{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare XGBoost and Deep Learning (DNN) Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Add current directory to path\n",
    "os.sys.path.append(os.path.dirname(os.path.abspath('.')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import protac_degradation_predictor as pdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import protac_degradation_predictor as pdp\n",
    "import torch\n",
    "from rdkit import Chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Heavy Atom Count</th>\n",
       "      <th>Ring Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>...</th>\n",
       "      <th>Name</th>\n",
       "      <th>Assay (DC50/Dmax)</th>\n",
       "      <th>Exact Mass</th>\n",
       "      <th>XLogP3</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "      <th>POI Sequence</th>\n",
       "      <th>E3 Ligase Uniprot</th>\n",
       "      <th>E3 Ligase Sequence</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "      <th>Active - OR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
       "      <td>1486.282</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>24</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>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
       "      <td>1500.309</td>\n",
       "      <td>102</td>\n",
       "      <td>10</td>\n",
       "      <td>25</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>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
       "      <td>1514.336</td>\n",
       "      <td>103</td>\n",
       "      <td>10</td>\n",
       "      <td>26</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>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
       "      <td>1528.363</td>\n",
       "      <td>104</td>\n",
       "      <td>10</td>\n",
       "      <td>27</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>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
       "      <td>1542.390</td>\n",
       "      <td>105</td>\n",
       "      <td>10</td>\n",
       "      <td>28</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>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound ID Uniprot                                             Smiles  \\\n",
       "0            1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "1            2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "2            3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "3            4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "4            5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "\n",
       "  E3 Ligase                                              InChI  \\\n",
       "0       VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
       "1       VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
       "2       VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
       "3       VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
       "4       VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
       "\n",
       "                     InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "0  SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
       "1  HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
       "2  ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
       "3  FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
       "4  PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
       "\n",
       "   Ring Count  Rotatable Bond Count  ...  Name  Assay (DC50/Dmax)  Exact Mass  \\\n",
       "0          10                    24  ...   NaN                NaN         NaN   \n",
       "1          10                    25  ...   NaN                NaN         NaN   \n",
       "2          10                    26  ...   NaN                NaN         NaN   \n",
       "3          10                    27  ...   NaN                NaN         NaN   \n",
       "4          10                    28  ...   NaN                NaN         NaN   \n",
       "\n",
       "  XLogP3  Target (Parsed)                                       POI Sequence  \\\n",
       "0    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "1    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "2    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "3    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "4    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "\n",
       "   E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
       "0             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "1             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "3             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "4             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "\n",
       "  Cell Line Identifier Active - OR  \n",
       "0               MOLT-4         NaN  \n",
       "1               MOLT-4         NaN  \n",
       "2               MOLT-4         NaN  \n",
       "3               MOLT-4         NaN  \n",
       "4               MOLT-4        True  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "      <th>Dmax (%)</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q07817</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q07817</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>P00533</td>\n",
       "      <td>H1975/WR</td>\n",
       "      <td>C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>25.3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>P00533</td>\n",
       "      <td>H1975/WR</td>\n",
       "      <td>C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>5.9</td>\n",
       "      <td>100.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>PH1-PBMCs-hiPSC4F1</td>\n",
       "      <td>COc1cc2c(OC[C@@H]3CCC(=O)N3)ncc(C#CCCCCCCCCCCC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>820.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2122</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>250.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>857 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Uniprot Cell Line Identifier  \\\n",
       "4     Q07817               MOLT-4   \n",
       "7     Q07817               MOLT-4   \n",
       "60    P00533             H1975/WR   \n",
       "69    P00533             H1975/WR   \n",
       "72    Q9NWZ3   PH1-PBMCs-hiPSC4F1   \n",
       "...      ...                  ...   \n",
       "2121  O60885              HEK293T   \n",
       "2122  O60885              HEK293T   \n",
       "2125  O60885              HEK293T   \n",
       "2126  O60885              HEK293T   \n",
       "2127  O60885              HEK293T   \n",
       "\n",
       "                                                 Smiles E3 Ligase  DC50 (nM)  \\\n",
       "4     Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...       VHL       53.0   \n",
       "7     Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...       VHL       93.0   \n",
       "60    C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...       VHL       25.3   \n",
       "69    C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...       VHL        5.9   \n",
       "72    COc1cc2c(OC[C@@H]3CCC(=O)N3)ncc(C#CCCCCCCCCCCC...       VHL     3000.0   \n",
       "...                                                 ...       ...        ...   \n",
       "2121  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B      820.0   \n",
       "2122  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...     FEM1B      250.0   \n",
       "2125  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B     1100.0   \n",
       "2126  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B     3600.0   \n",
       "2127  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B     1600.0   \n",
       "\n",
       "      Dmax (%) Active  \n",
       "4        100.0   True  \n",
       "7         90.0   True  \n",
       "60        90.0   True  \n",
       "69       100.0   True  \n",
       "72        50.0  False  \n",
       "...        ...    ...  \n",
       "2121      81.0   True  \n",
       "2122      94.0   True  \n",
       "2125      85.0  False  \n",
       "2126      60.0  False  \n",
       "2127      80.0  False  \n",
       "\n",
       "[857 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "protac_df = pdp.load_curated_dataset()\n",
    "display(protac_df.head())\n",
    "\n",
    "pDC50_threshold = 6.0\n",
    "Dmax_threshold = 0.6\n",
    "\n",
    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')\n",
    "protac_df['Active'] = protac_df.apply(\n",
    "    lambda x: pdp.is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1\n",
    ")\n",
    "\n",
    "protac_df = protac_df[~protac_df['Active'].isna()]\n",
    "\n",
    "relevant_cols = ['Uniprot', 'Cell Line Identifier', 'Smiles', 'E3 Ligase', 'DC50 (nM)', 'Dmax (%)', 'Active']\n",
    "display(protac_df[relevant_cols])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the predictions over the whole dataset:\n",
    "\n",
    "NOTE: This will include the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Heavy Atom Count</th>\n",
       "      <th>Ring Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>...</th>\n",
       "      <th>dnn_mean</th>\n",
       "      <th>dnn_majority_vote</th>\n",
       "      <th>dnn_model_n0</th>\n",
       "      <th>dnn_model_n1</th>\n",
       "      <th>dnn_model_n2</th>\n",
       "      <th>xgb_mean</th>\n",
       "      <th>xgb_majority_vote</th>\n",
       "      <th>xgb_model_n0</th>\n",
       "      <th>xgb_model_n1</th>\n",
       "      <th>xgb_model_n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
       "      <td>1542.390</td>\n",
       "      <td>105</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.926091</td>\n",
       "      <td>True</td>\n",
       "      <td>0.922488</td>\n",
       "      <td>0.971058</td>\n",
       "      <td>0.884728</td>\n",
       "      <td>0.977803</td>\n",
       "      <td>True</td>\n",
       "      <td>0.979807</td>\n",
       "      <td>0.972958</td>\n",
       "      <td>0.980646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C76H96ClF3N10O9S4/c1-51(53-17-19-55(2...</td>\n",
       "      <td>FQLHSKINBMZPFV-WTFVIAODSA-N</td>\n",
       "      <td>1514.380</td>\n",
       "      <td>103</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934987</td>\n",
       "      <td>True</td>\n",
       "      <td>0.938649</td>\n",
       "      <td>0.967957</td>\n",
       "      <td>0.898355</td>\n",
       "      <td>0.975625</td>\n",
       "      <td>True</td>\n",
       "      <td>0.975480</td>\n",
       "      <td>0.972628</td>\n",
       "      <td>0.978767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>P00533</td>\n",
       "      <td>C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C62H79N11O7S/c1-8-53(75)66-45-19-18-2...</td>\n",
       "      <td>HQGHIKHHXKBFOJ-OIQPADLRSA-N</td>\n",
       "      <td>1122.451</td>\n",
       "      <td>81</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532295</td>\n",
       "      <td>True</td>\n",
       "      <td>0.651652</td>\n",
       "      <td>0.704779</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.917975</td>\n",
       "      <td>True</td>\n",
       "      <td>0.946647</td>\n",
       "      <td>0.901747</td>\n",
       "      <td>0.905530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>P00533</td>\n",
       "      <td>C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C56H67N11O7S/c1-8-47(69)60-39-13-12-1...</td>\n",
       "      <td>CBWIPIZLLNOTMZ-PDMHGPQCSA-N</td>\n",
       "      <td>1038.289</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>0.586461</td>\n",
       "      <td>True</td>\n",
       "      <td>0.713173</td>\n",
       "      <td>0.802957</td>\n",
       "      <td>0.243252</td>\n",
       "      <td>0.922217</td>\n",
       "      <td>True</td>\n",
       "      <td>0.919568</td>\n",
       "      <td>0.927843</td>\n",
       "      <td>0.919240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73</td>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>COc1cc2c(OC[C@@H]3CCC(=O)N3)ncc(C#CCCCCCCCCCCC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C51H65N7O8S/c1-32-45(67-31-55-32)34-2...</td>\n",
       "      <td>DFCKZXBDTTURLE-ABFWCBLXSA-N</td>\n",
       "      <td>936.189</td>\n",
       "      <td>67</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.378277</td>\n",
       "      <td>False</td>\n",
       "      <td>0.456931</td>\n",
       "      <td>0.610394</td>\n",
       "      <td>0.067505</td>\n",
       "      <td>0.103941</td>\n",
       "      <td>False</td>\n",
       "      <td>0.107219</td>\n",
       "      <td>0.095667</td>\n",
       "      <td>0.108936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>3262</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>InChI=1S/C38H41Cl2N9O5S/c1-23-24(2)55-38-35(23...</td>\n",
       "      <td>QVNLOGQWJMAEJT-LJAQVGFWSA-N</td>\n",
       "      <td>806.777</td>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.461836</td>\n",
       "      <td>False</td>\n",
       "      <td>0.631028</td>\n",
       "      <td>0.531522</td>\n",
       "      <td>0.222957</td>\n",
       "      <td>0.937156</td>\n",
       "      <td>True</td>\n",
       "      <td>0.934651</td>\n",
       "      <td>0.940516</td>\n",
       "      <td>0.936302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>3263</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>InChI=1S/C39H43Cl2N9O4S/c1-24-25(2)55-39-36(24...</td>\n",
       "      <td>IEXUFNUFUSSKQI-PMERELPUSA-N</td>\n",
       "      <td>804.805</td>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.478040</td>\n",
       "      <td>False</td>\n",
       "      <td>0.602999</td>\n",
       "      <td>0.603999</td>\n",
       "      <td>0.227121</td>\n",
       "      <td>0.512707</td>\n",
       "      <td>True</td>\n",
       "      <td>0.478524</td>\n",
       "      <td>0.512765</td>\n",
       "      <td>0.546831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>3264</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>InChI=1S/C40H45Cl2N9O6S/c1-25-26(2)58-40-37(25...</td>\n",
       "      <td>CPDVGNBJFIONLX-HKBQPEDESA-N</td>\n",
       "      <td>850.830</td>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.410410</td>\n",
       "      <td>False</td>\n",
       "      <td>0.426177</td>\n",
       "      <td>0.582066</td>\n",
       "      <td>0.222986</td>\n",
       "      <td>0.118575</td>\n",
       "      <td>False</td>\n",
       "      <td>0.107306</td>\n",
       "      <td>0.111763</td>\n",
       "      <td>0.136655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>3265</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>InChI=1S/C42H49Cl2N9O7S/c1-27-28(2)61-42-39(27...</td>\n",
       "      <td>QMBOIOPJFSHXPV-XIFFEERXSA-N</td>\n",
       "      <td>894.883</td>\n",
       "      <td>61</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.418736</td>\n",
       "      <td>False</td>\n",
       "      <td>0.438183</td>\n",
       "      <td>0.595072</td>\n",
       "      <td>0.222955</td>\n",
       "      <td>0.184872</td>\n",
       "      <td>False</td>\n",
       "      <td>0.159074</td>\n",
       "      <td>0.173036</td>\n",
       "      <td>0.222507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>3266</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>InChI=1S/C44H53Cl2N9O8S/c1-29-30(2)64-44-41(29...</td>\n",
       "      <td>UUCUKSPUFPMKNK-DHUJRADRSA-N</td>\n",
       "      <td>938.936</td>\n",
       "      <td>64</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.472904</td>\n",
       "      <td>False</td>\n",
       "      <td>0.470890</td>\n",
       "      <td>0.724864</td>\n",
       "      <td>0.222957</td>\n",
       "      <td>0.122594</td>\n",
       "      <td>False</td>\n",
       "      <td>0.107752</td>\n",
       "      <td>0.154437</td>\n",
       "      <td>0.105594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>857 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Compound ID Uniprot                                             Smiles  \\\n",
       "0              5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "1              8  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "2             61  P00533  C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...   \n",
       "3             70  P00533  C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...   \n",
       "4             73  Q9NWZ3  COc1cc2c(OC[C@@H]3CCC(=O)N3)ncc(C#CCCCCCCCCCCC...   \n",
       "..           ...     ...                                                ...   \n",
       "852         3262  O60885  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...   \n",
       "853         3263  O60885  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...   \n",
       "854         3264  O60885  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...   \n",
       "855         3265  O60885  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...   \n",
       "856         3266  O60885  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...   \n",
