiIqLKoEoXigMGDEBSUhJmzZqF+Ph4NGvWDPv371c94HL37l3o6FTZTlciIiKq4qp0oQgA48aNw7hx4ySXHTt27IXbBgUFlX+DiIiIiP4j2F1GRERERJJYKBIRERGRJBaKRERERCSJhSIRERERSaryD7MQ0X9P4uaSvUv9eRk56m8BSto+DpkGpXsLEFpp740VRET/NexRJCIiIiJJLBSJiIiISBIvPVcxW5dOQrfBU2FhU/R91uUl8UkWDHR1YGFS8I7ra3ceIioqCrm5uTAwMED4PRN417XTWHwAiLkch3vXE5D+OAsyHRks7U1Rr6ULbJwsNBoXAB5lPEJ4XDgeZT4CAFgbW8Pd0R3WJtYaj61NcTHheBB9DRmpBZ/bxNwaTrUawdHVXeOxlUoBHR2Z5PzElExUszTWeBu0gblWsbkmhMCDBw/g4OAAuVyOvLw8HD16FHl5eWjbtq3qbV2VUVXNNWKhWGndunxScv79W1cQdfU0zK0K7sOq27Rtucf+5vdz+LBDfbRu4IBT4XGYvekslEpjmJqaIicnB1+tP42AQS3RukH5F6sZKVnYNj8YcbceQiaTQQgB+1rWiPz3Do78dgGtejZC52He5R4XALJys7Do8CIcjTwKmUwGMwMzAEBaThqEEOjUoBP8u/jDUM9QI/G1JSP1MXavnY0Ht6/CzKoaTMyt/3/+I6Q9/glOtRuj10cBMDG3Kv/Y2XlYtCsMZyISYGygiy7Na0AII8hkBUXjk8wcjFwSjANze5Z7bEW+Esc2hSLi9B0Ymxmgua8bmnWpp1qe/jgLy0Zuw5c7hpV7bOZaxefanTt3MG7cOCQkJMDZ2RkrVqzAtGnTEBMTAyEEDA0NsW7dOtSoUaPcYzPXSJtYKFZSu1bPBGQyQIgiy45sX17wH5kMk5cHl3vsmMQ01KxWcDLZcvwmhnRqgH3RBqrlfeorsOFIpEYKxYNrz8LUyhifb3wLcl05gtefR05GLkYu7IGYy3HY8eMxmFkbo2WPhuUee8WxFYiIj8D8PvPhVcMLcp2ChygUSgVC74Zi+dHlWHF0BSa/NbncY2vT4W1LIIQSI2YGwdpe/UvyUcJd7N/4Aw5vW4JeHwWUe+z1wRGIjk/FlL6eSM/Ow29HInHzIVC3bl2Nv37z5B+XceVoFFr1aoSczFwcXncWD24k4Z0xbVTrCInfv/LAXKv4XFu2bBnq16+PxYsXY8+ePZg4cSJq1KiBwMBAKJVKTJs2DWvXrsXXX39d7rGZa6RNvEexknJ1b4FaDVti9Pw/MXnFEdU/mY4cfl+tK5jWQJEIAHIdGbJy8wEA8Y8z4VlX/SlSz7p2uJeUppHYUaH30f7D5jAw1oeuvhwdh3jiWkg0cjJz4drUEV1HtETo/kiNxP7n5j+Y4jsFLV1bqk6mACDXkaOFawt88dYXOH7zuEZia1PM9XPo0n9CkS9uALC2r4FO/cYh5vo5jcQ+eT0eE3p54M3GTnjHuyYWjWqH/Px83Lx5E0qlUiMxC109HoV3x7aBT5/G6DDIEyN+7IGYK3H437ITqi/twp7N8sZcq/hcu3z5Mj7++GPUrVsXo0ePRkxMDIYMGQJdXV3o6+tj2LBhCA0N1Uhs5hppEwvFSqrv2O9Q080TG3/4FFFXTlVo7KauNjh6ORYAUMfJAldjHqotvxLzELbmRhqJLdeTq50wCy8/KxQFRUP1BtWQkpiukdhCCOjJ9Ypdrqujq7G/+rVJrqePnKyMYpfnZmdBrqevkdipmTmoZvE0l8yN9eHm5galUombN28iJ0/xgq1fTdqjLNjVsFRNWzuZY/A33XA/MhF7loRAKDX3s2auSdNkrmVlZcHCouAeZyMjIxgZGcHW1la13MHBAY8ePdJIbOYaaRMLxUrMu1M/9P74GxzfvQYHf1+IvNzsCok74q2G2Hf+Dr7/IxSNa9pg49EI3L59Gw8ePMDt27exet9VfNC+3st3VAYu7tXwz5aLyM3OK7ivZ2MoLO1NYWxWcP9MZmo2jEw180XSunZrLDq8CDcTbxZZdjPxJpYEL4FPbR+NxNYmN88O2Pfbt7gRFqL2JZ6TlYEbYSHYv/E7uHt10kjsahbGuJekXvjL5XLUr18fSqUSC7Zd0EhcADC1NMTjePWecXMbEwz+uhse3ErG/5ad0Fhs5lrF55qtrS3i4+NV05999hmsrJ7eC/n48WOYm5trJDZzjbSJ9yhWcvYu9TBkyioc/XMl1i8YJXnPYnmrWc0Myz55E0HB4dgecgu5eUqYIQ3y3HQ0qW6JD970whuNnDQSu7NfC2yecxALB/0OANAz1MX7UzqoliffS0GTjnU1EvuzTp9h3t/z8MnGT2BmaAZLI0sAQEpWCtJz0tGiZgt81ukzjcTWpo7vjYFQKvFX4FwolQrI5QWnFYUiHzo6cjTxeRvt+3yqkdiede1wIPQuWrrZq80vLBb18+9rJC4A1GziiGsh0ajloZ7LZjbGGPS1LzbO2K+x2My1is+1Vq1aISYmBs2aNQMA9O3bV235v//+Czc3N43EZq6RNskE+4wrVGpqKiwsLPDkyZMy/fW5/FBKmWPfunwSd2+EodVbH5bpqcABSZ+XehshBB6n50AAsDDWh668bJ3Yu0vxtoy8nHzcu54ARb4Szm52MDZ/tafx+v1vaqnWv/PwDq7FXcPjjMcAACsTKzR2aowa1qV/GtJyomWptykvpc21nKwMxN+NRGZawec2MbeGvUt9GBiZlDp2SXMtLTMXD9Oy4Wpf8LuUkaPARxvvqZYv6+eABw/T4VHLtrhdFFHSXEtJTMfD+09Qx9NZum0PMxF96QGadir5HybMtZLJycpAwr0basPjlDXXxne1LPU2UmJjY2FgYKB2Ofpl1kRNL9F6zLWnXvU7lEqPPYpVSN2mbTUyHM6LyGQyWJtV7LAJega6qN1c+oRaEWra1ERNm5pai68tBkYmqOnmWaExzYz1YWZc/K0ExgZ6pSoSS8Oymiksq5kWu9zMxrhUX9xlUZVzrUb95tpuhhpnZ82dc5hrpE0sFCuxzPQnuHp6n+TAtI1bd4OxmaVW2pX4JAu/BUfg8/c0c6LPy8lHfNRDGJoaqN0AXrgs/FQMmmro8nOeIg8nbp3A9bjreJTx/wPTmlijkWMjtK3b9oU3hb/OtJVr2h7cPTMtG4kxj1HN1QrGZobITM1G2OGbUOQq4N7WFbYulhqLXVVz7Vm5OVmIDD2KlKQHMLGwgbt3JxiZaG5Q/ZCQEFy7dg0+Pj7w8PDAuXPnsHHjRiiVSnTs2BHvvfeexmIz10hbWChWUnEx4fhj5VTo6RughpsXrKq5ACgYmDb0+A6cPfQ73h/7HRxrNqjwtqVl5uLgxXsaKRQfxj7B73MO4klyBmQyGVzcq6H35+1hZl3wZo6czDz8tfykRgrF+4/vY+qOqUhOT4a7ozusjAsu799MvIk9l/bAzswO3/b5FtWtqpd7bG3SZq5pc3D3BzeSsHnOQeRk5cHQRB8fznkLO344Bh25DpRKgVM7r2Lo/LfhWMem3GNX1VxbN9cPA/2XwsjEAqmPE/H7os+Qk5UO62ouSEl+gNP7fsOgyStgaVv+90Dv2LED33//PerVq4ctW7ZgypQp+O6779C1a1fI5XIsWrQIOTk5GDhwYLnHZq6RNrFQrKSCty+Hm2d7dP3Av8j4WkIIHNqyCEe2L8egySvLPfbp8PgXLn/wqPjhLV7VkQ0XYFfTCiMW9kB2ei4OrTuLDdP/xuBvusHCrvhLN+VhSfAS1LKthdWDV8PEQP1eqYycDCzYvwBLjyzFD+//oNF2VDRt5po2B3c/tuki3Nu6osvwFgg9cAPbvz2KOs2d8O7Ygts7/lp+Aie2XUK/6eX/FG5VzbVHCXeh/P+hrkJ2r4GZpR38vlwLAyNT5GZnYteaWTjxv1/RffjMco+9ZcsWTJ06FX369MH58+cxYcIETJw4Ef369QMANG7cGBs2bNBIochcI23i8DiVVFJsFLw69pMchFUmk8GrYz8k3r+lkdhzNp994b/V+69pJC4A3I9IRMfBXjA2N4S1kzn6f9UZtZs5Y8OX+4oML1Herj64ipFtRxY5mQKAiYEJRrQZgSuxVzTaBm3QZq5pc3D3uKhktOrZCAbG+mjZoyHSH2Wiedf6quVe77gj7layRmJX1Vx71oPo62jzzjAYGBX8AahvaIy27wxD7O2rmon34AF8fAqGgfH29oZCoYCn59N7cr28vBAXF6eR2Mw10ib2KFZSJubWiL8TDhsH6SfS4u+Ew9is/N+HCgDWpgYY37Mp2rg7Si6/9eAJxv6smZH883MV0JGrD7j99mgf7F/9L36bsR+9J7XTSFwAMDUwRdyTONSyrSW5PC41DqYGmu3V1AZt5lrh4O61HSyeGdz96WlNk4O7K/KV0NUveFOFXFcHegZyGD3zhL2xuQGy0nI0Eruq5hrw9A0k+Xk5MLFQv9RqammHzLQUjcS1sLBAXFwcHBwckJSUBIVCgfj4eNSpUwcAEBcXpxqQu7wx10ibWChWUt6d++PA7wsRf/cGarp5qr6oM9Me405kKK6c2qux8cbqOVviZuyTYgtFDb1pCgBgW90CcVEPi9zY3e3j1gCAbfOPaCz2O43fwbf7v8WQ1kPgWcNTdS/P48zHCL0bio1nNqJPsz4ai68t2sy1EW81xOdrT+BharZqcPekXGMYGhoiOzsbq+9kYGJvD43ENrcxQUpCOiztCy599/68PUytnhal6Y+z1L7My1NVzTUA2LbMHzpyXeRmZ+Jxwj3YOT0tYFIfJcDIRDNDprRv3x5z585F9+7d8c8//+Ddd9/F4sWLIZPJIJPJsHTpUrRu3VojsZlrpE0sFCspz/Z9YGRigQtH/0BYyB4IZcGrzGQ6cti71EO3wVPRwKujRmL3e6Musv//cqAUJxsTfD+iTbHLX0X9VjVwLeQ2mnSoU2RZt49bQygFQg9o5l3PI9qOgJGeEbae34qfj/+s6vkQQsDaxBoDWwzEBy0+0EhsbdJmrj0/uHt2rgIPHz6ETCaDiYkJJg/xRGcPzQxb0rBdLWQ8efq2o3reLmrLb569B6d6mhmap6rmms/bQ9Wm9QzUi6Ooq6dRvW5TjcQeP3488vLycPDgQTRt2hRffPEFtmzZgs8//xz5+fnw9PTE2LFjNRKbuUbaxAG3K5g2BtxWKPKRlf4EAGBkaqF6m0FplWXA7fJSmgG3y1tpB6YFgAcpD/Ao8/+HkTC2hpNl2Z7CfJ0GQQa0m2tCCMQ+ysSEbbHQ1dWFjo4O1g52gYmBvFT7Ka9cy8vJh0wmU10yLAnmWsV71QG3c3JykJ+fDxOT0g/2XdIBt1+mKuUaB9yueOxRrALkcl2YWpT/sAmlpVAqIdfR3vNTQgjJBy40wcnSqcwn0deZNnNNJpPBytQQ+vqaeZd3aekZVMzptarm2vMq8vf7WQYGBjAwMHj5ihrwOD4NFnYmzDXSKBaKlVji/Vs4f2Q77kddQcaTgstxlrZOqNO0LVp2+aBMr7sqiXM3EmBrboRaDuZQKgU2H7+BvWdj8Cg9BzZmhujZqhYGvFlXIyf1/FwFjm0KxYObyajrXR1t3muCE9su4dSOgqfy6rVwwTujfWDwgrd5lJfk9GT87/L/EJsSCxsTG7zb5N0yve7qdaCtXAOAC7cSce3OIzRxtUH96tZIS0tDXFwclEolDl8U6NVa+ib88pb2MBOhByPxOC4NplZGaNa1HmyrW1ZI7KqSa/l5uQj536+IvxOB2o1ao9VbA3F63284c2gzAKBukzbo+oG/xvItOTkZZ8+ehYWFBVq2bAk9vacDTWdlZWHjxo0YNWqURmJLWTV2J0Yt6anRwbafV1VyjZ7i8DiVVPT1s9i8cBzycrPhXLsxZDIZmvi8g1qNWiPywlFs+PZj1Rs0ytuqv68hPTsPALA15CZ2nb6Nvm/UxTdDWqHvG3Ww8/RtbAvRzHApRzdewLWQaDjVs8XlI7ew/5d/cfHQDbw92gfvjmmDuFvJOLbpokZid1vWDSmZKQCAmOQY+AX5ITgiGPmKfPx7+198vPFjRCVFaSS2Nmkz1w6H3cOMDWfwb2QC5mw6i+Cwe7h58yb09PRgYGCAn/++in+uPtBI7O8GbFTdN5Z0NwW/jN+Ja//chjJfiVvn7+PXz/9CQoxmPndVzbWQPWsRcT4YDjUb4NqZAzi0dQkun/oLb33gD98PJyP+TiRO/O9XjcS+du0a+vXrh++//x5Tp05F//79ERX19BhnZmZizZo1Gon9x7dHJP8JIXBw7VnVtCZU1Vyjp9ijWEn9s3sNOrw3Bs3a9QQAxLR8C8Hbl2PkrPV4o8cI/LlyKv7ZvQZvDyn9fSovE5+SCXvLgifyjl6OxfieTdG+ccEDBS3q28PZxgQ//30VA96sV+6xI07fQc8Jb6CWhxO83m6An8fswPtTO8KtVcFfvEbmhvh75Un4jmpV7rFz83NReMvv2pNr4VHdA1/3/BpyHTmUSiXm7ZuHX0/8ivl95pd7bG3SZq79eTIKH7/dCH18aiM0KgkzNpyBk5MTHBwKBtjuXicXO0/fxpuNy/9yWX5uPvD/P+9jGy+gRiMH9J3WUfW2jN2L/8GxjaEYMKNLuceuqrl2I+w43hk6HTUbeKH5m72wNmAIeo36GvX+/x32RiYWOLD5R3Tu/1m5x/7pp5/QsWNHzJgxA1lZWVi+fDk++eQTrFy5Em5ubuUe71mRZ+6iRiMHyfc96xnqavQKSVXNNXqKPYqV1KOEu6jVsIVqumYDL6QkP0D6k4eQy3Xh884w3L76r0ZimxnpITm1oKflSUYunK3VT27ONqZ4mJottekry0zNgbVTwQ3OVg5mBfetOZiplls7miEjVTPjjT3rZuJNDPAeALlOwc3lOjo6+KDFB7iReEPjsSuaNnMt9mEGWrvZAwA869hBoVSq3eDuXc9eYwNuPys++hFa924EHXnBKVVHRwafPo0RH/VQ47GrUq5lpT+BVbWCV8VZ2jpBJtNRe12fVTVnZKWnaCR2eHg4hg0bBh0dHZiYmGDatGkYPHgwRo8ejWvXNPcSAQDo7d8ej+PT4NLQHj0+e0P1TyaTocMgT9W0plWlXKOn2KNYSZla2OJRwj1Y2BSMZZiSFAsIoRpjzMzSFnm5WRqJ3bahI34/fhNzBrWAj7sD9pyJxqTeHqp7Enf/G43ajpoZmNbC1gT3I5JgYWeKBzeSIJMBD24mo1rNgrG/Ym8kw9zGWCOxC8dTAwAZZEXeZGCib4K0bM0XLRVNm7km15EhX/F04AY9XR3I5U+f/NSVy5CTp9BIbJlMphoUVAbAwES9V8fAWB/ZGbkai10Vc83Myh6xt6/B3NoecTHhkMlkiL8TrhpL8UF0OEwt7Uq1z8TNI0u0Xm5CBOL+/BxGDk/PXe/oAxluCnzcrws+79MceQ+jS7w/AEAJn7Bv1K4WnOvbYs+SENy6cB/vjmkDI7OKeYCmquYaPcVCsZJq1OotHNj8I1r7DoZcVxcXjvyBOk18INctuPk68X4UzG2kB8R+VSO6umNq4CmMXHoEDV2s8c/VBwiNSkJ1GxM8eJSJtKxczB/mo5HYzX3r46/lJ3Dp8E3ERSWj8/AWOL4pFI8ePAEgQ+j+CLTq1VgjsYUQGBI4BDLIkJWXhaikKNSxezqe44MnD2BtYq2R2NqkzVxztjHBveQ0uPz/e7wDJ3XB+G1PX6OW8DgTdhaaeTOLEAI/j9kBmQzIzcpHYsxj2Ls+/fk+jk+DiaXmYlfFXPN4ozv2b/wOV07/jYS7N9Chz6cI+d86PEq4D5kMCAvZA+/O/TUS27WaGa7ffYzaDup/5PZrVxcCAgu2X9BI3EKW9mYYPO9thGwNw1r/PXhntGbGon1eVc01eoqFYiXV2ncw8nJzcHrfBigU+XBt4I1O/carlptZ2qLrgIkaiW1iqIclH7fD/gt3cToiHvaWxhAQyFcKdGzqjHdbuqKahr68W/VsBBMLI8TeSIJH57po9GZtVKtpheObLyIvJx8tezZC236aGZB3iu8UtWlnS/WBnq/HXUe7upp7haC2aDPXPnizPkwNnz55amygp/Y0/c0HTzRyfyIA9BivfqnPytFMbTo2MglurTXzNGhVzTXvTv1gbGaFuOjraOLzNty9O8PWqTZO7g1EXm42vDr2RWvfwRqJ3aW5C67EPET3lq5FlvVvVw9CAH+di9FI7EI6OjK0H9gctT2csGdpCCpiGOSqmmv0FAfcrmDaGHC7vHDA7YpXVQdBLmuuCSGQmatUTRvr65R6GCbmWsV7HXOtPLxKruVk5SElPg021S2gq1e6QeWB1zfXOOB2xWOPIhFVGjKZrNRvYiF6HRkY6cG+Fi/5kubxqecq6p89a7Bv4/daib3u4HUs3KGZsQxf5uhvF/DX8hNaib3mxBp8f0A7x1ybmGsVj7lW8ZhrVFmxR7GKSk9JRtrjRK3ETk7NRtITzTwF+zJpDzOR+jBDK7GT05KRmKadY65NzLWKx1yreMw1qqxYKFZR7wwtn5fRl8WUvp5ai91zovZuup7+tvaOuTYx1yoec63iMdeosmKhWIllpj/B1dP78CD6muoVaibm1nCq1QiNW3eDsZmlxmI/ycjBgdC7uH73MR6nFwxwbWVqgIY1rPCWZw1YmmhuDLDM1GxcCr6J+xFJyEgp+AvfxNII1RvYoWmnejCxMNRY7CdZT7Dv6j5ce3ANjzILjrm1sTUaOTVCt0bdYGlsqbHY2sRcY65VFOYac40qFp96rmAV9dRzXEw4/lg5FXr6Bqjh5gUT84KbnjNSH+HujVDk5+bg/bHfwbFmgxLHLunTgRH3H+PL9f/CUE+O5nVsYWVacAJ7nJ6NsNvJyM5TYP7Q1nCrblXi2CV9OvDBjST8/vUh6BnowrWpI0ytCobhSX+chZgr8cjPyccHs7rCqZ5tiWOX9OnA8LhwTN0xFQZ6BvCq4QVr44Jj/ijzEULvhiInPwffvfcdGjiU/Ji/Dk+iMteYa6+KufZizLWn+NRzxWOPYiUVvH053Dzbo+sH/kWGBxFC4NCWRTiyfTkGTV5Z7rF/+usK3mzshAk9m0rGXrrnMn7aexVLPyn/yyUH1p6BextXvD3aRzL2vp9P4+DaM/D77t1yj7386HK0r98e/l2kj/miw4uw/OhyrBxY/sdcm5hrzLWKwlxjrlHF41PPlVRSbBS8OvaTHENOJpPBq2M/JN6/pZHYt+NT8V6b2sXGfq9NbUTFPdFI7MSYx2jZs2GxsVv2bIiE6EcaiR2VFIV+XsUf835e/XArUTPHXJuYa8y1isJcY65RxWOhWEmZmFsj/k54scvj74TD2Kzkl0hKw8rMAJH3U4pdHnk/BZammrmXx8TSCA9uJhe7/MHNZI3dy2NtYo3wuOKPeXhcOKyMNXPMtYm5Jo25Vv6Ya9KYa6RJvPRcSXl37o8Dvy9E/N0bqOnmqTp5ZqY9xp3IUFw5tRft+3yqkdh929bBkt2XcPNBCprXtlOdPFPSc3DxdhL2nb+DUd0aaSR2616N8PdPpxEf9RCuTR1V79rNSMlCzOU4XDx0E52HeWskdn+v/lh4eCFuJN6AZw1P1cnzceZjhN4Nxd4re/Hpm5o55trEXGOuVRTmGnONKh4LxUrKs30fGJlY4MLRPxAWsgdCqQAAyHTksHeph26Dp6KBV0eNxO7VujYsjA2w41QU/ncmBsr/f15KRyZDPScLTH6vOdo3cX7JXsrG+113GJkb4uz/ruHCvkgolQWvc9PR0YFDHWv0GN8WDd+opZHYfZr3gYWRBf4I/QN7Lu2B4v+PuVxHjnrV6mGq71R0dNPMMdcm5hpzraIw15hrVPH41HMF08a7nhWKfGSlF9w7Y2RqAbm8bH8flOWdqPkKJZ5k5gIALIz1oSsv290OZXknqiJficzUbACAsbkh5Lpli12Wd6LmK/LxJKvgmFsYWUC3jMf8dXgS9VnMNeZaWTDXSq6q5xqfeq547FGsAuRyXZha2Ggltq5cBzZmmhvf60XkujowszbWSmxduS5sTLVzzLWJuVbxmGsVj7lGVQkfZiEiIiIiSSwUiYiIiEgSC0UiIiIiksRCkYiIiIgkVflCceXKlXB1dYWhoSFatWqFs2fPFrvumjVr0K5dO1hZWcHKygpdunR54fpEREREr7MqXShu3boV/v7+mD17NkJDQ+Hh4QFfX18kJiZKrn/s2DEMHDgQR48exenTp+Hi4oK33noLsbGxFdxyIiIiIs2r0oXiokWLMGrUKAwfPhwNGzbEqlWrYGxsjHXr1kmuv2nTJowZMwbNmjVDgwYNsHbtWiiVSgQHB1dwy4mIiIg0r8oWirm5ubhw4QK6dOmimqejo4MuXbrg9OnTJdpHZmYm8vLyYG1tXew6OTk5SE1NVftHRERE9DqosgNuJycnQ6FQwN7eXm2+vb09IiIiSrSPqVOnwsnJSa3YfN6CBQsQEBBQZH5sbGyZikbj/MxSb1NekuWaeT1VSRikaG8E/kQT6VsRKkL6/XStxWauVTzmWsVjrlW8V8m1tLS0cmwJlUSVLRRf1bfffostW7bg2LFjMDQsfoT+6dOnw9/fXzWdmpoKFxcXODs7l+n1Q5nhKWVpbrmwVWjvXswcyzytxa6WUfrXbJUXy+qWWovNXKt4zLWKx1yreK+Sa7wqV/GqbKFoa2sLuVyOhIQEtfkJCQlwcHB44bY//vgjvv32Wxw+fBhNmzZ94boGBgYwMDB45fYSERERVbQqe4+ivr4+vLy81B5EKXwwxcfHp9jtvv/+e8ydOxf79++Ht7d3RTSViIiISCuqbI8iAPj7+2PYsGHw9vZGy5YtsWTJEmRkZGD48OEAgKFDh8LZ2RkLFiwAAHz33XeYNWsWNm/eDFdXV8THxwMATE1NYWpqqrXPQURERKQJVbpQHDBgAJKSkjBr1izEx8ejWbNm2L9/v+oBl7t370JH52mn688//4zc3Fz07dtXbT+zZ8/GnDlzKrLpRERERBpXpQtFABg3bhzGjRsnuezYsWNq0zExMZpvEBEREdF/RJW9R5GIiIiIXoyFIhERERFJYqFIRERERJJYKBIRERGRJBaKRERERCSJhSIRERERSWKhSERERESSWCgSERERkSQWikREREQkiYUiEREREUlioUhEREREklgoEhEREZEkFopEREREJImFIhERERFJYqFIRERERJJYKBIRERGRJBaKRERERCSJhSIRERERSWKhSERERESSWCgSERERkSQWikREREQkiYUiEREREUlioUhEREREklgoEhEREZEkFopEREREJImFIhERERFJYqFIRERERJJYKBIRERGRJBaKRERERCSJhSIRERERSWKhSERERESSWCgSERERkSQWikREREQkiYUiEREREUlioUhEREREklgoEhEREZEkFopEREREJImFIhERERFJYqFIRERERJJYKBIRERGRJBaKRERERCSJhSIRERERSWKhSERERESSWCgSERERkSQWikREREQkiYUiEREREUlioUhEREREklgoEhEREZEkFopEREREJImFIhERERFJYqFIRERERJKqfKG4cuVKuLq6wtDQEK1atcLZs2dfuP727dvRoEEDGBoaokmTJvj7778rqKVEREREFatKF4pbt26Fv78/Zs+ejdDQUHh4eMDX1xeJiYmS6586dQoDBw7EyJEjcfHiRfTu3Ru9e/fG1atXK7jlRERERJpXpQvFRYsWYdSoURg+fDgaNmyIVatWwdjYGOvWrZNcf+nSpejWrRu++OILuLu7Y+7cufD09MSKFSsquOVEREREmqer7QZoS25uLi5cuIDp06er5uno6KBLly44ffq05DanT5+Gv7+/2jxfX1/s2rWr2Dg5OTnIyclRTT958gQAkJqaWqZ2Z2WUbbvykJaZq7XYWWk5L19JQ1KztXfMdVK197ccc63iMdcqHnOt4r1KrhV+dwohyqs59BJVtlBMTk6GQqGAvb292nx7e3tERERIbhMfHy+5fnx8fLFxFixYgICAgCLzXVxcytBq7Zqq7QZoyQQs1l7w6S9fpTJirmkBc61Ked1zLS0tDRYWFq++I3qpKlsoVpTp06er9UIqlUo8evQINjY2kMlkWmzZ6yM1NRUuLi64d+8ezM3Ntd0cqsSYa1RRmGtlI4RAWloanJyctN2UKqPKFoq2traQy+VISEhQm5+QkAAHBwfJbRwcHEq1PgAYGBjAwMBAbZ6lpWXZGl3FmZub84RKFYK5RhWFuVZ67EmsWFX2YRZ9fX14eXkhODhYNU+pVCI4OBg+Pj6S2/j4+KitDwCHDh0qdn0iIiKi11mV7VEEAH9/fwwbNgze3t5o2bIllixZgoyMDAwfPhwAMHToUDg7O2PBggUAgAkTJqB9+/ZYuHAh3n33XWzZsgXnz5/H6tWrtfkxiIiIiDSiSheKAwYMQFJSEmbNmoX4+Hg0a9YM+/fvVz2wcvfuXejoPO10bdOmDTZv3owZM2bgyy+/RL169bBr1y40btxYWx+hSjAwMMDs2bOLXMInKm/MNaoozDV6XcgEnzEnIiIiIglV9h5FIiIiInoxFopEREREJImFIhERERFJYqFIKh06dMDEiRNV066urliyZMkLt5HJZC98hWFJldd+XgdlOc5U+QUFBamNsTpnzhw0a9ZMa+2hyou5RqXBQrES6NGjB7p16ya5LCQkBDKZDJcvXy71fs+dO4ePP/74VZunprgTUlxcHN5+++1yjfW8oKAgyGQyyGQy6OjowNHREQMGDMDdu3c1GpderPBnUty/OXPmvNK+S/IHyLPxzM3N0aJFC+zevbvMcem/iblGVHosFCuBkSNH4tChQ7h//36RZYGBgfD29kbTpk1LvV87OzsYGxuXRxNfysHBoUKGiTA3N0dcXBxiY2Px559/IjIyEv369dN4XCpeXFyc6t+SJUtUP6PCf5MnT66QdgQGBiIuLg7nz59H27Zt0bdvX1y5cqVCYlPFYK4RlR4LxUqge/fusLOzQ1BQkNr89PR0bN++HSNHjsTDhw8xcOBAODs7w9jYGE2aNMHvv//+wv0+f0n05s2bePPNN2FoaIiGDRvi0KFDRbaZOnUq6tevD2NjY9SuXRszZ85EXl4egIIevYCAAFy6dEn1F3Vhm5//a/zKlSvo1KkTjIyMYGNjg48//hjp6emq5X5+fujduzd+/PFHODo6wsbGBmPHjlXFKo5MJoODgwMcHR3Rpk0bjBw5EmfPnkVqaqpqnd27d8PT0xOGhoaoXbs2AgICkJ+fr1qekpKCTz75BPb29jA0NETjxo3x119/AUCZjnNV5+DgoPpnYWGh+hkV/tuyZQvc3d1haGiIBg0a4KefflJtm5ubi3HjxsHR0RGGhoaoWbOmaoB8V1dXAECfPn0gk8lU08WxtLSEg4MD6tevj7lz5yI/Px9Hjx5VLb937x769+8PS0tLWFtbo1evXoiJiVHbx7p169CoUSMYGBjA0dER48aNUy1btGgRmjRpAhMTE7i4uGDMmDFqOU2ax1wjKj0WipWArq4uhg4diqCgIDw7LOb27duhUCgwcOBAZGdnw8vLC3v37sXVq1fx8ccfY8iQITh79myJYiiVSrz33nvQ19fHmTNnsGrVKkydOrXIemZmZggKCsL169exdOlSrFmzBosXLwZQMMD5559/jkaNGqn+gh8wYECRfWRkZMDX1xdWVlY4d+4ctm/fjsOHD6udCAHg6NGjiIqKwtGjR7F+/XoEBQUVKZZfJDExETt37oRcLodcLgdQcKl+6NChmDBhAq5fv45ffvkFQUFBmDdvnuo4vP322zh58iQ2btyI69ev49tvv1Vt/6rHmdRt2rQJs2bNwrx58xAeHo758+dj5syZWL9+PQBg2bJl2LNnD7Zt24bIyEhs2rRJ9SV97tw5AE97bwqnXyY/Px+//vorgIJXfQJAXl4efH19YWZmhpCQEJw8eRKmpqbo1q0bcnNzAQA///wzxo4di48//hhXrlzBnj17ULduXdV+dXR0sGzZMly7dg3r16/HkSNHMGXKlHI5TvTqmGtExRBUKYSHhwsA4ujRo6p57dq1E4MHDy52m3fffVd8/vnnqun27duLCRMmqKZr1qwpFi9eLIQQ4sCBA0JXV1fExsaqlu/bt08AEDt37iw2xg8//CC8vLxU07NnzxYeHh5F1nt2P6tXrxZWVlYiPT1dtXzv3r1CR0dHxMfHCyGEGDZsmKhZs6bIz89XrdOvXz8xYMCAYtsSGBgoAAgTExNhbGwsAAgA4rPPPlOt07lzZzF//ny17X777Tfh6OioOg46OjoiMjKy2DjPK81xruoCAwOFhYWFarpOnTpi8+bNauvMnTtX+Pj4CCGEGD9+vOjUqZNQKpWS+3tZfj67nqGhoTAxMRE6OjoCgHB1dRUPHz4UQhTkgJubm1qcnJwcYWRkJA4cOCCEEMLJyUl89dVXJf6s27dvFzY2Nqrp5z97cb8rVD6YaxaqaeYavUiVfoVfZdKgQQO0adMG69atQ4cOHXDr1i2EhITg66+/BgAoFArMnz8f27ZtQ2xsLHJzc5GTk1PiexDDw8Ph4uICJycn1TwfH58i623duhXLli1DVFQU0tPTkZ+fD3Nz81J9lvDwcHh4eMDExEQ1r23btlAqlYiMjFS9YrFRo0aqnjwAcHR0fOl9PmZmZggNDUVeXh727duHTZs2qXoLAeDSpUs4efKk2jyFQoHs7GxkZmYiLCwM1atXR/369SX3/6rHmZ7KyMhAVFQURo4ciVGjRqnm5+fnw8LCAkDBLQhdu3aFm5sbunXrhu7du+Ott94qU7zFixejS5cuuH37NiZNmoRly5bB2toaQEFe3Lp1C2ZmZmrbZGdnIyoqComJiXjw4AE6d+5c7P4PHz6MBQsWICIiAqmpqcjPz1flFfNDu5hrRMVjoViJjBw5EuPHj8fKlSsRGBiIOnXqoH379gCAH374AUuXLsWSJUtU965MnDhRdSmjPJw+fRqDBg1CQEAAfH19YWFhgS1btmDhwoXlFuNZenp6atMymQxKpfKF2+jo6Kgu0bi7uyMqKgqjR4/Gb7/9BqDgvs6AgAC89957RbY1NDSEkZHRC/dfEce5qii8p2rNmjVo1aqV2rLCPxA8PT0RHR2Nffv24fDhw+jfvz+6dOmCP/74o9TxHBwcULduXdStWxeBgYF45513cP36dVSrVg3p6enw8vLCpk2bimxnZ2en9k54KTExMejevTtGjx6NefPmwdraGidOnMDIkSORm5vLL28tY64RFY+FYiXSv39/TJgwAZs3b8aGDRswevRoyGQyAMDJkyfRq1cvDB48GEDBvXY3btxAw4YNS7Rvd3d33Lt3D3FxcXB0dAQA/Pvvv2rrnDp1CjVr1sRXX32lmnfnzh21dfT19aFQKF4aKygoCBkZGapexZMnT0JHRwdubm4lam9JTZs2DXXq1MGkSZPg6ekJT09PREZGqt3v86ymTZvi/v37uHHjhmSv4qseZ3rK3t4eTk5OuH37NgYNGlTseubm5hgwYAAGDBiAvn37olu3bnj06BGsra2hp6f30nyT0rJlS3h5eWHevHlYunQpPD09sXXrVlSrVq3YHnJXV1cEBwejY8eORZZduHABSqUSCxcuVH3Rb9u2rdTtIs1grhEVjw+zVCKmpqYYMGAApk+fjri4OPj5+amW1atXD4cOHcKpU6cQHh6OTz75BAkJCSXed5cuXVC/fn0MGzYMly5dQkhIiFpBWBjj7t272LJlC6KiorBs2TLs3LlTbR1XV1dER0cjLCwMycnJyMnJKRJr0KBBMDQ0xLBhw3D16lUcPXoU48ePx5AhQ1SXncuLi4sL+vTpg1mzZgEAZs2ahQ0bNiAgIADXrl1DeHg4tmzZghkzZgAA2rdvjzfffBPvv/8+Dh06pOph2L9/v+oYvMpxJnUBAQFYsGABli1bhhs3buDKlSsIDAzEokWLABQ83fn7778jIiICN27cwPbt2+Hg4KAaTLjwCzU+Ph6PHz8uVeyJEyfil19+QWxsLAYNGgRbW1v06tULISEhiI6OxrFjx/DZZ5+phqWaM2cOFi5ciGXLluHmzZsIDQ3F8uXLAQB169ZFXl4eli9fjtu3b+O3337DqlWryu9A0StjrhFJY6FYyYwcORKPHz+Gr6+v2v2EM2bMgKenJ3x9fdGhQwc4ODigd+/eJd6vjo4Odu7ciaysLLRs2RIfffSR2n18ANCzZ09MmjQJ48aNQ7NmzXDq1CnMnDlTbZ33338f3bp1Q8eOHWFnZyc5dIyxsTEOHDiAR48eoUWLFujbty86d+6MFStWlO5glNCkSZOwd+9enD17Fr6+vvjrr79w8OBBtGjRAq1bt8bixYtRs2ZN1fp//vknWrRogYEDB6Jhw4aYMmWKqifhVY8zqfvoo4+wdu1aBAYGokmTJmjfvj2CgoJQq1YtAAX3nH7//ffw9vZGixYtEBMTg7///lvVk7Jw4UIcOnQILi4uaN68ealid+vWDbVq1cK8efNgbGyMf/75BzVq1MB7770Hd3d3jBw5EtnZ2apen2HDhmHJkiX46aef0KhRI3Tv3h03b94EAHh4eGDRokX47rvv0LhxY2zatEk1tAr9NzDXiKTJhHhmPBUiIiIiov/HHkUiIiIiksRCkYiIiIgksVAkIiIiIkksFImIiIhIEgtFIiqzhw8folq1aoiJidF2U6gYH3zwgcYGvdemisi9OXPmoFmzZq+8n6CgINUwOpXFqlWr0KNHD203gyoAC0UiKrN58+ahV69ecHV1LbLM19cXcrkc586dq/iGVbDMzExMnz4dderUgaGhIezs7NC+fXvs3r1b203DjBkzMG/ePDx58kTbTSlXz+deTEwMZDIZ5HI5YmNj1daNi4uDrq4uZDJZqQrLyZMnIzg4+JXbOmDAANy4cUM1XR4F6J9//in5WQvVq1cP/v7+L91PWdsyYsQIhIaGIiQkpNTb0uuFhSIRlUlmZiZ+/fVXjBw5ssiyu3fv4tSpUxg3bhzWrVun8bZo+xWJn376KXbs2IHly5cjIiIC+/fvR9++ffHw4UONxSzpZ27cuDHq1KmDjRs3aqwtFe1Fuefs7IwNGzaozVu/fj2cnZ1LHcfU1BQ2NjZlbicA5OXlwcjICNWqVXul/TyvZ8+esLGxwfr164ss++eff3Dr1i3J41Ne9PX18eGHH2LZsmUai0H/EYKIqAy2b98u7OzsJJfNmTNHfPDBByI8PFxYWFiIzMxMIYQQkZGRAoAIDw9XW3/RokWidu3aqukrV66Ibt26CRMTE1GtWjUxePBgkZSUpFrevn17MXbsWDFhwgRhY2MjOnToIIQQYuHChaJx48bC2NhYVK9eXYwePVqkpaWpxVq9erWoXr26MDIyEr179xYLFy4UFhYWauvs2rVLNG/eXBgYGIhatWqJOXPmiLy8vGKPhYWFhQgKCnrh8crOzhZTpkwR1atXF/r6+qJOnTpi7dq1quXHjh0TLVq0EPr6+sLBwUFMnTpVLWZxn/llx0oIIQICAsQbb7zxwva9TqRyLzo6WgAQM2bMEPXq1VNbVr9+fTFz5kwBQERHRwshhMjPzxcjRowQrq6uwtDQUNSvX18sWbJEbbvZs2cLDw8P1bRCoRABAQHC2dlZ6OvrCw8PD7Fv374ibdiyZYt48803hYGBgQgMDBSBgYGqHAsMDBQA1P4FBgaK4cOHi3fffVctfm5urrCzs1PLk2f5+/sX+axCCDFs2DDRqlUrIYQQd+7cET179hQmJibCzMxM9OvXT8THx7+wLUII8fjxYzFy5Ehha2srzMzMRMeOHUVYWJhanOPHjwt9fX3V7zdVTiwUiahMPvvsM9GtW7ci85VKpahZs6b466+/hBBCeHl5iQ0bNqiWe3t7ixkzZqht4+XlpZr3+PFjYWdnJ6ZPny7Cw8NFaGio6Nq1q+jYsaNq/fbt2wtTU1PxxRdfiIiICBERESGEEGLx4sXiyJEjIjo6WgQHBws3NzcxevRo1XYnTpwQOjo64ocffhCRkZFi5cqVwtraWq1Q/Oeff4S5ubkICgoSUVFR4uDBg8LV1VXMmTOn2GPh5uYm+vfvL1JTU4tdp3///sLFxUXs2LFDREVFicOHD4stW7YIIYS4f/++MDY2FmPGjBHh4eFi586dwtbWVsyePfuFn7kkx0oIIfbt2yf09fVFdnZ2se17nUjlXmGRdvbsWWFraytCQkKEEEKEhIQIOzs7cfbsWbVCMTc3V8yaNUucO3dO3L59W2zcuFEYGxuLrVu3qvb5fKG4aNEiYW5uLn7//XcREREhpkyZIvT09MSNGzfU2uDq6ir+/PNPcfv2bfHgwQO1QjEzM1N8/vnnolGjRiIuLk7ExcWJzMxMcfLkSSGXy8WDBw9U8Xbs2CFMTEyK/LFT6Nq1awKAOH78uGpeWlqaMDExEatXrxYKhUI0a9ZMvPHGG+L8+fPi33//FV5eXqJ9+/YvbIsQQnTp0kX06NFDnDt3Tty4cUN8/vnnwsbGRjx8+FAVKyMjQ+jo6IijR4+W/IdHrx0WikRUJr169RIjRowoMv/gwYPCzs5O1Ru2ePFi1RdT4XSdOnVU08/3Ms6dO1e89dZbavu8d++eACAiIyOFEAVFU/PmzV/axu3btwsbGxvV9IABA4r02gwaNEitUOzcubOYP3++2jq//fabcHR0LDbO8ePHRfXq1YWenp7w9vYWEydOFCdOnCjyGQ8dOiS5/Zdffinc3NyEUqlUzVu5cqUwNTUVCoWi2M9ckmMlhBCXLl0SAERMTEyxn+F1IpV7hUXaxYsXxcSJE8Xw4cOFEEIMHz5cTJo0SVy8eFGtUJQyduxY8f7776umny8UnZycxLx589S2adGihRgzZoxaG57vmXy2UJTab6GGDRuK7777TjXdo0cP4efnV2x7hRCidevWYtiwYarpX3/9VRgbG4vU1FRx8OBBIZfLxd27d1XLC4vLs2fPFtuWkJAQYW5uXuQPizp16ohffvlFbZ6VldVLe9Pp9cZ7FImoTLKysmBoaFhk/rp16zBgwADo6uoCAAYOHIiTJ08iKioKQMFTuDExMfj3338BAJs2bYKnpycaNGgAALh06RKOHj0KU1NT1b/CZYX7AAAvL68isQ8fPozOnTvD2dkZZmZmGDJkCB4+fIjMzEwAQGRkJFq2bKm2zfPTly5dwtdff60Wf9SoUYiLi1Pt53lvvvkmbt++jeDgYPTt2xfXrl1Du3btMHfuXABAWFgY5HI52rdvL7l9eHg4fHx8IJPJVPPatm2L9PR03L9/v9jPXNJjZWRkBADFtv91U1zuFRoxYgS2b9+O+Ph4bN++HSNGjJBcb+XKlfDy8oKdnR1MTU2xevVq3L17V3Ld1NRUPHjwAG3btlWb37ZtW4SHh6vN8/b2LuUnKvDRRx8hMDAQAJCQkIB9+/YV2/ZCI0aMwB9//IG0tDQABb9//fr1g5mZGcLDw+Hi4gIXFxfV+g0bNoSlpWWRNj/r0qVLSE9Ph42NjVpuRUdHq+UVUJBblSWvSJquthtARK8nW1tbPH78WG3eo0ePsHPnTuTl5eHnn39WzVcoFFi3bh3mzZsHBwcHdOrUCZs3b0br1q2xefNmjB49WrVueno6evToge+++65ITEdHR9X/TUxM1JbFxMSge/fuGD16NObNmwdra2ucOHECI0eORG5uLoyNjUv0udLT0xEQEID33nuvyLIXFSd6enpo164d2rVrh6lTp+Kbb77B119/jalTp6oKtVf1/Gcu6bF69OgRAMDOzq5c2qFtUrn3rCZNmqBBgwYYOHAg3N3d0bhxY4SFhamts2XLFkyePBkLFy6Ej48PzMzM8MMPP+DMmTOv3L7nf04lNXToUEybNg2nT5/GqVOnUKtWLbRr1+6F23zwwQeYNGkStm3bhjfffBMnT57EggULyhS/UHp6OhwdHXHs2LEiy54f5ufRo0eVJq9IGgtFIiqT5s2bF3mSdtOmTahevTp27dqlNv/gwYNYuHAhvv76a8jlcgwaNAhTpkzBwIEDcfv2bXzwwQeqdT09PfHnn3/C1dVV1StZEhcuXIBSqcTChQuho1NwsWTbtm1q67i5uRUZruf5aU9PT0RGRqJu3bolji2lYcOGyM/PR3Z2Npo0aQKlUonjx4+jS5cuRdZ1d3fHn3/+CSGEqlfx5MmTMDMzQ/Xq1YuNUdJjdfXqVVSvXh22trav9Jn+K6Ry73kjRozAmDFj1P5gedbJkyfRpk0bjBkzRjXv+d6yZ5mbm8PJyQknT55U6xk+efJkkV7pl9HX14dCoSgy38bGBr1790ZgYCBOnz6N4cOHv3RfZmZm6NevH9atW4eoqCjUr19fVVy6u7vj3r17uHfvnqpX8fr160hJSUHDhg2LbYunpyfi4+Ohq6srOfRVoaioKGRnZ6N58+Yl/ej0OtL2tW8iej1dvnxZ6OrqikePHqnmeXh4iKlTpxZZNyUlRejr66secElNTRVGRkbCw8NDdO7cWW3d2NhYYWdnJ/r27SvOnj0rbt26Jfbv3y/8/PxEfn6+EKLgfr0JEyaobRcWFqa6PywqKkps2LBBODs7CwDi8ePHQoinD7MsXLhQ3LhxQ6xatUrY2NgIS0tL1X72798vdHV1xZw5c8TVq1fF9evXxe+//y6++uqrYo9F+/btxapVq8T58+dFdHS02Lt3r3BzcxOdOnVSrePn5ydcXFzEzp07xe3bt8XRo0dVD04UPswyduxYER4eLnbt2iX5MMvzn7kkx0qIgqdgpe4nfV1J5d6z9ygKIUReXp5ISkpS3Sv7/D2KS5cuFebm5mL//v0iMjJSzJgxQ5ibm6vdr/f8/XuLFy8W5ubmYsuWLSIiIkJMnTpV8mGWwjYUev4exU2bNgkTExNx8eJFkZSUpHYv4MGDB4W+vr6Qy+UiNja2RMcjJCREABBWVlbi22+/Vc1XKpWiWbNmol27duLChQvizJkzag+zFNcWpVIp3njjDeHh4SEOHDggoqOjxcmTJ8WXX34pzp07p/a5nh2tgConFopEVGYtW7YUq1atEkIIcf78ebWb5J/39ttviz59+qim+/fvLwCIdevWFVn3xo0bok+fPsLS0lIYGRmJBg0aiIkTJ6oe9pAqmoQoeCrV0dFRGBkZCV9fX7Fhwwa1QlGIguFxnJ2dVcPjfPPNN8LBwUFtP/v37xdt2rQRRkZGwtzcXLRs2VKsXr262OMwf/584ePjI6ytrYWhoaGoXbu2+Oyzz0RycrJqnaysLDFp0iTh6Ogo9PX1Rd26ddU+e0mGx5H6zC87VllZWcLCwkKcPn262Pa/jp7NPSGKL9IKPV8oZmdnCz8/P2FhYSEsLS3F6NGjxbRp015YKCoUCjFnzhzh7Ows9PT0ih0e52WFYnZ2tnj//feFpaWl2pA0QjwdNeCdd94p1fFwc3Mr8tS0EC8eHudFbUlNTRXjx48XTk5OQk9PT7i4uIhBgwapPRjz1ltviQULFpSqnfT6kQkhhDZ6Mono9bd371588cUXuHr1qupy7+tm1KhRiIiIqLRvmPj555+xc+dOHDx4UNtNKVcVkXvTp09HSEgITpw4oZH9S0lPT4ezszMCAwMl75P9r7h27Ro6deqEGzduwMLCQtvNIQ3iPYpEVGbvvvsubt68idjYWLUnK//LfvzxR3Tt2hUmJibYt28f1q9fj59++knbzdIYPT09LF++XNvNKHeazD0hhOop9oq6/06pVCI5ORkLFy6EpaUlevbsWSFxyyouLg4bNmxgkVgFsEeRiKqU/v3749ixY0hLS0Pt2rUxfvx4fPrpp9puFv2HpKSkwN7eHi1atMCmTZtQs2ZNjceMiYlBrVq1UL16dQQFBaFz584aj0lUEiwUiYiIiEjS63lTERERERFpHAtFIiIiIpJU6odZFAoF8vLyNNEWIiIiItIwPT09yOXyEq1b4kJRCIH4+HikpKSUtV1ERERE9B9gaWkJBwcHtXfMSylxoVhYJFarVg3GxsYv3TERERER/bcIIZCZmYnExEQA6u+Fl1KiQlGhUKiKRBsbm1dvJRERERFphZGREQAgMTER1apVe+Fl6BI9zFJ4T6KxsXE5NI+IiIiItKmwpnvZcyeleuqZl5uJiIiIXn8lrek4PA4RERERSWKhWIFiYmIgk8kQFhb2Wu37WceOHYNMJlM9/R4UFARLS0uNxqTKZ86cOWjWrJlq2s/PD71799ZaeyojmUyGXbt2vdI+nv+5dOjQARMnTnylfQJFf/7/Na6urliyZIlqujyOJdGrKu158vnv67Iq9TiKz1t+6NUaUBrju1qWav2kpCTMmjULe/fuRUJCAqysrODh4YFZs2ahbdu2AApOADt37qwSX1LR0dH46quvcOzYMTx69Ai2trbw8vLCd999hwYNGpRpnwMGDMA777yjmp4zZw527dql8YL1RRI3j6zQeNU+/LXE676sq3/27NmYM2fOK7aobEr6u3D8+HEEBAQgLCwM2dnZcHZ2Rps2bbBmzRro6+uXKfbSpUvx7NtEO3TogGbNmql9WVekNVHTKzTeqDoLSrV+Sc5tcXFxsLKyeqV2Pf9zKS+TJ0/G