       "\n",
       "    E3 Ligase                                              InChI  \\\n",
       "0         VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
       "1         VHL  InChI=1S/C76H96ClF3N10O9S4/c1-51(53-17-19-55(2...   \n",
       "2         VHL  InChI=1S/C62H79N11O7S/c1-8-53(75)66-45-19-18-2...   \n",
       "3         VHL  InChI=1S/C56H67N11O7S/c1-8-47(69)60-39-13-12-1...   \n",
       "4         VHL  InChI=1S/C51H65N7O8S/c1-32-45(67-31-55-32)34-2...   \n",
       "..        ...                                                ...   \n",
       "852     FEM1B  InChI=1S/C38H41Cl2N9O5S/c1-23-24(2)55-38-35(23...   \n",
       "853     FEM1B  InChI=1S/C39H43Cl2N9O4S/c1-24-25(2)55-39-36(24...   \n",
       "854     FEM1B  InChI=1S/C40H45Cl2N9O6S/c1-25-26(2)58-40-37(25...   \n",
       "855     FEM1B  InChI=1S/C42H49Cl2N9O7S/c1-27-28(2)61-42-39(27...   \n",
       "856     FEM1B  InChI=1S/C44H53Cl2N9O8S/c1-29-30(2)64-44-41(29...   \n",
       "\n",
       "                       InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "0    PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
       "1    FQLHSKINBMZPFV-WTFVIAODSA-N          1514.380               103   \n",
       "2    HQGHIKHHXKBFOJ-OIQPADLRSA-N          1122.451                81   \n",
       "3    CBWIPIZLLNOTMZ-PDMHGPQCSA-N          1038.289                75   \n",
       "4    DFCKZXBDTTURLE-ABFWCBLXSA-N           936.189                67   \n",
       "..                           ...               ...               ...   \n",
       "852  QVNLOGQWJMAEJT-LJAQVGFWSA-N           806.777                55   \n",
       "853  IEXUFNUFUSSKQI-PMERELPUSA-N           804.805                55   \n",
       "854  CPDVGNBJFIONLX-HKBQPEDESA-N           850.830                58   \n",
       "855  QMBOIOPJFSHXPV-XIFFEERXSA-N           894.883                61   \n",
       "856  UUCUKSPUFPMKNK-DHUJRADRSA-N           938.936                64   \n",
       "\n",
       "     Ring Count  Rotatable Bond Count  ...  dnn_mean  dnn_majority_vote  \\\n",
       "0            10                    28  ...  0.926091               True   \n",
       "1            10                    28  ...  0.934987               True   \n",
       "2             8                    25  ...  0.532295               True   \n",
       "3             8                    19  ...  0.586461               True   \n",
       "4             6                    21  ...  0.378277              False   \n",
       "..          ...                   ...  ...       ...                ...   \n",
       "852           6                    15  ...  0.461836              False   \n",
       "853           6                    15  ...  0.478040              False   \n",
       "854           6                    18  ...  0.410410              False   \n",
       "855           6                    21  ...  0.418736              False   \n",
       "856           6                    24  ...  0.472904              False   \n",
       "\n",
       "     dnn_model_n0 dnn_model_n1  dnn_model_n2  xgb_mean  xgb_majority_vote  \\\n",
       "0        0.922488     0.971058      0.884728  0.977803               True   \n",
       "1        0.938649     0.967957      0.898355  0.975625               True   \n",
       "2        0.651652     0.704779      0.240453  0.917975               True   \n",
       "3        0.713173     0.802957      0.243252  0.922217               True   \n",
       "4        0.456931     0.610394      0.067505  0.103941              False   \n",
       "..            ...          ...           ...       ...                ...   \n",
       "852      0.631028     0.531522      0.222957  0.937156               True   \n",
       "853      0.602999     0.603999      0.227121  0.512707               True   \n",
       "854      0.426177     0.582066      0.222986  0.118575              False   \n",
       "855      0.438183     0.595072      0.222955  0.184872              False   \n",
       "856      0.470890     0.724864      0.222957  0.122594              False   \n",
       "\n",
       "    xgb_model_n0 xgb_model_n1 xgb_model_n2  \n",
       "0       0.979807     0.972958     0.980646  \n",
       "1       0.975480     0.972628     0.978767  \n",
       "2       0.946647     0.901747     0.905530  \n",
       "3       0.919568     0.927843     0.919240  \n",
       "4       0.107219     0.095667     0.108936  \n",
       "..           ...          ...          ...  \n",
       "852     0.934651     0.940516     0.936302  \n",
       "853     0.478524     0.512765     0.546831  \n",
       "854     0.107306     0.111763     0.136655  \n",
       "855     0.159074     0.173036     0.222507  \n",
       "856     0.107752     0.154437     0.105594  \n",
       "\n",
       "[857 rows x 45 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "dnn_preds = []\n",
    "xgb_preds = []\n",
    "batch_size = 256\n",
    "\n",
    "for i in range(0, len(protac_df), batch_size):\n",
    "    # Accumulate the rows into a batch\n",
    "    batch = protac_df.iloc[i:i+batch_size]\n",
    "    if len(batch) == 0:\n",
    "        continue\n",
    "    \n",
    "    # Get predictions for DNN model\n",
    "    pred_dnn = pdp.get_protac_active_proba(\n",
    "        protac_smiles=batch['Smiles'].tolist(),\n",
    "        e3_ligase=batch['E3 Ligase'].tolist(),\n",
    "        target_uniprot=batch['Uniprot'].tolist(),\n",
    "        cell_line=batch['Cell Line Identifier'].tolist(),\n",
    "        device=device,\n",
    "    )\n",
    "    \n",
    "    for j in range(len(batch)):\n",
    "        dnn_pred = {\n",
    "            'mean': pred_dnn['mean'][j],\n",
    "            'majority_vote': pred_dnn['majority_vote'][j],\n",
    "        }\n",
    "        for k in range(pred_dnn['preds'].shape[0]):  # Assuming num_models is 3\n",
    "            dnn_pred[f'model_n{k}'] = pred_dnn['preds'][k][j]\n",
    "        dnn_preds.append(dnn_pred)\n",
    "    \n",
    "    # Get predictions for XGBoost model\n",
    "    pred_xgb = pdp.get_protac_active_proba(\n",
    "        protac_smiles=batch['Smiles'].tolist(),\n",
    "        e3_ligase=batch['E3 Ligase'].tolist(),\n",
    "        target_uniprot=batch['Uniprot'].tolist(),\n",
    "        cell_line=batch['Cell Line Identifier'].tolist(),\n",
    "        device=device,\n",
    "        use_xgboost_models=True,\n",
    "    )\n",
    "    \n",
    "    for j in range(len(batch)):\n",
    "        xgb_pred = {\n",
    "            'mean': pred_xgb['mean'][j],\n",
    "            'majority_vote': pred_xgb['majority_vote'][j],\n",
    "        }\n",
    "        for k in range(pred_xgb['preds'].shape[0]):  # Assuming num_models is 3\n",
    "            xgb_pred[f'model_n{k}'] = pred_xgb['preds'][k][j]\n",
    "        xgb_preds.append(xgb_pred)\n",
    "\n",
    "# Convert accumulated predictions into DataFrames\n",
    "dnn_df = pd.DataFrame(dnn_preds)\n",
    "xgb_df = pd.DataFrame(xgb_preds)\n",
    "\n",
    "# Concatenate the original DataFrame with predictions\n",
    "result_df = pd.concat([\n",
    "        protac_df.reset_index(drop=True),\n",
    "        dnn_df.add_prefix('dnn_'),\n",
    "        xgb_df.add_prefix('xgb_'),\n",
    "    ], axis=1)\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe columns with XGBoost predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xgb_mean</th>\n",
       "      <th>xgb_model_n0</th>\n",
       "      <th>xgb_model_n1</th>\n",
       "      <th>xgb_model_n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>857.000000</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>857.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.519258</td>\n",
       "      <td>0.519648</td>\n",
       "      <td>0.518779</td>\n",
       "      <td>0.519347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.444579</td>\n",
       "      <td>0.443365</td>\n",
       "      <td>0.443698</td>\n",
       "      <td>0.447897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010143</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.011076</td>\n",
       "      <td>0.008641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.030862</td>\n",
       "      <td>0.030901</td>\n",
       "      <td>0.033069</td>\n",
       "      <td>0.029464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.767127</td>\n",
       "      <td>0.766466</td>\n",
       "      <td>0.729913</td>\n",
       "      <td>0.788420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.961856</td>\n",
       "      <td>0.962569</td>\n",
       "      <td>0.961087</td>\n",
       "      <td>0.965674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.988086</td>\n",
       "      <td>0.988136</td>\n",
       "      <td>0.986411</td>\n",
       "      <td>0.990691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         xgb_mean  xgb_model_n0  xgb_model_n1  xgb_model_n2\n",
       "count  857.000000    857.000000    857.000000    857.000000\n",
       "mean     0.519258      0.519648      0.518779      0.519347\n",
       "std      0.444579      0.443365      0.443698      0.447897\n",
       "min      0.010143      0.010036      0.011076      0.008641\n",
       "25%      0.030862      0.030901      0.033069      0.029464\n",
       "50%      0.767127      0.766466      0.729913      0.788420\n",
       "75%      0.961856      0.962569      0.961087      0.965674\n",
       "max      0.988086      0.988136      0.986411      0.990691"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df.filter(like='xgb_').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the mean of the preditions of the \"best\" models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "# Plot histogram of dnn_mean and xgb_mean\n",
    "sns.histplot(result_df['dnn_mean'], label='DNN (mean)', kde=True, color='C0', alpha=0.3)\n",
    "sns.histplot(result_df['xgb_mean'], label='XGB (mean)', kde=True, color='C1', alpha=0.3)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the \"best\" models used in majority voting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the KDE plots without histograms\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i in range(3):\n",
    "    sns.kdeplot(result_df[f'dnn_model_n{i}'], label=f'DNN (model {i})', color=f'C{i}')\n",
    "    sns.kdeplot(result_df[f'xgb_model_n{i}'], label=f'XGB (model {i})', color=f'C{i+3}', linestyle='dashed')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the entries for which the majority vote is different between DNN and XGBoost:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "      <th>Dmax (%)</th>\n",
       "      <th>Active</th>\n",
       "      <th>dnn_majority_vote</th>\n",
       "      <th>xgb_majority_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>PH1-PBMCs-hiPSC4F1</td>\n",
       "      <td>CC[C@@H]1[C@H](F)C(=O)N[C@@H]1COc1ncc(C#CCCCCC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>259.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>PH1-PBMCs-hiPSC4F1</td>\n",
       "      <td>CC[C@@H]1[C@H](F)C(=O)N[C@@H]1COc1ncc(C#CCN2CC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>151.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>P10275</td>\n",
       "      <td>LNCaP</td>\n",
       "      <td>CC(=O)N[C@H](C(=O)N1C[C@@H](O)C[C@@H]1C(=O)N[C...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>O60885</td>\n",
       "      <td>22Rv1</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>P10275</td>\n",
       "      <td>VCaP</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>15.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>P10415</td>\n",
       "      <td>293T FOXP3</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>Q07817</td>\n",
       "      <td>293T FOXP3</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>810.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>820.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>250.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Uniprot Cell Line Identifier  \\\n",
       "5    Q9NWZ3   PH1-PBMCs-hiPSC4F1   \n",
       "6    Q9NWZ3   PH1-PBMCs-hiPSC4F1   \n",
       "60   P10275                LNCaP   \n",
       "61   O60885                22Rv1   \n",
       "68   P10275                 VCaP   \n",
       "..      ...                  ...   \n",
       "831  P10415           293T FOXP3   \n",
       "844  Q07817           293T FOXP3   \n",
       "851  O60885              HEK293T   \n",
       "852  O60885              HEK293T   \n",
       "853  O60885              HEK293T   \n",
       "\n",
       "                                                Smiles E3 Ligase  DC50 (nM)  \\\n",
       "5    CC[C@@H]1[C@H](F)C(=O)N[C@@H]1COc1ncc(C#CCCCCC...       VHL      259.0   \n",
       "6    CC[C@@H]1[C@H](F)C(=O)N[C@@H]1COc1ncc(C#CCN2CC...       VHL      151.0   \n",
       "60   CC(=O)N[C@H](C(=O)N1C[C@@H](O)C[C@@H]1C(=O)N[C...       VHL        NaN   \n",
       "61   Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...      CRBN        NaN   \n",
       "68   Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...       VHL       15.0   \n",
       "..                                                 ...       ...        ...   \n",
       "831  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...       VHL        NaN   \n",
       "844  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...       VHL        NaN   \n",
       "851  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B      810.0   \n",
       "852  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B      820.0   \n",
       "853  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...     FEM1B      250.0   \n",
       "\n",
       "     Dmax (%) Active  dnn_majority_vote  xgb_majority_vote  \n",
       "5        90.0   True              False               True  \n",
       "6        90.0   True              False               True  \n",
       "60        5.0  False               True              False  \n",
       "61       36.0  False               True              False  \n",
       "68       76.0   True              False               True  \n",
       "..        ...    ...                ...                ...  \n",
       "831      47.0  False               True              False  \n",
       "844      38.0  False               True              False  \n",
       "851      71.0   True              False               True  \n",
       "852      81.0   True              False               True  \n",
       "853      94.0   True              False               True  \n",
       "\n",
       "[134 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = result_df[result_df['dnn_majority_vote'] != result_df['xgb_majority_vote']]\n",
    "tmp[relevant_cols + ['dnn_majority_vote', 'xgb_majority_vote']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries for which the majority vote is different between DNN and XGBoost: 134\n",
      "Number of entries for which the majority vote of XGBoost is different from the Active column: 7\n",
      "Number of entries for which the majority vote of DNN is different from the Active column: 127\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of entries for which the majority vote is different between DNN and XGBoost: {len(tmp)}')\n",
    "print(f'Number of entries for which the majority vote of XGBoost is different from the Active column: {len(tmp[tmp[\"Active\"] != tmp[\"xgb_majority_vote\"]])}')\n",
    "print(f'Number of entries for which the majority vote of DNN is different from the Active column: {len(tmp[tmp[\"Active\"] != tmp[\"dnn_majority_vote\"]])}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KL Divergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate and plot KL divergence between DNN and XGBoost predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DNN vs. XGB model N.0: 7.322032476908708\n",
      "DNN vs. XGB model N.1: 5.150240075698975\n",
      "DNN vs. XGB model N.2: 6.874510246266886\n",
      "DNN vs. XGB mean: 7.969119274983867\n",
      "DNN vs. XGB majority vote: 0.07029106924951611\n",
      "True vs. DNN majority voto: 0.004405425756712149\n",
      "True vs. XGB majority voto: 0.03965522705930391\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import entropy\n",
    "\n",
    "def kl_divergence(p, q, bins=20):\n",
    "    # Histogram bins\n",
    "    hist_p, bin_edges = np.histogram(p, bins=bins, density=True)\n",
    "    hist_q, _ = np.histogram(q, bins=bin_edges, density=True)\n",
    "\n",
    "    # Make sure no zero values in densities (adjust as necessary)\n",
    "    hist_p += 1e-10\n",
    "    hist_q += 1e-10\n",
    "\n",
    "    return entropy(hist_p, hist_q)\n",
    "\n",
    "\n",
    "bins = 50\n",
    "kl_divergences = {}\n",
    "for i in range(3):\n",
    "    kl_divergences[f'DNN vs. XGB model N.{i}'] = kl_divergence(result_df[f'dnn_model_n{i}'], result_df[f'xgb_model_n{i}'], bins=bins)\n",
    "\n",
    "kl_divergences['DNN vs. XGB mean'] = kl_divergence(result_df['dnn_mean'], result_df['xgb_mean'], bins=bins)\n",