+PHjVdN+fn5ISUl55WJMoVDghx9+QFBQEO7cuQMjIyPUq1cPo0aNwkcffVTm/T57LAvfyXzx4kWtFbspS1IqNJ7lRMtSre/n54f169cDAHR1dWFtbY2mTZti4MCB8PPzg44O+6heJ69cKP6Xvf/++8jNzcX69etRu3ZtJCQkIDg4GA8fPtR208osNze3TF/GeXl56Nq1K9zc3LBjxw44Ojri/v372Ldv3yv9tWFkZKR6eopeLi4uTvX/rVu3YtasWYiMjFTNMzU1LdX+ypoPZXX9+nV069YN48ePx7Jly2BkZISbN2/izz//hEKhKPN+LSwsyrGVlV9Jzm0ODg6vHKe8fy5CCCgUCpiampY610siICAAv/zyC1asWAFvb2+kpqbi/PnzePz48SvttzyOZVXTrVs3BAYGQqFQICEhAfv378eECRPwxx9/YM+ePdDVrdTlR6VSacv6lJQUhISE4LvvvkPHjh1Rs2ZNtGzZEtOnT0fPnj0BFFxeAIA+ffpAJpOppqOiotCrVy/Y29vD1NQULVq0wOHDh9X27+rqivnz52PEiBEwMzNDjRo1sHr1arV1zp49i+bNm8PQ0BDe3t64ePGi2nKFQoGRI0eiVq1aMDIygpubG5YuXaq2TmFX87x58+Dk5AQ3N7cS7ft5165dQ1RUFH766Se0bt0aNWvWRNu2bfHNN9+gdevWAJ5evt6yZQvatGkDQ0NDNG7cGMePHy92v89eeg4KCkJAQAAuXboEmUwGmUyGoKCgF7arqnFwcFD9s7CwgEwmU01nZGRg0KBBL827uXPnYujQoTA3N8fHH38MAFizZg1cXFxgbGyMPn36YNGiRUVuCdi9ezc8PT1haGiI2rVrIyAgAPn5+ar9AkV/F5538OBBODg44Pvvv0fjxo1Rp04ddOvWDWvWrFH9wVCYE7t27UK9evVgaGgIX19f3Lt3r9jj8uwlFT8/Pxw/fhxLly5V5VFMTEzpDnQlVpJzG6B+ubTwd3vbtm1o164djIyM0KJFC9y4cQPnzp2Dt7c3TE1N8fbbbyMpKUm1j5dd6vrtt9/g7e0NMzMzODg44MMPP1SNzQY8vfS1b98+eHl5wcDAACdOnFC79DxnzhysX78eu3fvVv28jx07hk6dOmHcuHFq8ZKSkqCvr4/g4GDJ9uzZswdjxoxBv379UKtWLXh4eGDkyJGYPHmyap0OHTpg3LhxGDduHCwsLGBra4uZM2e+sOf02WNZq1YtAEDz5s0hk8nQoUOHYrerygwMDODg4ABnZ2d4enriyy+/xO7du7Fv3z7V94LULVMpKSmqHACe5tCBAwfQvHlzGBkZoVOnTkhMTMS+ffvg7u4Oc3NzfPjhh8jMzFTtp0OHDhg/fjwmTpwIKysr2NvbY82aNcjIyMDw4cNhZmaGunXrYt++fQAK/oipW7cufvzxR7XPERYWBplMhlu3bkl+zsLfkfnz58Pe3h6Wlpb4+uuvkZ+fjy+++ALW1taoXr06AgMD1ba7cuUKOnXqBCMjI9jY2ODjjz9Genq6arlCoYC/vz8sLS1hY2ODKVOmFMlRpVKJBQsWqGoIDw8P/PHHH6X6OZVEpS0UC/9i3bVrF3JyciTXOXfuHAAgMDAQcXFxqun09HS88847CA4OxsWLF9GtWzf06NEDd+/eVdt+4cKFqiJtzJgxGD16tKp3KD09Hd27d0fDhg1x4cIFzJkzR+1kBRT8kKtXr47t27fj+vXrmDVrFr788kts27ZNbb3g4GBERkbi0KFD+Ouvv0q07+fZ2dlBR0cHf/zxx0t7fr744gt8/vnnuHjxInx8fNCjR48S9cIOGDAAn3/+ORo1aoS4uDjExcVhwIABL92OCpQ073788Ud4eHjg4sWLmDlzJk6ePIlPP/0UEyZMQFhYGLp27Yp58+apbRMSEoKhQ4diwoQJuH79On755RcEBQWp1ivud+F5Dg4OiIuLwz///PPCz5KZmYl58+Zhw4YNOHnyJFJSUvDBBx+U6DgsXboUPj4+GDVqlCqPXFxcSrRtVVCSc1txZs+ejRkzZiA0NBS6urr48MMPMWXKFCxduhQhISG4desWZs2aVeL95eXlYe7cubh06RJ27dqFmJgY+Pn5FVlv2rRp+PbbbxEeHo6mTZuqLZs8eTL69++Pbt26qX7ebdq0wUcffYTNmzerfcaNGzfC2dkZnTp1kmyPg4MDjhw5olbsSlm/fj10dXVx9uxZLF26FIsWLcLatWtL9JnPnj0LADh8+DDi4uKwY8eOEm1HQKdOneDh4VGmYzZnzhysWLECp06dwr1799C/f38sWbIEmzdvxt69e3Hw4EEsX75cbZv169fD1tYWZ8+exfjx4zF69Gj069cPbdq0QWhoKN566y0MGTIEmZmZkMlkGDFiRJGCLjAwEG+++Sbq1q1bbNuOHDmCBw8e4J9//sGiRYswe/ZsdO/eHVZWVjhz5gw+/fRTfPLJJ7h//z4AICMjA76+vrCyssK5c+ewfft2HD58WO0Po4ULFyIoKAjr1q3DiRMn8OjRI+zcuVMt7oIFC7BhwwasWrUK165dw6RJkzB48OAXdu6URaUtFHV1dREUFIT169fD0tISbdu2xZdffonLly+r1rGzswPw9DU2hdMeHh745JNP0LhxY9SrVw9z585FnTp1sGfPHrUY77zzDsaMGYO6deti6tSpsLW1xdGjRwEAmzdvhlKpxK+//opGjRqhe/fu+OKLL9S219PTQ0BAALy9vVGrVi0MGjQIw4cPL1IompiYYO3atWjUqBEaNWpUon0/z9nZGcuWLcOsWbNgZWWFTp06Ye7cubh9+3aRdceNG4f3338f7u7u+Pnnn2FhYYFff335fXhGRkYwNTWFrq6uqpeMl6VLrqR516lTJ3z++eeoU6cO6tSpg+XLl+Ptt9/G5MmTUb9+fYwZMwZvv/222jYBAQGYNm0ahg0bhtq1a6Nr166YO3cufvnlFwDF/y48r1+/fhg4cCDat28PR0dH9OnTBytWrEBqaqraenl5eVixYgV8fHzg5eWF9evX49SpU6ov2RexsLCAvr4+jI2NVXlU0neSVgUlObcVZ/LkyfD19YW7uzsmTJiACxcuYObMmWjbti2aN2+OkSNHqs5hJTFixAi8/fbbqF27Nlq3bo1ly5Zh3759aj0jAPD111+ja9euqFOnDqytrdWWmZqawsjISNUD5eDgAH19fbz33nsACnrCCwUFBcHPz6/Ye30XLVqEpKQkODg4oGnTpvj0009VPUbPcnFxweLFi+Hm5oZBgwZh/PjxWLx4cYk+c+Hvho2NDRwcHIp8HnqxBg0alOkKwTfffKOWp8ePH8fPP/+M5s2bo127dujbt2+R3PXw8MCMGTNQr149TJ8+HYaGhrC1tcWoUaNQr149zJo1Cw8fPlT97vj5+SEyMlJ1nsrLy8PmzZsxYsSIF7bN2toay5Ytg5ubG0aMGAE3NzdkZmbiyy+/VMXW19fHiRMnABTUB9nZ2diwYQMaN26MTp06YcWKFfjtt9+QkJAAAFiyZAmmT5+O9957D+7u7li1apXarSA5OTmYP38+1q1bB19fX9SuXRt+fn4YPHiw6rxeXiptoQgU3Mfz4MED7NmzB926dcOxY8fg6en50suh6enpmDx5Mtzd3WFpaQlTU1OEh4cX6dl59i/jwkuIhZddCv9yNjQ0VK3j4+NTJNbKlSvh5eUFOzs7mJqaYvXq1UXiNGnSRO0+tJLu+3ljx45FfHw8Nm3aBB8fH2zfvh2NGjXCoUOH1NZ7dl+6urrw9vZGeHj4S/dPr6akeeft7a02HRkZiZYtW6rNe3760qVL+Prrr1W9Uaampqoeu2cv17yMXC5HYGAg7t+/j++//x7Ozs6YP3++qhe5kK6uLlq0aKGabtCgASwtLZlH5aSs57Znz1n29vYACs4vz8579tLxy1y4cAE9evRAjRo1YGZmhvbt2wPAS3O2JAwNDTFkyBCsW7cOABAaGoqrV69K9lgWatiwIa5evYp///0XI0aMQGJiInr06FHkQZbWrVurFZs+Pj64efPmK91nSyUjhCjTmMzP566xsTFq166tNu/53H12G7lcDhsbmyL5DkC1nZOTE959911Vzv3vf/9DTk4O+vXr98K2NWrUSO0BHXt7e7U4hbGfrQ88PDxgYmKiWqdt27ZQKpWIjIzEkydPEBcXh1atWqmWF34XF7p16xYyMzPRtWtXtfP6hg0bEBUV9cL2llalLhSBgpNN165dMXPmTJw6dQp+fn6YPXv2C7eZPHkydu7cifnz5yMkJARhYWFo0qQJcnNz1dbT09NTm5bJZFAqlSVu25YtWzB58mSMHDkSBw8eRFhYGIYPH14kzrPJ9KrMzMzQo0cPzJs3D5cuXUK7du3wzTfflNv+qexKmndlyYf09HTVk8qF/65cuYKbN2+q/cFRUs7OzhgyZAhWrFiBa9euITs7G6tWrSr1fqjsynJue/acVfhl/fy8kp7DCi+fmZubY9OmTTh37pzq0lh5ncM++ugjHDp0CPfv30dgYCA6deqEmjVrvnAbHR0dtGjRAhMnTsSOHTsQFBSEX3/9FdHR0WVqA5Wv8PBw1X2ehcXVs/feFfeWkOfztCTfv1LrSP0OPLvdRx99hC1btiArKwuBgYEYMGDAS99K97I4xbXvVRT22u/du1ftvH79+vVyv0+x0heKz2vYsCEyMjJU03p6ekX+ijx58iT8/PzQp08fNGnSBA4ODqXuKnd3d8fly5eRnZ2tmvfvv/8WidOmTRuMGTMGzZs3R926dUv0l0BJ9l0SMpkMDRo0UDsez+8rPz8fFy5cgLu7e4n2qa+vz7/Ky6iseefm5lbknsLnpz09PREZGYm6desW+Vd4spb6XSgJKysrODo6quVRfn4+zp8/r5qOjIxESkoK80iDnj+3aVpERAQePnyIb7/9Fu3atUODBg1K1Rv5rOJ+3k2aNIG3tzfWrFlTokuAUho2bAgAasfmzJkzauv8+++/qFevXolucSi8usP8LL0jR47gypUreP/99wE8vYz/7NUIbQ6tBhTcUmZiYoKff/4Z+/fvL1POvYy7uzsuXbqklpMnT56Ejo4O3NzcYGFhAUdHR7U8LfwuLtSwYUMYGBjg7t27Rc7p5X1Pd6UtFB8+fIhOnTph48aNuHz5MqKjo7F9+3Z8//336NWrl2o9V1dXBAcHIz4+XjWEQr169bBjxw6EhYXh0qVL+PDDD0v9l8CHH34ImUyGUaNG4fr16/j777+LPE1Vr149nD9/HgcOHMCNGzcwc+bMYh8iKO2+nxcWFoZevXrhjz/+wPXr13Hr1i38+uuvWLdundrxAAouh+/cuRMREREYO3YsHj9+XOJfFldXV0RHRyMsLAzJycmlvtm+Kitr3o0fPx5///03Fi1ahJs3b+KXX37Bvn371C7vzJo1Cxs2bEBAQACuXbuG8PBwbNmyBTNmzFCtI/W78LxffvkFo0ePxsGDBxEVFYVr165h6tSpuHbtGnr06KFaT09PD+PHj8eZM2dw4cIF+Pn5oXXr1kUuiRfH1dUVZ86cQUxMDJKTk8v1L/HXXUnPbZpWo0YN6OvrY/ny5bh9+zb27NmDuXPnlmlfrq6uuHz5MiIjI5GcnKzWq/TRRx/h22+/hRACffr0eeF++vbti8WLF+PMmTO4c+cOjh07hrFjx6J+/fpqY8XevXsX/v7+iIyMxO+//47ly5djwoQJJWprtWrVYGRkhP379yMhIQFPnjwp02eu7HJychAfH4/Y2FiEhoZi/vz56NWrF7p3746hQ4cCKLivvXXr1qoHnY4fP652TtIGuVwOPz8/TJ8+HfXq1SvRbV2lNWjQIBgaGmLYsGG4evUqjh49ivHjx2PIkCGqy+ETJkzAt99+i127diEiIgJjxoxRG8rOzMwMkydPxqRJk7B+/XpERUUhNDQUy5cvV41hWW5ECWRlZYnr16+LrKyskqz+n5CdnS2mTZsmPD09hYWFhTA2NhZubm5ixowZIjMzU7Xenj17RN26dYWurq6oWbOmEEKI6Oho0bFjR2FkZCRcXFzEihUrRPv27cWECRNU29WsWVMsXrxYLaaHh4eYPXu2avr06dPCw8ND6Ovri2bNmok///xTABAXL15UtdHPz09YWFgIS0tLMXr0aDFt2jTh4eGh2sewYcNEr169iny+l+37eUlJSeKzzz4TjRs3FqampsLMzEw0adJE/Pjjj0KhUKg+NwCxefNm0bJlS6Gvry8aNmwojhw5otrP0aNHBQDx+PFjIYQQgYGBwsLCQu24v//++8LS0lIAEIGBgZLtoaLHrqx5J4QQq1evFs7OzsLIyEj07t1bfPPNN8LBwUFtnf3794s2bdoIIyMjYW5uLlq2bClWr16tWi71u/C80NBQMXjwYFGrVi1hYGAgbGxsxJtvvin27NlT5HP9+eefonbt2sLAwEB06dJF3LlzR7XO7NmzX5jnkZGRonXr1sLIyEgAENHR0S88llVJSc9tAMTOnTuFEE9/t589Pzz/uyxE0Zx8/ufyfD5u3rxZuLq6CgMDA+Hj4yP27NmjFkcqhhBFf/6JiYmia9euwtTUVAAQR48eVS1LS0sTxsbGYsyYMS89NqtXrxYdO3YUdnZ2Ql9fX9SoUUP4+fmJmJgYtc8wZswY8emnnwpzc3NhZWUlvvzyS6FUKlXrPP979uyxFEKINWvWCBcXF6GjoyPat2//0nZVNcOGDRMABAChq6sr7OzsRJcuXcS6detU3zeFrl+/Lnx8fISRkZFo1qyZOHjwoFoOlCRPhSiaU8/nqhDS58/nf7ZCCBEVFSUAiO+//75En/X57+iSxL58+bLo2LGjMDQ0FNbW1mLUqFEiLS1NtTwvL09MmDBBmJubC0tLS+Hv7y+GDh2qFkupVIolS5YINzc3oaenJ+zs7ISvr684fvy4EKL4379CJa3tZEK8fNj97OxsREdHo1atWmW6n4leD/+FNw5Q+Rg1ahQiIiIQEhJS4bGDgoIwceLEV35tFBFQcF6qU6cOzp07B09Pz1fen7bf+kP/fSEhIejcuTPu3bun6uGrjEpa23FodKJK4Mcff0TXrl1hYmKCffv2Yf369fjpp5+03SyiMsvLy8PDhw8xY8YMtG7dulyKRKIXycnJQVJSEubMmYN+/fpV6iKxNCrtPYpEVcnZs2fRtWtXNGnSBKtWrcKyZcte6d22RNp28uRJODo64ty5c3yinirE77//jpo1ayIlJQXff/+9tpvzn8FLz0RERERVTElrO/YoEhEREZGkUhWKJeh8JCIiIqL/uJLWdCUqFAtHGC/Nq76IiIiI6L+psKZ7/i0yzyvRU89yuRyWlpaqUfeNjY3L9K5GIiIiItIeIQQyMzORmJgIS0vLl76RqEQPsxTuOD4+nmOjEREREb3mLC0t4eDg8NKOvxIXioUUCkWxL+0mIiIiov82PT29Er3bHChDoUhEREREVQOHxyEiIiIiSSwUiYiIiEgSC0UiIiIiksRCkYiIiIgksVAkIiIiIkksFImIiIhIEgtFIiIiIpL0f/16WApPeMXIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric            |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| XGBoost      |      0 | random       | Validation Recall | 0.8875   | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation Recall | 0.925    | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation Recall | 0.860759 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation Recall | 0.924051 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation Recall | 0.924051 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation Recall | 0.465116 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation Recall | 0.835821 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation Recall | 0.303571 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation Recall | 0.511811 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation Recall | 0.963636 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation Recall | 0.866667 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation Recall | 0.878505 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation Recall | 0.878788 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation Recall | 0.833333 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation Recall | 0.686747 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |    nan | random       | Test Recall       | 0.875    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Recall       | 0.925    | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Recall       | 0.9      | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall       | 0.304348 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall       | 0.456522 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall       | 0.326087 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall       | 0.540541 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall       | 0.567568 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall       | 0.486486 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Recall       | 0.85     | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Recall       | 0.282609 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Recall       | 0.432432 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Recall | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Recall | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Recall | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)   |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Recall       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)   |          |                 |                  |              |\n",
      "Metric: Precision\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric               |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:---------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| XGBoost      |      0 | random       | Validation Precision | 0.816092 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation Precision | 0.860465 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation Precision | 0.894737 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation Precision | 0.858824 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation Precision | 0.858824 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation Precision | 0.588235 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation Precision | 0.811594 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation Precision | 0.62963  | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation Precision | 0.607477 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation Precision | 0.791045 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation Precision | 0.8125   | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation Precision | 0.895238 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation Precision | 0.773333 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation Precision | 0.744681 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation Precision | 0.730769 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.7      | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.711538 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.692308 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.482759 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.567568 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.441176 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.833333 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.84     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.72     | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Precision       | 0.723404 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Precision       | 0.481481 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Precision       | 0.842105 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Precision | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)      |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Precision       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)      |          |                 |                  |              |\n",
      "Metric: ROC AUC\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric             |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:-------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| XGBoost      |      0 | random       | Validation ROC AUC | 0.917167 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation ROC AUC | 0.959291 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation ROC AUC | 0.917975 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation ROC AUC | 0.937384 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation ROC AUC | 0.939241 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation ROC AUC | 0.727944 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation ROC AUC | 0.857252 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation ROC AUC | 0.63211  | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation ROC AUC | 0.622604 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation ROC AUC | 0.80303  | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation ROC AUC | 0.875    | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation ROC AUC | 0.907403 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation ROC AUC | 0.926599 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation ROC AUC | 0.888095 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation ROC AUC | 0.775226 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC       | 0.879348 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC       | 0.884239 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC       | 0.882065 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC       | 0.499443 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC       | 0.486065 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC       | 0.5      | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC       | 0.835586 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC       | 0.827703 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC       | 0.759572 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test ROC AUC       | 0.880978 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test ROC AUC       | 0.487179 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test ROC AUC       | 0.831081 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation ROC AUC | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)    |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test ROC AUC       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)    |          |                 |                  |              |\n",
      "Metric: F1 Score\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric              |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:--------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| XGBoost      |      0 | random       | Validation F1 Score | 0.850299 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation F1 Score | 0.891566 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation F1 Score | 0.877419 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation F1 Score | 0.890244 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation F1 Score | 0.890244 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation F1 Score | 0.519481 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation F1 Score | 0.823529 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation F1 Score | 0.409639 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation F1 Score | 0.555556 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation F1 Score | 0.868852 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation F1 Score | 0.83871  | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation F1 Score | 0.886792 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation F1 Score | 0.822695 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation F1 Score | 0.786517 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation F1 Score | 0.708075 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |    nan | random       | Test F1 Score       | 0.777778 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test F1 Score       | 0.804348 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test F1 Score       | 0.782609 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score       | 0.373333 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score       | 0.506024 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score       | 0.375    | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score       | 0.655738 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score       | 0.677419 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score       | 0.580645 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test F1 Score       | 0.781609 | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test F1 Score       | 0.356164 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test F1 Score       | 0.571429 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation F1 Score | 0.5      | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation F1 Score | 0.5      | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation F1 Score | 0.5      | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test F1 Score       | 0.5      | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "Metric: Accuracy\n"
     ]
    },
    {
     "data": {
      "image/png": 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JBw8elFn/3LlzMXjw4BLbHiIiIqKSVioLxdevX8PPzw/q6uoIDQ3FzZs3MX/+fBgbG0vnmTNnDpYsWYJVq1bh7Nmz0NXVRWBgINLS0gAAa9aswcWLF3H69Gn07dsX33zzDYQQAICoqCisXbsWM2bMUMr2EREREZUENWU3QBFmz54Ne3t7BAcHS8c5OjpK/18IgUWLFmH8+PFo06YNAGDTpk2wtLTErl270KVLF0REROCrr76Cu7s7KlSogNGjR+Ply5cwNzdH//79MXv2bBgYGJT4thERERGVlFJ5RnH37t3w8fFBx44dYWFhAW9vb6xdu1Y6PSoqCrGxsWjcuLF0nKGhIWrVqoXTp08DADw9PXHixAmkpqZi3759sLa2hpmZGTZv3gwtLS20a9euQG1JT09HYmKizB8RERHRl6BUnlG8f/8+Vq5ciREjRuCnn37C+fPnMWTIEGhoaKBHjx6IjY0FAFhaWsosZ2lpKZ3Wq1cvXL16FW5ubjAzM8PWrVvx+vVrTJw4EWFhYRg/fjy2bNkCJycnrF+/Hra2tnLbMmvWLEyZMiXf+CdPnhSpaNR5m1LoZYrLS1X52/gxKapvAURLh+NUbZCqWrjU04xX3tnbx48fKy22Mikz1x4/TlJabM14U6XFZq6VPObal+XNmzfKbkKZUyoLxezsbPj4+GDmzJkAAG9vb1y/fh2rVq1Cjx49CrQOdXV1LF++XGZcz549MWTIEFy+fBm7du3ClStXMGfOHAwZMgTbt2+Xu55x48ZhxIgR0uHExETY29vD1ta2SJeuUyLiC71McTHLelKk5ZKzsmSGTbOeQjdLtVDrSDfKLFLs4mBnZ6e02MqkzFyzszNSWuz09DilxWaulTzm2peFV+VKXqm89GxtbQ03NzeZca6urnj06BEAwMrKCgDw7NkzmXmePXsmnZbXkSNHcOPGDQwaNAhhYWFo0aIFdHV10alTJ4SFhb23LZqamjAwMJD5IyIiIvoSlMpC0c/PD5GRkTLjbt++jfLlywPIubHFysoKhw4dkk5PTEzE2bNn4evrm299aWlpGDhwIFavXg1VVVVkZWUhMzPnDFdmZiay8pwxIyIiIioNSmWhOHz4cJw5cwYzZ87E3bt3ERISgjVr1mDgwIEAAIlEgmHDhmH69OnYvXs3rl27hu+++w42NjZo27ZtvvVNmzYNLVq0gLe3N4CcQnTHjh24evUqli1bBj8/v5LcPCIiIqISUSr7KNaoUQM7d+7EuHHjMHXqVDg6OmLRokXo1q2bdJ4xY8YgOTkZffv2RXx8POrWrYu9e/dCS0tLZl3Xr1/H1q1bER4eLh3XoUMHhIWFwd/fHy4uLggJCSmpTSMiIiIqMaWyUASAVq1aoVWrVu+dLpFIMHXqVEydOvWD6/Hw8JB5YwsAqKioYMWKFVixYkWxtJWIiIjoc1QqLz0TERER0adjoUhEREREcrFQJCIiIiK5Sm0fRSKiskIIgeTkZOmwrq4uJBKJEltEpRVzrexhoUhE9IVLTk5GmzZtpMN//fUX9PT0lNgiKq2Ya2UPLz0TERERkVwsFImIiIhILhaKRERERCQXC0UiIiIikouFIhERERHJxbueiYg+E/GL4ou0XHJmssxwwsoEvFV/W6h1GA0zKlJs+jIx16igeEaRiIiIiORioUhEREREcrFQJCIiIiK52EeRFE5HQwW/dLeXGS4L+KorIiL60rFQJIWTSCTQ1VRVdjNKHF91RUREXzoWikREXzgdNR1sarZJZphIEZhrZQ8LRSKiL5xEIoGuuq6ym0FlAHOt7GGhSPQRfN7Yl4P9QomIihcLRSIqNdgvlIioeJWN20+JiIiIqNBYKBIRERGRXLz0TKQgvDuQiIi+dCwUiRSEdwcSEdGXjpeeiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5FJTdgOIiPJ6HtK7SMslp2fJDL/YNggpmqqFW0ktiyLFJiIqjXhGkYiIiIjkYqFIRERERHLx0jMREZVZRe3mUCzYzYG+ADyjSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrnKRKH4888/QyKRYNiwYdJxaWlpGDhwIExNTaGnp4f27dvj2bNn0umvXr1C69atoaenB29vb1y+fFlmnQMHDsT8+fNLahOIiIiISlypLxTPnz+P1atXo2rVqjLjhw8fjr///hvbtm3D0aNH8fTpU3z99dfS6TNmzMCbN29w6dIlNGjQAH369JFOO3PmDM6ePStTeBIRERGVNqW6UExKSkK3bt2wdu1aGBsbS8cnJCRg3bp1WLBgAQICAlC9enUEBwfj1KlTOHPmDAAgIiICXbp0QaVKldC3b19EREQAADIzM9GvXz+sWrUKqqqqStkuIiIiopJQqgvFgQMHomXLlmjcuLHM+IsXLyIzM1NmfOXKlVGuXDmcPn0aAODp6YnDhw/j7du32Ldvn/SM5Jw5c9CgQQP4+PgUqA3p6elITEyU+SMiIiL6EqgpuwGKsmXLFly6dAnnz5/PNy02NhYaGhowMjKSGW9paYnY2FgAwI8//oj+/fvDyckJDg4OWLduHe7cuYONGzfi9OnT6NevH/bv3w8fHx+sXbsWhoaGctsxa9YsTJkyJd/4J0+eFKlo1HmbUuhlistLVVulxdaMN1Ba7Oe6z5UWO+lxktJif4m5JrQFZvewkQ6naKgiVSIp1DqYayXvS8y14sBcK7w3b94UY0uoIEploRgdHY2hQ4fiwIED0NLSKtI6DA0NERISIjMuICAAc+fOxebNm3H//n1ERkaiT58+mDp16ntvbBk3bhxGjBghHU5MTIS9vT1sbW1hYFD4nURKRHyhlykuZllPlBY73ShTabEtki2UFtvIzkhpsb/YXHt3r5Zd+MWZayXvi821T8RcKzxelSt5pfLS88WLF/H8+XNUq1YNampqUFNTw9GjR7FkyRKoqanB0tISGRkZiI+Pl1nu2bNnsLKykrvO4OBgGBkZoU2bNggLC0Pbtm2hrq6Ojh07Iiws7L1t0dTUhIGBgcwfERER0ZegVJ5RbNSoEa5duyYzrmfPnqhcuTLGjh0Le3t7qKur49ChQ2jfvj0AIDIyEo8ePYKvr2++9b148QJTp07FiRMnAABZWVnIzMw5EszMzERWVpaCt4iIiIio5JXKQlFfXx8eHh4y43R1dWFqaiod37t3b4wYMQImJiYwMDDA4MGD4evri9q1a+db37BhwzBy5EjY2ub0ZfHz88Ovv/6Kpk2bYs2aNfDz81P8RhERERGVsFJZKBbEwoULoaKigvbt2yM9PR2BgYFYsWJFvvn27duHu3fv4tdff5WOGzRoEC5cuIBatWqhZs2amDRpUkk2nYiIiKhElJlCMW8/Qi0tLSxfvhzLly//4HKBgYEIDAyUGaejo4OtW7cWdxOJiIiIPiul8mYWIiIiIvp0LBSJiIiISC4WikREREQkFwtFIiIiIpKLhWIZk5GeivTUZKXETs14i+Q05b2JgMoO5hoRUfEoM3c9l3UvYx7g302z8Dz6DiCRwMyqPJp9OxZW5VwUHvvB80TM/fMy7sYkAADKW+hj1NfeqGRrpPDYAJD1NhtxTxIghICpjSHUNFRLJC6VPGXnGilHRnoqRHY2NLV1Syxm3Js0rNhzDVeiXiJbAO7lTDCgpQesTUqmDdyvUUlhoVhG7P99AbzrtUXl6g2R9TYTFw//iX83/Yxe44MVHnvxX1fxVW1H1PewwdssgR2n7mHu9ktYOyRA4bEf3YjFzvnHkJ2VjeysbKioquCroXXhVM1O4bHjkuKw5MgSXIm+gmyRDQ8bDwxqOAg2RjYKj61MSQlxOLRtCaLvXIHIzoZtBQ8EdBwEIzPFb7cyc+3NqxTs/+UsHl6PhcgWsKtsgabf14Kxlb7CY5fVXFPmAfD8HeGobG+Ebxu54O1bgd1nozBr60Us6VdP4bG5X6OSxEvPpdTO1ePx5vUL6XBqUgKcq9aBuoYWtHT04eheCylvXisk9qTfzuJFQqp0ODE5A76VraCloQY9bXXUqGSJ10npComdnS1khg+sO4+2I+ph+MYuGPnbN6j/jTdCV51RSOy85uyfA0dTRyzqtAjzO86Hsa4xpv87vURiK9Pe3+bAzNoRXYYtQqch86Gjb4w9wYrZbmXmWl7/LDsJ83LG+HZ6c3SbGghdI23sWnC0RGKX1VzLPQAeuuBfDJq9CxU9/fHvpp8VEmvFP9eQmvFWOvz0VRI6+1eEg4UBnG0M0c63AqJfJikkNvdrpEwsFEsptxqNsXXpSFw8sh1CCHjXb4vgGb3x9/qp+GvtRPy5YiyqN2ivkNgBXnYYG3wKO0/fhxACX9V2QN+lRzDjjwuYGnIeP208jXZ1nBQSe8OYPYi5FycdzsrKhoHZf5eCDM11kZWpmHdzLz2yFKkZ/xUtT+KfoGvNrnAwc0BFi4po790e0a+jFRJbmQ5tW4qM9P+2O/7FE9Rs0hVm1g6wtK+I6g3b49VzxWy3MnNt/y9nkfFOP8hXMYnwbecB83JGsKpgipqt3BD3JFEhsctqrinzANjMQAuDVhzD6YhYAECDKrYYvOoYftl3E6tCr2Pi5rNo5KmYM3rcr5Ey8dJzKeVSrQEcXH1wdNcabJ43EE26DEfHgXMQfScc2dlZqNmkK6wdXBUSu76HLXycLfDLvpsYuvo4hnzliVk9zHAl6iWysgU61XNGZTtjhcQO7Fsb/y4/hXLulqjfzRv+nT2xbuTfMLU1QPZbgZdPEhDYp5ZCYpvrmaPf5n7oW68v/Jz80NClIfqH9Edth9p4m/0Wx+8eR+PKjRUSW5n0jczx6+x+qN+2L5yr+sGlekNsntsfju61kZ31FneuHIdbDcVstzJzTd9UB+tH7kFAj+qoVLMc3Oo6InjMP3Cuboust9mIPPMIHvUrKCR2Wc213ANgL/82qNbga+kBsL1zVWRnvcXD25dRI6CTQmJ38q8If3cbLP37GvZffoQBLavAxdYYVx68hMgW+D7QDfXcFXP5lfs1UiaJEEJ8fDYqLomJiTA0NERCQgIMDAwKvfzSA/GFXubxvWs4uGUhylf2Qd3WvaCuoVXodQBA5xcjC73M9QdxWPL3VVRzNkdQo8rQ0ijasclftSwKPG92VjZO77yOa0fuIaBHddhWMseT2y8gsgWsK5rBwLRwnc07/j22wPM+jX+KxYcXQ0NNA0MaDsHt57dl+vLUr1QfEomkwOszGmZUqLYWp8LkWvzLpzj4x2KoqWugUccheBZ9O6ePosjpo1jJu3Db/aXk2uvYN9i35gzUNFTRtE8txN6L+6+PoqslXOuUL9R2M9c+Lj01CUd3rcHzx3fRpMtwqKioSg+AbSt4FPoAuCi5dig8GpsOR6KdbwW0qe1YqM/5XdyvFd6n/oZS4bFQLGElWSimJicgIS4Wxua2UNPQwpm9v+HWxcNo2H4AKrjXLnTswuxQE1MyEPs6BbamutBUV0XI0dsIu/oE/Zp7oKaLZaFjF2aHmut1TCJCV52Bho46Ar+vBX1TnUKvAyjcDjXXgYgD2HBqA9pXa492Xu2K/EPypfx457p57gBO/rMB1Ru2h3f9om/3l5Zr147ew7Hfw1GztRt8WlQu8nYz1wpOWQfAiSkZMNDRwJuUDKzZewMPn7/BsLaeqGBlWOjY3K8VHgvFksdCsYSVVKF48/xB7A+ZBw0tXbzNTEeL78bBuaof4mIf4cCWBdDRN0KjjkOga2BS4NgF3aEevvIYC3eFQ0dTHRlvszCmfTX4ulrh0Ys3WLL7Kgx1NTCwZRWY6Bd8x16YHerzh6/x6mkCzMsZw9TWEFcP38XxrVdQ6yt3+LSoXOD15CrsDjUhNQGG2oZITEvEqqOr8CDuAUY2GQkn88L3lfuSfrxTkxOgrWuI1OREhO1chbiYB2j6zUhY2BZ+u7+UXAOAlDdp0NHXQuqbdBzacB4vouPRYkAdWDoU/LuVi7n2cco6AL507wV+3noRCSkZMNXXwv+6+MC9nAnC77/E8j1XUbOSJb5rVBma6gV/TA33a4XHQrHksY9iKXV891oEdhsDV58AxD6KxN7f5sC5qh9Mrcqhy7BFuHJiDzbPH4S+U0KKPfa6/REY2c4bDara4vaTeMzfeRm+rlYoZ66Peb398O/5Bxi25gQ2jSz+fi1n/7qBsJDLsChvhNcxb9Dw22rwbuoCZx87HFx/HhuO3kOL/nVg4VD8/dYuPryIGf/OQHxqPEz1TDGp1SSMCRyDy48uY/o/01G7Qm0E1QmCpppmscdWpoe3LmLPhhlITYqHnqEpWveehObdx+DR7cvYEzwdTu61UadlENQ1in+7lZlrUVeeYteCY0hJTIO+iQ6+Ht0ArQbXxYNrMdg1/xicfexQr4sX1DWLfzdbVnNN3gGwX8sgVK4egANbFuD6mb2FPgAuqOV/X0Mnf2e0ruWIC3eeY/W/17GkXz14VTDD8gH1sfnIbfRfHob1wxoVe2zu10iZeNdzKZWZngYTS3sAgJGZDd5myD4ixLNuK3QbuVwhsdMy38LOTA8AYGOii/Q8d+O1qOGAxT/4KyT26Z3X0Xl8I/Sc0wq95rfG2b9uAgB0DLTw1TB/1OvqjR1zwxQSe8nhJehcozNCh4RiaMBQrAhbAQDwLueN1d1XQ1VFFX1+7aOQ2Mp0cOsS1GzcGUMXhKJRp6E4sj1nu8tV8sZ3Y1dDRVUVm35WzHYrM9f2rTkL33YeGLOlOwL71MKB9ecAAA5VrNF7fmuoqqrglxG7FRK7rOZa7gHwgFnb0WnIfJzYsx4ApAfA5V18sHn+IIXEfpWUhpoultBUV4VPRQvEJ2dIp2moqaJnE1dM7FpDIbG5XyNl4hnFUsq9ViC2rxyHchU9EfvoNtxqNsk3j66BYu4GbeJtj/G/nkFVR1PceZKARp72+eYx1lPM0acQQtpvRkUlf/+ZCl426L2gtUJixyXHwbeCLzTVNFHDoQZWHF0hnaahpoHv636PRpWL/2yDsiUnxqGChy/UNTTh4FoDR3b8t91q6hrw/+p7VPZRzHYrM9eSXqfA2ccO6ppqqOBti4PB56XT1DRU0aB7Nbj5OyokdlnNtYIcADtX9VNI7NqVrTDt9wvwrWyJ6w9foWal/JeNHSwVcymU+zVSJhaKpVTD9gNgX9ETr55Fw712Mzi6KuZIV55+zT3g6WCG6Jdv0NS7HHwqFr7DdlHVbuuBLdMOwtLRGK+eJqJBt2r55lHEpUAAqONUB5P/ngxfJ19cf3IdtRzyP67C0UwxhYMyOVWpg93rJsOpii+e3LuOCu75t9vcRjHbrcxcq1jTHjvmhKFiDXtERzyHU/X8z9CzKK+Yg7GymmvKPAAe0dYL/5x/gOiXSWjkZYdm1cspJI483K+RMrFQLMUUdWRdEL6uVvCFVcnHbeeBCt42iHuSCIvyRjCzMyqx2KObjsbfV//Go1eP0Ni1MVp4tCix2MrUrNtoXDnxN+KePYJbjcao4luy262sXGs50A+X999G3OMEeNSvAK/GFUssdlnNNWUeAKurqaCtr2Kei/kx3K+RMrFQLMXiYh7i6YMbsHH0gKlVOcTFPsLFI38iK+st3Go0RnmX/EelipCa8RZHrz1FzKtkmOhromFVOxjoaCgsnqWDSZHuOP1U6qrq+Nr76xKPq2yqauqo1kB5252emYUjV5/gxsM4xL1Jh0SS01/R19UK1ZzMFRZXTV0VNVoq5qH1H1NWcw1Q3gHwnyfuwt/dBpbGRXsczafifo2UhYViKXX/xlnsWjMBGprayMxIQ5s+UxG66WeY2zlBiGz8uWwMOgyao5Bi8fvFh7GgT10Y6GjgeUIqRv5yAkmpmbAz08PTV8nYfOQ2Fv/gD2uTwj0gtqAu/BOBp3dewqm6Hdz9HXEt7B5O/nkVEIBL7XKo/403VFQVcx9XZlYmTtw9gZsxN/Eq+RUAwETXBO7W7vBz9oO6qrpC4n5OhBCIvhOO18+fQM/QBA5uNaGqqphdzZO4JIwNPo2MzCyoq6niZWIqalSyQOTj1/j7XBT83KzxU6fqUFUp/n/vM7uuo3IdBxhZ6BX7uj/mjwt/oH7F+rAyLPkzqcqWmZGOWxcP4fG960hOiINEIoGRmQ2cq/qhfOXqCou7dt9N/LL/JrwczdCsenn4uVlDXa3k7gdV1n6tLOca5WChWEqd3vsrajTuDP/WvRFx4TD+2TADXv5fwf+r7wEAx/5ag7P7QxRSKEa/TELW/7/Efv3+mzDV18KqgQ2gq6WOlPS3mBpyDsEHI/BTJ59ij31i6xWc3nkdFbxtcHD9OSS8SMKZXddRs7UbJBIJzv19EypqKqjf1bvYYz9+/Rhjd4zFy6SXcLV2hbFOTl+pO8/vYPeV3TDXN8fP7X6GnbFi3gerLNtX/IhWPcdDU1sPqckJ2L5iHGIf3sp5pmJKIozN7dB1+GLo6BsVe+wV/1xHjYoWGPJVVUgkEvxx7A6uPojDkn718PhlEsZtPI3NYbfxXUDhnzP3MYc2XsDhXy/CwcMano0rwqV2OagV4hl6n2L1sdVYe3wtvOy90MKjBfwr+peJg5DXzx9j69JReJuZATV1dbx5/QKO7rUQ8/AWwo/vRkWvumgVNAEqqor5dxje1gunbsZg9p+XoKulhkaedmjuU15hN7HkUuZ+razmGv2HhWIpFRfzAC2+/RFAznuf/900C5W860unu9Zogutn9im8HRHRrzHkq6rQ1crZsehoquHbABfM3HpJIfGuHL6L1kP8UNnXAc+iXmHdqL/RekhdVKmf81BYU1tDHN50QSE71EWHFsHRzBFruq+Brqbs2dLk9GTM2jsLiw8vxtz2c4s9tjJF3TyHt5mZ0NQGTvy9Hhnpqfh+8m8wMrNB4uvn2LVmAk7sWY+mXUcUe+yrUS+xcmAD6R2hX9dxwoaDt5CYkgE7Mz30b+GBlf9eV0ihCAAtB9TB7bOPsHvRcWjqqsOjXgV4NamksJtY3jWyyUicvHcSs/bOwpLDS9DYtTFaVmlZqm8sOPznMji61USTLsMhkUhwdv/veHz3CrqPXoFXz6Px57IxOL33V/i1DFJI/JqVLBFYrRxeJ6XjwOVH2HcxGrvORKGSrRGaVy+H+lVspfu64qTM/RpQNnON/sNCsTSTPk5BBWpq6tDU/q940dDURnpqkqJDIyMzC6Z53ophZqCNhOR0OUt9uqTXqbB2NgMAWDqaQAKJTL8eKydTvHmVqpDY159ex8pvVuYrEgFAV1MXver0woDfBygk9ufi0e1w1G/bF0ZmNgAAA2ML1G/TF/t+n6+QeHpa6kjNeCsdTs/MQrYQUP3/R4g4Whrg1RvF5BoAOFe3g2ejikiOT8XVI3dx5eBdXPj3FqydTOHVpCLc6jpCU0H9cWs71kZzj+Z4nfwa+27uQ+j1UOwM34lKFpXQskpLNHRpKDcXv2TRd8Lx3bi10gMDn4AOOLFnPVKTE2BiYY+GHQbhyJ/LFFYo5jLW00Qn/4ro5F8R1x7EYe/Fh1gVegOrQm9g98SWxR5Pmfs1oGzmGv2HD9wupQxMLPH6+WPp8DejlsPA+L/33ia+fg5dQ1OFxR+z/hQGrDiKlPS3iH4pW5A+i09R2M0sekZaeBkdDwCIe5oAIQRePk6QTn8ZHQ9dw6K9E/ajsTX1EJMQ897pMYkx0NMs+f5sJSH3hzst5Q0M/79IzGVkboukhJcKiVvN2RyrQ2/g0Ys3iHmVjMW7r8DJ2lB6Vud5QiqMdBX/xghdI234tquCfsvbofu0QJjaGeLA+vNY3GurwmMb6xqjS40u2NhzIxZ2XIjypuWxPGw5OqzuoPDYJU1TWw+Zaf8VRJkZaRDZWVBRyTnnYW7jiKTEuBJtUxUHU4xuXw2/j2mKfs3dFRJDmfu1d5WlXKP/8IxiKeXl3wZCZEuH8z7HLurmWZSrqJjLFN0aVnpnyBJaGrJpdibyGTwUdPeee70K2L34BCrVtMeDqzGo3dYDhzacR+qbNEgkEpz88yoq+5ZXSOwWHi3w896f8W3tb1GtXDVpH8XXKa9x6dEl/Hb2N7TzaqeQ2MoW+uvPUFVTR3bWWyTGxcjkW3LiK2hp6ysk7veB7pi8+Rz6LDkCADA31Mbkb2pKpyckZ6Bj3cK/i7YgcovjvMq5W6GcuxUC+9TCzRNRJRq7ql1VVLWrisENB+NI5BGFxFam8pV9cGTHCjTpMgKqamo4vvsXWNg5S6+WJL56Dl19xV/2l0dXSx0tajgoZN3K3K+V1Vyj/7BQLKW8/L/64PR6XynulUsf6w/Wt5lijroBoF5Xb6hpqOJJ5At4NamEOu2rwNLRBIc3XUBm+ltU9LFH/W8UUyD38usFbXVt/HHhD6w8ulK6gxVCwETXBF1rdEWXGl0UEluZ3GsFSv/fuaofMvO8LeNO+HFY2CmmWDPW08TiH/zx+GUS3mZlw95cT+YO53oeNh9Y+tMIIT44XVNHA95NXZQSW1dTF62qtlJIbGWq3/YH7FozHsHTgwCJBPpG5mjbd5p0empyAmo06qyQ2PumfXifqkjK3K+V1Vyj/7BQpFJFRUWCuh09Zca5+zvCXUGvUsura82u6FqzK57GP8WrlP9/PI6OCWyMFFewKFvzb8d+cLpvi+8gkSi2l0vu+55L0v92BpV4zFyHRxxWWmxl0jUwRrdRy/HqeTSy3r6FqWU5mTucXd65Ya80UeZ+razmGv2HhWIZdfnYLqQmJaBOix4lHnv32SgkJGfg2wDFnG35HNgY2ZTq4rAwNDS1lRb7VEQMktPeool3/ndA05fLxOLz+/dkrlFpxZtZyqjb4cdw/aziH48jz4kbMThwOVopsS/8ewvHt4QrJfbJuyex74ZyPnNlunP1JG4oKdfW7YvAvB2XlRI78uwjXD1yVymxmWslT5m5xv0aKRLPKJZRnYcsUFrsOb3qKC125JmHiH/2Bv5dvEo89prja/A4/jEC3QM/PnMpcmzXGrx+8VimL2NJWTcsoMRj5jqy6SJexSSiakPnEo/NXCtbucb9GikSC0UqU7pNVd7ObGPPjUqLrUy9J5bN7e63XHl3uDPXyhbu10iReOm5lDp/8A8kxMUqJfafJ+7i2esUpcQminmVjIt3n+PBs0RlN4XKgNHrTnJ/R6UazyiWUkd3rcaxv9bCvpIXqvi2QCUvf6iqlcz7Odfuu4lf9t+El6MZmlUvDz83a6irlewxyYOrMYi++QxJr1MhUZHAyFIPFWvaw9TGUOGxs7OzoaKSf3uzs7PxIukFLA0s5Sz15bp9+Rgc3WtCXUPxD/zNa8nuK/g+0B06mmpIz8zCnD8v4cTN/x56XtXBFFO614KOpmJ2dUIIxD9LgqG5LlRUVfA2MwuRZx4h620WnKvbQcegZD+T4VuHY2zgWFgZWpVo3JKizFw7HSH/wPvawziciYyFhaEOAMDXteQ/+9Q36bhzIbpEuzmU9lyj/7BQLMWafjMSd6+exL+bZuHQtiVwq9EYVeq0zPfwbUUY3tYLp27GYPafl6CrpYZGnnZo7lMeDpYGCo2bHJ+KrTMPIeZuHCQSCYQQsHQ0QeSZhzj860XU+sodjXr4KCZ2ejLm7p+L0/dPQ1cj59liPXx7QFUl5/Ed8anx+GbdNzg0/JBC4ivL7nWToaGlA5dqDVDFtwVsHN1KLPY/5x/i24DK0NFUw+Yjkbj1+DVm96yDynZGuBeTgDnbL+P3o7fRu2nxtynuSQJ+n7wfiXEpMLLUQ9dJTbFjbhjiniRACEBdUxU9fm6hkIOTk/dOyh1/7ck1nL5/GhYGFgAAPye/Yo+tTMrMtckh5947bcU/16X/r4znLSa+TMaepScVUiiW1Vyj/7BQLMUquNdGFd/mSE58jRtn9+Ha6VBcOroTVvaVUKVOS1Su3lDm/c/FqWYlSwRWK4fXSek4cPkR9l2Mxq4zUahka4Tm1cuhfhVb6WvWitP+X85Bz1gHI39rClU1VRzaeAHpyRnoPb81HlyNwY55YdA30UHN1sX/A7P+1Hrcf3kfPzX/CUnpSfj1zK+48/wOpn41FeqqOdv6sYfXfql8GnXC3SsncO3UvzC1dkAV3+Zwr9UU2rqKP4Ob60zkM3wf6AavCjnvxHUvb4p+zT2wdt8NhRSKhzdegKWjCTr9rzGuHLqDP6YfhKmNAYJmt4TIFtgxNwwntl5Bm2H1ij32hL8mSA+E8lp6ZCmAnDdqlLaDEkB5uVbd2QIqKsDIdt4w1vvvtZDNJ/6NlYPqw8FCcQfB6SkZH5yekZqpsNhlOdcoBwvFMkDXwBg1m3RBzSZdEH33Kq6d+hdHdizHkR3LMWxBqEJjG+tpopN/RXTyr4hrD+Kw9+JDrAq9gVWhN7B7Ystij3fv0mN8N6sFNP//XdINv62G+d1+R2DfWnCoao0mvWri5LarCikUT9w9gXHNxsHL3gsAUNe5LsbtHIefdv2EGW1mAHj/67C+dJ5+rVGn+XeIfRSJa6f+xanQTTi2ey2cq9RB1Tqt4OCqmLO4AJD7kb56kwbHPGesK1gZ4EVCqpylPt3jyBfoOqkJLByMUb+bN87tuYmWA+pA9f+7Wfh+7YFdC44pJHYNhxpQkahgTNMxMNb975V1TRY1wdrua+Fg5qCQuJ8DZeXazB61sf3kPQxaeQyDW1dB7cold8l1XreQD+47hBAK27eU5VyjHCwUS6v37DTsnavC3rkqGnUcjFsXS/b9nFUcTFHFwRQDWmbi6LUnComhqq4qs8PMPRLOysp577VdZQvEP09SSOyE1ARY6FtIhw21DTG3/VyM3TEWP+78EaOajFJI3M+JVTkXWJVzQYOvB+B2+FFcO/Uv/lwxFgbGFug79XeFxNx48BY0NVShIpEg7k2aTPeGxNQMaKqrfmDpostIewtt/ZwzSxpa6tDQUoOe8X8PFzc010NyfJpCYs/+eja2XdyGfiH9MDRgKOo4Ke+RU8qijFxr7+cET0czzP7zIs5EPkO/5op7Hem7NLXV4dexKmwqmsud/iomEaErTyskNnONWCiWVh97D622LjzrKuf9nLpa6mhRw0Eh67Z3tcCxLZfRekhdqKqpIuy3SzCy1IOOfk7n95TENGjraSgktqW+JR69eiTzRhZdTV3MaT8HY7aPwcS/JyokrtLJOShR19CEe82mcK/ZFK+fP8b1M3sVErqKgymiX+YU/uUs9PEsXvbs4bnbzxXWL1bPWBuJL5JhaJ7z+sCA73ygY/jfTRbJCYrLNQDoWL0jvOy9MPPfmTh9/zQGNhiosFifDSXmWi5nG0Ms618fq/69jv7Lj0JA8d1JLCuYAgDKe8g/i6mlq6HQbi1lMtdIioViKTVqmfLez6mMzty5GgXVQMjk/ZjfLeeMgrqWGtqPaSCd/jI6HlUUdGdg9fLVEXojFLUr1JYZr6Ohg9lfz8bo7aMVElfpPvIDZWxhB/+vvldI6Hm95Xegz70UF1DVVmGvVKvgaYOXTxJg75ZzF3v15pVlpkeFP4FlBROFxM5V0aIiVnVfheVhy9Hn1z6ltg+slBJz7V2a6qoY2sYTpyNicfn+CxjqaH58oU/gUc8RmelZ752ua6QN/85eCm1Dmcs1kmKhSKWKsZU++i5ug+ibz5D1Nhu2LuYyjyjxbFRRYbGD6gQhLilO7jRdTV3MbT8Xd57fUVh8ZekzJQQ6+kbKboaMFpP2YNWgBihvoa+wGM37+35wuqufo8IOSt6lqaaJEY1H4OS9kwiPDoehdsndQFTSPrdc83W1wtTfz6NlDQeZG1yKm3dTlw9O1zPWRr0SeCtLWco1+g8LxVLs0tGdiH14C45uteDqE4Ab5/bj7L4QCCFQ0asu6rbsBRXV4u+/dedpPPS01GFtknNH9cHwaOw59wAvElJhYaSDNrUc0aCqbbHHBYB9a8/CtU55VPBWzPo/xEDLAAZa/13mTM1IxZHbR/A0/ilMdU0RUDlAeqNLaXLh0FZUqtYA9s5VSzz2qtDrcsdnC4E/jt2BgW7Opd9+zT2KPXZurpVzl3850NhKcUVqXqkZqUhITYCmmibCbochoHJAqfwRZ64p/5mFZSXX6D8sFEup06G/4tzBLXBw9cGRHSuQ+OoZzh/6A9UbdoBEIsHFw39CVUUNfq16FnvseTsu44fmHrA20cW/Fx5i5T/X0NynPBp72ePxyyQs3BWOtMwsNKterthjX/g3AhdDb8HYSh+ejSuiakNnmRsMFCloQxAWd14MQ21DPH/zHEP/GIo3aW9gb2yPpwlP8euZX7Gs6zKZPoylweVju3D5+F8wMrNBFd8W8KgdCF0DxV5yzbXz1H1UsDKQ+6il6JdJ0ExQhaLuM/+ccm3IliFISk9irikQc61s5Rr9h4ViKXX9zF407z4Wlbzr4fnju/h1dj80/3Ys3Go2AQCYWJbDsV2rFVIoPo1Lhu3/n03cc+4B+rfwkLl5pZKtEX4/elshhSIAdJ3UBHfOR+PMrus4uvkynKvbwqtJJThVt4OKiuIeT/Po1SNkZ+fcXb32+FqY6Zlh7bdroaeph5SMFEzcPRHrTq7DhJYTFNYGZek4cA7uXT+N84f+wIk961HBvRaq1mkBR/fact9SU1x6NnbFvxceom8zd3g7/XdHaPOJf2Pk114KfbYd8Pnkmrm+OX757hfmGnOt2JXlXKMcLBRLqeTEOFiVz+nXYmHnDEgkMLf7r7+UpX1FJCW8VEhsTXVVJKRkwNJYBy8TUuFiZywzvbKdMWIV+G5Ui/LGcPS0QaOgGog88xBXDt3Btp8PQ9dQG54Bzqga4AwTG8Xu1G/G3MTwxsOhp5lzR6yOhg56+PbA9H+nKzSuspjZVED5ytVRv10/3Ak/jutnQrFrzUTo6BvBo3YzeNRuBmMLu2KP26V+RXg5mWHOn5dQy8UKvZu6Qk215F4XyVwrecw15hqVrJJ9AS+VGB0DE7yMeQAAePU8GkJk41XsQ+n0uNgH0NE3fs/Sn6ZGJUvsOZcTu4qjKY7feCoz/ej1J9L+i4qkqqYCt7qO6DqpKQauag/vppVw/eg9rBq0U2Exc5/hmP42Haa6pjLTzPXMEZ8Sr7DYnwNVVTVUrt4QHQbOQZ8pm1HVrxVunj+IddN6KCxmZTtjLOtfHwnJ6Ri48igePEtUWKz3Ya6VPOYac41KBs8ollKuPo0QuulnOFf1w8Pbl1CzcWeE7VyJ1ORESCQSnNn3Gyp51VdI7N5NXTF87QmM/OUEKtoaYfvJe7gSFYdy5np4/DIJEdGvMfmbmgqJ/T6G5nqo18UL/p09EXUlRmFxRvw5AmoqakjJSEH062g4mv33Xu1nic9goK3YI/7PiYGJJfxaBqFOix54eOuiQmPpaKphTIdqCLv6BGODTyNbiY/uYK6VPOYac40Uh4ViKeXXsifU1DURE3UTVeu0RK2m38Dc1hnH/lqNzIx0OHn4KqR/IgCYGWhj5YAG2HLsDs5GxkIIIPLxa7xISIV7ORMs7OOe73J0cTE014NE9f39dSQSCSp4KabT9Xe1v5MZ1lLXkhk+ff80qtqW/N2aimZgYgmVD1yCk0gkCn2F37saVLWFW3kT3H0aD0sjHYXGYq6VPOaafMw1UiSJ4FMzS1RiYiIMDQ2RkJAAA4PCH4UtPRBf/I0qoM4vRiot9l+1LD4+k4J0/Hus0mIbDTNSWmzmWsljrpU85lrJ+5Rc+9TfUCo89lEkIiIiIrlYKBIRERGRXKW2UJw1axZq1KgBfX19WFhYoG3btoiMjJSZJy0tDQMHDoSpqSn09PTQvn17PHv2TDr91atXaN26NfT09ODt7Y3Lly/LLD9w4EDMnz+/RLaHiIiIqKSV2kLx6NGjGDhwIM6cOYMDBw4gMzMTTZs2RXJysnSe4cOH4++//8a2bdtw9OhRPH36FF9//bV0+owZM/DmzRtcunQJDRo0QJ8+faTTzpw5g7Nnz2LYsGEluVlEREREJabU3vW8d+9emeENGzbAwsICFy9eRL169ZCQkIB169YhJCQEAQEBAIDg4GC4urrizJkzqF27NiIiItClSxdUqlQJffv2xZo1awAAmZmZ6NevH3755ReoKuBdyURERESfg1J7RjGvhIQEAICJSc57QS9evIjMzEw0btxYOk/lypVRrlw5nD59GgDg6emJw4cP4+3bt9i3bx+qVs15BMCcOXPQoEED+Ph8/DEM6enpSExMlPkjIiIi+hKU2jOK78rOzsawYcPg5+cHDw8PAEBsbCw0NDRgZGQkM6+lpSViY2MBAD/++CP69+8PJycnODg4YN26dbhz5w42btyI06dPo1+/fti/fz98fHywdu1aGBoa5os9a9YsTJkyJd/4J0+eFKlo1HmruFfffcxLVVulxdaMV95jEJ7rPlda7KTHSUqLzVwrecy1ksdcK3mfkmtv3rwpxpZQQZSJQnHgwIG4fv06Tpw4UajlDA0NERISIjMuICAAc+fOxebNm3H//n1ERkaiT58+mDp1qtwbW8aNG4cRI0ZIhxMTE2Fvbw9bW9siPQMqJSK+0MsUF7OsJ0qLnW6UqbTYFsnKe9aZkZ2R0mIz10oec63kMddK3qfkGq/KlbxSf+l50KBB2LNnD44cOQI7u/9eFG9lZYWMjAzEx8fLzP/s2TNYWVnJXVdwcDCMjIzQpk0bhIWFoW3btlBXV0fHjh0RFhYmdxlNTU0YGBjI/BERERF9CUptoSiEwKBBg7Bz504cPnwYjo6OMtOrV68OdXV1HDp0SDouMjISjx49gq+vb771vXjxAlOnTsXSpUsBAFlZWcjMzDkazMzMRFZWlgK3hoiIiKjkldpLzwMHDkRISAj++usv6OvrS/sdGhoaQltbG4aGhujduzdGjBgBExMTGBgYYPDgwfD19UXt2rXzrW/YsGEYOXIkbG1z+rP4+fnh119/RdOmTbFmzRr4+fmV6PYRERERKVqpPaO4cuVKJCQkoEGDBrC2tpb+/fHHH9J5Fi5ciFatWqF9+/aoV68erKyssGPHjnzr2rdvH+7evYsBAwZIxw0aNAgVKlRArVq1kJGRgUmTJpXIdhERERGVlFJ7RlEI8dF5tLS0sHz5cixfvvyD8wUGBiIwMFBmnI6ODrZu3fpJbSQiIiL6nJXaM4pERERE9GlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEguFopEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXCwUiYiIiEiuMl8oLl++HA4ODtDS0kKtWrVw7tw56bQRI0bAxMQE9vb22Lx5s8xy27ZtQ+vWrUu6uUREREQlRk3ZDVCmP/74AyNGjMCqVatQq1YtLFq0CIGBgYiMjMTZs2cREhKC/fv3486dO+jVqxcCAwNhZmaGhIQE/O9//8PBgweVvQlEREREClOmzyguWLAAffr0Qc+ePeHm5oZVq1ZBR0cH69evR0REBBo0aAAfHx907doVBgYGiIqKAgCMGTMG/fv3R7ly5ZS8BURERESKU2bPKGZkZODixYsYN26cdJyKigoaN26M06dPY8CAAVizZg1ev36N+/fvIzU1Fc7Ozjhx4gQuXbqEFStWFChOeno60tPTpcMJCQkAgMTExCK1OzW5aMsVhzcpGUqLnfom/eMzKUhimvI+c5VE5R3LMddKHnOt5DHXSt6n5Frub6cQoriaQx8jyqgnT54IAOLUqVMy40ePHi1q1qwphBBi0qRJwsnJSXh4eIgdO3aI9PR04eHhIS5cuCCWLl0qKlWqJOrUqSOuX7/+3jiTJk0SAPjHP/7xj3/8418x/UVHRyu0RqD/SIQom2X506dPYWtri1OnTsHX11c6fsyYMTh69CjOnj2bb5kpU6YgPj4ePXv2RNOmTXHt2jXs2bMHy5Ytw8WLF+XGyXtGMTs7G69evYKpqSkkEknxb1gplJiYCHt7e0RHR8PAwEDZzaFSjLlGJYW5VjRCCLx58wY2NjZQUSnTvedKTJm99GxmZgZVVVU8e/ZMZvyzZ89gZWWVb/5bt27ht99+w+XLl7F+/XrUq1cP5ubm6NSpE3r16oU3b95AX18/33KamprQ1NSUGWdkZFSs21JWGBgYcIdKJYK5RiWFuVZ4hoaGym5CmVJmy3ENDQ1Ur14dhw4dko7Lzs7GoUOHZM4wAjlHMD/88AMWLFgAPT09ZGVlITMzEwCk/83Kyiq5xhMRERGVgDJ7RhHIeU5ijx494OPjg5o1a2LRokVITk5Gz549Zeb75ZdfYG5uLn1uop+fHyZPnowzZ84gNDQUbm5uPEtIREREpU6ZLhQ7d+6MFy9eYOLEiYiNjYWXlxf27t0LS0tL6TzPnj3DjBkzcOrUKem4mjVrYuTIkWjZsiUsLCywceNGZTS/zNDU1MSkSZPyXcInKm7MNSopzDX6UpTZm1mIiIiI6MPKbB9FIiIiIvowFopEREREJBcLRSIiIiKSi4ViKdegQQMMGzZMOuzg4IBFixZ9cBmJRIJdu3Z9cuziWg8REREpBwvFz1Tr1q3RrFkzudOOHz8OiUSCq1evFnq958+fR9++fT+1eTImT54MLy+vfONjYmLQvHnzYo31PqmpqTAxMYGZmZnMm3BIOSQSyQf/Jk+e/EnrLswByA8//ABVVVVs27atyDHp88VcI1IsFoqfqd69e+PAgQN4/PhxvmnBwcHw8fFB1apVC71ec3Nz6OjoFEcTP8rKyqrEHv2wfft2uLu7o3Llyko/iymEwNu3b5XaBmWLiYmR/i1atAgGBgYy40aNGlUi7UhJScGWLVswZswYrF+/vkRifkhGRoaym1DqMNfkY65RcWGh+Jlq1aoVzM3NsWHDBpnxSUlJ2LZtG3r37o24uDh07doVtra20NHRQZUqVfD7779/cL15Lz3fuXMH9erVg5aWFtzc3HDgwIF8y4wdOxaVKlWCjo4OKlSogAkTJkjfSLNhwwZMmTIFV65ckR7B57Y579H4tWvXEBAQAG1tbZiamqJv375ISkqSTg8KCkLbtm0xb948WFtbw9TUFAMHDpTG+pB169ahe/fu6N69O9atW5dv+o0bN9CqVSsYGBhAX18f/v7+uHfvnnT6+vXr4e7uDk1NTVhbW2PQoEEAgAcPHkAikSA8PFw6b3x8PCQSCcLCwgAAYWFhkEgkCA0NRfXq1aGpqYkTJ07g3r17aNOmDSwtLaGnp4caNWrg4MGDMu1KT0/H2LFjYW9vD01NTTg7O2PdunUQQsDZ2Rnz5s2TmT88PBwSiQR379796GeiTFZWVtI/Q0NDSCQSmXFbtmyBq6srtLS0ULlyZaxYsUK6bEZGBgYNGgRra2toaWmhfPnymDVrFoCc/AWAdu3aQSKRSIffZ9u2bXBzc8OPP/6IY8eOITo6Wmb6+z7/XB/Km7zdOgCgbdu2CAoKkg47ODhg2rRp+O6772BgYCA9m/+h71Suv//+GzVq1ICWlhbMzMzQrl07AMDUqVPh4eGRb1u9vLwwYcKED34epRFzDdL2MtdIIQR9tkaPHi2cnJxEdna2dNz69euFtra2iI+PF48fPxZz584Vly9fFvfu3RNLliwRqqqq4uzZs9L569evL4YOHSodLl++vFi4cKEQQoisrCzh4eEhGjVqJMLDw8XRo0eFt7e3ACB27twpXWbatGni5MmTIioqSuzevVtYWlqK2bNnCyGESElJESNHjhTu7u4iJiZGxMTEiJSUFCGEkFlPUlKSsLa2Fl9//bW4du2aOHTokHB0dBQ9evSQxunRo4cwMDAQ/fr1ExEREeLvv/8WOjo6Ys2aNR/8nO7evSs0NTXFq1evRFxcnNDS0hIPHjyQTn/8+LEwMTERX3/9tTh//ryIjIwU69evF7du3RJCCLFixQqhpaUlFi1aJCIjI8W5c+ekn1FUVJQAIC5fvixd3+vXrwUAceTIESGEEEeOHBEARNWqVcX+/fvF3bt3RVxcnAgPDxerVq0S165dE7dv3xbjx48XWlpa4uHDh9J1derUSdjb24sdO3aIe/fuiYMHD4otW7YIIYSYMWOGcHNzk9nWIUOGiHr16n3w8/jcBAcHC0NDQ+nwb7/9JqytrcX27dvF/fv3xfbt24WJiYnYsGGDEEKIuXPnCnt7e3Hs2DHx4MEDcfz4cRESEiKEEOL58+cCgAgODhYxMTHi+fPnH4zt7+8vli1bJoQQon379mLq1Kky0z/0+X8sb/J+t4QQok2bNjI5Xb58eWFgYCDmzZsn7t69K+7evSuE+PB3Sggh9uzZI1RVVcXEiRPFzZs3RXh4uJg5c6YQQojo6GihoqIizp07J53/0qVLQiKRiHv37n3w8yjtmGvMNSp+LBQ/YxERETIFiRA5O6Pu3bu/d5mWLVuKkSNHSoc/VCju27dPqKmpiSdPnkinh4aG5isU85o7d66oXr26dHjSpEnC09Mz33zvrmfNmjXC2NhYJCUlSaf/888/QkVFRcTGxgohcgrF8uXLi7dv30rn6dixo+jcufN72yKEED/99JNo27atdLhNmzZi0qRJ0uFx48YJR0dHkZGRIXd5Gxsb8b///U/utMIUirt27fpgO4UQwt3dXSxdulQIIURkZKQAIA4cOCB33idPnsgU/hkZGcLMzEz6I/elyPvj7eTkJP0xzjVt2jTh6+srhBBi8ODBIiAgQOYA6V0fy89ct2/fFurq6uLFixdCCCF27twpHB0dpev92Of/sbwp6I/3u7n5Pnm/U76+vqJbt27vnb958+aif//+0uHBgweLBg0afDROacdcY65R8eOl589Y5cqVUadOHWl/l7t37+L48ePo3bs3ACArKwvTpk1DlSpVYGJiAj09Pezbtw+PHj0q0PojIiJgb28PGxsb6ThfX9988/3xxx/w8/ODlZUV9PT0MH78+ALHeDeWp6cndHV1peP8/PyQnZ2NyMhI6Th3d3eoqqpKh62trfH8+fP3rjcrKwsbN25E9+7dpeO6d++ODRs2IDs7G0DO5Vp/f3+oq6vnW/758+d4+vQpGjVqVKjtkcfHx0dmOCkpCaNGjYKrqyuMjIygp6eHiIgI6WcXHh4OVVVV1K9fX+76bGxs0LJlS+m//99//4309HR07Njxk9uqLMnJybh37x569+4NPT096d/06dOll9mCgoIQHh4OFxcXDBkyBPv37y9SrPXr1yMwMBBmZmYAgBYtWiAhIQGHDx8G8PHP/0N5Uxh58wL4+HcqPDz8gznZp08f/P7770hLS0NGRgZCQkLQq1evT2pnacNcy8Fco09Vpt/1/CXo3bs3Bg8ejOXLlyM4OBhOTk7Snc3cuXOxePFiLFq0CFWqVIGuri6GDRtWrJ2YT58+jW7dumHKlCkIDAyEoaEhtmzZgvnz5xdbjHfl3VFKJBJpwSfPvn378OTJE3Tu3FlmfFZWFg4dOoQmTZpAW1v7vct/aBoAqKjkHEuJd950+b4+k+8WwQAwatQoHDhwAPPmzYOzszO0tbXRoUMH6b/Px2IDwPfff49vv/0WCxcuRHBwMDp37lxiNyMpQm6f1LVr16JWrVoy03IPEKpVq4aoqCiEhobi4MGD6NSpExo3bow///yzwHFyDyBiY2OhpqYmM379+vVo1KjRRz//guSGyPMGVHm5kTcvCvKd+ljs1q1bQ1NTEzt37oSGhgYyMzPRoUOHDy5T1jDXmGtUPFgofuY6deqEoUOHIiQkBJs2bUL//v0hkUgAACdPnkSbNm2kZ9Oys7Nx+/ZtuLm5FWjdrq6uiI6ORkxMDKytrQEAZ86ckZnn1KlTKF++PP73v/9Jxz18+FBmHg0NDWRlZX001oYNG5CcnCzdmZ08eRIqKipwcXEpUHvlWbduHbp06SLTPgCYMWMG1q1bhyZNmqBq1arYuHEjMjMz8xWi+vr6cHBwwKFDh9CwYcN86zc3NweQc2elt7c3AMjc2PIhJ0+eRFBQkLRjeFJSEh48eCCdXqVKFWRnZ+Po0aNo3Lix3HW0aNECurq6WLlyJfbu3Ytjx44VKPbnytLSEjY2Nrh//z66dev23vkMDAzQuXNndO7cGR06dECzZs3w6tUrmJiYQF1d/aP59u+//+LNmze4fPmyzBnq69evo2fPnoiPj//o5/+hvAFyciMmJkY6nJWVhevXr8vNo3cV5DtVtWpVHDp0CD179pS7DjU1NfTo0QPBwcHQ0NBAly5dCnTgUZYw15hrVEyUfOmbCqB3797C2NhYqKqqyvQnHD58uLC3txcnT54UN2/eFN9//70wMDAQbdq0kc7zsZtZ3NzcRJMmTUR4eLg4duyYqF69uky/nL/++kuoqamJ33//Xdy9e1csXrxYmJiYyPQD2rx5s9DV1RWXL18WL168EGlpaUII2f49ycnJwtraWrRv315cu3ZNHD58WFSoUCHfzSzvtl0IIYYOHSrq168v93N5/vy5UFdXF6Ghofmm/fvvv0JTU1PExcWJly9fClNTU2lH8du3b4tNmzZJO4pv2LBBaGlpicWLF4vbt2+LixcviiVLlkjXVbt2beHv7y9u3rwpwsLCRM2aNeX2UXz9+rVMG9q1aye8vLzE5cuXRXh4uGjdurXQ19eX+fcICgoS9vb2YufOneL+/fviyJEj4o8//pBZz08//SQ0NDSEq6ur3M/hc5e339jatWuFtra2WLx4sYiMjBRXr14V69evF/PnzxdCCDF//nwREhIiIiIiRGRkpOjdu7ewsrISWVlZQgghKlasKPr37y9iYmLEq1ev5MZs06aN3L6tWVlZwsrKSnrTwYc+/4/lzapVq4SOjo7Ys2ePiIiIEH369BEGBgb5+o3lft9yFeQ7deTIEaGioiK9weDq1avi559/llnP7du3haqqqlBVVRVnzpz5+D9EGcBcWyjTBuYaFQcWil+AU6dOCQCiRYsWMuPj4uJEmzZthJ6enrCwsBDjx48X3333XYELRSFyOlnXrVtXaGhoiEqVKom9e/fm68A9evRoYWpqKvT09ETnzp3FwoULZXY0aWlpon379sLIyEh6l6AQ+TuCX716VTRs2FBoaWkJExMT0adPH/HmzRvp9MIWivPmzRNGRkZyO4Cnp6cLIyMjsXjxYiGEEFeuXBFNmzYVOjo6Ql9fX/j7+8vctbdq1Srh4uIi1NXVhbW1tRg8eLB02s2bN4Wvr6/Q1tYWXl5eYv/+/QUqFKOiokTDhg2Ftra2sLe3F8uWLcv375GamiqGDx8urK2thYaGhnB2dhbr16+XWc+9e/cEADFnzhy5n8PnLu+PtxA5BxdeXl5CQ0NDGBsbi3r16okdO3YIIXJufPLy8hK6urrCwMBANGrUSFy6dEm67O7du4Wzs7NQU1MT5cuXzxcvNjZWqKmpia1bt8ptT//+/YW3t7cQ4uOf/4fyJiMjQ/Tv31+YmJgICwsLMWvWLLk3GOT98Rbi498pIYTYvn279DMyMzMTX3/9db71+Pv7C3d3d7nbWRYx1xbmawNzjT6VRIg8HR+I6LNy/PhxNGrUCNHR0bC0tFR2c+gzIYRAxYoVMWDAAIwYMULZzaFSjLlWtrGPItFnKj09HS9evMDkyZPRsWNHFokk9eLFC2zZsgWxsbHv7VtGVByYa8RCkegz9fvvv6N3797w8vLCpk2blN0c+oxYWFjAzMwMa9asgbGxsbKbQ6UYc4146ZmIiIiI5OIDt4mIiIhILhaKRFRkcXFxsLCwkHk+JH1eunTporAH5CtTSeTe5MmT4eXl9cnr2bBhA4yMjD55PZ+TVatWoXXr1spuBpUAFopEVGQzZsxAmzZt4ODgkG9aYGAgVFVVcf78+ZJvWAlLSUnBuHHj4OTkBC0tLZibm6N+/fr466+/lN00jB8/HjNmzEBCQoKym1Ks8ubegwcPIJFIoKqqiidPnsjMGxMTAzU1NUgkkkIVlqNGjcKhQ4c+ua2dO3fG7du3pcPFUYBu375d7rbmqlixYoHuUC5qW3r16oVLly7h+PHjhV6WviwsFImoSFJSUrBu3Trpu8ff9ejRI5w6dQqDBg2SvqtakYrztZVF0a9fP+zYsQNLly7FrVu3sHfvXnTo0AFxcXEKi1nQbfbw8ICTkxN+++03hbWlpH0o92xtbfPd/LVx40bY2toWOo6enh5MTU2L3E4g51V72trasLCw+KT15PXVV1/B1NQUGzduzDft2LFjuHv3rtzPp7hoaGjgm2++wZIlSxQWgz4TynyIIxF9ubZt2ybMzc3lTps8ebLo0qWLiIiIEIaGhiIlJUUIkfOAdwAiIiJCZv4FCxaIChUqSIevXbsmmjVrJnR1dYWFhYXo3r27ePHihXR6/fr1xcCBA8XQoUOFqampaNCggRAi500bHh4eQkdHR9jZ2Yn+/fvLPNRdiJyHLNvZ2QltbW3Rtm1bMX/+/HwPIN61a5fw9vYWmpqawtHRUUyePFlkZma+97MwNDQUGzZs+ODnlZaWJsaMGSPs7OyEhoaGcHJyEr/88ot0elhYmKhRo4bQ0NAQVlZWYuzYsTIx37fNH/ushBBiypQpom7duh9s35dEXu5FRUUJAGL8+PGiYsWKMtMqVaokJkyYIACIqKgoIYQQb9++Fb169RIODg5CS0tLVKpUSSxatEhmuUmTJglPT0/pcFZWlpgyZYqwtbUVGhoawtPTU+bNULlt2LJli6hXr57Q1NQUwcHBMg8CDw4OFgBk/oKDg0XPnj1Fy5YtZeJnZGQIc3NzmTx514gRI/JtqxA5Ly+oVauWEEKIhw8fiq+++kro6uoKfX190bFjRxEbG/vBtgghxOvXr0Xv3r2FmZmZ0NfXFw0bNhTh4eEycY4ePSo0NDSk328qnVgoElGRDBkyRDRr1izf+OzsbFG+fHmxZ88eIYQQ1atXF5s2bZJO9/HxEePHj5dZpnr16tJxr1+/Fubm5mLcuHEiIiJCXLp0STRp0kQ0bNhQOn/9+vWFnp6eGD16tLh165b0VWcLFy4Uhw8fFlFRUeLQoUPCxcVF9O/fX7rciRMnhIqKipg7d66IjIwUy5cvz/dKs2PHjgkDAwOxYcMGce/ePbF//37h4OAgJk+e/N7PwsXFRXTq1EkkJia+d55OnToJe3t7sWPHDnHv3j1x8OBBsWXLFiGEEI8fPxY6OjpiwIABIiIiQuzcuVOYmZmJSZMmfXCbC/JZCSFEaGio0NDQkL5e80snL/dyi7Rz584JMzMzcfz4cSGEEMePHxfm5ubi3LlzMoViRkaGmDhxojh//ry4f/+++O2334SOjo7MKzTzFooLFiwQBgYG4vfffxe3bt0SY8aMEerq6uL27dsybXBwcBDbt28X9+/fF0+fPpUpFFNSUsTIkSOFu7u7iImJETExMSIlJUWcPHlSqKqqiqdPn0rj7dixQ+jq6uY72Ml148YNAUAcPXpUOu7NmzdCV1dXrFmzRmRlZQkvLy9Rt25dceHCBXHmzBlRvXp16duu3tcWIYRo3LixaN26tfSVgiNHjhSmpqYiLi5OGis5OVmoqKhI31JFpRMLRSIqkjZt2ohevXrlG79//35hbm4uPRu2cOFCmdcwLly4UDg5OUmH855lnDZtmmjatKnMOqOjowUAERkZKYTIKZpyX432Idu2bROmpqbS4c6dO+c7a9OtWzeZQrFRo0Zi5syZMvP8+uuvwtra+r1xjh49Kuzs7IS6urrw8fERw4YNEydOnMi3jQcOHJC7/E8//SRcXFxEdna2dNzy5cuFnp6e9L3D8ra5IJ+VEDmvhwMgHjx48N5t+JLIy73cIu3y5cti2LBhomfPnkIIIXr27CmGDx8uLl++LFMoyjNw4EDRvn176XDeQtHGxkbMmDFDZpkaNWqIAQMGyLQh75nJvK8WzLveXG5ubmL27NnS4datW4ugoKD3tleInHfRv/sqv3Xr1gkdHR2RmJgo9u/fL1RVVcWjR4+k03OLy3Pnzr23LcePHxcGBgb5DiycnJzE6tWrZcYZGxt/9Gw6fdnYR5GIiiQ1NRVaWlr5xq9fvx6dO3eGmlrO8/y7du2KkydP4t69ewBy7sJ98OABzpw5AwDYvHkzqlWrhsqVKwMArly5giNHjkBPT0/6lzstdx0AUL169XyxDx48iEaNGsHW1hb6+vr49ttvERcXh5SUFABAZGQkatasKbNM3uErV65g6tSpMvH79OmDmJgY6XryqlevHu7fv49Dhw6hQ4cOuHHjBvz9/TFt2jQAQHh4OFRVVVG/fn25y0dERMDX1xcSiUQ6zs/PD0lJSXj8+PF7t7mgn5W2tjYAvLf9X5r35V6uXr16Ydu2bYiNjcW2bdvQq1cvufMtX74c1atXh7m5OfT09LBmzRo8evRI7ryJiYl4+vQp/Pz8ZMb7+fkhIiJCZpyPj08htyjH999/j+DgYADAs2fPEBoa+t625+rVqxf+/PNPvHnzBkDO969jx47Q19dHREQE7O3tYW9vL53fzc0NRkZG+dr8ritXriApKQmmpqYyuRUVFSWTV0BObpWWvCL5+GYWIioSMzMzvH79Wmbcq1evsHPnTmRmZmLlypXS8VlZWVi/fj1mzJgBKysrBAQEICQkBLVr10ZISAj69+8vnTcpKQmtW7fG7Nmz88W0traW/r+urq7MtAcPHqBVq1bo378/ZsyYARMTE5w4cQK9e/dGRkYGdHR0CrRdSUlJmDJlCr7++ut80z5UnKirq8Pf3x/+/v4YO3Yspk+fjqlTp2Ls2LHSQu1T5d3mgn5Wr169AgCYm5sXSzuUTV7uvatKlSqoXLkyunbtCldXV3h4eCA8PFxmni1btmDUqFGYP38+fH19oa+vj7lz5+Ls2bOf3L68/04F9d133+HHH3/E6dOncerUKTg6OsLf3/+Dy3Tp0gXDhw/H1q1bUa9ePZw8eRKzZs0qUvxcSUlJsLa2RlhYWL5peR/z8+rVq1KTVyQfC0UiKhJvb+98d9Ju3rwZdnZ22LVrl8z4/fv3Y/78+Zg6dSpUVVXRrVs3jBkzBl27dsX9+/fRpUsX6bzVqlXD9u3b4eDgID0rWRAXL15EdnY25s+fDxWVnIslW7dulZnHxcUl3+N68g5Xq1YNkZGRcHZ2LnBsedzc3PD27VukpaWhSpUqyM7OxtGjR9G4ceN887q6umL79u0QQkjPKp48eRL6+vqws7N7b4yCflbXr1+HnZ0dzMzMPmmbPhfyci+vXr16YcCAATIHLO86efIk6tSpgwEDBkjH5T1b9i4DAwPY2Njg5MmTMmeGT548me+s9MdoaGggKysr33hTU1O0bdsWwcHBOH36dIHerayvr4+OHTti/fr1uHfvHipVqiQtLl1dXREdHY3o6GjpWcWbN28iPj4ebm5u721LtWrVEBsbCzU1NbmPvsp17949pKWlwdvbu6CbTl8iZV/7JqIv09WrV4Wampp49eqVdJynp6cYO3Zsvnnj4+OFhoaG9AaXxMREoa2tLTw9PUWjRo1k5n3y5IkwNzcXHTp0EOfOnRN3794Ve/fuFUFBQeLt27dCiJz+ekOHDpVZLjw8XNo/7N69e2LTpk3C1tZWABCvX78WQvx3M8v8+fPF7du3xapVq4SpqakwMjKSrmfv3r1CTU1NTJ48WVy/fl3cvHlT/P777+J///vfez+L+vXri1WrVokLFy6IqKgo8c8//wgXFxcREBAgnScoKEjY29uLnTt3ivv374sjR45Ib5zIvZll4MCBIiIiQuzatUvuzSx5t7kgn5UQOXfByutP+qWSl3vv9lEUQojMzEzx4sULaV/ZvH0UFy9eLAwMDMTevXtFZGSkGD9+vDAwMJDpr5e3/97ChQuFgYGB2LJli7h165YYO3as3JtZctuQK28fxc2bNwtdXV1x+fJl8eLFC5m+gPv37xcaGhpCVVVVPHnypECfx/HjxwUAYWxsLH7++Wfp+OzsbOHl5SX8/f3FxYsXxdmzZ2VuZnlfW7Kzs0XdunWFp6en2Ldvn4iKihInT54UP/30kzh//rzMdr37tAIqnVgoElGR1axZU6xatUoIIcSFCxdkOsnn1bx5c9GuXTvpcKdOnQQAsX79+nzz3r59W7Rr104YGRkJbW1tUblyZTFs2DDpzR7yiiYhcu5Ktba2Ftra2iIwMFBs2rRJplAUIufxOLa2ttLH40yfPl1YWVnJrGfv3r2iTp06QltbWxgYGIiaNWuKNWvWvPdzmDlzpvD19RUmJiZCS0tLVKhQQQwZMkS8fPlSOk9qaqoYPny4sLa2FhoaGsLZ2Vlm2wvyeBx52/yxzyo1NVUYGhqK06dPv7f9X6J3c0+I9xdpufIWimlpaSIoKEgYGhoKIyMj0b9/f/Hjjz9+sFDMysoSkydPFra2tkJdXf29j8f5WKGYlpYm2rdvL4yMjGQeSSPEf08NaNGiRaE+DxcXl3x3TQvx4cfjfKgtiYmJYvDgwcLGxkaoq6sLe3t70a1bN5kbY5o2bSpmzZpVqHbSl0cihBDKOJNJRF++f/75B6NHj8b169ell3u/NH369MGtW7dK7RsmVq5ciZ07d2L//v3KbkqxKoncGzduHI4fP44TJ04oZP3yJCUlwdbWFsHBwXL7yX4ubty4gYCAANy+fRuGhobKbg4pEPsoElGRtWzZEnfu3MGTJ09k7qz8nM2bNw9NmjSBrq4uQkNDsXHjRqxYsULZzVIYdXV1LF26VNnNKHaKzD0hhPQu9pLqf5ednY2XL19i/vz5MDIywldffVUicYsqJiYGmzZtYpFYBvCMIhGVKZ06dUJYWBjevHmDChUqYPDgwejXr5+ym0Wfkfj4eFhaWqJGjRrYvHkzypcvr/CYDx48gKOjI+zs7LBhwwY0atRI4TGJCoKFIhERERHJ9WV2KiIiIiIihWOhSERERERyFfpmlqysLGRmZiqiLURERESkYOrq6lBVVS3QvAUuFIUQiI2NRXx8fFHbRURERESfASMjI1hZWcm8Y16eAheKuUWihYUFdHR0PrpiIiIiIvq8CCGQkpKC58+fA5B9L7w8BSoUs7KypEWiqanpp7eSiIiIiJRCW1sbAPD8+XNYWFh88DJ0gW5mye2TqKOjUwzNIyIiIiJlyq3pPnbfSaHueublZiIiIqIvX0FrOj4eh4iIiIjkYqFYgh48eACJRILw8PAvat3vCgsLg0Qikd79vmHDBhgZGSk0JpU+kydPhpeXl3Q4KCgIbdu2VVp7SiOJRIJdu3Z90jry/rs0aNAAw4YN+6R1Avn//T83Dg4OWLRokXS4OD5Lok9V2P1k3t/roir0cxTzWnrg0xpQGIObGBVq/hcvXmDixIn4559/8OzZMxgbG8PT0xMTJ06En58fgJwdwM6dO8vEj1RUVBT+97//ISwsDK9evYKZmRmqV6+O2bNno3LlykVaZ+fOndGiRQvp8OTJk7Fr1y6FF6wf8jykd4nGs/hmXYHn/dip/kmTJmHy5Mmf2KKiKeh34ejRo5gyZQrCw8ORlpYGW1tb1KlTB2vXroWGhkaRYi9evBjvvk20QYMG8PLykvmxLklr740r0Xh9nGYVav6C7NtiYmJgbGz8Se3K++9SXEaNGoXBgwdLh4OCghAfH//JxVhWVhbmzp2LDRs24OHDh9DW1kbFihXRp08ffP/990Ve77ufZe47mS9fvqy0Yjd+UXyJxjMaZlSo+YOCgrBx40YAgJqaGkxMTFC1alV07doVQUFBUFHhOaovyScXip+z9u3bIyMjAxs3bkSFChXw7NkzHDp0CHFxccpuWpFlZGQU6cc4MzMTTZo0gYuLC3bs2AFra2s8fvwYoaGhn3S0oa2tLb17ij4uJiZG+v9//PEHJk6ciMjISOk4PT29Qq2vqPlQVDdv3kSzZs0wePBgLFmyBNra2rhz5w62b9+OrKysIq/X0NCwGFtZ+hVk32ZlZfXJcYr730UIgaysLOjp6RU61wtiypQpWL16NZYtWwYfHx8kJibiwoULeP369Settzg+y7KmWbNmCA4ORlZWFp49e4a9e/di6NCh+PPPP7F7926oqZXq8qNUKbVlfXx8PI4fP47Zs2ejYcOGKF++PGrWrIlx48bhq6++ApBzeQEA2rVrB4lEIh2+d+8e2rRpA0tLS+jp6aFGjRo4ePCgzPodHBwwc+ZM9OrVC/r6+ihXrhzWrFkjM8+5c+fg7e0NLS0t+Pj44PLlyzLTs7Ky0Lt3bzg6OkJbWxsuLi5YvHixzDy5p5pnzJgBGxsbuLi4FGjded24cQP37t3DihUrULt2bZQvXx5+fn6YPn06ateuDeC/y9dbtmxBnTp1oKWlBQ8PDxw9evS963330vOGDRswZcoUXLlyBRKJBBKJBBs2bPhgu8oaKysr6Z+hoSEkEol0ODk5Gd26dfto3k2bNg3fffcdDAwM0LdvXwDA2rVrYW9vDx0dHbRr1w4LFizI1yXgr7/+QrVq1aClpYUKFSpgypQpePv2rXS9QP7vQl779++HlZUV5syZAw8PDzg5OaFZs2ZYu3at9IAhNyd27dqFihUrQktLC4GBgYiOjn7v5/LuJZWgoCAcPXoUixcvlubRgwcPCvdBl2IF2bcBspdLc7/bW7duhb+/P7S1tVGjRg3cvn0b58+fh4+PD/T09NC8eXO8ePFCuo6PXer69ddf4ePjA319fVhZWeGbb76RPpsN+O/SV2hoKKpXrw5NTU2cOHFC5tLz5MmTsXHjRvz111/Sf++wsDAEBARg0KBBMvFevHgBDQ0NHDp0SG57du/ejQEDBqBjx45wdHSEp6cnevfujVGjRknnadCgAQYNGoRBgwbB0NAQZmZmmDBhwgfPnL77WTo6OgIAvL29IZFI0KBBg/cuV5ZpamrCysoKtra2qFatGn766Sf89ddfCA0Nlf4uyOsyFR8fL80B4L8c2rdvH7y9vaGtrY2AgAA8f/4coaGhcHV1hYGBAb755hukpKRI19OgQQMMHjwYw4YNg7GxMSwtLbF27VokJyejZ8+e0NfXh7OzM0JDQwHkHMQ4Oztj3rx5MtsRHh4OiUSCu3fvyt3O3O/IzJkzYWlpCSMjI0ydOhVv377F6NGjYWJiAjs7OwQHB8ssd+3aNQQEBEBbWxumpqbo27cvkpKSpNOzsrIwYsQIGBkZwdTUFGPGjMmXo9nZ2Zg1a5a0hvD09MSff/5ZqH+ngii1hWLuEeuuXbuQnp4ud57z588DAIKDgxETEyMdTkpKQosWLXDo0CFcvnwZzZo1Q+vWrfHo0SOZ5efPny8t0gYMGID+/ftLzw4lJSWhVatWcHNzw8WLFzF58mSZnRWQ849sZ2eHbdu24ebNm5g4cSJ++uknbN26VWa+Q4cOITIyEgcOHMCePXsKtO68zM3NoaKigj///POjZ35Gjx6NkSNH4vLly/D19UXr1q0LdBa2c+fOGDlyJNzd3RETE4OYmBh07tz5o8tRjoLm3bx58+Dp6YnLly9jwoQJOHnyJPr164ehQ4ciPDwcTZo0wYwZM2SWOX78OL777jsMHToUN2/exOrVq7FhwwbpfO/7LuRlZWWFmJgYHDt27IPbkpKSghkzZmDTpk04efIk4uPj0aVLlwJ9DosXL4avry/69OkjzSN7e/sCLVsWFGTf9j6TJk3C+PHjcenSJaipqeGbb77BmDFjsHjxYhw/fhx3797FxIkTC7y+zMxMTJs2DVeuXMGuXbvw4MEDBAUF5Zvvxx9/xM8//4yIiAhUrVpVZtqoUaPQqVMnNGvWTPrvXadOHXz//fcICQmR2cbffvsNtra2CAgIkNseKysrHD58WKbYlWfjxo1QU1PDuXPnsHjxYixYsAC//PJLgbb53LlzAICDBw8iJiYGO3bsKNByBAQEBMDT07NIn9nkyZOxbNkynDp1CtHR0ejUqRMWLVqEkJAQ/PPPP9i/fz+WLl0qs8zGjRthZmaGc+fOYfDgwejfvz86duyIOnXq4NKlS2jatCm+/fZbpKSkQCKRoFevXvkKuuDgYNSrVw/Ozs7vbdvhw4fx9OlTHDt2DAsWLMCkSZPQqlUrGBsb4+zZs+jXrx9++OEHPH78GACQnJyMwMBAGBsb4/z589i2bRsOHjwoc2A0f/58bNiwAevXr8eJEyfw6tUr7Ny5UyburFmzsGnTJqxatQo3btzA8OHD0b179w+e3CmKUlsoqqmpYcOGDdi4cSOMjIzg5+eHn376CVevXpXOY25uDuC/19jkDnt6euKHH36Ah4cHKlasiGnTpsHJyQm7d++WidGiRQsMGDAAzs7OGDt2LMzMzHDkyBEAQEhICLKzs7Fu3Tq4u7ujVatWGD16tMzy6urqmDJlCnx8fODo6Ihu3bqhZ8+e+QpFXV1d/PLLL3B3d4e7u3uB1p2Xra0tlixZgokTJ8LY2BgBAQGYNm0a7t+/n2/eQYMGoX379nB1dcXKlSthaGiIdes+3g9PW1sbenp6UFNTk54l42Xpgito3gUEBGDkyJFwcnKCk5MTli5diubNm2PUqFGoVKkSBgwYgObNm8ssM2XKFPz444/o0aMHKlSogCZNmmDatGlYvXo1gPd/F/Lq2LEjunbtivr168Pa2hrt2rXDsmXLkJiYKDNfZmYmli1bBl9fX1SvXh0bN27EqVOnpD+yH2JoaAgNDQ3o6OhI86ig7yQtCwqyb3ufUaNGITAwEK6urhg6dCguXryICRMmwM/PD97e3ujdu7d0H1YQvXr1QvPmzVGhQgXUrl0bS5YsQWhoqMyZEQCYOnUqmjRpAicnJ5iYmMhM09PTg7a2tvQMlJWVFTQ0NPD1118DyDkTnmvDhg0ICgp6b1/fBQsW4MWLF7CyskLVqlXRr18/6Rmjd9nb22PhwoVwcXFBt27dMHjwYCxcuLBA25z73TA1NYWVlVW+7aEPq1y5cpGuEEyfPl0mT48ePYqVK1fC29sb/v7+6NChQ77c9fT0xPjx41GxYkWMGzcOWlpaMDMzQ58+fVCxYkVMnDgRcXFx0u9OUFAQIiMjpfupzMxMhISEoFevXh9sm4mJCZYsWQIXFxf06tULLi4uSElJwU8//SSNraGhgRMnTgDIqQ/S0tKwadMmeHh4ICAgAMuWLcOvv/6KZ8+eAQAWLVqEcePG4euvv4arqytWrVol0xUkPT0dM2fOxPr16xEYGIgKFSogKCgI3bt3l+7Xi0upLRSBnH48T58+xe7du9GsWTOEhYWhWrVqH70cmpSUhFGjRsHV1RVGRkbQ09NDREREvjM77x4Z515CzL3sknvkrKWlJZ3H19c3X6zly5ejevXqMDc3h56eHtasWZMvTpUqVWT6oRV03XkNHDgQsbGx2Lx5M3x9fbFt2za4u7vjwIEDMvO9uy41NTX4+PggIiLio+unT1PQvPPx8ZEZjoyMRM2aNWXG5R2+cuUKpk6dKj0bpaenJz1j9+7lmo9RVVVFcHAwHj9+jDlz5sDW1hYzZ86UnkXOpaamhho1akiHK1euDCMjI+ZRMSnqvu3dfZalpSWAnP3Lu+PevXT8MRcvXkTr1q1Rrlw56Ovro379+gDw0ZwtCC0tLXz77bdYv349AODSpUu4fv263DOWudzc3HD9+nWcOXMGvXr1wvPnz9G6det8N7LUrl1bptj09fXFnTt3PqmfLRWMEKJIz2TOm7s6OjqoUKGCzLi8ufvuMqqqqjA1Nc2X7wCky9nY2KBly5bSnPv777+Rnp6Ojh07frBt7u7uMjfoWFpaysTJjf1ufeDp6QldXV3pPH5+fsjOzkZkZCQSEhIQExODWrVqSafn/hbnunv3LlJSUtCkSROZ/fqmTZtw7969D7a3sEp1oQjk7GyaNGmCCRMm4NSpUwgKCsKkSZM+uMyoUaOwc+dOzJw5E8ePH0d4eDiqVKmCjIwMmfnU1dVlhiUSCbKzswvcti1btmDUqFHo3bs39u/fj/DwcPTs2TNfnHeT6VPp6+ujdevWmDFjBq5cuQJ/f39Mnz692NZPRVfQvCtKPiQlJUnvVM79u3btGu7cuSNzwFFQtra2+Pbbb7Fs2TLcuHEDaWlpWLVqVaHXQ0VXlH3bu/us3B/rvOMKug/LvXxmYGCAzZs34/z589JLY8W1D/v+++9x4MABPH78GMHBwQgICED58uU/uIyKigpq1KiBYcOGYceOHdiwYQPWrVuHqKioIrWBildERIS0n2ducfVu37v3vSUkb54W5PdX3jzyvgPvLvf9999jy5YtSE1NRXBwMDp37vzRt9J9LM772vcpcs/a//PPPzL79Zs3bxZ7P8VSXyjm5ebmhuTkZOmwurp6vqPIkydPIigoCO3atUOVKlVgZWVV6FPlrq6uuHr1KtLS0qTjzpw5ky9OnTp1MGDAAHh7e8PZ2blARwIFWXdBSCQSVK5cWebzyLuut2/f4uLFi3B1dS3QOjU0NHhUXkRFzTsXF5d8fQrzDlerVg2RkZFwdnbO95e7s5b3XSgIY2NjWFtby+TR27dvceHCBelwZGQk4uPjmUcKlHffpmi3bt1CXFwcfv75Z/j7+6Ny5cqFOhv5rvf9e1epUgU+Pj5Yu3ZtgS4ByuPm5gYAMp/N2bNnZeY5c+YMKlasWKAuDrlXd5ifhXf48GFcu3YN7du3B/DfZfx3r0Yo89FqQE6XMl1dXaxcuRJ79+4tUs59jKurK65cuSKTkydPnoSKigpcXFxgaGgIa2trmTzN/S3O5ebmBk1NTTx69CjfPr24+3SX2kIxLi4OAQEB+O2333D16lVERUVh27ZtmDNnDtq0aSOdz8HBAYcOHUJsbKz0EQoVK1bEjh07EB4ejitXruCbb74p9JHAN998A4lEgj59+uDmzZv4999/891NVbFiRVy4cAH79u3D7du3MWHChPfeRFDYdecVHh6ONm3a4M8//8TNmzdx9+5drFu3DuvXr5f5PICcy+E7d+7ErVu3MHDgQLx+/brAXxYHBwdERUUhPDwcL1++LHRn+7KsqHk3ePBg/Pvvv1iwYAHu3LmD1atXIzQ0VObyzsSJE7Fp0yZMmTIFN27cQEREBLZs2YLx48dL55H3Xchr9erV6N+/P/bv34979+7hxo0bGDt2LG7cuIHWrVtL51NXV8fgwYNx9uxZXLx4EUFBQahdu3a+S+Lv4+DggLNnz+LBgwd4+fJlsR6Jf+kKum9TtHLlykFDQwNLly7F/fv3sXv3bkybNq1I63JwcMDVq1cRGRmJly9fypxV+v777/Hzzz9DCIF27dp9cD0dOnTAwoULcfbsWTx8+BBhYWEYOHAgKlWqJPOs2EePHmHEiBGIjIzE77//jqVLl2Lo0KEFaquFhQW0tbWxd+9ePHv2DAkJCUXa5tIuPT0dsbGxePLkCS5duoSZM2eiTZs2aNWqFb777jsAOf3aa9euLb3R6ejRozL7JGVQVVVFUFAQxo0bh4oVKxaoW1dhdevWDVpaWujRoweuX7+OI0eOYPDgwfj222+ll8OHDh2Kn3/+Gbt27cKtW7cwYMAAmUfZ6evrY9SoURg+fDg2btyIe/fu4dKlS1i6dKn0GZbFRhRAamqquHnzpkhNTS3I7J+FtLQ08eOPP4pq1aoJQ0NDoaOjI1xcXMT48eNFSkqKdL7du3cLZ2dnoaamJsqXLy+EECIqKko0bNhQaGtrC3t7e7Fs2TJRv359MXToUOly5cuXFwsXLpSJ6enpKSZNmiQdPn36tPD09BQaGhrCy8tLbN++XQAQly9flrYxKChIGBoaCiMjI9G/f3/x448/Ck9PT+k6evToIdq0aZNv+z627rxevHghhgwZIjw8PISenp7Q19cXVapUEfPmzRNZWVnS7QYgQkJCRM2aNYWGhoZwc3MThw8flq7nyJEjAoB4/fq1EEKI4OBgYWhoKPO5t2/fXhgZGQkAIjg4WG57KP9nV9S8E0KINWvWCFtbW6GtrS3atm0rpk+fLqysrGTm2bt3r6hTp47Q1tYWBgYGombNmmLNmjXS6fK+C3ldunRJdO/eXTg6OgpNTU1hamoq6tWrJ3bv3p1vu7Zv3y4qVKggNDU1RePGjcXDhw+l80yaNOmDeR4ZGSlq164ttLW1BQARFRX1wc+yLCnovg2A2LlzpxDiv+/2u/uHvN9lIfLnZN5/l7z5GBISIhwcHISmpqbw9fUVu3fvlokjL4YQ+f/9nz9/Lpo0aSL09PQEAHHkyBHptDdv3ggdHR0xYMCAj342a9asEQ0bNhTm5uZCQ0NDlCtXTgQFBYkHDx7IbMOAAQNEv379hIGBgTA2NhY//fSTyM7Ols6T93v27mcphBBr164V9vb2QkVFRdSvX/+j7SprevToIQAIAEJNTU2Ym5uLxo0bi/Xr10t/b3LdvHlT+Pr6Cm1tbeHl5SX2798vkwMFyVMh8udU3lwVQv7+M++/rRBC3Lt3TwAQc+bMKdC25v2NLkjsq1evioYNGwotLS1hYmIi+vTpI968eSOdnpmZKYYOHSoMDAyEkZGRGDFihPjuu+9kYmVnZ4tFixYJFxcXoa6uLszNzUVgYKA4evSoEOL9379cBa3tJEJ8/LH7aWlpiIqKgqOjY5H6M9GX4XN44wAVjz59+uDWrVs4fvx4icfesGEDhg0b9smvjSICcvZLTk5OOH/+PKpVq/bJ61P2W3/o83f8+HE0atQI0dHR0jN8pVFBazs+Gp2oFJg3bx6aNGkCXV1dhIaGYuPGjVixYoWym0VUZJmZmYiLi8P48eNRu3btYikSiT4kPT0dL168wOTJk9GxY8dSXSQWRqnto0hUlpw7dw5NmjRBlSpVsGrVKixZsuST3m1LpGwnT56EtbU1zp8/zzvqqUT8/vvvKF++POLj4zFnzhxlN+ezwUvPRERERGVMQWs7nlEkIiIiIrkKVSgW4OQjEREREX3mClrTFahQzH3CeGFe9UVEREREn6fcmi7vW2TyKtBdz6qqqjAyMpI+dV9HR6dI72okIiIiIuURQiAlJQXPnz+HkZHRR99IVKCbWXJXHBsby2ejEREREX3hjIyMYGVl9dETfwUuFHNlZWW996XdRERERPR5U1dXL9C7zYEiFIpEREREVDbw8ThEREREJBcLRSIiIiKSi4UiEREREcnFQpGIiIiI5GKhSERERERysVAkIiIiIrlYKBIRERGRXP8HaGwKH6P/FmAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| model_type   |   fold | split_type   | Metric              |    Score | Stage           | Split Type       |   Experiment |\n",
      "|:-------------|-------:|:-------------|:--------------------|---------:|:----------------|:-----------------|-------------:|\n",
      "| XGBoost      |      0 | random       | Validation Accuracy | 0.83871  | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      1 | random       | Validation Accuracy | 0.883117 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      2 | random       | Validation Accuracy | 0.876623 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      3 | random       | Validation Accuracy | 0.883117 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      4 | random       | Validation Accuracy | 0.883117 | Validation      | Standard Split   |          nan |\n",
      "| XGBoost      |      0 | uniprot      | Validation Accuracy | 0.650943 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      1 | uniprot      | Validation Accuracy | 0.834483 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      2 | uniprot      | Validation Accuracy | 0.554545 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      3 | uniprot      | Validation Accuracy | 0.539823 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      4 | uniprot      | Validation Accuracy | 0.797468 | Validation      | Target Split     |          nan |\n",