    "kl_divergences['DNN vs. XGB majority vote'] = kl_divergence(result_df['dnn_majority_vote'].astype(int), result_df['xgb_majority_vote'].astype(int), bins=bins)\n",
    "\n",
    "# Get df where the Active column is not NaN\n",
    "active_df = result_df[~result_df['Active'].isna()]\n",
    "kl_divergences['True vs. DNN majority voto'] = kl_divergence(active_df['Active'].astype(int), active_df['dnn_majority_vote'].astype(int), bins=bins)\n",
    "kl_divergences['True vs. XGB majority voto'] = kl_divergence(active_df['Active'].astype(int), active_df['xgb_majority_vote'].astype(int), bins=bins)\n",
    "\n",
    "for key, value in kl_divergences.items():\n",
    "    print(f'{key}: {value}')\n",
    "\n",
    "# Plot KL divergences\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(kl_divergences.keys(), kl_divergences.values())\n",
    "plt.ylabel('KL Divergence')\n",
    "# Put values on top of the bars\n",
    "for key, value in kl_divergences.items():\n",
    "    plt.text(key, value, f'{value:.4f}', ha='center', va='bottom')\n",
    "# Rotate x-axis labels\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC-AUC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dFpEeKmgSPyGEUCqVombNmqJmzZoaE/AplUrx+++/izZt2ggrKythYmIiGjRoIObOnVvo7LlBQUGiW7duwtbWVhgaGgonJycxePBgcezYsQLXiYmJEYaGhmL48OEFLpOWlibMzMxEv379NNr/+usv0bJlS2Fubi4UCoXw8PAQc+fO1bj0+N9evHghZs2aJRo1aiTMzMzUk9JNnz5dREVFFbj/f7tz544YO3ascHNzE8bGxsLS0lK0adNG/PTTTxr7TUtLE++8846wtrYWlpaWYtCgQeLZs2cFXgpe0GXtQgixYsUKAUBYWlqK9PT0fJe5cuWK6N+/v6hcubJQKBSievXqYtCgQeLw4cOv/EyXL18WAPLM3pw7id9/ARATJkzQaMu9/H3hwoUa7eHh4WLEiBHC0dFRGBkZCRcXF+Hn5yeCgoLUy2RkZIhPPvlEODk5CVNTU9GmTRtx5swZ0aFDB41L+nMvBQ8MDMx33/+99L5FixZi2LBhr/z8RKVBJkQBw/aJiKhEdO7cGc7Ozvjzzz+ljqITISEhaNq0KS5fvlzgQGui0sTihoiolJ07dw7t2rXD3bt39eLO4G+++SZUKlW+MzwTSYHFDREREekVXi1FREREeoXFDREREekVFjdERESkV1jcEBERkV6pcJP4qVQqPH36FJaWlrzBGxERUTkhhEBycjKcnZ1feWPfClfcPH36tFg38yMiIiLpPXr0CFWrVi10mQpX3FhaWgJ4eXCsrKwkTkNERERFkZSUBFdXV/X3eGEqXHGTeyrKysqKxQ0REVE5U5QhJRxQTERERHqFxQ0RERHpFRY3REREpFdY3BAREZFeYXFDREREeoXFDREREekVFjdERESkV1jcEBERkV5hcUNERER6hcUNERER6RVJi5sTJ06gd+/ecHZ2hkwmw/bt21+5zrFjx9C0aVMoFArUqlULa9asKfGcREREVH5IWtykpqbC09MTS5cuLdLyERER6NWrF3x8fBASEoKPP/4YY8aMwf79+0s4KREREZUXkt44s0ePHujRo0eRl1++fDlq1KiBH374AQBQr149BAcH48cff4Svr29JxSSipChAlS11inIpLUuJxPQsqWMQlSojhSnsHKtJtv9ydVfwM2fOoEuXLhptvr6++PjjjwtcJzMzE5mZmerXSUlJJRWvbMpKBe4dBnIyX70sUX6OLQDiw6VOUW6Z/e9BVJHcNqwHuxlnJdt/uSpuoqOj4eDgoNHm4OCApKQkpKenw9TUNM86CxYswNy5c0srom49vghc2wQIVfG3cWGl7vIQGZqU+i4FgByl+N+zkqNUlez2iSoSpVza8qJcFTfFMX36dEyePFn9OikpCa6urhImKoRKBaQ+A45/Bzw6D8Rc1+32a3TQ7fao4rCwB3p8B5jZAgBepGZh0uYQxCSVfI/grajS721tU6uyzrYll8kwrGV1+DZw1Nk2icqSBw8eICgoCFWqVMGwYcMgl8vRQOJM5aq4cXR0RExMjEZbTEwMrKys8u21AQCFQgGFQlEa8Yov7i4Qfx9YPyj/9xsFALY1i799M1ugyXDAmJ3jFVVmjhKXIl8g53V6Jx4rAcQCAC5ExuNYWKxuwhWRqZEB3m3vXqL7sLdSYFAzVxgZcJYMolcRQuDkyZM4evQohBBQKBRITU2FpaWl1NHKV3HTqlUr7N27V6Pt4MGDaNWqlUSJdOB5OPBzM822yrWATjMAE2vAuhpgV0uabKQ3Zmy7gcBLj3W+3YYuVpja3UPn2/0vM2MDNHG1gVwuK/F9EdGrpaSkYNu2bQgPfzkez9PTE7169YKxsbHEyV6StLhJSUnBvXv31K8jIiIQEhICW1tbVKtWDdOnT8eTJ0+wdu1aAMC4cePw888/47PPPsPbb7+NI0eOYPPmzdizZ49UH+H1ZCQBv/d8+dxAAdjVAep0AzrNBGT8JU7aEUJg7q5Q3IlJzvPejSeJAACXSqawNjXSyf4MDWSY4FML7WpX0cn2iKh8iIiIwJYtW5CSkgIjIyP06tULXl5eUsfSIGlxc/HiRfj4+Khf546NGTlyJNasWYOoqCg8fPhQ/X6NGjWwZ88eTJo0CUuWLEHVqlWxcuXK8nsZ+P1jQEr0y+dubYDh2ySNQyXnWVIGPt92HSfvxpXYsNisnFcPPP9hkCdauutuPAkRVSwqlQp79+5FSkoK7O3tERAQgCpVyt4fODIhRIW6RCApKQnW1tZITEyElZVV6e48/QVwc/s/l2VHXQWurn/5/NP7gDm/dPRV98UncDs6b49KSVnypleeNgcrE7SoYQsZewWJ6DVER0fj4sWL8PX1hZGRbnqCi0Kb7+9yNeam3Du+EDibz2zMtbqwsCkDYpIysPjQHSRl5Oh823efpQAAfh/9Buo6lNxgO5kMcLQyYQFDRDoTHh6OhIQEeHt7A3h5cY+fn5/EqQrH4qY0CAEkPQES/3eKzaERUKXOy+dyQ+CNsdJlI7UVJ+5jw/lHJbZ9A7kMnlUrwda8bAy4IyIqjEqlwtGjRxEcHAy5XA5nZ2c4OTlJHatIWNyUhr8/A87/9s/rxoOANh9Kl4fy2HLpMVYGRwAAmlW3QW9PZ53vw8PRkoUNEZULSUlJCAoKUo97bdKkSZkcW1MQFjel4emVl/+VGwHmVQB3TqZXljx4nopPAq+qX4/3qYlOHg6FrEFEpL/u3r2Lbdu2IS0tDQqFAn369EGDBlJPy6cdFjeladBawKOn1CkqHKVKICNbqdG27twDbL38BAA0BvpO6+HBwoaIKqzDhw/j5MmTAAAnJycEBATA1tZW4lTaY3FDekkIgZtPkxAem4KPNoYUaZ033GwwtIV0d7ElIpJa7mz/LVq0QNeuXWFoWD7LhPKZmug/niakY8ulx8hSvpzr5XT4c1x68OKV6y17qyksTYxQycwIDZyteJUREVU4WVlZ6pmFW7VqhapVq6JatfL9hx6LG9ILiw/dweaL+d9eoIGzFQK8q+LN5po/rEYGchhwOn8iqqCUSiUOHjyIe/fu4d1334WxsTFkMlm5L2wAFjdUxgghcPdZSp4xMoVZczpSPX6meQ1b1HN8OY+MoYEcQ5pXQy17ixLJSkRUXr148QKBgYF4+vQpACAsLAyNGjWSOJXusLgpafH3gccXpE5RLsQmZ6Lvz8F4mphR7G1M7V4X3tXL3+A3IqLSEhoaih07diAzMxOmpqbw9/dH3bp1pY6lUyxuStrWd/95bqiQLkcZd/b+c7z521mNNpdKpkVeX2Eox6pRb6CGnbmuoxER6YWcnBwcOHAA58+fBwC4urpi4MCBsLa2ljiZ7rG4KUkq5T9z3FRtDri1lTZPGbb40B31c2drE+z6oC0qW7AYJCLSlX8XNm3btoWPjw8MDAwkTlUyWNyUpHUDAdX/7lPU9Uv23OTjaUI6zkU8R/T/TkX19XLGDwGeMDSQS5yMiEi/tG/fHpGRkejWrRtq1aoldZwSxeKmJOX22hgYA476M1BLl0b/fgFhMf9MotfXy5mFDRGRDmRnZ+P27dvqgcIWFhZ4//33K8SUFyxuSooQQOb/vrTHBQMKXrGTS6kSSMvKwQ8H7qgLG+/qNqhZxRyt3O0kTkdEVP7FxcUhMDAQMTExkMvl6tsnVITCBmBxU3LW9v3nlBSpZeYo0X3xSUTEpWq0rxrZDJXMeFNJIqLXdfXqVezevRvZ2dkwNzdXzzpckbC4KSkRx1/+16QSUKm6pFHKkqVH7uUpbE5+5sPChojoNWVlZeHvv//GlSsvh0TUqFED/fv3h6WlpcTJSh+Lm5I24TxgZCJ1CsllZCsx+NczuPo4EQBgb6nAic98oDCUV5huUiKikvLs2TMEBgYiNjYWMpkMHTt2RLt27SCXV8wxjCxuSpqsYv6P9V/nI+LVhQ0ALBrkBRMj/bwEkYiotL148QKxsbGwtLTEgAED4ObmJnUkSbG4oVKhVAn184szusCOc9gQEb0WIYS657tu3bro06cP6tatC3NzTmbKbgUqcSmZOZi18wYAoJGLNQsbIqLXFB0djdWrVyMx8Z8e8aZNm7Kw+R8WN1TijofF4lF8OgDA1pwDh4mIiksIgYsXL2LlypV49OgRDhw4IHWkMomnpajEZSn/ucP3twMaS5iEiKj8yszMxK5du3Djxsue8Dp16qBXr14SpyqbWNzQa9lzLQqnwuMKXSYi9uWl3+1q28HRmleOERFpKyoqCoGBgYiPj4dcLkeXLl3QqlUrXm1aABY3VGxZOSpMWH+5yMtbmRqVYBoiIv0UERGBv/76C0qlEtbW1ggICEDVqlWljlWmsbihYslRqtB98Qn16wk+NaEwLPjSbkMDGfp4OpdGNCIivVK1alXY2dnBxsYGffv2rZAzDmuLxQ0Vy++nInH/fzMNN6tugynd6rJ7lIhIR549ewY7OzvI5XIYGRlh5MiRMDU15e/ZIuLVUqS1xy/SMG/vLQCAwlCOwHE870tEpAtCCJw5cwa//vorgoOD1e1mZmb8PasF9txQkT18noaI56kYufq8um35cG/+wBER6UB6ejq2b9+OsLAwAC97b/49UR8VHYsbKpKz95/jzd/OarR1re8An7r2EiUiItIfjx49QlBQEBITE2FgYIDu3bujWbNmLGyKicUNFUnk/8bXmBjJUdlcgT5ezvjMt67EqYiIyjchBE6fPo3Dhw9DpVLB1tYWAQEBcHJykjpaucbihorkeWoWAKBtrSpYObKZxGmIiPRDfHw8jh49CpVKhUaNGsHPzw8KBW9R87pY3FChMnOUmLMzFBvOP5Q6ChGR3qlcuTJ69uwJIQSaNm3K01A6wuKG8rjxJBEbLzyEUiWw4fwjjfd6NnKUKBURUfknhEBwcDDc3d3h4uIC4OUNL0m3WNxQHt/tD8OJO7F52vd+2A71na0kSEREVP6lpKRg27ZtCA8Px6VLlzB+/HgYG/NmwiWBxQ1pSM7IVhc2vT2dUcfeAuYKQwzwrgpr3j6BiKhYIiIisGXLFqSkpMDIyAgdO3ZkYVOCWNyQ2uWHL9D/l9Pq136NneDbgKehiIiKS6VS4cSJEzh+/DiEELC3t0dAQACqVKkidTS9xuKmglKqBGZsv4Hw2BR12/mIePVzJ2sTtKpZWYpoRER6ITMzExs2bEBkZCQAoEmTJujZsyeMjNgLXtJY3FRQYdHJBV4BNdGnFj7pVoej9omIXoOxsbH64efnh8aNG0sdqcJgcVPBKFUCu689RcijBACAjZkR5vVrpH7fvYo5PBw5aJiIqDhUKhWUSiWMjIwgk8ng7++PtLQ02NnZSR2tQmFxU8EE34vDRxtD1K8rmRmjZyPOhElE9LqSkpKwZcsWVKpUCf369QPw8oaXZmZmEiereFjcVDAJaS9nGrazUKB9HTv08XSWOBERUfl39+5dbNu2DWlpaYiOjkZCQgIqVaokdawKi8VNBVXX0QKLBnlJHYOIqFxTKpU4cuQITp06BQBwcnJCQEAACxuJsbghIiIqhsTERAQFBeHRo5czubdo0QJdu3aFoSG/WqXGfwEiIiItCSHw119/ITY2FiYmJujbty/q1asndSz6HxY3FYgQAlceJkgdg4io3JPJZOjevTuOHj2KAQMGwMbGRupI9C8sbiqQg6ExWHM6EgAg5xw2RERaefHiBeLj41GzZk0AQM2aNeHu7s45wcogFjcVSFRihvr56DZu0gUhIipnQkNDsWPHDgDAe++9B1tbWwBgYVNGsbipgHo1dkInDwepYxARlXk5OTk4cOAAzp8/DwBwdXWFgYGBxKnoVVjcEBER5eP58+cICgpCVFQUAKBNmzbo1KkTi5tygMUNERHRf9y4cQO7du1CZmYmzMzM0K9fP9SuXVvqWFRELG6IiIj+4/Hjx8jMzET16tUxYMAAWFnxnnvlCYsbIiIivJwuI3eAcNeuXWFra4tmzZpBLpdLnIy0xeJGz92PTcGa05HIzFbhzrNkqeMQEZVJ165dw/Xr1zFkyBDI5XIYGBigefPmUseiYmJxo2eEEEhMz1a//vnIPWy98kRjGWtTo9KORURUJmVlZeHvv//GlStXAABXrlyBt7e3xKnodbG40TNj117CoVsxedp96lZBMzdbKAzl6OPFO4ETET179gyBgYGIjY2FTCZDhw4d0KRJE6ljkQ5IfiJx6dKlcHNzg4mJCVq0aKGeS6AgixcvRt26dWFqagpXV1dMmjQJGRkZha5TkZyLeJ6nzcrEEJO61sEEn1oY084d9pYmEiQjIiobhBC4cuUKVqxYgdjYWFhYWGDEiBHo2LEjx9foCUl7bjZt2oTJkydj+fLlaNGiBRYvXgxfX1+EhYXB3t4+z/Lr16/HtGnTsHr1arRu3Rp37tzBqFGjIJPJsGjRIgk+Qdl1aHIH1LAzBwDIAMjlnEWTiAgAjh8/jmPHjgF4eQuF/v37w9zcXNpQpFOSlqiLFi3C2LFjMXr0aNSvXx/Lly+HmZkZVq9ene/yp0+fRps2bTB06FC4ubmhW7duGDJkyCt7eyoiA7lM/WBhQ0T0jwYNGkChUKBz584YNmwYCxs9JFlxk5WVhUuXLqFLly7/hJHL0aVLF5w5cybfdVq3bo1Lly6pi5n79+9j79696NmzZ4H7yczMRFJSksaDiIgqDiEEoqOj1a+rVKmCjz/+GO3ateO9ofSUZKel4uLioFQq4eCgeY8jBwcH3L59O991hg4diri4OLRt2xZCCOTk5GDcuHH4/PPPC9zPggULMHfuXJ1mJyKi8iEzMxO7du3CzZs3MWrUKFSvXh0AYGpqKnEyKknlauTUsWPHMH/+fPzyyy+4fPkytm7dij179uCrr74qcJ3p06cjMTFR/Xj06FEpJiYiIqlERUXh119/xY0bNyCTyRAXFyd1JColkvXc2NnZwcDAADExmpctx8TEwNHRMd91Zs6cieHDh2PMmDEAgEaNGiE1NRXvvvsuvvjii3xHuSsUCigUCt1/ACIiKpOEELhw4QL2798PpVIJa2trDBw4EK6urlJHo1IiWc+NsbExvL29cfjwYXWbSqXC4cOH0apVq3zXSUtLy1PA5N6dVQhRcmHLiZ1XnyI5I0fqGEREksnIyEBgYCD27t0LpVKJunXrYty4cSxsKhhJLwWfPHkyRo4ciWbNmqF58+ZYvHgxUlNTMXr0aADAiBEj4OLiggULFgAAevfujUWLFqFJkyZo0aIF7t27h5kzZ6J3794V/hb0KpXAlMCr6tcWCs7PSEQVz+3btxEaGgoDAwN07doVLVq04KDhCkjSb8DBgwcjNjYWs2bNQnR0NLy8vLBv3z71IOOHDx9q9NTMmDEDMpkMM2bMwJMnT1ClShX07t0b8+bNk+ojlBkCQFaOCgCwaJAnqljyVBwRVTyenp6IiYlBw4YN4eLiInUckohMVLDzOUlJSbC2tkZiYmLJ3sJ+jvXL/065B1hUKbn9/M+ea1GYsP4yAODKzK6wMTcu8X0SEUktPT0dR44cQefOnWFiwtnX9Zk23988d6EHkjOy8cGGl4WNoVwGY8NydREcEVGxPHr0CEFBQUhMTERGRgYGDBggdSQqI1jc6IHMHBVU/+t/+3loE5hzvA0R6TEhBE6fPo3Dhw9DpVLB1tYWrVu3ljoWlSH8FtQz3Rs6SR2BiKjEpKWlYdu2bbh79y4AoGHDhujduzen/CANLG6IiKhciI6Oxvr165GUlARDQ0P06NEDTZs25dVQlAeLGyIiKhdyB5Ha2dkhICAgz+17iHKxuCEiojIrMzNTfcrJzMwMw4YNQ6VKlWBszCtCqWC8rIaIiMqkiIgI/PTTTwgJCVG32dvbs7ChV2JxU86pVALj112WOgYRkc6oVCocO3YMa9euRUpKCi5cuMBb7JBWeFqqJLyILLVdRTxPxfmIeABANVuzUtsvEVFJSE5OxtatWxEREQEAaNKkCXr06MFBw6QVFje6lpMJLG//z2t5yd7z6t9/zOz+sG2J7ouIqCSFh4dj69atSE1NhbGxMXr16gVPT0+pY1E5xOJG17JSgczEl89bTQTMbEtlt5XMjGBlYlQq+yIi0rUXL15g3bp1UKlUcHBwQEBAAOzs7KSOReUUi5uS1PXLEt9FVGJ6ie+DiKik2djYoE2bNkhPT4evry+MjPjHGhUfi5ty7GlCOoavOg8AkPN8NBGVM3fv3oWdnR1sbGwAAJ06deLYGtIJXi1Vjj1N+KfX5p22NSRMQkRUdEqlEgcPHsS6desQFBQEpVIJACxsSGfYc6MHatiZY4JPLaljEBG9UmJiIoKCgvDo0SMAgIuLCy/zJp1jcUNERKUiLCwM27dvR3p6OkxMTNCnTx/Ur19f6likh1jcEBFRiVIqlTh06BDOnDkD4GVvzcCBA9VjbYh0jcUNERGVKCEEHjx4AABo2bIlunbtCgODkp0DjCo2FjdERFQihBCQyWQwNDREQEAAYmJi4OHhIXUsqgBY3BARkU7l5OTgwIEDMDExQadOnQC8nMeGp6GotLC4ISIinYmPj0dgYCCioqIgk8ng5eUFW9vSmamdKBeLGyIi0ombN29i586dyMzMhJmZGfz9/VnYkCRY3BAR0WvJzs7G/v37cfHiRQBAtWrVMHDgQFhZWUmcjCoqFjdERFRsQgisXbsWjx49gkwmQ9u2beHj4wO5nBPgk3RY3JRDD5+nYdSa84hJzJA6ChFVcDKZDN7e3oiPj0f//v1Rs2ZNqSMRsbgpj85GPMf92FT16/pO7PolotKTnZ2NhIQEVKlSBQDg5eWFunXrwtTUVOJkRC+xuCnHWtSwxbcDGqOarZnUUYiogoiNjUVgYCAyMjIwbtw4mJm9/P3DwobKEhY35Zi5whBuduZSxyCiCiIkJAR79uxBdnY2LCwskJCQoC5uiMoSFjdERFSorKws7NmzB1evXgUAuLu7o3///rCwsJA4GVH+WNwQEVGBYmJiEBgYiLi4OMhkMvj4+KBdu3aQyWRSRyMqEIubcubh8zR8FnRN6hhEVEGcOnUKcXFxsLS0xMCBA1G9enWpIxG9EoubcuTEnViMWH1e/drJ2kTCNERUEfTs2ROGhobo3LkzzM05xo/KB86yVI6cuf9c/bxHQ0fM9KsvYRoi0kdRUVE4cOAAhBAAABMTE/Tp04eFDZUrr9Vzk5GRARMT9h6UtsHNXPHtwMZSxyAiPSKEwMWLF7Fv3z4olUpUqVIFTZo0kToWUbFo3XOjUqnw1VdfwcXFBRYWFrh//z4AYObMmVi1apXOA1Je5gqeTSQi3cnIyEBgYCD27NkDpVKJunXrwsPDQ+pYRMWmdXHz9ddfY82aNfjuu+9gbGysbm/YsCFWrlyp03D0j5TMHGy7/ETqGESkZ548eYJff/0VoaGhMDAwgK+vL958801OykflmtZdAGvXrsVvv/2Gzp07Y9y4cep2T09P3L59W6fh6B/Lj4UjOunlvaSMDHkJJhG9vitXrmD37t1QKpWoVKkSAgIC4OLiInUsotemdXHz5MkT1KpVK0+7SqVCdna2TkKRpkOhMfj56D316yFvVJMwDRHpC1tbW6hUKtSrVw99+/blGErSG1oXN/Xr18fJkyfzzHUQFBTEwWclZOuVx+rnPw1pwlsuEFGx/ftCkOrVq2Ps2LFwcnLipHykV7QubmbNmoWRI0fiyZMnUKlU2Lp1K8LCwrB27Vrs3r27JDJWeP+7IhOjWruht6eztGGIqFwSQuDMmTM4ceIExowZAzs7OwCAszN/p5D+0XpAcd++fbFr1y4cOnQI5ubmmDVrFm7duoVdu3aha9euJZGxwstRvaxuatrzPi5EpL20tDRs2LABBw4cQEZGhvoeUUT6qljXFLdr1w4HDx7UdRbKx7Jj4TgYGiN1DCIqpx4+fIigoCAkJSXB0NAQ3bt3h7e3t9SxiEqU1j037u7ueP78eZ72hIQEuLu76yQU/eP4nWfq555VrSVMQkTliRACJ0+exJo1a5CUlITKlStjzJgxaNasGcfXkN7TuucmMjISSqUyT3tmZiaePOE8LCXlhwBPNK5aSeoYRFROhISE4PDhwwCAxo0bw8/PT2NuMiJ9VuTiZufOnern+/fvh7X1P70ISqUShw8fhpubm07D0T8URrwNGBEVnaenJ27cuIGGDRvCy8uLvTVUoRS5uPH39wcAyGQyjBw5UuM9IyMjuLm54YcfftBpOCIiKhqVSoUrV67Ay8sLBgYGkMvlGDZsGIsaqpCKXNyoVCoAQI0aNXDhwgX1ZYRERCStlJQUbNmyBREREYiLi4Ovry8AsLChCkvrMTcRERElkYP+QwiBHw/ewdn78VJHIaIy7P79+9i6dStSUlJgZGQER0dHqSMRSa5Yl4Knpqbi+PHjePjwIbKysjTe+/DDD3USrKKbsP4y9l6PVr92q8xZiYnoHyqVCseOHcPJkychhICDgwMCAgLYq06EYhQ3V65cQc+ePZGWlobU1FTY2toiLi4OZmZmsLe3Z3GjI5cevFA/3/NhWzRw5mXgRPRSUlIStmzZggcPHgAAvL290b17dxgZGUmcjKhs0PoSnEmTJqF379548eIFTE1NcfbsWTx48ADe3t74/vvvSyJjhbb7AxY2RKQpJycH0dHRMDY2xoABA9C7d28WNkT/onXPTUhICH799VfI5XIYGBggMzMT7u7u+O677zBy5Ej079+/JHISEVVoQgj1AGFbW1sEBATAxsYGlStXljgZUdmjdc+NkZER5PKXq9nb2+Phw4cAAGtrazx69Ei36YiICImJiVizZg3u37+vbqtVqxYLG6ICaF3cNGnSBBcuXAAAdOjQAbNmzcK6devw8ccfo2HDhjoPSERUkYWFhWH58uV48OAB9uzZo56Wg4gKpnVxM3/+fDg5OQEA5s2bBxsbG7z//vuIjY3Fr7/+qvOAREQVkVKpxP79+7Fhwwakp6fD2dkZw4YNU/ecE1HBtB5z06xZM/Vze3t77Nu3T6eBiIgquoSEBAQGBqrv19eyZUt06dIFhobFmr2DqMLR2Z8Aly9fhp+fn9brLV26FG5ubjAxMUGLFi1w/vz5QpdPSEjAhAkT4OTkBIVCgTp16mDv3r3FjV0m7br6FDFJmVLHICIJJCYmYvny5Xjy5AlMTEzw5ptvonv37ixsiLSgVXGzf/9+TJkyBZ9//rl6YNvt27fh7++PN954Q+tzwZs2bcLkyZMxe/ZsXL58GZ6envD19cWzZ8/yXT4rKwtdu3ZFZGQkgoKCEBYWhhUrVsDFxUWr/ZZ1n2+7rn5eyYyXdxJVJFZWVqhbty6qVq2KcePGwcPDQ+pIROVOkf8UWLVqFcaOHQtbW1u8ePECK1euxKJFi/DBBx9g8ODBuHHjBurVq6fVzhctWoSxY8di9OjRAIDly5djz549WL16NaZNm5Zn+dWrVyM+Ph6nT59Wz+mgj3ciz8x5WSTO69cQVW3MJE5DRCUtPj4eJiYmMDMzg0wmg5+fn3q6DSLSXpF7bpYsWYJvv/0WcXFx2Lx5M+Li4vDLL7/g+vXrWL58udaFTVZWFi5duoQuXbr8E0YuR5cuXXDmzJl819m5cydatWqFCRMmwMHBAQ0bNsT8+fOhVCoL3E9mZiaSkpI0HuVFx7r2UkcgohJ28+ZN/Prrr9ixYweEEABeTrnBwoao+Ipc3ISHhyMgIAAA0L9/fxgaGmLhwoWoWrVqsXYcFxcHpVIJBwcHjXYHBwdER0fnu879+/cRFBQEpVKJvXv3YubMmfjhhx/w9ddfF7ifBQsWwNraWv1wdXUtVl4iIl3KycnB7t27ERgYiMzMTKSnpyMzk2PtiHShyKel0tPTYWb28hSJTCaDQqFQXxJeWlQqFezt7fHbb7/BwMAA3t7eePLkCRYuXIjZs2fnu8706dMxefJk9eukpCQWOEQkqefPnyMwMFD9h1y7du3g4+PDy7yJdESr4fcrV66EhYUFgJd/daxZsybPHWiLeuNMOzs7GBgYICYmRqM9JiYGjo6O+a7j5OSUp7u2Xr16iI6ORlZWFoyNjfOso1AooFAoipSJiKikXbt2Dbt370ZWVhbMzc3Rr18/1KpVS+pYRHqlyMVNtWrVsGLFCvVrR0dH/PnnnxrLyGSyIhc3xsbG8Pb2xuHDh+Hv7w/gZc/M4cOHMXHixHzXadOmDdavXw+VSqX+C+fOnTtwcnLKt7AhIipLsrOzceTIEWRlZcHNzQ0DBgyApaWl1LGI9E6Ri5vIyEid73zy5MkYOXIkmjVrhubNm2Px4sVITU1VXz01YsQIuLi4YMGCBQCA999/Hz///DM++ugjfPDBB7h79y7mz59f5IKKiEhKRkZGCAgIwN27d9G+fXuehiIqIZLOCjV48GDExsZi1qxZiI6OhpeXF/bt26ceZPzw4UONH35XV1fs378fkyZNQuPGjeHi4oKPPvoIU6dOleoj6NyNJ4nIyuG9Y4j0RUhICIQQaNKkCQDAxcVF7+bmIiprJJ/ycuLEiQWehjp27FietlatWuHs2bMlnEoa2UoV3vztn89mZCCTMA0RvY6srCzs2bMHV69ehaGhIapVq8a7eBOVEsmLG/pHjlIgJTMHAPBh59qwtzSROBERFUdMTAwCAwMRFxcHmUyG9u3bw8bGRupYRBUGi5syalwHd6kjEJGWhBC4cuUK9u7di5ycHFhaWmLAgAF6OZM6UVnG4qYM2XL5sdQRiKiYhBDYvn07rl69CgCoVasW+vXrB3Nzc4mTEVU8xRqqHx4ejhkzZmDIkCHqm1z+/fffuHnzpk7DVSTxqVmYsf0GAEBhKIchr6IgKldkMhlsbW3Vt5F56623WNgQSUTrb9Djx4+jUaNGOHfuHLZu3YqUlBQAwNWrVwucJZheLSP7n/tj/TaiGYwNWdwQlXVCCKSnp6tft2vXDu+++y7atm0LmYwXBBBJRetv0GnTpuHrr7/GwYMHNSbO69Spk95exVSajA3l6FCnitQxiOgVMjIyEBQUhDVr1iA7OxvAy5v/FjTDOhGVHq3H3Fy/fh3r16/P025vb4+4uDidhCIiKsuePn2KwMBAvHjxAnK5HI8ePYK7Oy8CICortC5uKlWqhKioKNSoUUOj/cqVK5yYioj0mhAC58+fx4EDB6BUKlGpUiUMHDgQVatWlToaEf2L1sXNm2++ialTpyIwMBAymQwqlQqnTp3ClClTMGLEiJLIWCGcusdeL6KyLD09HTt27MDt27cBAB4eHujbty9MTU0lTkZE/6V1cTN//nxMmDABrq6uUCqVqF+/PpRKJYYOHYoZM2aUREa99yQhHZ8GXQMAKAw4kJioLNqzZw9u374NAwMDdOvWDc2bN+egYaIySuvixtjYGCtWrMDMmTNx48YNpKSkoEmTJqhdu3ZJ5KsQEtKy1M+/8m8oYRIiKkjXrl3x4sUL9OrVC87OzlLHIaJCaF3cBAcHo23btqhWrRqqVatWEpkqnCsPEwAADlYK+DfhuCWisiAtLQ137tyBl5cXAMDa2hpjxoxhbw1ROaB1cdOpUye4uLhgyJAhGDZsGOrXr18SuSqMuzHJ6sn7OHEfUdnw8OFDBAUFISkpCaampqhbty4AsLAhKie0/jZ9+vQpPvnkExw/fhwNGzaEl5cXFi5ciMePeesAbSWkZcHvp2D168ld60iYhoiEEAgODsaaNWuQlJSEypUrw9raWupYRKQlrYsbOzs7TJw4EadOnUJ4eDgCAgLwxx9/wM3NDZ06dSqJjHrrdPhzZOaoAACdPewxwJuXkxJJJTU1FevWrcOhQ4egUqnQqFEjvPvuu5yUj6gceq0bZ9aoUQPTpk2Dp6cnZs6ciePHj+sqV4WgEkL9/KehTSRMQlSxRUZGYsuWLUhOToahoSF69uyJJk2a8DQUUTlV7OLm1KlTWLduHYKCgpCRkYG+fftiwYIFusym1+JSMvHhhisAgJbutjAz5g3aiaSSkpKC5ORkVKlSBQEBAbC3t5c6EhG9Bq2/UadPn46NGzfi6dOn6Nq1K5YsWYK+ffvCzMysJPLprUOhMVD9r+PG3tJE2jBEFZAQQt0z07BhQyiVStSrV0/jnnlEVD5pXdycOHECn376KQYNGgQ7O7uSyFQhZKv+OSXFuW2IStf9+/dx4MABDBs2DBYWFgAAT09PiVMRka5oXdycOnWqJHJUWN0bOMLa1EjqGEQVgkqlwvHjx3HixAkIIXDs2DH4+flJHYuIdKxIxc3OnTvRo0cPGBkZYefOnYUu26dPH50EIyLSpeTkZGzZsgWRkZEAgKZNm8LX11faUERUIopU3Pj7+yM6Ohr29vbw9/cvcDmZTAalUqmrbHonPUuJ43dikZmjxLVHCVLHIaow7t27h61btyItLQ3Gxsbo3bs3GjVqJHUsIiohRSpuVCpVvs9JO0sO38Xy4+EabYYGvNSUqCTdvHkTgYGBAABHR0cEBASgcuXKEqciopKk9SR+a9euRWZmZp72rKwsrF27Vieh9NHR28/UhY1bZTO0qVUZPnWrYHQbN2mDEem5WrVqoXLlynjjjTcwZswYFjZEFYBMiH/NJFcEBgYGiIqKyjMPxPPnz2Fvb1/mT0slJSXB2toaiYmJsLKy0v0O0uKB72q8fD4rHpAbAADG/HEBh249AwAsHdoUvRo76X7fRAQAePz4MVxcXNSXemdmZkKhUEiciohehzbf31r33Px7boh/e/z4Me/BUojcK7+Ht6yOno04nTtRSVAqlThw4ABWrlyJs2fPqttZ2BBVLEW+FDx3KnKZTIbOnTvD0PCfVZVKJSIiItC9e/cSCalPGlW15pTuRCUgISEBQUFB6pv4JicnS5yIiKRS5OIm9yqpkJAQ+Pr6qie+AgBjY2O4ublhwIABOg9IRPQqt2/fxvbt25GRkQETExP4+/vDw8ND6lhEJJEiFzezZ88GALi5uWHw4MEwMeEtA4hIWjk5OTh48CDOnTsHAKhatSoGDhyISpUqSRuMiCSl9QzFI0eOLIkcRERai42NxYULFwAArVu3RufOnWFgYCBxKiKSWpGKG1tbW9y5cwd2dnawsbEpdMxIfHy8zsIRERXGyckJPXv2hJWVFerUqSN1HCIqI4pU3Pz444+wtLRUP+eAWCKSQu5pqKZNm8LBwQEA0KxZM4lTEVFZU6Ti5t+nokaNGlVSWYiICvT8+XMEBgYiOjoa4eHhGD9+PORyrWezIKIKQOvfDJcvX8b169fVr3fs2AF/f398/vnnyMrK0mk4IiIAuH79On799VdER0fD3Nwc3bt3Z2FDRAXS+rfDe++9hzt37gAA7t+/j8GDB8PMzAyBgYH47LPPdB6QiCqu7Oxs7Ny5E1u2bEFWVhbc3Nwwbtw41KpVS+poRFSGaV3c3LlzB15eXgCAwMBAdOjQAevXr8eaNWuwZcsWXecjogoqJSUFK1aswOXLlyGTydChQweMGDFCPf6PiKggWl8KLoRQ3xn80KFD8PPzAwC4uroiLi5Ot+mIqMIyMzODubk5LCws0L9/f7i7u0sdiYjKCa2Lm2bNmuHrr79Gly5dcPz4cSxbtgwAEBERob56gYioOLKysiCXy2FoaAi5XK6e9fzfM6ITEb2K1qelFi9ejMuXL2PixIn44osv1Oe+g4KC0Lp1a50HJKKK4dmzZ1ixYgX27dunbrOwsGBhQ0Ra07rnpnHjxhpXS+VauHAhZwYlIq0JIXDlyhXs3bsXOTk5yMjIQKdOnWBmZiZ1NCIqp7QubnJdunQJt27dAgDUr18fTZs21VkoIqoYMjMzsWfPHly7dg0AUKtWLfTr14+FDRG9Fq2Lm2fPnmHw4ME4fvy4+uZ0CQkJ8PHxwcaNG1GlShVdZyQiPRQdHY3AwEA8f/4ccrkcnTp1Qps2bTgDOhG9Nq3H3HzwwQdISUnBzZs3ER8fj/j4eNy4cQNJSUn48MMPSyIjEemZnJwcrFu3Ds+fP4eVlRVGjRqFtm3bsrAhIp3Quudm3759OHToEOrVq6duq1+/PpYuXYpu3brpNBwR6SdDQ0P4+fnh0qVL8Pf352koItIprYsblUoFIyOjPO1GRkbq+W+IiP7r6dOnyMjIUM9