      "| XGBoost      |      0 | tanimoto     | Validation Accuracy | 0.821429 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      1 | tanimoto     | Validation Accuracy | 0.868852 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      2 | tanimoto     | Validation Accuracy | 0.839744 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      3 | tanimoto     | Validation Accuracy | 0.781609 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |      4 | tanimoto     | Validation Accuracy | 0.680272 | Validation      | Similarity Split |          nan |\n",
      "| XGBoost      |    nan | random       | Test Accuracy       | 0.767442 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Accuracy       | 0.790698 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Accuracy       | 0.767442 | (Average Score) | Standard Split   |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy       | 0.447059 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy       | 0.517647 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy       | 0.411765 | (Average Score) | Target Split     |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy       | 0.752941 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy       | 0.764706 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy       | 0.694118 | (Average Score) | Similarity Split |          nan |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | random       | Test Accuracy       | 0.77907  | (Majority Vote) | Standard Split   |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | uniprot      | Test Accuracy       | 0.447059 | (Majority Vote) | Target Split     |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| XGBoost      |    nan | tanimoto     | Test Accuracy       | 0.717647 | (Majority Vote) | Similarity Split |          nan |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.514914 | nan             | Dummy model      |            0 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.534884 | nan             | Dummy model      |            0 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.526994 | nan             | Dummy model      |            1 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.541176 | nan             | Dummy model      |            1 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Validation Accuracy | 0.518175 | nan             | Dummy model      |            2 |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Average Score)     |          |                 |                  |              |\n",
      "| nan          |    nan | nan          | Test Accuracy       | 0.564706 | nan             | Dummy model      |            2 |\n",
      "|              |        |              | (Majority Vote)     |          |                 |                  |              |\n",
      "Plotting performance for main part of the paper...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Pytorch performances:')\n",
    "plot_performance_metrics(\n",
    "    df_cv=reports['cv_train'],\n",
    "    df_test=reports['test'],\n",
    "    df_test_majority=reports['majority_vote'][reports['majority_vote']['cv_models'].isna()],\n",
    "    title=f'pytorch_performance',\n",
    "    show_plot=True,\n",
    "     metrics_to_plot = {\n",
    "        'val_acc': 'Validation Accuracy',\n",
    "        'val_roc_auc': 'Validation ROC AUC',\n",
    "        'val_f1_score': 'Validation F1 Score',\n",
    "        'val_precision': 'Validation Precision',\n",
    "        'val_recall': 'Validation Recall',\n",
    "        'test_acc': 'Test Accuracy',\n",
    "        'test_roc_auc': 'Test ROC AUC',\n",
    "        'test_f1_score': 'Test F1 Score',\n",
    "        'test_precision': 'Test Precision',\n",
    "        'test_recall': 'Test Recall',\n",
    "    },\n",
    ")\n",
    "\n",
    "print('XGBoost performances:')\n",
    "plot_performance_metrics(\n",
    "    df_cv=reports['xgboost_cv_train'],\n",
    "    df_test=reports['xgboost_test'],\n",
    "    df_test_majority=reports['xgboost_majority_vote'],\n",
    "    title=f'xgboost_performance',\n",
    "    show_plot=True,\n",
    "     metrics_to_plot = {\n",
    "        'val_acc': 'Validation Accuracy',\n",
    "        'val_roc_auc': 'Validation ROC AUC',\n",
    "        'val_f1_score': 'Validation F1 Score',\n",
    "        'val_precision': 'Validation Precision',\n",
    "        'val_recall': 'Validation Recall',\n",
    "        'test_acc': 'Test Accuracy',\n",
    "        'test_roc_auc': 'Test ROC AUC',\n",
    "        'test_f1_score': 'Test F1 Score',\n",
    "        'test_precision': 'Test Precision',\n",
    "        'test_recall': 'Test Recall',\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance_metrics(\n",
    "    df_cv=reports['xgboost_cv_train'],\n",
    "    df_test=reports['xgboost_test'],\n",
    "    df_test_majority=reports['xgboost_majority_vote'],\n",
    "    title=f'xgboost_summary_performance-best_models_as_test',\n",
    "    show_plot=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ablation Studies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3069606/464303105.py:72: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
      "  sns.barplot(data=final_df,\n",
      "/tmp/ipykernel_3069606/464303105.py:72: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
      "  sns.barplot(data=final_df,\n",
      "/tmp/ipykernel_3069606/464303105.py:72: UserWarning: The palette list has more values (4) than needed (1), which may not be intended.\n",
      "  sns.barplot(data=final_df,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_ablation_study(report):\n",
    "    # Define the ablation study combinations\n",
    "    ablation_study_combinations = [\n",
    "        'disabled smiles',\n",
    "        'disabled poi',\n",
    "        'disabled e3',\n",
    "        'disabled cell',\n",
    "        'disabled poi e3',\n",
    "        'disabled poi e3 smiles',\n",
    "        'disabled poi e3 cell',\n",
    "    ]\n",
    "\n",
    "    for group in report['split_type'].unique():    \n",
    "        baseline = report[report['disabled_embeddings'].isna()].copy()\n",
    "        baseline = baseline[baseline['split_type'] == group]\n",
    "        baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
    "        # metrics_to_show = ['val_acc', 'test_acc']\n",
    "        metrics_to_show = ['test_acc']\n",
    "        # baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "        baseline = baseline.melt(id_vars=['disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "\n",
    "        ablation_dfs = []\n",
    "        for disabled_embeddings in ablation_study_combinations:\n",
    "            tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
    "            tmp = tmp[tmp['split_type'] == group]\n",
    "            # tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "            tmp = tmp.melt(id_vars=['disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "            ablation_dfs.append(tmp)\n",
    "        ablation_df = pd.concat(ablation_dfs)\n",
    "\n",
    "        dummy_test_df = pd.DataFrame()\n",
    "        tmp = report[report['split_type'] == group]\n",
    "        dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
    "        dummy_test_df['metric'] = 'test_acc'\n",
    "        dummy_test_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "        # dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
    "        dummy_df = dummy_test_df\n",
    "\n",
    "        final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
    "\n",
    "        final_df['metric'] = final_df['metric'].map({\n",
    "            'val_acc': 'Validation Accuracy',\n",
    "            'test_acc': 'Test Accuracy',\n",
    "            'val_roc_auc': 'Val ROC-AUC',\n",
    "            'test_roc_auc': 'Test ROC-AUC',\n",
    "        })\n",
    "\n",
    "        final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
    "            'all embeddings enabled': 'All embeddings enabled',\n",
    "            'dummy': 'Dummy model',\n",
    "            'disabled smiles': 'Disabled PROTAC information',\n",
    "            'disabled e3': 'Disabled E3 information',\n",
    "            'disabled poi': 'Disabled POI information',\n",
    "            'disabled cell': 'Disabled cell information',\n",
    "            'disabled poi e3': 'Disabled E3 and POI info',\n",
    "            'disabled poi e3 smiles': 'Disabled compound, E3, and POI info\\n(only cell information left)',\n",
    "            'disabled poi e3 cell': 'Disabled cell, E3, and POI info\\n(only PROTAC information left)',\n",
    "        })\n",
    "\n",
    "        # Print final_df to latex\n",
    "        tmp  = final_df.groupby(['disabled_embeddings', 'metric']).mean().round(3)\n",
    "        # Remove fold column to tmp\n",
    "        tmp = tmp.reset_index() #.drop('fold', axis=1)\n",
    "\n",
    "        # print('DF to plot:\\n', tmp.to_markdown(index=False))\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(3, 5))\n",
    "        \n",
    "        # fig, ax = plt.subplots()\n",
    "\n",
    "        sns.barplot(data=final_df,\n",
    "            y='disabled_embeddings',\n",
    "            x='score',\n",
    "            hue='metric',\n",
    "            ax=ax,\n",
    "            errorbar=('sd', 1),\n",
    "            palette=sns.color_palette(palette, len(palette)),\n",
    "            saturation=1,\n",
    "        )\n",
    "\n",
    "        # ax.set_title(f'{group.replace(\"random\", \"standard\")} CV split')\n",
    "        ax.grid(axis='x', alpha=0.5)\n",
    "        ax.tick_params(axis='y', rotation=0)\n",
    "        ax.set_xlim(0, 1.0)\n",
    "        ax.xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "        ax.set_ylabel('')\n",
    "        ax.set_xlabel('')\n",
    "\n",
    "        # Plot the legend below the x-axis, outside the plot\n",
    "        ax.legend(loc='upper center', bbox_to_anchor=(0.02, -0.1))\n",
    "\n",
    "        # For each bar, add the rotated value (as percentage), inside the bar\n",
    "        for i, p in enumerate(plt.gca().patches):\n",
    "            # TODO: For some reasons, there is an additional bar being added at\n",
    "            # the end of the plot... it's not in the dataframe\n",
    "            if i == len(plt.gca().patches) - 1:\n",
    "                continue\n",
    "            value = '{:.1f}%'.format(100 * p.get_width())\n",
    "            y = p.get_y() + p.get_height() / 2\n",
    "            x = 0.2 # p.get_height() - p.get_height() / 2\n",
    "            plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, alpha=0.8)\n",
    "\n",
    "        plt.savefig(f'plots/ablation_study_{group}.pdf', bbox_inches='tight')\n",
    "\n",
    "reports['test']['disabled_embeddings'] = pd.NA\n",
    "plot_ablation_study(pd.concat([\n",
    "    reports['ablation'],\n",
    "    reports['test'],\n",
    "]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
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}