XU7duXdSpU4e9NUSkc1qflurUqRM++ugjPH36VN325MkTTJo0CZ07d9ZpOCIq/4QQOHfuHFatWoXAwEAkJiaq32NhQ0QlQeuem59//hl9+vSBm5sbXF1dAQCPHj1Cw4YN8ddff+k8IBGVX+np6di5c6f6ysrq1avD2NhY4lREpO+0Lm5cXV1x+fJlHD58WP0Lq169eujSpYvOwxFR+fX48WMEBQUhISEBBgYG6NatG5o3b87eGiIqcVoVN5s2bcLOnTuRlZWFzp0744MPPiipXERUTgkhcPbsWRw8eBAqlQo2NjYICAiAs7Oz1NGIqIIocnGzbNkyTJgwAbVr14apqSm2bt2K8PBwLFy4sCTzEVE5I5PJEBcXB5VKhQYNGqB3794wMTGROhYRVSBFHlD8888/Y/bs2QgLC0NISAj++OMP/PLLLyWZjYjKESGE+nn37t3Rv39/DBw4kIUNEZW6Ihc39+/fx8iRI9Wvhw4dipycHERFRZVIMCIqH4QQCA4Oxvr169UFjpGRERo3bszxNUQkiSKflsrMzIS5ubn6tVwuh7GxMdLT00skGBGVfampqdi2bRvu3bsHALh9+7bGBJ9ERFLQakDxzJkzNWYSzcrKwrx582Btba1uW7Roke7SEVGZ9eDBAwQFBSE5ORmGhobo2bMnPDw8pI5FRFT04qZ9+/YICwvTaGvdujXu37+vfs0uaCL9p1KpEBwcjKNHj0IIgSpVqiAgIAD29vZSRyMiAqBFcXPs2LESjEFE5cWePXtw6dIlAICXlxd69uzJifmIqEzR+vYLJWHp0qVwc3ODiYkJWrRogfPnzxdpvY0bN0Imk8Hf379kAxKR2htvvAFTU1P069cP/v7+LGyIqMyRvLjZtGkTJk+ejNmzZ+Py5cvw9PSEr68vnj17Vuh6kZGRmDJlCtq1a1dKSYsvR6nC9SeJr16QqAxSqVR49OiR+rWjoyMmTZoET09PCVMRERVM8uJm0aJFGDt2LEaPHo369etj+fLlMDMzw+rVqwtcR6lU4q233sLcuXPVdxguyz7bcg2xyZkAAI5KovIkOTkZa9euxZo1a/DkyRN1O3triKgsk7S4ycrKwqVLlzTuSyWXy9GlSxecOXOmwPW+/PJL2Nvb45133imNmK8tIi5V/bxNLTsJkxAV3b1797B8+XJERkbCwMAAycnJUkciIioSrW+cqUtxcXFQKpVwcHDQaHdwcMDt27fzXSc4OBirVq1CSEhIkfaRmZmJzMxM9eukpKRi531dK0Y0g3MlU8n2T1QUKpUKR44cQXBwMICXp6ECAgJQuXJliZMRERVNsXpuTp48iWHDhqFVq1bqruo///xT/cuwpCQnJ2P48OFYsWIF7OyK1gOyYMECWFtbqx+urq4lmpGoPEtMTMSaNWvUP8tvvPEGxowZw8KGiMoVrYubLVu2wNfXF6amprhy5Yq6VyQxMRHz58/Xalt2dnYwMDBATEyMRntMTAwcHR3zLB8eHo7IyEj07t0bhoaGMDQ0xNq1a7Fz504YGhoiPDw8zzrTp09HYmKi+vHvgZFEpOnWrVt4+PAhFAoFAgIC0KtXLxgaStrBS0SkNa2Lm6+//hrLly/HihUrYGRkpG5v06YNLl++rNW2jI2N4e3tjcOHD6vbVCoVDh8+jFatWuVZ3sPDA9evX0dISIj60adPH/j4+CAkJCTfXhmFQgErKyuNR2lLycgp9X0SFUeLFi3Qpk0bvPfee2jQoIHUcYiIikXrP8nCwsLQvn37PO3W1tZISEjQOsDkyZMxcuRINGvWDM2bN8fixYuRmpqK0aNHAwBGjBgBFxcXLFiwACYmJmjYsKHG+pUqVQKAPO1lxefbruPusxSpYxDlKyEhAUePHkWvXr1gbGwMmUyGrl27Sh2LiOi1aF3cODo64t69e3Bzc9NoDw4OLtZl2YMHD0ZsbCxmzZqF6OhoeHl5Yd++fepBxg8fPoRcLvkV68V2Jvy5+nkjF+tCliQqXbdv38b27duRkZEBY2Nj9OrVS+pIREQ6oXVxM3bsWHz00UdYvXo1ZDIZnj59ijNnzmDKlCmYOXNmsUJMnDgREydOzPe9V932Yc2aNcXaZ2nb9G5LOFqbSB2DCEqlEgcPHsTZs2cBAC4uLmjTpo3EqYiIdEfr4mbatGlQqVTo3Lkz0tLS0L59eygUCkyZMgUffPBBSWTUC3I5p+8j6b148QKBgYF4+vQpgJc3v+3cuTMMDAwkTkZEpDtaFzcymQxffPEFPv30U9y7dw8pKSmoX78+LCwsSiIfEelIZGQkNmzYgMzMTPW9oerUqSN1LCIinSv2NZ7GxsaoX7++LrMQUQmqXLkyDA0N4eDggAEDBsDammPAiEg/aV3c+Pj4QCYr+BTLkSNHXisQEelOWloazMzMAACWlpYYPXo0bGxseBqKiPSa1sWNl5eXxuvs7GyEhITgxo0bGDlypK5yEdFrun79Onbv3o2+ffuqe1mLOrM3EVF5pnVx8+OPP+bbPmfOHKSkcD4XIqllZ2dj3759uHTpEgDg6tWrPIVMRBWKziaQGTZsGFavXq2rzRFRMcTFxWHlypW4dOkSZDIZOnTogMGDB0sdi4ioVOnspjFnzpyBiQnncSGSytWrV7F7925kZ2fDwsIC/fv3L9bEmkRE5Z3WxU3//v01XgshEBUVhYsXLxZ7Ej8iej1RUVHYtm0bAKBGjRoYMGAAp2cgogpL6+Lmv5ePyuVy1K1bF19++SW6deums2BEVHROTk5o3bo1FAoF2rVrV65vWUJE9Lq0Km6USiVGjx6NRo0awcbGpqQy6Y3Pt11DRFyq1DFIDwkhcPXqVbi7u6vvdM8/LoiIXtLqzzsDAwN069atWHf/roj23YhWP3e3M5cwCemTzMxMbNu2Ddu3b0dQUBBUKpXUkYiIyhStT0s1bNgQ9+/fR40aNUoij17a/UFbVLZQSB2D9EB0dDQCAwPx/PlzyOVy1KlTp9BJNYmIKiKti5uvv/4aU6ZMwVdffQVvb2+Ym2v2SOR2kdM/TIw4/oFejxACly5dwr59+5CTkwMrKysMHDgQ1apVkzoaEVGZU+Ti5ssvv8Qnn3yCnj17AgD69Omj8RejEAIymQxKpVL3KYkqsMzMTOzcuRM3b94EANSpUwf+/v7q2yoQEZGmIhc3c+fOxbhx43D06NGSzENE/yGXyxEbGwu5XI4uXbqgVatWPBVFRFSIIhc3QggAQIcOHUosDBG9lPvzJpPJYGRkhICAAGRmZqJq1aoSJyMiKvu0GnPDvxaJSl5GRgZ27NgBZ2dntGvXDgBQpUoViVMREZUfWhU3RbkyIz4+/rUCEVVkT548QWBgIBISEnDv3j00adKEMw0TEWlJq+Jm7ty5eWYoJqLXJ4TA2bNncejQISiVStjY2CAgIICFDRFRMWhV3Lz55puwt7cvqSxEFVJ6ejq2b9+OsLAwAED9+vXRp08f3oiWiKiYilzccLxNEYUfkToBlSNKpRIrV67E8+fPYWhoCF9fXzRr1ow/b0REr0Hrq6WoECmxwJZ3Xj43MIZQ8QuKCmdgYICWLVvi7NmzCAgIgKOjo9SRiIjKvSIXN7x/TRFkJv3zvO9SiO1yAJzUkDSlpaUhNTVVfQVUs2bN4OXlBSMjI4mTERHpB94XoCQorIHGg6ROQWXQgwcPsGzZMqxfvx4ZGRkA/pnLhoiIdEPre0tR0ey5FoWEtGypY1AZIYTAyZMncfToUQghYGdnh7S0NA4aJiIqASxuSsgngSHq55Ym/Ku8IktJScG2bdsQHh4OAPDy8kLPnj1hbGwscTIiIv3E4qaEZGS/HKP07YBGcLDiX+cVVUREBLZs2YKUlBQYGRmhV69e8PLykjoWEZFeY3FTwjrXc5A6AknozJkzSElJgb29PQICAngbBSKiUsDihqgE+fv7Izg4GD4+Phw0TERUSni1FJEOhYeH48CBA+rXZmZm6NatGwsbIqJSxJ4bIh1QqVQ4evQogoODIYSAq6sr6tWrJ3UsIqIKicUN0WtKSkrCli1b8ODBAwAvJ+WrVauWxKmIiCouFjdEr+Hu3bvYtm0b0tLSoFAo0KdPHzRo0EDqWEREFRqLG6JiOnnyJA4fPgwAcHZ2xsCBA2FraytxKiIiYnFDVExOTk6QyWRo3rw5unbtCkND/jgREZUF/G1MpIXU1FSYm5sDAGrVqoXx48dz7hoiojKGl4ITFYFSqcS+ffvw008/4cWLF+p2FjZERGUPixuiV3jx4gVWr16Ns2fPIiMjA3fv3pU6EhERFYKnpYgKERoaip07dyIjIwOmpqbw9/dH3bp1pY5FRESFYHFDlI+cnBwcOHAA58+fBwC4urpi4MCBsLa2ljgZERG9CosbonycO3dOXdi0bdsWPj4+MDAwkDgVEREVBYsbony0aNECERERaNGiBWrXri11HCIi0gIHFJeAtOwcqSOQlrKzs3H69GmoVCoAgKGhIYYNG8bChoioHGLPTQnIUQoAgIOVAtamvBt0WRcXF4fAwEDExMQgIyMDnTp1kjoSERG9BhY3JejwJx1hZMDOsbLs6tWr2LNnD7KysmBhYQE3NzepIxER0WticVOCTI04ALWsysrKwt9//40rV64AAGrUqIEBAwbAwsJC4mRERPS6WNxQhRMbG4vNmzcjNjYWMpkMHTt2RLt27SCXs5eNiEgfsLihCkcIgYSEBFhaWmLAgAE8FUVEpGdY3FCFoFKp1D0z9vb2GDx4MJycnNQ3wSQiIv3BfnjSe9HR0Vi2bBkePnyobqtVqxYLGyIiPcXihvSWEAIXL17EypUrERsbiwMHDkAIIXUsIiIqYTwtRXopMzMTu3btwo0bNwAAtWvXRr9+/SCTySRORkREJY3FDemdqKgoBAYGIj4+HnK5HF26dEGrVq1Y2BARVRAsbkivPHv2DCtXroRSqYS1tTUGDhwIV1dXqWMREVEpYnGjK0IAJ76XOkWFV6VKFdSpUwcqlQr+/v4wNTWVOhIREZWyMjGgeOnSpXBzc4OJiQlatGiB8+fPF7jsihUr0K5dO9jY2MDGxgZdunQpdPlSE3MDuLoeAJAgeBVOaXr69CkyMjIAADKZDP3798ebb77JwoaIqIKSvLjZtGkTJk+ejNmzZ+Py5cvw9PSEr68vnj17lu/yx44dw5AhQ3D06FGcOXMGrq6u6NatG548eVLKyf8jO0P9dFz2JAmDVBxCCJw5cwarVq3Crl271FdCGRkZcXwNEVEFJnlxs2jRIowdOxajR49G/fr1sXz5cpiZmWH16tX5Lr9u3TqMHz8eXl5e8PDwwMqVK6FSqXD48OFSTp4/ZSU3hAo3qWPovfT0dGzcuBH79++HUqmEEAJKpVLqWEREVAZIOuYmKysLly5dwvTp09VtuVe3nDlzpkjbSEtLQ3Z2NmxtbUsqJpUxjx49QlBQEBITE2FgYIDu3bujWbNm7K0hIiIAEhc3cXFxUCqVcHBw0Gh3cHDA7du3i7SNqVOnwtnZGV26dMn3/czMTGRmZqpfJyUlFT8wSUoIgdOnT+Pw4cNQqVSwtbVFQEAAnJycpI5GRERlSLm+Wuqbb77Bxo0bcezYMZiYmOS7zIIFCzB37txSTkYlISMjA2fPnoVKpUKjRo3g5+cHhUIhdSwiIipjJC1u7OzsYGBggJiYGI32mJgYODo6Frru999/j2+++QaHDh1C48aNC1xu+vTpmDx5svp1UlIS5z0pp0xNTTFw4EDExcWhadOmPA1FRET5knRAsbGxMby9vTUGA+cODm7VqlWB63333Xf46quvsG/fPjRr1qzQfSgUClhZWWk8qHwQQuDEiRO4du2auq169erw9vZmYUNERAWS/LTU5MmTMXLkSDRr1gzNmzfH4sWLkZqaitGjRwMARowYARcXFyxYsAAA8O2332LWrFlYv3493NzcEB0dDQCwsLCAhYWFZJ+DdCslJQXbtm1DeHg4jIyM4ObmxsKUiIiKRPLiZvDgwYiNjcWsWbMQHR0NLy8v7Nu3Tz3I+OHDh5DL/+lgWrZsGbKysjBw4ECN7cyePRtz5swpzehUQiIiIrBlyxakpKTAyMgIPXv2hKWlpdSxiIionJC8uAGAiRMnYuLEifm+d+zYMY3XkZGRJR+IJKFSqXDixAkcP34cQgjY29sjICAAVapUkToaERGVI2WiuCFSqVT466+/cP/+fQBA06ZN0aNHDxgZGUmcjIiIyhsWN1QmyOVyODs74/Hjx/Dz8yv0CjgiIqLCsLghyahUKqSnp8Pc/OWNRn18fNC0aVPONk1ERK+FxQ1JIikpCUFBQVAqlXj77bdhYGAAAwMDFjZERPTaWNzoWHoWb974Knfv3sW2bduQlpYGhUKBZ8+e8RYKRESkMyxudOx5ysv7WCkM5eA0c5qUSiWOHDmCU6dOAQCcnJwQEBDA3hoiItIpFjcl5IdBnpDLWd7kSkhIQFBQEB4/fgwAaNGiBbp27QpDQ/4vSEREusVvlhJgaWIIv8bOUscoU3bu3InHjx/DxMQEffv2Rb169aSOREREeorFDZUKPz8/7N69G71794aNjY3UcYiISI9JeuNM0l8vXrzA5cuX1a9tbW0xYsQIFjZERFTi2HNDOhcaGoqdO3ciMzMTlSpVgru7u9SRiIioAmFxQzqTk5ODAwcO4Pz58wAAV1dXXglFRESljsUN6UR8fDwCAwMRFRUFAGjTpg06deoEAwMDiZMREVFFw+KGXtvNmzfVp6HMzMzQr18/1K5dW+pYRERUQbG4odeWlZWFzMxMVK9eHQMGDICVlZXUkYiIqAJjcUPFolKpIJe/vNjOy8sLxsbGqFevnrqNiIhIKvwmIq1dvXoVy5YtQ1paGgBAJpOhQYMGLGyIiKhM4LcRFVlWVhZ27NiBbdu2ITY2FufOnZM6EhERUR48LUVF8uzZMwQGBiI2NhYymQwdOnRA+/btpY5FRESUB4sbKpQQAiEhIdi7dy+ys7NhYWGBAQMGoEaNGlJHIyIiyheLGyrUhQsXsHfvXgBAzZo10a9fP1hYWEicioiIqGAsbqhQjRs3xtmzZ9GkSRO0bdsWMplM6khERESFYnFDGoQQuH//Ptzd3SGTyWBiYoLx48fD0JD/qxARUfnAq6VILTMzE1u2bMGff/6pcUdvFjZERFSe8FuLAABRUVEIDAxEfHw85HI5srOzpY5EVG4olUr+zBDpgLGxsU7mTGNxU8EJIXDhwgXs378fSqUS1tbWGDhwIFxdXaWORlTmCSEQHR2NhIQEqaMQ6QW5XI4aNWrA2Nj4tbbD4qYCy8jIwM6dOxEaGgoAqFu3Lvz9/WFqaipxMqLyIbewsbe3h5mZGQfcE70GlUqFp0+fIioqCtWqVXutnycWNxVYTEwMbt26BQMDA3Tt2hUtWrTgL2eiIlIqlerCpnLlylLHIdILVapUwdOnT5GTkwMjI6Nib4fFTQVWvXp19OzZE87OznBxcZE6DlG5kjvGxszMTOIkRPoj93SUUql8reKGV0tVIOnp6diyZQvi4uLUbW+88QYLG6LXwN5OIt3R1c8Te24qiEePHiEoKAiJiYmIj4/HmDFj+EuZiIj0EntudERASB0hX0IInDp1Cr///jsSExNha2sLPz8/FjZEVKaEhYXB0dERycnJUkepMOLi4mBvb4/Hjx9LHUXnWNzoyIP4NPVzS0XZ6BBLS0vD+vXrcfDgQahUKjRs2BDvvfcenJycpI5GRBIaNWoUZDIZZDIZjIyM4ODggK5du2L16tVQqVQay7q5uUEmk+Hs2bMa7R9//DE6duyofj1nzhzIZDKMGzdOY7mQkBDIZDJERkYWmmn69On44IMPYGlpmec9Dw8PKBQKREdH53nPzc0NixcvztM+Z84ceHl5abRFR0fjgw8+gLu7OxQKBVxdXdG7d28cPny40GyvKzAwEB4eHjAxMUGjRo3U9+srzNKlS1GvXj2Ympqibt26WLt2bYHLbty4ETKZDP7+/hrtuf/G/30sXLgQAGBnZ4cRI0Zg9uzZr/X5yiIWNzqSka1UP/9paBMJk7wUHx+P5cuX4+7duzA0NETv3r0xYMAAKBQKqaMRURnQvXt3REVFITIyEn///Td8fHzw0Ucfwc/PDzk5ORrLmpiYYOrUqa/cpomJCVatWoW7d+9qleXhw4fYvXs3Ro0alee94OBgpKenY+DAgfjjjz+02u6/RUZGwtvbG0eOHMHChQtx/fp17Nu3Dz4+PpgwYUKxt/sqp0+fxpAhQ/DOO+/gypUr8Pf3h7+/P27cuFHgOsuWLcP06dMxZ84c3Lx5E3PnzsWECROwa9eufD/XlClT0K5duzzvRUVFaTxWr14NmUyGAQMGqJcZPXo01q1bh/j4eN184DKibHQx6BFDAxm8q9tKHQPW1tawtraGsbExAgIC4ODgIHUkIr0nhED6v/7QKU2mRgZanW5WKBRwdHQEALi4uKBp06Zo2bIlOnfujDVr1mDMmDHqZd99910sX74ce/fuRc+ePQvcZt26dWFvb48vvvgCmzdvLnKWzZs3w9PTM9+LG1atWoWhQ4eiQ4cO+Oijj4pUZOVn/PjxkMlkOH/+PMzNzdXtDRo0wNtvv12sbRbFkiVL0L17d3z66acAgK+++goHDx7Ezz//jOXLl+e7zp9//on33nsPgwcPBgC4u7vjwoUL+Pbbb9G7d2/1ckqlEm+99Rbmzp2LkydP5plMMvffN9eOHTvg4+MDd3d3dVuDBg3g7OyMbdu24Z133tHFRy4TWNzokdTUVJiYmMDAwAAGBgYYNGgQFArFa8/0SERFk56tRP1Z+yXZd+iXvjAzfr1f6Z06dYKnpye2bt2qUdzUqFED48aNw/Tp09G9e/dCp8f/5ptv8MYbb+DixYto1qxZkfZ78uTJfJdNTk5GYGAgzp07Bw8PDyQmJuLkyZP59lIUJj4+Hvv27cO8efM0CptclSpVKnDddevW4b333it0+3///XeBmc6cOYPJkydrtPn6+mL79u0Fbi8zMxMmJiYabaampjh//jyys7PVl0h/+eWXsLe3xzvvvIOTJ08WmjEmJgZ79uzJt/erefPmOHnypF4VNzwtpSciIiKwbNkyjXPHlpaWLGyISCseHh75jo+ZMWMGIiIisG7dukLXb9q0KQYNGqRVD8uDBw/g7Oycp33jxo2oXbs2GjRoAAMDA7z55ptYtWpVkbeb6969exBCwMPDQ+t1+/Tpg5CQkEIfhRVx0dHReXrOHRwc8h0/lMvX1xcrV67EpUuXIITAxYsXsXLlSmRnZ6un8ggODsaqVauwYsWKIn2OP/74A5aWlujfv3+e95ydnfHgwYMibae8YM9NOadSqXDixAkcP34cQgjcu3cPPj4+rzX5EREVj6mRAUK/9JVs37oghMj39FaVKlUwZcoUzJo1S326pCBff/016tWrhwMHDsDe3v6V+0xPT8/TUwEAq1evxrBhw9Svhw0bhg4dOuCnn37Kd+BxQYQo/tWslpaWWu1LF2bOnIno6Gi0bNkSQgg4ODhg5MiR+O677yCXy5GcnIzhw4djxYoVsLOzK9I2V69ejbfeeivf42xqaoq0tLR81iq/2HNTjiUnJ+PPP//EsWPHIIRAkyZNMHbsWBY2RBKRyWQwMzaU5KGr6R1u3bqFGjVq5Pve5MmTkZ6ejl9++aXQbdSsWRNjx47FtGnTilRY2NnZ4cWLFxptoaGhOHv2LD777DMYGhrC0NAQLVu2RFpaGjZu3KhezsrKComJiXm2mZCQAGtrawBA7dq1IZPJcPv27Vdm+a9169bBwsKi0Edhp4QcHR0RExOj0RYTE5NnPMy/mZqaYvXq1UhLS0NkZCQePnwINzc3WFpaokqVKggPD0dkZCR69+6tPjZr167Fzp07YWhoiPDwcI3tnTx5EmFhYRqnGv8tPj4eVapU0eKolH3suSmnwsPDsXXrVqSmpsLY2Bi9evWCp6en1LGIqBw7cuQIrl+/jkmTJuX7voWFBWbOnIk5c+agT58+hW5r1qxZqFmzpkYhUpAmTZqob+Cba9WqVWjfvj2WLl2q0f77779j1apVGDt2LICXg5gvXbqUZ5uXL19G3bp1AQC2trbw9fXF0qVL8eGHH+YZd5OQkFDguJs+ffqgRYsWheYvbJb3Vq1a4fDhw/j444/VbQcPHkSrVq0K3SYAGBkZoWrVqgBenqLz8/ODXC6Hh4cHrl+/rrHsjBkzkJycjCVLlsDV1VXjvVWrVsHb27vA74gbN25oXNavF0QFk5iYKACIxMREnW731oVDQsy2Eo/n1NbpdvOTnp4uFixYIGbPni1++eUXERsbW+L7JCJN6enpIjQ0VKSnp0sdRWsjR44U3bt3F1FRUeLx48fi0qVLYt68ecLCwkL4+fmJnJwc9bLVq1cXP/74o/p1VlaWqFmzpjAxMREdOnRQt8+ePVt4enpq7GfmzJnCxMREABAREREF5tm5c6ewt7dX7zcrK0tUqVJFLFu2LM+yoaGhAoC4ceOGEEKIU6dOCblcLr7++msRGhoqrl+/Lj7//HNhaGgorl+/rl4vPDxcODo6ivr164ugoCBx584dERoaKpYsWSI8PDy0OHraOXXqlDA0NBTff/+9uHXrlpg9e7YwMjLSyDZt2jQxfPhw9euwsDDx559/ijt37ohz586JwYMHC1tb20KP4ciRI0Xfvn3ztCcmJgozM7N8j6UQQqSmpgpTU1Nx4sSJYn9GXSrs50qb72+eliqHTExM4OfnB29vb4wZM6bI51yJiHLt27cPTk5OcHNzQ/fu3XH06FH83//9H3bs2AEDg4LH7xgZGeGrr75CRkbGK/cxZcoUWFhYvHK5Hj16wNDQEIcOHQIA7Ny5E8+fP0e/fv3yLFuvXj3Uq1dPPbC4devW+Pvvv/H333+jTZs26NixI06fPo3Dhw+jYcOG6vXc3d1x+fJl+Pj44JNPPkHDhg3RtWtXHD58GMuWLXtlxuJq3bo11q9fj99++w2enp4ICgrC9u3bNbJFRUXh4cOH6tdKpRI//PADPD090bVrV2RkZOD06dNwc3PTev8bN26EEAJDhgzJ9/0dO3agWrVqWl+BVtbJhHiNkVblUFJSEqytrZGYmAgrKyudbff2xcPw2N0fT2QOcJl9R2fbzZU7GV9B58KJqHRlZGQgIiICNWrUyHeQJmln6dKl2LlzJ/bvl+ZS+oqqZcuW+PDDDzF06FCpowAo/OdKm+9vjrkp45RKJY4cOYJTp07BwsIC48aNK9JfQkRE5cl7772HhIQEJCcnl/rVSRVVXFwc+vfvX2CvTnnG4qYMS0xMRFBQEB49egQAqF+/Pv9CJCK9ZGhoiC+++ELqGBWKnZ0dPvvsM6ljlAgWN2VUWFgYtm/frp7/oU+fPqhfv77UsYiIiMo8FjdljEqlwsGDB3HmzBkALy8xHDhwIGxsbCRORkREVD6wuCljZDIZUlNTAbwc6NW1a9dCr1wgIiIiTSxuygiVSgW5XA6ZTIZevXqhUaNGqF27ttSxiIiIyh3OcyOxnJwc7N27F5s2bVJPU65QKFjYEBERFRN7biQUHx+PwMBAREVFAQAePnyI6tWrS5yKiIiofGNxI5EbN25g165dyMzMhJmZGfz9/VnYEBER6QBPS5Wy7Oxs7N69G0FBQcjMzES1atUwbtw41KlTR+poREQVRlhYGBwdHZGcnCx1lAojKysLbm5uuHjxYonvi8VNKQsKCsLFixchk8nQrl07jBo1Sqe3gSAiKoxSqUTr1q3Rv39/jfbExES4urrmmUhvy5Yt6NSpE2xsbGBqaoq6devi7bffxpUrV9TLrFmzBjKZTP2wsLCAt7c3tm7dWiqfKVfHjh017r5dmOnTp+ODDz7IdzZkDw8PKBQKREdH53nPzc0NixcvztM+Z84ceHl5abRFR0fjgw8+gLu7OxQKBVxdXdG7d28cPny4SBmLKzAwEB4eHjAxMUGjRo2wd+/eV66zdOlS1KtXT/1vvHbtWo33s7Oz8eWXX6JmzZowMTGBp6cn9u3bV+D2vvnmG8hkMo1/D2NjY0yZMgVTp04t9mcrKhY3paxdu3awsrLCsGHD0LlzZ8jl/CcgotJjYGCANWvWYN++fVi3bp26/YMPPoCtrS1mz56tbps6dSoGDx4MLy8v7Ny5E2FhYVi/fj3c3d0xffp0je1aWVkhKioKUVFRuHLlCnx9fTFo0CCEhYWV2mcrqocPH2L37t0YNWpUnveCg4ORnp6OgQMH4o8//ij2PiIjI+Ht7Y0jR45g4cKFuH79Ovbt2wcfHx9MmDDhNdIX7vTp0xgyZAjeeecdXLlyBf7+/vD398eNGzcKXGfZsmWYPn065syZg5s3b2Lu3LmYMGECdu3apV5mxowZ+PXXX/HTTz8hNDQU48aNQ79+/TSK3FwXLlzAr7/+isaNG+d576233kJwcDBu3rypmw9cEF3frrys0+aW6dq4deGQELOtxOM5tTXas7Ky8tymPjs7W6f7JqLSl56eLkJDQ0V6evo/jSqVEJkp0jxUKq3yL1myRNjY2IinT5+K7du3CyMjIxESEqJ+/8yZMwKAWLJkSb7rq/61v99//11YW1trvK9UKoWRkZHYvHmzui0+Pl4MHz5cVKpUSZiamoru3buLO3fuaKwXFBQk6tevL4yNjUX16tXF999/r/H+0qVLRa1atYRCoRD29vZiwIABQgghRo4cKQBoPP77uzfXwoULRbNmzfJ9b9SoUWLatGni77//FnXq1MnzfvXq1cWPP/6Yp3327NnC09NT/bpHjx7CxcVFpKSk5Fn2xYsX+e5bFwYNGiR69eql0daiRQvx3nvvFbhOq1atxJQpUzTaJk+eLNq0aaN+7eTkJH7++WeNZfr37y/eeustjbbk5GRRu3ZtcfDgQdGhQwfx0Ucf5dmfj4+PmDFjRr5Z8v25+h9tvr85oLgExcbGIjAwEPHx8Rg7diwcHBwAvLyHChHpoew0YL6zNPv+/ClgbF7kxT/44ANs27YNw4cPx/Xr1zFr1ix4enqq39+wYQMsLCwwfvz4fNeXyWQFblupVKpPazRt2lTdPmrUKNy9exc7d+6ElZUVpk6dip49eyI0NBRGRka4dOkSBg0ahDlz5mDw4ME4ffo0xo8fj8qVK2PUqFG4ePEiPvzwQ/z5559o3bo14uPjcfLkSQDAkiVLcOfOHTRs2BBffvklAKBKlSr55jt58iSaNWuWpz05ORmBgYE4d+4cPDw8kJiYiJMnT6Jdu3avOJqa4uPjsW/fPsybNw/m5nn/TSpVqlTguuvWrcN7771X6Pb//vvvAjOdOXMGkydP1mjz9fXF9u3bC9xeZmZmnvsWmpqa4vz588jOzoaRkVGBywQHB2u0TZgwAb169UKXLl3w9ddf57u/5s2bq//dSkqZ+JZdunQpFi5ciOjoaHh6euKnn35C8+bNC1w+MDAQM2fORGRkJGrXro1vv/0WPXv2LMXEhRNCICQkBHv37kV2djYsLCyQmZkpdSwiIjWZTIZly5ahXr16aNSoEaZNm6bx/p07d+Du7q7xx9iiRYswa9Ys9esnT57A2toawMsxOxYWFgCA9PR0GBkZ4bfffkPNmjUBQF3UnDp1Cq1btwbw8ovc1dUV27dvR0BAABYtWoTOnTtj5syZAIA6deogNDQUCxcuxKhRo/Dw4UOYm5vDz88PlpaWqF69Opo0aQIAsLa2hrGxMczMzODo6FjoZ3/w4EG+xc3GjRtRu3ZtNGjQAADw5ptvYtWqVVoXN/fu3YMQAh4eHlqtBwB9+vRBixYtCl3GxcWlwPeio6PVf0jncnBwyHf8UC5fX1+sXLkS/v7+aNq0KS5duoSVK1ciOzsbcXFxcHJygq+vLxYtWoT27dujZs2aOHz4MLZu3QqlUqnezsaNG3H58mVcuHCh0PzOzs548OBBocu8LsmLm02bNmHy5MlYvnw5WrRogcWLF8PX1xdhYWGwt7fPs3zu+cQFCxbAz88P69evh7+/Py5fvoyGDRtK8An+QwDbt2/H1atXAQA1a9ZEv3791D/0RKTHjMxe9qBItW8trV69GmZmZoiIiMDjx4/h5uZW6PJvv/02+vTpg3PnzmHYsGHqiUcBwNLSEpcvXwYApKWl4dChQxg3bhwqV66M3r1749atWzA0NNT44q5cuTLq1q2LW7duAQBu3bqFvn37auyzTZs2WLx4MZRKJbp27Yrq1avD3d0d3bt3R/fu3dGvXz+YmWn32XNvSJzf8Rg2bJj69bBhw9ChQwf89NNP+Q48Lsi/j4u2LC0ttdqXLsycORPR0dFo2bIlhBBwcHDAyJEj8d1336nHhS5ZsgRjx46Fh4cHZDIZatasidGjR2P16tUAgEePHuGjjz7CwYMH8z22/2Zqaoq0tLQS/UySj2ZdtGgRxo4di9GjR6N+/fpYvnw5zMzM1Afsv5YsWYLu3bvj008/Rb169fDVV1+hadOm+Pnnn0s5ef6Umam4evUqZDIZOnXqhGHDhrGwIaooZLKXp4akeBRymig/p0+fxo8//ojdu3ejefPmeOeddzS+lGvXro379+8jOztb3VapUiXUqlUr354DuVyOWrVqoVatWmjcuDEmT56Mjh074ttvvy3+8fyP3AJqw4YNcHJyUp9KS0hI0Go7dnZ2ePHihUZbaGgozp49i88++wyGhoYwNDREy5YtkZaWho0bN6qXs7KyQmJiYp5tJiQkqHuxateuDZlMhtu3b2v9GdetWwcLC4tCH4Wd0nF0dERMTIxGW0xMTKG9Waampli9ejXS0tIQGRmJhw8fws3NDZaWlupTe1WqVMH27duRmpqKBw8e4Pbt27CwsIC7uzsA4NKlS3j27BmaNm2qPn7Hjx/H//3f/8HQ0FCjhyc+Pr7AU4a6Imlxk5WVhUuXLqFLly7qNrlcji5duqjviv1fZ86c0VgeeNmlVtDymZmZSEpK0niUJKFSwdLSEqNGjUL79u0LPS9NRCSFtLQ0jBo1Cu+//z58fHywatUqnD9/HsuXL1cvM2TIEKSkpOCXX34p9n4MDAyQnp4OAKhXrx5ycnJw7tw59fvPnz9HWFgY6tevr17m1KlTGts4deoU6tSpo76BsKGhIbp06YLvvvsO165dQ2RkJI4cOQLg5aXG//4SLUiTJk0QGhqq0bZq1Sq0b98eV69eRUhIiPoxefJkrFq1Sr1c3bp1cenSpTzbvHz5snq+MltbW/j6+mLp0qXqGyH/W2HFWJ8+fTT2n98jv1NquVq1apXnUvODBw+iVatWBa6Ty8jICFWrVoWBgQE2btwIPz+/PFf0mpiYwMXFBTk5OdiyZYu6p61z5864fv16npxvvfUWQkJCNG4AfePGDfXpxBLzyiHHJejJkycCgDh9+rRG+6effiqaN2+e7zpGRkZi/fr1Gm1Lly4V9vb2+S4/e/bsPCPoUQJXS92+cFikz6oswr9wz3d0PBHpl8Ku6ijrPvzwQ1GrVi2Rmpqqblu+fLmwsLDQuMLok08+EQYGBmLSpEni5MmTIjIyUpw5c0YMGzZMyGQy9e/R33//XVhZWYmoqCgRFRUl7t+/L3799VdhYGAg5s6dq95e3759Rf369cXJkydFSEiI6N69u6hVq5bIysoSQghx6dIlIZfLxZdffinCwsLEmjVrhKmpqfj999+FEELs2rVLLFmyRFy5ckVERkaKX375RcjlcnHjxg0hhBBjx44Vb7zxhoiIiBCxsbFCqVTm+/l37twp7O3tRU5OjhDi5VWtVapUEcuWLcuzbGhoqACg3sepU6eEXC4XX3/9tQgNDRXXr18Xn3/+uTA0NBTXr19XrxceHi4cHR1F/fr1RVBQkLhz544IDQ0VS5YsER4eHtr+kxXZqVOnhKGhofj+++/FrVu3xOzZs4WRkZFGtmnTponhw4erX4eFhYk///xT3LlzR5w7d04MHjxY2Nraavy/cPbsWbFlyxYRHh4uTpw4ITp16iRq1KhR6JVfBV0tVb16dbF27dp819HV1VJ6X9xkZGSIxMRE9ePRo0clUtwQUcVSXoubY8eOCQMDA3Hy5Mk873Xr1k106tRJ4zLvTZs2iY4dOwpra2thZGQkqlatKoYOHSrOnj2rXub333/X+ONRoVCIOnXqiHnz5qkLCCH+uRTc2tpamJqaCl9f3wIvBTcyMhLVqlUTCxcuVL938uRJ0aFDB2FjYyNMTU1F48aNxaZNm9Tvh4WFiZYtWwpTU9NCLwXPzs4Wzs7OYt++fep9yuVyER0dne/y9erVE5MmTVK/3r9/v2jTpo2wsbERlStXFh07dhTHjx/Ps97Tp0/FhAkTRPXq1YWxsbFwcXERffr0EUePHs13P7qyefNmUadOHWFsbCwaNGgg9uzZo/H+yJEjRYcOHdSvQ0NDhZeXlzA1NRVWVlaib9++4vbt2xrrHDt2TNSrV08oFApRuXJlMXz4cPHkyZNCc+RX3Jw+fVpUqlRJpKWl5buOroobmRCvMfLpNWVlZcHMzAxBQUHw9/dXt48cORIJCQnYsWNHnnWqVauGyZMna8x6OHv2bI1BvIVJSkqCtbU1EhMTOTMwERVbRkYGIiIiUKNGjVcOoKSyZ+nSpdi5cyf2798vdZQKZfDgwfD09MTnn3+e7/uF/Vxp8/0t6ZgbY2NjeHt7a5wfVKlUOHz4cIHnB1/nfCIREREAvPfee2jfvj3vLVWKsrKy0KhRI0yaNKnE9yX5peCTJ0/GyJEj0axZMzRv3hyLFy9GamoqRo8eDQAYMWIEXFxcsGDBAgDARx99hA4dOuCHH35Ar169sHHjRly8eBG//fablB+DiIjKEUNDwzz30aKSZWxsjBkzZpTKviQvbgYPHozY2FjMmjUL0dHR8PLywr59+9STED18+FBjtHbr1q2xfv16zJgxA59//jlq166N7du3l405boiIiEhyko65kQLH3BCRLnDMDZHu6cWYGyKi8q6C/X1IVKJ09fPE4oaIqBiMjIwAoMSnkSeqSLKysgBAY9K/4pB8zA0RUXlkYGCASpUq4dmzZwAAMzMzzkhO9BpUKhViY2NhZmamccPW4mBxQ0RUTLn368ktcIjo9cjlclSrVu21/1BgcUNEVEwymQxOTk6wt7fXuMEkERWPsbFxnvtZFQeLGyKi12RgYPDaYwSISHc4oJiIiIj0CosbIiIi0issboiIiEivVLgxN7kTBCUlJUmchIiIiIoq93u7KBP9VbjiJvcOsK6urhInISIiIm0lJyfD2tq60GUq3L2lVCoVnj59CktLS51PuJWUlARXV1c8evSI960qQTzOpYPHuXTwOJceHuvSUVLHWQiB5ORkODs7v/Jy8QrXcyOXy1G1atUS3YeVlRV/cEoBj3Pp4HEuHTzOpYfHunSUxHF+VY9NLg4oJiIiIr3C4oaIiIj0CosbHVIoFJg9ezYUCoXUUfQaj3Pp4HEuHTzOpYfHunSUheNc4QYUExERkX5jzw0RERHpFRY3REREpFdY3BAREZFeYXFDREREeoXFjZaWLl0KNzc3mJiYoEWLFjh//nyhywcGBsLDwwMmJiZo1KgR9u7dW0pJyzdtjvOKFSvQrl072NjYwMbGBl26dHnlvwu9pO3/z7k2btwImUwGf3//kg2oJ7Q9zgkJCZgwYQKcnJygUChQp04d/u4oAm2P8+LFi1G3bl2YmprC1dUVkyZNQkZGRimlLZ9OnDiB3r17w9nZGTKZDNu3b3/lOseOHUPTpk2hUChQq1YtrFmzpsRzQlCRbdy4URgbG4vVq1eLmzdvirFjx4pKlSqJmJiYfJc/deqUMDAwEN99950IDQ0VM2bMEEZGRuL69eulnLx80fY4Dx06VCxdulRcuXJF3Lp1S4waNUpYW1uLx48fl3Ly8kXb45wrIiJCuLi4iHbt2om+ffuWTthyTNvjnJmZKZo1ayZ69uwpgoODRUREhDh27JgICQkp5eTli7bHed26dUKhUIh169aJiIgIsX//fuHk5CQmTZpUysnLl71794ovvvhCbN26VQAQ27ZtK3T5+/fvCzMzMzF58mQRGhoqfvrpJ2FgYCD27dtXojlZ3GihefPmYsKECerXSqVSODs7iwULFuS7/KBBg0SvXr002lq0aCHee++9Es1Z3ml7nP8rJydHWFpaij/++KOkIuqF4hznnJwc0bp1a7Fy5UoxcuRIFjdFoO1xXrZsmXB3dxdZWVmlFVEvaHucJ0yYIDp16qTRNnnyZNGmTZsSzalPilLcfPbZZ6JBgwYabYMHDxa+vr4lmEwInpYqoqysLFy6dAldunRRt8nlcnTp0gVnzpzJd50zZ85oLA8Avr6+BS5PxTvO/5WWlobs7GzY2tqWVMxyr7jH+csvv4S9vT3eeeed0ohZ7hXnOO/cuROtWrXChAkT4ODggIYNG2L+/PlQKpWlFbvcKc5xbt26NS5duqQ+dXX//n3s3bsXPXv2LJXMFYVU34MV7saZxRUXFwelUgkHBweNdgcHB9y+fTvfdaKjo/NdPjo6usRylnfFOc7/NXXqVDg7O+f5gaJ/FOc4BwcHY9WqVQgJCSmFhPqhOMf5/v37OHLkCN566y3s3bsX9+7dw/jx45GdnY3Zs2eXRuxypzjHeejQoYiLi0Pbtm0hhEBOTg7GjRuHzz//vDQiVxgFfQ8mJSUhPT0dpqamJbJf9tyQXvnmm2+wceNGbNu2DSYmJlLH0RvJyckYPnw4VqxYATs7O6nj6DWVSgV7e3v89ttv8Pb2xuDBg/HFF19g+fLlUkfTK8eOHcP8+fPxyy+/4PLly9i6dSv27NmDr776SupopAPsuSkiOzs7GBgYICYmRqM9JiYGjo6O+a7j6Oio1fJUvOOc6/vvv8c333yDQ4cOoXHjxiUZs9zT9jiHh4cjMjISvXv3VrepVCoAgKGhIcLCwlCzZs2SDV0OFef/ZycnJxgZGcHAwEDdVq9ePURHRyMrKwvGxsYlmrk8Ks5xnjlzJoYPH44xY8YAABo1aoTU1FS8++67+OKLLyCX829/XSjoe9DKyqrEem0A9twUmbGxMby9vXH48GF1m0qlwuHDh9GqVat812nVqpXG8gBw8ODBApen4h1nAPjuu+/w1VdfYd++fWjWrFlpRC3XtD3OHh4euH79OkJCQtSPPn36wMfHByEhIXB1dS3N+OVGcf5/btOmDe7du6cuHgHgzp07cHJyYmFTgOIc57S0tDwFTG5BKXjLRZ2R7HuwRIcr65mNGzcKhUIh1qxZI0JDQ8W7774rKlWqJKKjo4UQQgwfPlxMmzZNvfypU6eEoaGh+P7778WtW7fE7NmzeSl4EWh7nL/55hthbGwsgoKCRFRUlPqRnJws1UcoF7Q9zv/Fq6WKRtvj/PDhQ2FpaSkmTpwowsLCxO7du4W9vb34+uuvpfoI5YK2x3n27NnC0tJSbNiwQdy/f18cOHBA1KxZUwwaNEiqj1AuJCcniytXrogrV64IAGLRokXiypUr4sGDB0IIIaZNmyaGDx+uXj73UvBPP/1U3Lp1SyxdupSXgpdFP/30k6hWrZowNjYWzZs3F2fPnlW/16FDBzFy5EiN5Tdv3izq1KkjjI2NRYMGDcSePXtKOXH5pM1xrl69ugCQ5zF79uzSD17OaPv/87+xuCk6bY/z6dOnRYsWLYRCoRDu7u5i3rx5Iicnp5RTlz/aHOfs7GwxZ84cUbNmTWFiYiJcXV3F+PHjxYsXL0o/eDly9OjRfH/f5h7bkSNHig4dOuRZx8vLSxgbGwt3d3fx+++/l3hOmRDsfyMiIiL9wTE3REREpFdY3BAREZFeYXFDREREeoXFDREREekVFjdERESkV1jcEBERkV5hcUNERER6hcUNEWlYs2YNKlWqJHWMYpPJZNi+fXuhy4waNQr+/v6lkoeISh+LGyI9NGrUKMhksjyPe/fuSR0Na9asUeeRy+WoWrUqRo8ejWfPnulk+1FRUejRowcAIDIyEjKZDCEhIRrLLFmyBGvWrNHJ/goyZ84c9ec0MDCAq6sr3n33XcTHx2u1HRZiRNrjXcGJ9FT37t3x+++/a7RVqVJFojSarKysEBYWBpVKhatXr2L06NF4+vQp9u/f/9rbftXd4wHA2tr6tfdTFA0aNMChQ4egVCpx69YtvP3220hMTMSmTZtKZf9EFRV7boj0lEKhgKOjo8bDwMAAixYtQqNGjWBubg5XV1eMHz8eKSkpBW7n6tWr8PHxgaWlJaysrODt7Y2LFy+q3w8ODka7du1gamoKV1dXfPjhh0hNTS00m0wmg6OjI5ydndGjRw98+OGHOHToENLT06FSqfDll1+iatWqUCgU8PLywr59+9TrZmVlYeLEiXBycoKJiQmqV6+OBQsWaGw797RUjRo1AABNmjSBTCZDx44dAWj2hvz2229wdnbWuAs3APTt2xdvv/22+vWOHTvQtGlTmJiYwN3dHXPnzkVOTk6hn9PQ0BCOjo5wcXFBly5dEBAQgIMHD6rfVyqVeOedd1CjRg2Ympqibt26WLJkifr9OXPm4I8//sCOHTvUvUDHjh0DADx69AiDBg1CpUqVYGtri759+yIyMrLQPEQVBYsbogpGLpfj//7v/3Dz5k388ccfOHLkCD777LMCl3/rrbdQtWpVXLhwAZcuXcK0adNgZGQEAAgPD0f37t0xYMAAXLt2DZs2bUJwcDAmTpyoVSZTU1OoVCrk5ORgyZIl+OGHH/D999/j2rVr8PX1RZ8+fXD37l0AwP/93/9h586d2Lx5M8LCwrBu3Tq4ubnlu93z588DAA4dOoSoqChs3bo1zzIBAQF4/vw5jh49qm6Lj4/Hvn378NZbbwEATp48iREjRuCjjz5CaGgofv31V6xZswbz5s0r8meMjIzE/v37YWxsrG5TqVSoWrUqAgMDERoailmzZuHzzz/H5s2bAQBTpkzBoEGD0L17d0RFRSEqKgqtW7dGdnY2fH19YWlpiZMnT+LUqVOwsLBA9+7dkZWVVeRMRHqrxG/NSUSlbuTIkcLAwECYm5urHwMHDsx32cDAQFG5cmX1699//11YW1urX1taWoo1a9bku+4777wj3n33XY22kydPCrlcLtLT0/Nd57/bv3PnjqhTp45o1qyZEEIIZ2dnMW/ePI113njjDTF+/HghhBAffPCB6NSpk1CpVPluH4DYtm2bEEKIiIgIAUBcuXJFY5n/3tG8b9++4u2331a//vXXX4Wzs7NQKpVCCCE6d+4s5s+fr7GNP//8Uzg5OeWbQQghZs+eLeRyuTA3NxcmJibquycvWrSowHWEEGLChAliwIABBWbN3XfdunU1jkFmZqYwNTUV+/fvL3T7RBUBx9wQ6SkfHx8sW7ZM/drc3BzAy16MBQsW4Pbt20hKSkJOTg4yMjKQlpYGMzOzPNuZPHkyxowZgz///FN9aqVmzZoAXp6yunbtGtatW6deXggBlUqFiIgI1KtXL99siYmJsLCwgEqlQkZGBtq2bYuVK1ciKSkJT58+RZs2bTSWb9OmDa5evQrg5Smlrl27om7duujevTv8/PzQrVu31zpWb731FsaOHYtffvkFCoUC69atw5tvvgm5XK7+nKdOndLoqVEqlYUeNwCoW7cudu7ciYyMDPz1118ICQnBBx98oLHM0qVLsXr1ajx8+BDp6enIysqCl5dXoXmvXr2Ke/fuwdLSUqM9IyMD4eHhxTgCRPqFxQ2RnjI3N0etWrU02iIjI+Hn54f3338f8+bNg62tLYKDg/HOO+8gKysr3y/pOXPmYOjQodizZw/+/vtvzJ49Gxs3bkS/fv2QkpKC9957Dx9++GGe9apVq1ZgNktLS1y+fBlyuRxOTk4wNTUFACQlJb3yczVt2hQRERH4+++/cejQIQwaNAhdunRBUFDQK9ctSO/evSGEwJ49e/DGG2/g5MmT+PHHH9Xvp6SkYO7cuejfv3+edU1MTArcrrGxsfrf4JtvvkGvXr0wd+5cfPXVVwCAjRs3YsqUKfjhhx/QqlUrWFpaYuHChTh37lyheVNSUuDt7a1RVOYqK4PGiaTE4oaoArl06RJUKhV++OEHda9E7viOwtSpUwd16tTBpEmTMGTIEPz+++/o168fmjZtitDQ0DxF1KvI5fJ817GysoKzszNOnTqFDh06qNtPnTqF5s2bayw3ePBgDB48GAMHDkT37t0RHx8PW1tbje3ljm9RKpWF5jExMUH//v2xbt063Lt3D3Xr1kXTpk3V7zdt2hRhYWFaf87/mjFjBjp16oT3339f/Tlbt26N8ePHq5f5b8+LsbFxnvxNmzbFpk2bYG9vDysrq9fKRKSPOKCYqAKpVasWsrOz8dNPP+H+/fv4888/sXz58gKXT09Px8SJE3Hs2DE8ePAAp06dwoULF9Snm6ZOnYrTp09j4sSJCAkJwd27d7Fjxw6tBxT/26effopvv/0WmzZtQlhYGKZNm4aQkBB89NFHAIBFixZhw4YNuH37Nu7cuYPAwEA4OjrmO/Ggvb09TE1NsW/fPsTExCAxMbHA/b711lvYs2cPVq9erR5InGvWrFlYu3Yt5s6di5s3b+LWrVvYuHEjZsyYodVna9WqFRo3boz58+cDAGrXro2LFy9i//79uHPnDmbOnIkLFy5orOPm5oZr164hLCwMcXFxyM7OxltvvQU7Ozv07dsXJ0+eREREBI4dO4YPP/wQjx8/1ioTkV6SetAPEelefoNQcy1atEg4OTkJU1NT4evrK9auXSsAiBcvXgghNAf8ZmZmijfffFO4uroKY2Nj4ezsLCZOnKgxWPj8+fOia9euwsLCQpibm4vGjRvnGRD8b/8dUPxfSqVSzJkzR7i4uAgjIyPh6ekp/v77b/X7v/32m/Dy8hLm5ubCyspKdO7cWVy+fFn9Pv41oFgIIVasWCFcXV2FXC4XHTp0KPD4KJVK4eTkJACI8PDwPLn27dsnWrduLUxNTYWVlZVo3ry5+O233wr8HLNnzxaenp552jds2CAUCoV4+PChyMjIEKNGjRLW1taiUqVK4v333xfTpk3TWO/Zs2fq4wtAHD16VAghRFRUlBgxYoSws7MTCoVCuLu7i7Fjx4rExMQCMxFVFDIhhJC2vCIiIiLSHZ6WIiIiIr3C4oaIiIj0CosbIiIi0issboiIiEivsLghIiIivcLihoiIiPQKixsiIiLSKyxuiIiISK+wuCEiIiK9wuKGiIiI9AqLGyIiItIrLG6IiIhIr/w/o/5GCCbn8WgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC-AUC curves for DNN and XGBoost models\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['dnn_mean'])\n",
    "plt.plot(fpr, tpr, label=f'DNN (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"dnn_mean\"]):.3f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--', alpha=0.5)\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['xgb_mean'])\n",
    "plt.plot(fpr, tpr, label=f'XGBoost (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"xgb_mean\"]):.3f})')\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC-AUC Curve (mean)')\n",
    "plt.show()\n",
    "\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['dnn_majority_vote'])\n",
    "plt.plot(fpr, tpr, label=f'DNN (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"dnn_majority_vote\"]):.3f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--', alpha=0.5)\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['xgb_majority_vote'])\n",
    "plt.plot(fpr, tpr, label=f'XGBoost (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"xgb_majority_vote\"]):.3f})')\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC-AUC Curve (majority_vote)')\n",
    "plt.show()\n",
    "\n",
    "for i in range(3):\n",
    "    fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df[f'dnn_model_n{i}'])\n",
    "    plt.plot(fpr, tpr, label=f'DNN (model n.{i}) (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[f\"dnn_model_n{i}\"]):.3f})')\n",
    "    fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df[f'xgb_model_n{i}'])\n",
    "    plt.plot(fpr, tpr, label=f'XGBoost (model n.{i}) (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[f\"xgb_model_n{i}\"]):.3f})', linestyle='dashed')\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title(f'ROC-AUC Curve (models)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard Split - Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Heavy Atom Count</th>\n",
       "      <th>Ring Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>...</th>\n",
       "      <th>POI Sequence</th>\n",
       "      <th>E3 Ligase Uniprot</th>\n",
       "      <th>E3 Ligase Sequence</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "      <th>Active - OR</th>\n",
       "      <th>Active</th>\n",
       "      <th>Avg Tanimoto</th>\n",
       "      <th>Uniprot Group</th>\n",
       "      <th>E3 Group</th>\n",
       "      <th>Tanimoto Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2434</td>\n",
       "      <td>Q92769</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C48H62FN7O8S/c1-31-43(65-30-52-31)33-...</td>\n",
       "      <td>AGMBKWPLZPKEGR-OUZJXKGJSA-N</td>\n",
       "      <td>916.130</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>MAYSQGGGKKKVCYYYDGDIGNYYYGQGHPMKPHRIRMTHNLLLNY...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>HCT116-53BPI(+/-)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.378412</td>\n",
       "      <td>57</td>\n",
       "      <td>6</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C41H42N10O8/c1-22(2)47-29-17-32(50-15...</td>\n",
       "      <td>YMXWKCAXGFLWAH-MONLTZSXSA-N</td>\n",
       "      <td>802.849</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>MNKPITPSTYVRCLNVGLIRKLSDFIDPQEGWKKLAVAIKKPSGDD...</td>\n",
       "      <td>Q96SW2</td>\n",
       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.412830</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>549</td>\n",
       "      <td>P03372</td>\n",
       "      <td>CCN(CCCCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C55H65N5O8S2/c1-6-59(28-29-68-43-24-1...</td>\n",
       "      <td>IDYPSHVHIRZTPO-VADRYRRNSA-N</td>\n",
       "      <td>988.286</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>MTMTLHTKASGMALLHQIQGNELEPLNRPQLKIPLERPLGEVYLDS...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MCF-7</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.374558</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1098</td>\n",
       "      <td>P04035</td>\n",
       "      <td>CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCc3cn(CCO...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C59H67FN8O13/c1-37(2)54-53(57(75)62-4...</td>\n",
       "      <td>FLZOWPIEHOMVRX-OEUTXLNUSA-N</td>\n",
       "      <td>1115.226</td>\n",
       "      <td>81</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>MLSRLFRMHGLFVASHPWEVIVGTVTLTICMMSMNMFTGNNKICGW...</td>\n",
       "      <td>Q96SW2</td>\n",
       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.359937</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>937</td>\n",
       "      <td>O75530</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C43H48N10O6S/c1-26-36(60-25-48-26)29-...</td>\n",
       "      <td>PPNNFXIBKLCMTI-WXEAQWFJSA-N</td>\n",
       "      <td>832.988</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>MSEREVSTAPAGTDMPAAKKQKLSSDENSNPDLSGDENDDAVSIES...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>DB</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.406916</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1103</td>\n",
       "      <td>P04035</td>\n",
       "      <td>CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCCCc3cn(C...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C63H75FN8O14/c1-41(2)58-57(61(79)66-4...</td>\n",
       "      <td>METKOBDLEKDPEQ-MOHJDXDLSA-N</td>\n",
       "      <td>1187.333</td>\n",
       "      <td>86</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>MLSRLFRMHGLFVASHPWEVIVGTVTLTICMMSMNMFTGNNKICGW...</td>\n",
       "      <td>Q96SW2</td>\n",
       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.329734</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2424</td>\n",
       "      <td>O15379</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C46H59N7O7S/c1-30-41(61-29-49-30)32-1...</td>\n",
       "      <td>QANLZMZKZIPVPD-DRTKLYQKSA-N</td>\n",
       "      <td>854.087</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>MAKTVAYFYDPDVGNFHYGAGHPMKPHRLALTHSLVLHYGLYKKMI...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>HCT116-53BPI(+/-)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.390991</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>335</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCn...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C41H41ClN10O8S/c1-22-23(2)61-41-34(22...</td>\n",
       "      <td>QDKBKEFTDKCTPJ-UFXYQILXSA-N</td>\n",
       "      <td>869.361</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...</td>\n",
       "      <td>Q96SW2</td>\n",
       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
       "      <td>NCI-H661</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.376327</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2520</td>\n",
       "      <td>P10275</td>\n",
       "      <td>CCN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2cc...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C44H49ClN8O5/c1-2-52(35-10-5-29(27-46...</td>\n",
       "      <td>VZVAWYNZTBXESG-BULKPCKOSA-N</td>\n",
       "      <td>805.380</td>\n",
       "      <td>58</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>MEVQLGLGRVYPRPPSKTYRGAFQNLFQSVREVIQNPGPRHPEAAS...</td>\n",
       "      <td>Q96SW2</td>\n",
       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
       "      <td>VCaP</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.390809</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2528</td>\n",
       "      <td>P10275</td>\n",
       "      <td>CN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2ccc...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C41H43ClN8O5/c1-46(30-9-4-26(22-43)35...</td>\n",
       "      <td>RNZSZCNUPQYOIX-RVWRWSNSSA-N</td>\n",
       "      <td>763.299</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>MEVQLGLGRVYPRPPSKTYRGAFQNLFQSVREVIQNPGPRHPEAAS...</td>\n",
       "      <td>Q96SW2</td>\n",
       "      <td>MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...</td>\n",
       "      <td>VCaP</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.439834</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Compound ID Uniprot                                             Smiles  \\\n",
       "0          2434  Q92769  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "1          2004  Q9NWZ3  CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...   \n",
       "2           549  P03372  CCN(CCCCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1...   \n",
       "3          1098  P04035  CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCc3cn(CCO...   \n",
       "4           937  O75530  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "..          ...     ...                                                ...   \n",
       "81         1103  P04035  CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCCCc3cn(C...   \n",
       "82         2424  O15379  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "83          335  O60885  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCn...   \n",
       "84         2520  P10275  CCN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2cc...   \n",
       "85         2528  P10275  CN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2ccc...   \n",
       "\n",
       "   E3 Ligase                                              InChI  \\\n",
       "0        VHL  InChI=1S/C48H62FN7O8S/c1-31-43(65-30-52-31)33-...   \n",
       "1       CRBN  InChI=1S/C41H42N10O8/c1-22(2)47-29-17-32(50-15...   \n",
       "2        VHL  InChI=1S/C55H65N5O8S2/c1-6-59(28-29-68-43-24-1...   \n",
       "3       CRBN  InChI=1S/C59H67FN8O13/c1-37(2)54-53(57(75)62-4...   \n",
       "4        VHL  InChI=1S/C43H48N10O6S/c1-26-36(60-25-48-26)29-...   \n",
       "..       ...                                                ...   \n",
       "81      CRBN  InChI=1S/C63H75FN8O14/c1-41(2)58-57(61(79)66-4...   \n",
       "82       VHL  InChI=1S/C46H59N7O7S/c1-30-41(61-29-49-30)32-1...   \n",
       "83      CRBN  InChI=1S/C41H41ClN10O8S/c1-22-23(2)61-41-34(22...   \n",
       "84      CRBN  InChI=1S/C44H49ClN8O5/c1-2-52(35-10-5-29(27-46...   \n",
       "85      CRBN  InChI=1S/C41H43ClN8O5/c1-46(30-9-4-26(22-43)35...   \n",
       "\n",
       "                      InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "0   AGMBKWPLZPKEGR-OUZJXKGJSA-N           916.130                65   \n",
       "1   YMXWKCAXGFLWAH-MONLTZSXSA-N           802.849                59   \n",
       "2   IDYPSHVHIRZTPO-VADRYRRNSA-N           988.286                70   \n",
       "3   FLZOWPIEHOMVRX-OEUTXLNUSA-N          1115.226                81   \n",
       "4   PPNNFXIBKLCMTI-WXEAQWFJSA-N           832.988                60   \n",
       "..                          ...               ...               ...   \n",
       "81  METKOBDLEKDPEQ-MOHJDXDLSA-N          1187.333                86   \n",
       "82  QANLZMZKZIPVPD-DRTKLYQKSA-N           854.087                61   \n",
       "83  QDKBKEFTDKCTPJ-UFXYQILXSA-N           869.361                61   \n",
       "84  VZVAWYNZTBXESG-BULKPCKOSA-N           805.380                58   \n",
       "85  RNZSZCNUPQYOIX-RVWRWSNSSA-N           763.299                55   \n",
       "\n",
       "    Ring Count  Rotatable Bond Count  ...  \\\n",
       "0            5                    23  ...   \n",
       "1            7                    13  ...   \n",
       "2            7                    22  ...   \n",
       "3            8                    31  ...   \n",
       "4            7                    15  ...   \n",
       "..         ...                   ...  ...   \n",
       "81           8                    36  ...   \n",
       "82           5                    20  ...   \n",
       "83           8                    16  ...   \n",
       "84           8                     9  ...   \n",
       "85           8                     8  ...   \n",
       "\n",
       "                                         POI Sequence  E3 Ligase Uniprot  \\\n",
       "0   MAYSQGGGKKKVCYYYDGDIGNYYYGQGHPMKPHRIRMTHNLLLNY...             P40337   \n",
       "1   MNKPITPSTYVRCLNVGLIRKLSDFIDPQEGWKKLAVAIKKPSGDD...             Q96SW2   \n",
       "2   MTMTLHTKASGMALLHQIQGNELEPLNRPQLKIPLERPLGEVYLDS...             P40337   \n",
       "3   MLSRLFRMHGLFVASHPWEVIVGTVTLTICMMSMNMFTGNNKICGW...             Q96SW2   \n",
       "4   MSEREVSTAPAGTDMPAAKKQKLSSDENSNPDLSGDENDDAVSIES...             P40337   \n",
       "..                                                ...                ...   \n",
       "81  MLSRLFRMHGLFVASHPWEVIVGTVTLTICMMSMNMFTGNNKICGW...             Q96SW2   \n",
       "82  MAKTVAYFYDPDVGNFHYGAGHPMKPHRLALTHSLVLHYGLYKKMI...             P40337   \n",
       "83  MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...             Q96SW2   \n",
       "84  MEVQLGLGRVYPRPPSKTYRGAFQNLFQSVREVIQNPGPRHPEAAS...             Q96SW2   \n",
       "85  MEVQLGLGRVYPRPPSKTYRGAFQNLFQSVREVIQNPGPRHPEAAS...             Q96SW2   \n",
       "\n",
       "                                   E3 Ligase Sequence Cell Line Identifier  \\\n",
       "0   MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...    HCT116-53BPI(+/-)   \n",
       "1   MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...              HEK293T   \n",
       "2   MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...                MCF-7   \n",
       "3   MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...                  NaN   \n",
       "4   MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...                   DB   \n",
       "..                                                ...                  ...   \n",
       "81  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...                  NaN   \n",
       "82  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...    HCT116-53BPI(+/-)   \n",
       "83  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...             NCI-H661   \n",
       "84  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...                 VCaP   \n",
       "85  MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI...                 VCaP   \n",
       "\n",
       "    Active - OR  Active  Avg Tanimoto Uniprot Group E3 Group Tanimoto Group  \n",
       "0         False   False      0.378412            57        6             64  \n",
       "1         False   False      0.412830            63        0             95  \n",
       "2         False   False      0.374558            13        6             61  \n",
       "3         False   False      0.359937            14        0             47  \n",
       "4         False    True      0.406916             8        6             90  \n",
       "..          ...     ...           ...           ...      ...            ...  \n",
       "81        False   False      0.329734            14        0             21  \n",
       "82        False   False      0.390991             3        6             75  \n",
       "83        False   False      0.376327             7        0             62  \n",
       "84         True    True      0.390809            17        0             75  \n",
       "85         True    True      0.439834            17        0            117  \n",
       "\n",
       "[86 rows x 39 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_df = pd.read_csv('data/studies/standard_test_10split_Active_Dmax_0.6_pDC50_6.0.csv')\n",
    "# Drop column \"Active\"\n",
    "protac_df.drop(columns='Active', inplace=True)\n",
    "# Rename \"Active (Dmax 0.6, pDC50 6.0)\" column to \"Active\"\n",
    "protac_df.rename(columns={'Active (Dmax 0.6, pDC50 6.0)': 'Active'}, inplace=True)\n",
    "protac_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Heavy Atom Count</th>\n",
       "      <th>Ring Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>...</th>\n",
       "      <th>dnn_mean</th>\n",
       "      <th>dnn_majority_vote</th>\n",
       "      <th>dnn_model_n0</th>\n",
       "      <th>dnn_model_n1</th>\n",
       "      <th>dnn_model_n2</th>\n",
       "      <th>xgb_mean</th>\n",
       "      <th>xgb_majority_vote</th>\n",
       "      <th>xgb_model_n0</th>\n",
       "      <th>xgb_model_n1</th>\n",
       "      <th>xgb_model_n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2434</td>\n",
       "      <td>Q92769</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C48H62FN7O8S/c1-31-43(65-30-52-31)33-...</td>\n",
       "      <td>AGMBKWPLZPKEGR-OUZJXKGJSA-N</td>\n",
       "      <td>916.130</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079379</td>\n",
       "      <td>False</td>\n",
       "      <td>0.170209</td>\n",
       "      <td>0.063416</td>\n",
       "      <td>0.004514</td>\n",
       "      <td>0.026209</td>\n",
       "      <td>False</td>\n",
       "      <td>0.024188</td>\n",
       "      <td>0.029409</td>\n",
       "      <td>0.025029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C41H42N10O8/c1-22(2)47-29-17-32(50-15...</td>\n",
       "      <td>YMXWKCAXGFLWAH-MONLTZSXSA-N</td>\n",
       "      <td>802.849</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.435443</td>\n",
       "      <td>False</td>\n",
       "      <td>0.269258</td>\n",
       "      <td>0.663059</td>\n",
       "      <td>0.374012</td>\n",
       "      <td>0.721998</td>\n",
       "      <td>True</td>\n",
       "      <td>0.766466</td>\n",
       "      <td>0.608752</td>\n",
       "      <td>0.790776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>549</td>\n",
       "      <td>P03372</td>\n",
       "      <td>CCN(CCCCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C55H65N5O8S2/c1-6-59(28-29-68-43-24-1...</td>\n",
       "      <td>IDYPSHVHIRZTPO-VADRYRRNSA-N</td>\n",
       "      <td>988.286</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257002</td>\n",
       "      <td>False</td>\n",
       "      <td>0.435150</td>\n",
       "      <td>0.277998</td>\n",
       "      <td>0.057857</td>\n",
       "      <td>0.199748</td>\n",
       "      <td>False</td>\n",
       "      <td>0.306645</td>\n",
       "      <td>0.227117</td>\n",
       "      <td>0.065481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1098</td>\n",
       "      <td>P04035</td>\n",
       "      <td>CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCc3cn(CCO...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C59H67FN8O13/c1-37(2)54-53(57(75)62-4...</td>\n",
       "      <td>FLZOWPIEHOMVRX-OEUTXLNUSA-N</td>\n",
       "      <td>1115.226</td>\n",
       "      <td>81</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048816</td>\n",
       "      <td>False</td>\n",
       "      <td>0.046254</td>\n",
       "      <td>0.098125</td>\n",
       "      <td>0.002068</td>\n",
       "      <td>0.010581</td>\n",
       "      <td>False</td>\n",
       "      <td>0.010565</td>\n",
       "      <td>0.012536</td>\n",
       "      <td>0.008641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>937</td>\n",
       "      <td>O75530</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C43H48N10O6S/c1-26-36(60-25-48-26)29-...</td>\n",
       "      <td>PPNNFXIBKLCMTI-WXEAQWFJSA-N</td>\n",
       "      <td>832.988</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.538979</td>\n",
       "      <td>True</td>\n",
       "      <td>0.644068</td>\n",
       "      <td>0.732764</td>\n",
       "      <td>0.240103</td>\n",
       "      <td>0.902249</td>\n",
       "      <td>True</td>\n",
       "      <td>0.873345</td>\n",
       "      <td>0.916330</td>\n",
       "      <td>0.917072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1103</td>\n",
       "      <td>P04035</td>\n",
       "      <td>CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCCCc3cn(C...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C63H75FN8O14/c1-41(2)58-57(61(79)66-4...</td>\n",
       "      <td>METKOBDLEKDPEQ-MOHJDXDLSA-N</td>\n",
       "      <td>1187.333</td>\n",
       "      <td>86</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042708</td>\n",
       "      <td>False</td>\n",
       "      <td>0.052732</td>\n",
       "      <td>0.073325</td>\n",
       "      <td>0.002068</td>\n",
       "      <td>0.010631</td>\n",
       "      <td>False</td>\n",
       "      <td>0.010794</td>\n",
       "      <td>0.012440</td>\n",
       "      <td>0.008659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2424</td>\n",
       "      <td>O15379</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C46H59N7O7S/c1-30-41(61-29-49-30)32-1...</td>\n",
       "      <td>QANLZMZKZIPVPD-DRTKLYQKSA-N</td>\n",
       "      <td>854.087</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146975</td>\n",
       "      <td>False</td>\n",
       "      <td>0.251751</td>\n",
       "      <td>0.182186</td>\n",
       "      <td>0.006988</td>\n",
       "      <td>0.616503</td>\n",
       "      <td>True</td>\n",
       "      <td>0.532181</td>\n",
       "      <td>0.683957</td>\n",
       "      <td>0.633372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>335</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCn...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C41H41ClN10O8S/c1-22-23(2)61-41-34(22...</td>\n",
       "      <td>QDKBKEFTDKCTPJ-UFXYQILXSA-N</td>\n",
       "      <td>869.361</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190578</td>\n",
       "      <td>False</td>\n",
       "      <td>0.189161</td>\n",
       "      <td>0.351043</td>\n",
       "      <td>0.031529</td>\n",
       "      <td>0.029169</td>\n",
       "      <td>False</td>\n",
       "      <td>0.033773</td>\n",
       "      <td>0.033058</td>\n",
       "      <td>0.020677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2520</td>\n",
       "      <td>P10275</td>\n",
       "      <td>CCN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2cc...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C44H49ClN8O5/c1-2-52(35-10-5-29(27-46...</td>\n",
       "      <td>VZVAWYNZTBXESG-BULKPCKOSA-N</td>\n",
       "      <td>805.380</td>\n",
       "      <td>58</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558503</td>\n",
       "      <td>True</td>\n",
       "      <td>0.788242</td>\n",
       "      <td>0.655330</td>\n",
       "      <td>0.231937</td>\n",
       "      <td>0.910048</td>\n",
       "      <td>True</td>\n",
       "      <td>0.901576</td>\n",
       "      <td>0.915484</td>\n",
       "      <td>0.913085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2528</td>\n",
       "      <td>P10275</td>\n",
       "      <td>CN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2ccc...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C41H43ClN8O5/c1-46(30-9-4-26(22-43)35...</td>\n",
       "      <td>RNZSZCNUPQYOIX-RVWRWSNSSA-N</td>\n",
       "      <td>763.299</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519538</td>\n",
       "      <td>True</td>\n",
       "      <td>0.898071</td>\n",
       "      <td>0.482898</td>\n",
       "      <td>0.177645</td>\n",
       "      <td>0.960892</td>\n",
       "      <td>True</td>\n",
       "      <td>0.956234</td>\n",
       "      <td>0.954487</td>\n",
       "      <td>0.971954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Compound ID Uniprot                                             Smiles  \\\n",
       "0          2434  Q92769  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "1          2004  Q9NWZ3  CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...   \n",
       "2           549  P03372  CCN(CCCCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1...   \n",
       "3          1098  P04035  CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCc3cn(CCO...   \n",
       "4           937  O75530  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "..          ...     ...                                                ...   \n",
       "81         1103  P04035  CC(C)c1c(C(=O)Nc2ccccc2)c(-c2cccc(OCCCCCc3cn(C...   \n",
       "82         2424  O15379  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "83          335  O60885  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCn...   \n",
       "84         2520  P10275  CCN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2cc...   \n",
       "85         2528  P10275  CN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2ccc...   \n",
       "\n",
       "   E3 Ligase                                              InChI  \\\n",
       "0        VHL  InChI=1S/C48H62FN7O8S/c1-31-43(65-30-52-31)33-...   \n",
       "1       CRBN  InChI=1S/C41H42N10O8/c1-22(2)47-29-17-32(50-15...   \n",
       "2        VHL  InChI=1S/C55H65N5O8S2/c1-6-59(28-29-68-43-24-1...   \n",
       "3       CRBN  InChI=1S/C59H67FN8O13/c1-37(2)54-53(57(75)62-4...   \n",
       "4        VHL  InChI=1S/C43H48N10O6S/c1-26-36(60-25-48-26)29-...   \n",
       "..       ...                                                ...   \n",
       "81      CRBN  InChI=1S/C63H75FN8O14/c1-41(2)58-57(61(79)66-4...   \n",
       "82       VHL  InChI=1S/C46H59N7O7S/c1-30-41(61-29-49-30)32-1...   \n",
       "83      CRBN  InChI=1S/C41H41ClN10O8S/c1-22-23(2)61-41-34(22...   \n",
       "84      CRBN  InChI=1S/C44H49ClN8O5/c1-2-52(35-10-5-29(27-46...   \n",
       "85      CRBN  InChI=1S/C41H43ClN8O5/c1-46(30-9-4-26(22-43)35...   \n",
       "\n",
       "                      InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "0   AGMBKWPLZPKEGR-OUZJXKGJSA-N           916.130                65   \n",
       "1   YMXWKCAXGFLWAH-MONLTZSXSA-N           802.849                59   \n",
       "2   IDYPSHVHIRZTPO-VADRYRRNSA-N           988.286                70   \n",
       "3   FLZOWPIEHOMVRX-OEUTXLNUSA-N          1115.226                81   \n",
       "4   PPNNFXIBKLCMTI-WXEAQWFJSA-N           832.988                60   \n",
       "..                          ...               ...               ...   \n",
       "81  METKOBDLEKDPEQ-MOHJDXDLSA-N          1187.333                86   \n",
       "82  QANLZMZKZIPVPD-DRTKLYQKSA-N           854.087                61   \n",
       "83  QDKBKEFTDKCTPJ-UFXYQILXSA-N           869.361                61   \n",
       "84  VZVAWYNZTBXESG-BULKPCKOSA-N           805.380                58   \n",
       "85  RNZSZCNUPQYOIX-RVWRWSNSSA-N           763.299                55   \n",
       "\n",
       "    Ring Count  Rotatable Bond Count  ...  dnn_mean  dnn_majority_vote  \\\n",
       "0            5                    23  ...  0.079379              False   \n",
       "1            7                    13  ...  0.435443              False   \n",
       "2            7                    22  ...  0.257002              False   \n",
       "3            8                    31  ...  0.048816              False   \n",
       "4            7                    15  ...  0.538979               True   \n",
       "..         ...                   ...  ...       ...                ...   \n",
       "81           8                    36  ...  0.042708              False   \n",
       "82           5                    20  ...  0.146975              False   \n",
       "83           8                    16  ...  0.190578              False   \n",
       "84           8                     9  ...  0.558503               True   \n",
       "85           8                     8  ...  0.519538               True   \n",
       "\n",
       "    dnn_model_n0 dnn_model_n1  dnn_model_n2  xgb_mean  xgb_majority_vote  \\\n",
       "0       0.170209     0.063416      0.004514  0.026209              False   \n",
       "1       0.269258     0.663059      0.374012  0.721998               True   \n",
       "2       0.435150     0.277998      0.057857  0.199748              False   \n",
       "3       0.046254     0.098125      0.002068  0.010581              False   \n",
       "4       0.644068     0.732764      0.240103  0.902249               True   \n",
       "..           ...          ...           ...       ...                ...   \n",
       "81      0.052732     0.073325      0.002068  0.010631              False   \n",
       "82      0.251751     0.182186      0.006988  0.616503               True   \n",
       "83      0.189161     0.351043      0.031529  0.029169              False   \n",
       "84      0.788242     0.655330      0.231937  0.910048               True   \n",
       "85      0.898071     0.482898      0.177645  0.960892               True   \n",
       "\n",
       "   xgb_model_n0 xgb_model_n1 xgb_model_n2  \n",
       "0      0.024188     0.029409     0.025029  \n",
       "1      0.766466     0.608752     0.790776  \n",
       "2      0.306645     0.227117     0.065481  \n",
       "3      0.010565     0.012536     0.008641  \n",
       "4      0.873345     0.916330     0.917072  \n",
       "..          ...          ...          ...  \n",
       "81     0.010794     0.012440     0.008659  \n",
       "82     0.532181     0.683957     0.633372  \n",
       "83     0.033773     0.033058     0.020677  \n",
       "84     0.901576     0.915484     0.913085  \n",
       "85     0.956234     0.954487     0.971954  \n",
       "\n",
       "[86 rows x 49 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "dnn_preds = []\n",
    "xgb_preds = []\n",
    "batch_size = 256\n",
    "\n",
    "for i in range(0, len(protac_df), batch_size):\n",
    "    # Accumulate the rows into a batch\n",
    "    batch = protac_df.iloc[i:i+batch_size]\n",
    "    if len(batch) == 0:\n",
    "        continue\n",
    "    \n",
    "    # Get predictions for DNN model\n",
    "    pred_dnn = pdp.get_protac_active_proba(\n",
    "        protac_smiles=batch['Smiles'].tolist(),\n",
    "        e3_ligase=batch['E3 Ligase'].tolist(),\n",
    "        target_uniprot=batch['Uniprot'].tolist(),\n",
    "        cell_line=batch['Cell Line Identifier'].tolist(),\n",
    "        device=device,\n",
    "    )\n",
    "    \n",
    "    for j in range(len(batch)):\n",
    "        dnn_pred = {\n",
    "            'mean': pred_dnn['mean'][j],\n",
    "            'majority_vote': pred_dnn['majority_vote'][j],\n",
    "        }\n",
    "        for k in range(pred_dnn['preds'].shape[0]):  # Assuming num_models is 3\n",
    "            dnn_pred[f'model_n{k}'] = pred_dnn['preds'][k][j]\n",
    "        dnn_preds.append(dnn_pred)\n",
    "    \n",
    "    # Get predictions for XGBoost model\n",
    "    pred_xgb = pdp.get_protac_active_proba(\n",
    "        protac_smiles=batch['Smiles'].tolist(),\n",
    "        e3_ligase=batch['E3 Ligase'].tolist(),\n",
    "        target_uniprot=batch['Uniprot'].tolist(),\n",
    "        cell_line=batch['Cell Line Identifier'].tolist(),\n",
    "        device=device,\n",
    "        use_xgboost_models=True,\n",
    "    )\n",
    "    \n",
    "    for j in range(len(batch)):\n",
    "        xgb_pred = {\n",
    "            'mean': pred_xgb['mean'][j],\n",
    "            'majority_vote': pred_xgb['majority_vote'][j],\n",
    "        }\n",
    "        for k in range(pred_xgb['preds'].shape[0]):  # Assuming num_models is 3\n",
    "            xgb_pred[f'model_n{k}'] = pred_xgb['preds'][k][j]\n",
    "        xgb_preds.append(xgb_pred)\n",
    "\n",
    "# Convert accumulated predictions into DataFrames\n",
    "dnn_df = pd.DataFrame(dnn_preds)\n",
    "xgb_df = pd.DataFrame(xgb_preds)\n",
    "\n",
    "# Concatenate the original DataFrame with predictions\n",
    "result_df = pd.concat([\n",
    "        protac_df.reset_index(drop=True),\n",
    "        dnn_df.add_prefix('dnn_'),\n",
    "        xgb_df.add_prefix('xgb_'),\n",
    "    ], axis=1)\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xgb_mean</th>\n",
       "      <th>xgb_model_n0</th>\n",
       "      <th>xgb_model_n1</th>\n",
       "      <th>xgb_model_n2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.556621</td>\n",
       "      <td>0.560646</td>\n",
       "      <td>0.551290</td>\n",
       "      <td>0.557927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.376574</td>\n",
       "      <td>0.377049</td>\n",
       "      <td>0.378438</td>\n",
       "      <td>0.384407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010581</td>\n",
       "      <td>0.010565</td>\n",
       "      <td>0.012440</td>\n",
       "      <td>0.008641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.139769</td>\n",
       "      <td>0.149495</td>\n",
       "      <td>0.137282</td>\n",
       "      <td>0.128790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.655310</td>\n",
       "      <td>0.669479</td>\n",
       "      <td>0.657044</td>\n",
       "      <td>0.661293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.934663</td>\n",
       "      <td>0.938701</td>\n",
       "      <td>0.934932</td>\n",
       "      <td>0.932825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.983716</td>\n",
       "      <td>0.984821</td>\n",
       "      <td>0.984761</td>\n",
       "      <td>0.984929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        xgb_mean  xgb_model_n0  xgb_model_n1  xgb_model_n2\n",
       "count  86.000000     86.000000     86.000000     86.000000\n",
       "mean    0.556621      0.560646      0.551290      0.557927\n",
       "std     0.376574      0.377049      0.378438      0.384407\n",
       "min     0.010581      0.010565      0.012440      0.008641\n",
       "25%     0.139769      0.149495      0.137282      0.128790\n",
       "50%     0.655310      0.669479      0.657044      0.661293\n",
       "75%     0.934663      0.938701      0.934932      0.932825\n",
       "max     0.983716      0.984821      0.984761      0.984929"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df.filter(like='xgb_').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the mean of the preditions of the \"best\" models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "# Plot histogram of dnn_mean and xgb_mean\n",
    "sns.histplot(result_df['dnn_mean'], label='DNN (mean)', kde=True, color='C0', alpha=0.3)\n",
    "sns.histplot(result_df['xgb_mean'], label='XGB (mean)', kde=True, color='C1', alpha=0.3)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the \"best\" models used in majority voting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the KDE plots without histograms\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i in range(3):\n",
    "    sns.kdeplot(result_df[f'dnn_model_n{i}'], label=f'DNN (model {i})', color=f'C{i}')\n",
    "    sns.kdeplot(result_df[f'xgb_model_n{i}'], label=f'XGB (model {i})', color=f'C{i+3}', linestyle='dashed')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the entries for which the majority vote is different between DNN and XGBoost:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "      <th>Dmax (%)</th>\n",
       "      <th>Active</th>\n",
       "      <th>dnn_majority_vote</th>\n",
       "      <th>xgb_majority_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q06187</td>\n",
       "      <td>THP-1</td>\n",
       "      <td>NC(=O)c1c(-c2ccc(Oc3ccc(F)cc3F)cc2)nn([C@@H]2C...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>398.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>P11802</td>\n",
       "      <td>Jurkat</td>\n",
       "      <td>CC(=O)c1c(C)c2cnc(Nc3ccc(N4CCN(CC(=O)NCCOCCOCC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>2512.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>P10275</td>\n",
       "      <td>VCaP</td>\n",
       "      <td>N#Cc1ccc(O[C@H]2CC[C@H](NC(=O)c3ccc(N4CCN(CCNc...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>77.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>P00533</td>\n",
       "      <td>NCI-H3255</td>\n",
       "      <td>COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCCCOCCOCC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>22.3</td>\n",
       "      <td>96.6</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>P33981</td>\n",
       "      <td>HCT 116</td>\n",
       "      <td>CCC(=O)N[C@H]1CC[C@@H](n2c(=O)cc(C)c3cnc(Nc4cc...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>25.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Q06187</td>\n",
       "      <td>Ramos</td>\n",
       "      <td>NC(=O)c1c(-c2ccc(Oc3ccc(F)cc3F)cc2)nn([C@@H]2C...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>36.9</td>\n",
       "      <td>85.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Q9NWZ3</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>190.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Q9UM73</td>\n",
       "      <td>SR</td>\n",
       "      <td>CCc1cc2c(cc1N1CCC(N3CCN(C(=O)CNc4cccc5c4CN(C4C...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>87.7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>P10275</td>\n",
       "      <td>VCaP</td>\n",
       "      <td>N#Cc1ccc(O[C@H]2CC[C@H](NC(=O)c3ccc(NCCCCCCCCC...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Q9UBN7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CCCCN(Cc1ccc(C(=O)NO)cc1)C(=O)Nc1ccc(OCCCCCn2c...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Q16539</td>\n",
       "      <td>MDA-MB-231</td>\n",
       "      <td>COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>230.0</td>\n",
       "      <td>90.1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>P33981</td>\n",
       "      <td>HCT 116</td>\n",
       "      <td>CCC(=O)N[C@H]1CC[C@@H](n2c(=O)cc(C)c3cnc(Nc4cc...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>58.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Q96SW2</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>20.4</td>\n",
       "      <td>99.6</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>P01116</td>\n",
       "      <td>NCI-H358</td>\n",
       "      <td>C=C(F)C(=O)N1CCN(c2nc(OC[C@@H]3CCCN3CCCOCCC(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>520.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>O60885</td>\n",
       "      <td>HEK293T</td>\n",
       "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...</td>\n",
       "      <td>FEM1B</td>\n",
       "      <td>250.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>P10275</td>\n",
       "      <td>LNCaP</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>O15379</td>\n",
       "      <td>HCT116-53BPI(+/-)</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>640.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Uniprot Cell Line Identifier  \\\n",
       "1   Q9NWZ3              HEK293T   \n",
       "7   Q06187                THP-1   \n",
       "13  P11802               Jurkat   \n",
       "14  P10275                 VCaP   \n",
       "30  P00533            NCI-H3255   \n",
       "36  P33981              HCT 116   \n",
       "44  Q06187                Ramos   \n",
       "48  Q9NWZ3              HEK293T   \n",
       "50  Q9UM73                   SR   \n",
       "53  P10275                 VCaP   \n",
       "55  Q9UBN7                  NaN   \n",
       "58  Q16539           MDA-MB-231   \n",
       "61  P33981              HCT 116   \n",
       "62  Q96SW2              HEK293T   \n",
       "66  P01116             NCI-H358   \n",
       "69  O60885              HEK293T   \n",
       "76  P10275                LNCaP   \n",
       "82  O15379    HCT116-53BPI(+/-)   \n",
       "\n",
       "                                               Smiles E3 Ligase  DC50 (nM)  \\\n",
       "1   CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...      CRBN        NaN   \n",
       "7   NC(=O)c1c(-c2ccc(Oc3ccc(F)cc3F)cc2)nn([C@@H]2C...      CRBN      398.5   \n",
       "13  CC(=O)c1c(C)c2cnc(Nc3ccc(N4CCN(CC(=O)NCCOCCOCC...       VHL     2512.0   \n",
       "14  N#Cc1ccc(O[C@H]2CC[C@H](NC(=O)c3ccc(N4CCN(CCNc...      CRBN        0.8   \n",
       "30  COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCCCOCCOCC...       VHL       22.3   \n",
       "36  CCC(=O)N[C@H]1CC[C@@H](n2c(=O)cc(C)c3cnc(Nc4cc...      CRBN       25.4   \n",
       "44  NC(=O)c1c(-c2ccc(Oc3ccc(F)cc3F)cc2)nn([C@@H]2C...      CRBN       36.9   \n",
       "48  CC(C)Nc1cc(-n2ccc3cc(C#N)cnc32)ncc1C(=O)N[C@H]...      CRBN      190.0   \n",
       "50  CCc1cc2c(cc1N1CCC(N3CCN(C(=O)CNc4cccc5c4CN(C4C...      CRBN        6.1   \n",
       "53  N#Cc1ccc(O[C@H]2CC[C@H](NC(=O)c3ccc(NCCCCCCCCC...      CRBN     1000.0   \n",
       "55  CCCCN(Cc1ccc(C(=O)NO)cc1)C(=O)Nc1ccc(OCCCCCn2c...      CRBN       18.0   \n",
       "58  COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4...       VHL      230.0   \n",
       "61  CCC(=O)N[C@H]1CC[C@@H](n2c(=O)cc(C)c3cnc(Nc4cc...      CRBN       58.1   \n",
       "62  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...       VHL       20.4   \n",
       "66  C=C(F)C(=O)N1CCN(c2nc(OC[C@@H]3CCCN3CCCOCCC(=O...       VHL      520.0   \n",
       "69  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...     FEM1B      250.0   \n",
       "76  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...       VHL       50.0   \n",
       "82  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...       VHL      640.0   \n",
       "\n",
       "    Dmax (%)  Active  dnn_majority_vote  xgb_majority_vote  \n",
       "1       46.0   False              False               True  \n",
       "7       68.0    True              False               True  \n",
       "13       NaN   False               True              False  \n",
       "14      77.0    True              False               True  \n",
       "30      96.6    True              False               True  \n",
       "36      57.0   False              False               True  \n",
       "44      85.0    True              False               True  \n",
       "48      93.0    True              False               True  \n",
       "50      87.7    True              False               True  \n",
       "53      42.0   False              False               True  \n",
       "55      45.0   False              False               True  \n",
       "58      90.1    True              False               True  \n",
       "61      53.0   False              False               True  \n",
       "62      99.6    True              False               True  \n",
       "66      40.0   False              False               True  \n",
       "69      94.0    True              False               True  \n",
       "76      71.0    True              False               True  \n",
       "82      59.0   False              False               True  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = result_df[result_df['dnn_majority_vote'] != result_df['xgb_majority_vote']]\n",
    "tmp[relevant_cols + ['dnn_majority_vote', 'xgb_majority_vote']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries for which the majority vote is different between DNN and XGBoost: 18\n",
      "Number of entries for which the majority vote of XGBoost is different from the Active column: 7\n",
      "Number of entries for which the majority vote of DNN is different from the Active column: 11\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of entries for which the majority vote is different between DNN and XGBoost: {len(tmp)}')\n",
    "print(f'Number of entries for which the majority vote of XGBoost is different from the Active column: {len(tmp[tmp[\"Active\"] != tmp[\"xgb_majority_vote\"]])}')\n",
    "print(f'Number of entries for which the majority vote of DNN is different from the Active column: {len(tmp[tmp[\"Active\"] != tmp[\"dnn_majority_vote\"]])}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KL Divergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate and plot KL divergence between DNN and XGBoost predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DNN vs. XGB model N.0: 7.322032476908708\n",
      "DNN vs. XGB model N.1: 5.150240075698975\n",
      "DNN vs. XGB model N.2: 6.874510246266886\n",
      "DNN vs. XGB mean: 7.969119274983867\n",
      "DNN vs. XGB majority vote: 0.07029106924951611\n",
      "True vs. DNN majority vote: 0.004405425756712149\n",
      "True vs. XGB majority vote: 0.03965522705930391\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import entropy\n",
    "\n",
    "def kl_divergence(p, q, bins=20):\n",
    "    # Histogram bins\n",
    "    hist_p, bin_edges = np.histogram(p, bins=bins, density=True)\n",
    "    hist_q, _ = np.histogram(q, bins=bin_edges, density=True)\n",
    "\n",
    "    # Make sure no zero values in densities (adjust as necessary)\n",
    "    hist_p += 1e-10\n",
    "    hist_q += 1e-10\n",
    "\n",
    "    return entropy(hist_p, hist_q)\n",
    "\n",
    "\n",
    "bins = 50\n",
    "kl_divergences = {}\n",
    "for i in range(3):\n",
    "    kl_divergences[f'DNN vs. XGB model N.{i}'] = kl_divergence(result_df[f'dnn_model_n{i}'], result_df[f'xgb_model_n{i}'], bins=bins)\n",
    "\n",
    "kl_divergences['DNN vs. XGB mean'] = kl_divergence(result_df['dnn_mean'], result_df['xgb_mean'], bins=bins)\n",
    "kl_divergences['DNN vs. XGB majority vote'] = kl_divergence(result_df['dnn_majority_vote'].astype(int), result_df['xgb_majority_vote'].astype(int), bins=bins)\n",
    "\n",
    "# Get df where the Active column is not NaN\n",
    "active_df = result_df[~result_df['Active'].isna()]\n",
    "kl_divergences['True vs. DNN majority vote'] = kl_divergence(active_df['Active'].astype(int), active_df['dnn_majority_vote'].astype(int), bins=bins)\n",
    "kl_divergences['True vs. XGB majority vote'] = kl_divergence(active_df['Active'].astype(int), active_df['xgb_majority_vote'].astype(int), bins=bins)\n",
    "\n",
    "for key, value in kl_divergences.items():\n",
    "    print(f'{key}: {value}')\n",
    "\n",
    "# Plot KL divergences\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(kl_divergences.keys(), kl_divergences.values())\n",
    "plt.ylabel('KL Divergence')\n",
    "# Put values on top of the bars\n",
    "for key, value in kl_divergences.items():\n",
    "    plt.text(key, value, f'{value:.4f}', ha='center', va='bottom')\n",
    "# Rotate x-axis labels\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC-AUC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RjB07tsLPR9Qf0iwl6r377ruPXr16MX/+fHJycgCwt7fnxRdf5MyZM7z66qvFzlm7di2LFi1i+PDh5r4T9vb2vPTSS5w6dYqXXnqpxOaN77//nn379mFra0toaGiRLzc3N1auXElmZiZPPfUUkyZNKvY1evRoli9fTm5uLgAhISFYW1vz6aefFqsB+PzzzykoKOD+++83l02aNIlOnTrx9ttvs3v37mLxpaenl/h8b/eXv/wFBwcHZs2aRVxcXLH9Fy5c4MMPPwRMiYaHhwfbtm0rcswnn3xS5j1KMnHiRHQ6HT/88APh4eGMHj3a/MYH0L17d1q1asW///1vMjIyip2fkJBQ5vV9fX3x8/PjwIEDFY6tLIVJ7p1/D/Pnz7/ruaGhoVhbW/Pf//63yPkLFy4kNTWVUaNGVSqmwp9babP9Fs6u/c9//pOtW7dWuNamUGhoKO7u7ixbtoxly5bRq1evIjWWPj4+BAUF8c033xSJ5fjx46xfv56RI0feNeYJEyag0+l48803i/2MlVLmvlSFTp48SU5OTrWN9hN1g9TciAbhz3/+M2FhYSxatMjcofXll1/m8OHD/POf/2T37t1MnDgROzs7duzYwffff09gYCDffPNNseucOHGC//znP2zevJlJkybRuHFjYmNjWbVqFfv27WPXrl2lxrF48WIaNWpU6gvv2LFj+eKLL1i7di0TJkzAy8uLefPm8de//pWBAwcyduxY7O3t2bVrFz/88APDhg0rUruh1+tZsWIFoaGhDBw4kMmTJ9O/f3/0ej0nTpxgyZIluLm58fbbb5caY6tWrViyZAlTpkwhMDCQRx55hI4dO5KXl8euXbsIDw9nxowZ5uNnzZrFP/7xD2bNmkWPHj3Ytm0bZ8+eLc+vpQgvLy8GDx7M+++/T3p6epEaAACtVsuXX37J/fffT4cOHZg5cya+vr5cu3aNzZs34+zszM8//1zmPR544AFWrlxZpX0ynJ2dGThwIP/617/Iz8/H19eX9evXl6uGyNPTk7lz5/Lmm28yYsQIxo4dy5kzZ/jkk0/o2bNnpZOOVq1a4erqyoIFC3BycsLBwYHevXubEw+9Xs/UqVP56KOP0Ol05mHYFaXX65kwYQJLly4lMzOTf//738WOee+997j//vvp27cvjz76KNnZ2fzvf//DxcWFN954w3xc9+7dAdPyGFOnTkWv1zNmzBhatWrFW2+9xdy5c7l06RLjxo3DycmJqKgoVq5cyWOPPcaLL75ovs6GDRuwt7dn6NChlXpOop6wyBgtIapBaZP4KaWUwWBQrVq1Uq1atSoydNVgMKivv/5a9e/fXzk7OytbW1vVoUMH9eabb5Y5e25ERIQaNmyYcnd3V1ZWVsrHx0dNmTKlxEnNCsXFxSkrK6syp4bPyspS9vb2avz48UXKv//+e9WnTx/l4OCgbGxsVEBAgHrzzTdVTk5OiddJTk5W8+bNU506dVL29vbmSenmzp2rYmJiSr3/7c6ePatmz56t/P39lbW1tXJyclL9+/dX//vf/4rcNysrSz366KPKxcVFOTk5qcmTJ6v4+PhSh4KXNqxdKaW++OILBSgnJ6dSJ5M7fPiwmjBhgmrUqJGysbFRzZs3V5MnT1aRkZF3fU6HDh1SQLHZmwsn8bsToJ566qkiZYXD39977z1z2dWrV9X48eOVq6urcnFxUWFhYer69evFfgalTeL30UcfqYCAAKXX65W3t7f605/+VOokfiW5cyi4Ukr99NNPqn379srKyqrEYeGFQ+2HDRtW4jXLa8OGDQpQGo2myHQLt9u4caPq37+/srOzU87OzmrMmDFFJvEr9Pe//135+voqrVZb7Oe0fPlyFRwcrBwcHJSDg4MKCAhQTz31lDpz5kyRa/Tu3VtNmzbtnp6TqPs0St1lhikhhKhHQkJCaNKkCd99912N33vhwoXMmjWL6OjoIlMTWMLRo0cJCgri22+/vadJH2uTI0eO0K1bNw4dOlRqp3PRMEifGyFEg/LOO++wbNmySnV6vlcxMTFoNJoic75YyhdffIGjoyMTJkywdChV5h//+AeTJk2SxEZInxshRMPSu3dv85xDNSUuLo6IiAgWLFhA3759sbe3r9H73+7nn3/m5MmTfP755zz99NNFOm0DZGRklNhh+3aenp73vARJdVi6dKmlQxC1hDRLCSFENduyZQsjR46kV69efPHFFxadg8Xf35+4uDiGDx/Od999V2w26jfeeIM333yzzGtERUXh7+9fjVEKcW8kuRFCCGF28eLFIktLlCQ4OLhcC2wKYSmS3AghhBCiXpEOxUIIIYSoVxpch2Kj0cj169dxcnKShdWEEEKIOkIpRXp6Ok2aNEGrLbtupsElN9evX6/UgoJCCCGEsLzyzBPV4JKbwpEB0dHRODs7WzgaIYQQQpRHWloafn5+xUb4laTBJTeFTVHOzs6S3AghhBB1THm6lEiHYiGEEELUK5LcCCGEEKJekeRGCCGEEPWKJDdCCCGEqFckuRFCCCFEvSLJjRBCCCHqFUluhBBCCFGvSHIjhBBCiHpFkhshhBBC1CuS3AghhBCiXrFocrNt2zbGjBlDkyZN0Gg0rFq16q7nbNmyhW7dumFjY0Pr1q1ZtGhRtccphBBCiLrDoslNZmYmXbp04eOPPy7X8VFRUYwaNYrBgwdz5MgRnn/+eWbNmsVvv/1WzZEKIYQQoq6w6MKZ999/P/fff3+5j1+wYAEtWrTgP//5DwCBgYHs2LGDDz74gOHDh1dXmEIIIYQoj4JcEuKukZmdi3/rQIuFUadWBd+9ezehoaFFyoYPH87zzz9f6jm5ubnk5uaaH6elpVVXeEIIIUT9kp8DWYmQmXjr+x3bKiuRvLR4yEzExpCJJ5BoGwQvb7VY2HUquYmNjcXb27tImbe3N2lpaWRnZ2NnZ1fsnHfffZc333yzpkIUQgghaq/87DITlWKJTF7GXS+pAWxue1ygtCijEaUUGo2m2p5KWepUclMZc+fOZc6cOebHaWlp+Pn5WTAiIYQQoorkZd1MRBIg88ZtiUoCZN24LVG5uT8/s+L30FqBvQf5du7cMDpxOceBM+k2xBscScKZhEwjsVcu4erdnPEPPcqALm1o71y8sqEm1ankpnHjxsTFxRUpi4uLw9nZucRaGwAbGxtsbGxK3CeEEELUKnmZpSQqiXeU3Uxa8rMqfg+tHhw8wN7D9N283QgcPM3lyr4RZ9Jt2HAhh42n4zkanVrkMo2dbWirotGmnaJLh8409vZiaFALnJwsm9hAHUtu+vbtyy+//FKkbMOGDfTt29dCEQkhhBClUMrUrJOZeKsWxZyo3Fl2c7sgu+L30VmXkKjcsX17mY0zlNJclJNvYM/FG0Qeiify1BWup+YU2d+lqQshgd70bWbPqd2RXLx4GRrZ06VLF0aNGoW1tXVlflJVzqLJTUZGBufPnzc/joqK4siRI7i7u9OsWTPmzp3LtWvX+PbbbwF44okn+Oijj/jLX/7CH//4RzZt2sSPP/7I2rVrLfUUhBBCNBRKQW76rVqUUhOV22pZCnLuft076WxMNSgOjW5LSjzBvlHJyYuNU6nJSnkkZuSy6XQ8kafi2H4ukaw8g3mfrV5LcGsPQgK9CQnwwsvZlqioKJYv/4GMjAz0ej2jRo0iKCio0vevDhZNbg4cOMDgwYPNjwv7xkyfPp1FixYRExPDlStXzPtbtGjB2rVreeGFF/jwww9p2rQpX375pQwDF0IIUXFKQW5aKZ1pS0leDLl3v+6drGyLJiclJiq3lVk73lOycjdKKc7EpRN5Kp6Np+I4Ep2CUrf2ezvbMCTAm9BAL/q39sBWrzPvMxqN/PLLL2RkZODl5UVYWBienp7VFmtlaZS6/SnVf2lpabi4uJCamoqzs7OlwxFCCFFVlIKc1JuJSMLdRwJl3QBDXsXvo7cvoY9KYS2LZ9E+LPYeYO1QrclKeeQVGNkbdcOc0FxNLtr81dHXmZAAb0IDveno61zmKKfY2FgOHDjA8OHD0ev11R26WUXev+tUnxshhBANiFKQk1K+IcuFyYoxv+L30TsU60xrqkW5I1Ep3G9tX+VPtTokZeax+XQ8kafj2HY2kYzcAvM+Gyst/Vt7EBLoRUiAN41dbEu9zoULF0hJSaF79+6AaXDP6NGjqz3+eyHJjRBCiJphNN5KVu42ZDmrMFkpuOtli7F2LL0zbUnbesuP7qkKSinOx2ew8ZSp/8yhK8kYb2ub8XC0ISTAi5BAL4LbeGBvXXYKYDQa2bx5Mzt27ECr1dKkSRN8fHyq+VlUDUluhBBCVI7RCNnJdxmyfHvycgOU4e7XvZONc+mdaUsaFaQvvRaivsk3GNkXlcTGU3FEnornSlLRoeGBPs6EBnoREuhNZ18XtNryNY+lpaURERFh7vfatWvXWtm3pjSS3AghhDAxGkzJSnmGLBfWrChjxe9j43JHH5W7jAqykrnKbpeSlceWMwlsPBXH1jMJpN/W3GSt09K3VSNCA70YEuiNr2vFa6XOnTvHypUrycrKwsbGhrFjx9KhQ4eqfArVTpIbIYSor4wGyEoq35DlzARTYlOZZMXWpYT+Kh6U2NnWvhFY1Y65UOqSCwkZRJ6KY+OpeA5eTsZwW3tTIwdrBgd4ERroRXAbTxxtKv/WHhkZyfbt2wHw8fEhLCwMd3f3e46/pklyI4QQdYWhwJSc3HXI8s2RQtnJQCUGxNq5lZGo3NnZthHoam7ETENRYDCy/1IykafiiDwdT1Ri0WUT2nk7mToDB3oT5OeKrpzNTXdTONt/7969GTp0KFZWdTNNqJtRCyFEfWDIv6MzbVkjgQqTlYrSmJKVUqbYLzYqyN5dkhULSc3OZ8uZeCJPxbPlTDxpObeam/Q6DX1aNrrZIdgbP/eqG7GVl5dnnlm4b9++NG3alGbNmlXZ9S1BkhshhKgqBXlFa1bKSlQyE00jhypMY0pA7jbFfuG2nTvo5KW+trqUmGnuDLz/UhIFtzU3udnrbzY3eTOgjQdOtlWbdBoMBjZs2MD58+d57LHHsLa2RqPR1PnEBiS5EUKI0hXklW/IcmFZTurdr3knjdaUgNzezFPqqCBPUy2MVnf364paqcBg5NCVlJv9Z+K4kFC0uamNlyMhgabZgbs2c6uy5qY7JScnEx4ezvXr1wE4c+YMnTp1qpZ7WYIkN0KIhqMgt/TOtLd3ti1MWHLTKn4PjbYcHWtvK7NzlWSlnkvLyWfb2QQiT8Wz+Uw8KVm3Jhq00mro1cLdnNA0b+RQ7fGcPHmSn376idzcXOzs7Bg3bhzt2rWr9vvWJEluhBB1V3520Y61pc63crOWJS+94vfQ6O7SX+WOzra2rqDVVvlTFXXLlRtZpuam03HsvVi0ucnFTs/gdp6EBHozsK0nLnY108epoKCA9evXs2/fPgD8/PyYNGkSLi4uNXL/miTJjRCi9sjLKt+Q5cL9eRkVv4fWqoz+KiUkL5KsiHIwGBVHopPNswOfjSv6t9nS04HQmytrd2/uhpWu5v+mbk9sgoODGTx4MDpd/aw1lORGCFF98jLLN2S5cH9+5t2veSetvpyJSmHNiovFFzEU9UNGbgHbzyaw8WZzU1LmrUU4dVoNPf3dTAlNoDctPKq/ueluBg4cyKVLlxg2bBitW7e2dDjVSpIbIUT5KGVKVkpMVO4cCXRzf0H23a97J511CbPU3jGL7e01LzbOkqyIGnM1Ocu8svbei0nkGW5Neuhka8V97UyT6d3X1gsXe8sOqc/Pz+f06dPmjsKOjo786U9/KnPF7/pCkhshGiqlTM065RmyXFjLUpBT8ftY2RafpbbYqKDbkhcbJ0lWRK1hNCqOXk0xD9c+HVu035Z/I3tCAr0JCfSip787egs0N5UkMTGR8PBw4uLi0Gq15uUTGkJiA5LcCFF/KAW56eUbslyYvBhyK34fK7uSZ6ktcRZbD9MKzQ3kBVXUD1l5BWw/l0jkqTg2nU4gMePW/4lWAz2au5tnB27l6VDrEoajR4+yZs0a8vPzcXBwMM863JBIciNEbaWUad6UYonKnUOWb0teDHl3v+6d9PYldLAtqb/KzTJry/cdEKKqXU/JJvK0qTPwrgs3yCu41dzkaGPFoLaehLY3NTe5OdTOtbHy8vL49ddfOXz4MAAtWrRgwoQJODk5WTiymifJjRC1TU4qfDceYn4HY/7dj7+T3qGEyd/K6GxrXXXTuAtRVxiNimPXUs2LUZ6MKTqnkZ+7HSEB3oQGetOrhTvWVrWjuak08fHxhIeHk5CQgEaj4b777mPAgAFoG+hIP0luhKht9n0B1w7eemztdMfkb3cmKnd0ttU3vCpoIcojO8/AzvOJbDwVx6bT8cSn32pu0migWzM3QgJNyx208XKsdc1NZUlOTiYhIQEnJycmTpyIv7+/pUOyKEluhKhN8rJgz6em7dHzocuDoLe1aEhC1GVxaTlE3px7Zsf5RHJva25ysNYxsK1pMr3B7Txp5GhjwUgrTillTsDatWvH2LFjadeuHQ4O0nQsyY0Qtcnh7019Z1ybQdeHZcFDISpIKcWJ62nm0U3HrhVd78vX1c5cO9O7pTs2VnVzErvY2FjWrl1bZIbhbt26WTiq2kNeOYWoLQz5sOu/pu3+z0liI0Q55eQb2HUhkY2n4tl0Kp7YtFtTFmg00KWpK6E3RzcFNHaqU81Nd1JKcfDgQdatW2deTiEsLMzSYdU68uopRG1xLAJSo8HBC4KmWToaIWq1+PQcNp+OZ8PJeHaeTyQ732DeZ6fXMaCNB6GB3gwO8MLTqW41N5UmNzeXn3/+mePHjwPQtm1bRo0aZeGoaidJboSoDYxG2PGBabvvk9LPRog7KKU4FZNuGt10Op6j0SlF9vu42JrnnunbshG2+rrZ3FSamJgYwsPDSUpKQqvVEhoaSt++fet0LVR1kuRGiNrgzC+QeAZsXKDHo5aORohaIbfAwO4LN8wdgq+nFp0hu3NTF9Nw7fZetPdxrrdv9FFRUXz//fcYDAZcXFwICwujadOmlg6rVpPkRghLUwq2/8e03WsW2DpbNh4hLCgxI5dNNyfT234ukay8W81Ntnotwa09CAn0ZkiAF97ODaOGs2nTpnh4eODm5sYDDzzQIGccrihJboSwtKitcP2QaQ2m3n+ydDRC1CilFGfjMth4Ko6Np+I4Ep2CUrf2ezvbMCTAm9BAL/q39qh3zU2liY+Px8PDA61Wi16vZ/r06djZ2dXb2qmqJsmNEJa2/X3T926PgKOnZWMRogbkFRjZG3XDvLr21eSiq8d39HU2zw7c0bf+NjeVRCnFnj172LhxI4MGDWLgwIEA2NvLTOIVIcmNEJZ07aCp5kZrBf2esXQ0QlSbpMw8Np+OJ/J0HNvOJpKRW2DeZ22lpX+rRubVtX1cGmazS3Z2NqtWreLMmTOAqfbm9on6RPlJciOEJRXW2nQKM03cJ0Q9oZTifHwGG292Bj50JRnjbc1NHo42hAR4ERLoRXAbD+ytG/bbUXR0NBEREaSmpqLT6RgxYgQ9evSQxKaSGvZfkxCWlHAGTq8BNBD8gqWjEeKe5RuM7I9KYuPN5qYrSVlF9gf6OJsn0+vs64JWK2/cSil27dpFZGQkRqMRd3d3wsLC8PHxsXRodZokN0JYyo75pu8Bo8CznUVDEaKyUrLy2HImgY2n4th6NoH0nNuam3Ra+rZqRGigF0MCvfF1bZjNTWVJSkpi8+bNGI1GOnXqxOjRo7GxqR+TDlqSJDdCWEJKNBz70bQ9YI5lYxGigi4kZJgm0zsVz8HLyRhua29q5GDN4AAvQgO9CG7jiaONvM2UpVGjRowcORKlFN26dZNmqCoif3VCWMKu/4GxAFoMAt/ulo5GiDIVGIzsv5RM5Kk4Ik/HE5WYWWR/O28n8+zAQX6u6KS5qVRKKXbs2EHLli3x9fUFZMHL6iDJjRA1LSMBDn1j2pZaG1FLpWbns/VsApGn4thyJoHU7HzzPr1OQ5+WjW52CPbGz12GKZdHRkYGK1eu5MKFCxw8eJAnn3wSa2trS4dVL0lyI0RN2/spFORAk26mmhshaolLiZlsPBVH5Kl49l9KouC25iY3e/3N5iZvBrTxwMlWb8FI656oqCiWL19ORkYGer2e++67TxKbaiTJjRA1KScN9n1p2h4wB6R9XVhQgcHIoSspN/vPxHEhoWhzU2svR0ICTQlNt2Zu0txUCUajkW3btrF161aUUnh5eREWFoanp0zYWZ0kuRGiJh1YCLmp4NEO2o2ydDSiAUrLyWfb2QQiT8Wz+Uw8KVm3mpustBp6tXAnJNC03EHzRg4WjLTuy83N5YcffuDSpUsAdO3alZEjR6LXS61XdZPkRoiakp8Nuz8xbQc/D1qtRcMRDUd0Upa5uWlv1A3yDbeam1zs9Axu50lIoDcD23riYidvvFXF2tra/DV69Gg6d+5s6ZAaDEluhKgpRxZDZjy4+JlmJBaimhiMiiPRyebZgc/GZRTZ39LTgdBAb0ICvOje3A0rnSTaVcVoNGIwGNDr9Wg0GsaNG0dWVhYeHh6WDq1BkeRGiJpgKICd/zVt93sWdPLpWFStjNwCtp9NYOPN5qakzDzzPp1WQ4/mbqaEJtCLlp6OFoy0/kpLS2P58uW4uroyfvx4wLTgpSx6WfMkuRGiJpxYASmXwd4Duk6zdDSinrianGVeWXvvxSTyDEbzPidbK+5rZ5pM7762XrjYS0Jdnc6dO8fKlSvJysoiNjaWlJQUXF1dLR1WgyXJjRDVzWiEHR+Ytvv8CazlU5yoHKNRcfRqijmhOR2bXmS/fyN788raPf3d0UtzU7UzGAxs2rSJnTt3AuDj40NYWJgkNhYmyY0Q1e3sOog/CdZO0HOWpaMRdUxWXgHbzyUSeSqOTacTSMzINe/TaqC7ubnJm1aeDjJ9fw1KTU0lIiKC6OhoAHr37s3QoUOxspK3VkuT34AQ1Ukp2PG+abvno2DnatFwRN0Qk5pt7gy868IN8gpuNTc52lgxqK0nIYFeDG7nhZuDTARnCUopvv/+exISErC1teWBBx4gMDDQ0mGJmyS5EaI6XdoBV/eDzgb6PGnpaEQtZTQqjl9PZeNJ02KUJ2PSiuz3c7cjJMCb0EBverVwx9pKmpssTaPRMGLECDZv3szEiRNxc3OzdEjiNpLcCFGdCmttuk4DJ2/LxiJqlew8AzvPJxJ52jT/THz6reYmjQa6NXMzzw7cxstRmptqgeTkZJKSkmjVqhUArVq1omXLlvK7qYUkuRGiulw/DBc2gUYH/Z+1dDSiFohLyyHyZnPTjvOJ5N7W3ORgrWNgW9NkeoPbedLI0caCkYo7nTx5kp9++gmAxx9/HHd3dwBJbGopSW6EqC6FI6Q6TQI3f4uGIixDKcWJ62nm2YGPXUstst/X1c5cO9O7pTs2VjoLRSpKU1BQwPr169m3bx8Afn5+6HTye6rtJLkRojoknoOTq03bwS9YNhZRo3LyDey+cIMNp+LYdCqe2LQc8z6NBro0dSU00IuQQG8CGjvJJ/9a7MaNG0RERBATEwNA//79GTJkiCQ3dYAkN0JUh53zAQXtRoKXjKCo7+LTc9h8Op6Np+LZcS6R7HyDeZ+dXseANh6EBnozOMALTydpbqoLjh8/zs8//0xubi729vaMHz+eNm3aWDosUU6S3AhR1VKvwdFlpu3gOZaNRVQLpRSnYtKJPBXHxtPxHI1OKbLfx8WWkJu1M31bNsJWL5/065qrV6+Sm5tL8+bNmThxIs7OzpYOSVSAJDdCVLXdH4ExH/wHgF9PS0cjqkhugam5KfJUPJtOx3MtJbvI/s5NXUzDtdt70d7HWZqb6iCllPn3NnToUNzd3enRowdarQy9r2skuRGiKmXegIOLTNvS16bOS8zIvdncFMf2c4lk5d1qbrLVawlu7UFIoDdDArzwdra1YKTiXv3+++8cO3aMBx98EK1Wi06no1evXpYOS1SSJDdCVKW9CyA/C3y6QKshlo5GVJBSirNxGTdHN8VxODoFpW7t93a2YUiAN6GBXvRr5YGdtTQ31XV5eXn8+uuvHD58GIDDhw/TvXt3C0cl7pUkN0JUldx02PeZaTt4jmlojKj18gqM7I26YV6M8mpy0eamjr7O5tmBO/pKc1N9Eh8fT3h4OAkJCWg0GgYNGkTXrl0tHZaoAhZPbj7++GPee+89YmNj6dKlC//73//KrAqcP38+n376KVeuXMHDw4NJkybx7rvvYmsrVcLCwg58DTmp0Kg1BI6xdDSiDEmZeWw+HU/k6Ti2nU0kI7fAvM/aSkv/Vo3Mq2v7uNhZMFJRHZRSHDlyhF9++YX8/HwcHR2ZOHEiLVq0sHRooopYNLlZtmwZc+bMYcGCBfTu3Zv58+czfPhwzpw5g5eXV7HjlyxZwssvv8xXX31Fv379OHv2LDNmzECj0fD+++9b4BkIcVNBLuz+2LQd/AJopbmiNlFKcSEhg42n4tl4Mo5DV5Ix3tbc5OFoQ0iAFyGBXgS38cDe2uKf+0Q12rp1K1u2bAFMSyhMmDABBwcHywYlqpRF/4Pff/99Zs+ezcyZMwFYsGABa9eu5auvvuLll18udvyuXbvo378/Dz30EAD+/v48+OCD7N27t0bjFqKYI0sgIxacfaHTZEtHI4B8g5H9UUmm1bVPx3H5RlaR/YE+zubJ9Dr7uqDVSnNTQ9GhQwd2795NcHAwwcHB0tRYD1ksucnLy+PgwYPMnTvXXKbVagkNDWX37t0lntOvXz++//579u3bR69evbh48SK//PILDz/8cKn3yc3NJTf31oJ0aWlppR4rRKUYCmDnh6btfs+AlbVl42nAUrLy2HImgY2n4th6NoH0nNuam3Ra+rZqRGigF0MCvfF1leamhkIpRVxcHI0bNwbA09OT559/Hjs7+RuoryyW3CQmJmIwGPD2LrpSsre3N6dPny7xnIceeojExESCg4NRSlFQUMATTzzBK6+8Uup93n33Xd58880qjV2IIk6uguQosHOHbo9YOpoG50JChmkyvVPxHLycjOG29qZGDtYMDvAiNNCL4DaeONpIc1NDk5uby88//8yJEyeYMWMGzZs3B5DEpp6rU//pW7Zs4Z133uGTTz6hd+/enD9/nueee46///3vvPbaayWeM3fuXObMuTVLbFpaGn5+fjUVsqjvlLq1QGafP4G1tNtXtwKDkQOXk4m8uRjlxcTMIvvbeTuZZwcO8nNFJ81NDVZMTAzh4eEkJSWh1WpJTEw0JzeifrNYcuPh4YFOpyMuLq5I+e1Vh3d67bXXePjhh5k1axYAnTp1IjMzk8cee4xXX321xFkkbWxssLGRtVxENTm3HuKOg7Uj9Jpt6WjqrdTsfLaeTSDyVBxbziSQmp1v3qfXaejTstHNDsHe+LnbWzBSURsopdi/fz+//fYbBoMBFxcXJk2aJB9sGxCLJTfW1tZ0796dyMhIxo0bB4DRaCQyMpKnn366xHOysrKKJTCFq7Oq22faEqKmbL85Sq/HTLBzs2ws9cylxMybk+nFs/9SEgW3NTe52etvNjd5M6CNB062egtGKmqTnJwcVq9ezcmTJwFo164d48aNk2aoBsaizVJz5sxh+vTp9OjRg169ejF//nwyMzPNo6ceeeQRfH19effddwEYM2YM77//Pl27djU3S7322muMGTNGlqAXNe/yLojeAzpr6POUpaOp8woMRg5dSbnZfyaOCwlFm5taezkSEmhKaLo1c5PmJlGi06dPc/LkSXQ6HUOHDqV3794yGqoBsmhyM2XKFBISEpg3bx6xsbEEBQWxbt06cyfjK1euFKmp+etf/4pGo+Gvf/0r165dw9PTkzFjxvD2229b6imIhqyw1iboIXD2sWwsdVR6Tj7bziYSeSqOzWfiSc661dxkpdXQq4U7IYGm5Q6aN5L+TOLuunTpQlxcHB07dsTX19fS4QgL0agG1p6TlpaGi4sLqampsoS9qLyY3+GzAaDRwjMHwb2lpSOqM6KTsszNTXujbpBvuPUS5GKnZ3A7T0ICvRnY1hMXO2luEmXLzs5m06ZNhISEyEz19VxF3r/r1GgpIWqNwhFSHSZIYnMXBqPiSHSyaTK9U3Gcjcsosr+lpwOhgd6EBHjRvbkbVrriAwOEKEl0dDQRERGkpqaSk5PDxIkTLR2SqCUkuRGiom5cMM1tA6alFkQxGbkFbD+bwMZT8Ww5E8+NzDzzPp1WQ4/mbqaEJtCLlp6OFoxU1EVKKXbt2kVkZCRGoxF3d3f69etn6bBELSLJjRAVtfNDUEZoMxwad7R0NLXGtZRs82R6ey7cIM9gNO9zsrXivnamyfQGtfXE1V5mcRaVk5WVxcqVKzl37hwAHTt2ZMyYMTLlhyhCkhshKiLtOhz9wbQ9YE7Zx9ZzRqPi6NUUIk/Fs/FUHKdj04vs929kb15Zu6e/O3ppbhL3KDY2liVLlpCWloaVlRX3338/3bp1k9FQohhJboSoiN0fgyEPmvWDZn0sHU2Ny8orYPs50+imTacTSMy4tW6bVgPdzc1N3rTydJA3HVGlCjuRenh4EBYWVmz5HiEKSXIjRHllJcGBr03bDajWJiY129wZeNeFG+QV3GpucrSxYlBbT0ICvRjczgs3B2luElUrNzfX3ORkb2/PtGnTcHV1xdpa/tZE6SS5EaK89n0O+ZnQuBO0DrV0NNXGaFQcv55qTmhOXE8rst/P3Y6QAG9CA73p1cIdaytpbhLVIyoqiuXLlxMaGkpQUBAAXl5elg1K1AmS3AhRHrkZsHeBaTv4BahnzS3ZeQZ2nk8k8rRp/pn49FvNTRoNdGvmZp4duI2XozQ3iWplNBrZtm0bW7duNa8T1aVLF/m7E+UmyY0Q5XHoG8hONs1p036cpaOpEnFpOUTerJ3ZcT6R3NuamxysdQxo40loe28Gt/OkkaOMRBE1Iz09nRUrVhAVFQVA165duf/++yWxERUiyY0Qd1OQC7s+Mm33fx60dXMdM6UUJ66nmWcHPnYttch+X1c7QgJNK2v3aemOjVXdfJ6i7rpw4QIrVqwgMzMTa2trRo0aRZcuXSwdlqiDJLkR4m5+Xwbp18HJB7pMtXQ0FZKTb2D3hRtsPBXHptPxxKTmmPdpNNClqSuhNxOagMZO8ulYWExycjKLFy/GaDTi7e1NWFgYHh4elg5L1FGS3AhRFqMBdsw3bfd9Gqxqf/NMfHoOm0/Hs/FUPDvOJZKdbzDvs9PrGNDGg9BAbwYHeOHpVPufj2gY3Nzc6N+/P9nZ2QwfPhy9XtYVE5UnyY0QZTm1GpIugJ0bdJ9h6WhKpJTiVEy6aXbg0/EcjU4pst/HxZYhAV6Etvemb8tG2OqluUnUDufOncPDwwM3NzcAhgwZIrWHokpIciNEaZSC7f8xbfd6HGxqzxpIuQUG9lxMYuNJU3PTtZTsIvs7N3UhJMA0O3CHJs7yhiFqFYPBwKZNm9i5cye+vr788Y9/RKfTyd+pqDKS3AhRmvOREHsM9A7Q+3FLR8ONjFw2nY4n8lQ8288lkJl3q7nJVq8luLUHIYHeDAnwwtvZ1oKRClG61NRUIiIiiI6OBsDX1xellIWjEvWNJDdClGbH+6bv3WeAvXuN314pxdm4jJujm+I4HJ3C7e8B3s42DAnwJjTQi36tPLCzluYmUbudOXOGVatWkZ2dja2tLWPHjqV9+/aWDkvUQ5LcCFGSK3vh8k7Q6qHvUzV227wCI3ujbpgXo7yaXLS5qaOvs3l24A5NnNFqpRpf1H4Gg4GNGzeye/duwFRbM2nSJHNfGyGqmiQ3QpSksNamy1Rw8a3WWyVn5rH5jCmZ2XY2kYzcAvM+ayst/Vs1Mq+u7eNiV62xCFEdlFJcvnwZgD59+jB06FB0OqlpFNVHkhsh7hR3As6uA43WtNRCFVNKcSEhw7x208HLyRhva27ycLQhJMCLkEAvgtt4YG8t/6aiblJKodFosLKyIiwsjLi4OAICAiwdlmgA5FVTiDvt+MD0vf0D0KhVlVwy32Bkf1SSKaE5HcflG1lF9gf6OJsn0+vs6yLNTaJOKygoYP369dja2jJkyBDANI+NNEOJmiLJjRC3S4qC48tN2/dYa5OSlceWMwlsPBXH1rMJpOfc1tyk09K3VSNCA70YEuiNr6s0N4n6ISkpifDwcGJiYtBoNAQFBeHuXvMd8kXDJsmNELfb9V9QRmgdCj4VX9PmYkKGuTPwgcvJGG5rb2rkYM3gAC9CA70IbuOJo438+4n65cSJE6xevZrc3Fzs7e0ZN26cJDbCIuTVVYhC6bFw+HvTdvCccp1SYDBy4HIykTcXo7yYmFlkfztvJ/NilEF+ruikuUnUQ/n5+fz2228cOHAAgGbNmjFp0iScnZ0tHJloqCS5EaLQ7o/BkAd+vaF5v1IPS83OZ+vZBCJPxbHlTAKp2fnmfXqdhj4tG93sEOyNn7t9TUQuhMUopfj222+Jjo5Go9EQHBzM4MGD0Wq1lg5NNGCS3AgBkJ0MB74ybQfPMS2ZfZtLiZk3J9OLZ/+lJApua25ys9czuJ0pmRnY1gMnW1nwTzQcGo2G7t27k5SUxIQJE2jVqmo64QtxLyS5EQJg35eQlwFeHaDtcAxGxaEryeaE5nx8RpHDW3s5EhLoRWigN92auUlzk2hQ8vPzSUlJwdPTE4CgoCDatWuHnZ10jBe1gyQ3QuRlwd5PATjcfAbf/XiUzWfiSc661dxkpdXQq4U7IYGm5Q6aN3KwVLRCWFRCQgLh4eHk5OTwxBNPYG9vanqVxEbUJpLciAYtOimLa799QJ+sG1xRXkza3hgD1wBwsdMzuJ3nzeYmT1zspLlJNGxHjhxh7dq15Ofn4+joSEpKijm5EaI2keRGNCgGo+JIdLJ5duCouBS22CwEDSwoGENzT2dCA70JCfCie3M3rHTSKVKIvLw81q5dy9GjRwFo2bIlEyZMwNHR0cKRCVEySW5EvZeZW8D2cwlsPBXP5tPx3MjMM++bbLULX80Nsqw9mP34K7zj42HBSIWofeLi4ggPDycxMRGNRsPgwYMZMGAAGo30MxO1lyQ3ol66lpJN5Kk4Np6KZ8+FG+QZjOZ9TrZW3NfOi9CARozePg+SwH7Qs7SQxEaIYnbu3EliYiJOTk5MmjSJ5s2bWzokIe5KkhtRLxiNiqNXU8yzA5+OTS+y37+RvXll7Z7+7uh1Wjj5EySdB1sX6PFHC0UuRO02cuRIrKysCAkJwcFBOtKLukGSG1FnZeUVsP1cIpGn4th0OoHEjFzzPq0Gujd3M/WfCfSmladD0Wp0pWD7+6btXo+BjVMNRy9E7RQTE8OxY8cYOnQoGo0GW1tbxo4da+mwhKiQe0pucnJysLW1rapYhLirmNRsIm92Bt554QZ5BbeamxxtrBjU1pOQQC8Gt/PCzcG69Atd3AwxR8DKDno/Uf2BC1HLKaU4cOAA69atw2Aw4OnpSdeuXS0dlhCVUuHkxmg08vbbb7NgwQLi4uI4e/YsLVu25LXXXsPf359HH320OuIUDZxSihfDf2f5oatFyv3c7QgJ8CY00JteLdyxtirn6KbCWpvu08FB+tqIhi0nJ4fVq1dz8uRJANq1a0dAQICFoxKi8iqc3Lz11lt88803/Otf/2L27Nnm8o4dOzJ//nxJbkS1OHQlheWHrqLRQLdmbubZgdt4OVZ81MbVA3BpO2itoO/T1ROwEHXEtWvXiIiIIDk5GZ1OR2hoKH369JHRUKJOq3By8+233/L5558TEhLCE0/cqs7v0qULp0+frtLghCj04/5oACZ0bcp/Jne5t4sV1tp0ngqufvcYmRB11+HDh1mzZg0GgwFXV1fCwsLw9fW1dFhC3LMKJzfXrl2jdevWxcqNRiP5+fklnCHEvcnILeDn368DMKXnPSYj8afgzFpAA8HP33NsQtRl7u7uGI1GAgMDeeCBB6QPpag3KpzctG/fnu3btxeb6yAiIkI6n4lqsebodbLyDLT0cKCnv9u9XWzHfNP3wDHg0eaeYxOirrl9IEjz5s2ZPXs2Pj4+0gwl6pUKJzfz5s1j+vTpXLt2DaPRyIoVKzhz5gzffvsta9asqY4YRQO37ICpSWpKT797ewFOvgzHwk3bA+ZUQWRC1B1KKXbv3s22bduYNWsWHh6mjvRNmjSxcGRCVL0KL5zzwAMP8PPPP7Nx40YcHByYN28ep06d4ueff2bo0KHVEaNowM7GpXP4SgpWWg0TujW9t4vt+i8oA7QcDE2kllE0HFlZWfzwww+sX7+enJwc8xpRQtRXlZrnZsCAAWzYsKGqYxGimGU3OxKHBHrh6WRT+QtlxMPh703bUmsjGpArV64QERFBWloaVlZWjBgxgu7du1s6LCGqVYVrblq2bMmNGzeKlaekpNCyZcsqCUoIgNwCAytuzmtzzx2J93wCBTng2wP8B1RBdELUbkoptm/fzqJFi0hLS6NRo0bMmjWLHj16SP8aUe9VuObm0qVLGAyGYuW5ublcu3atSoISAmDjyXiSs/Jp7GzLwDaelb9QTirsX2jaHjAH5IVdNABHjhwhMjISgM6dOzN69GisrcuYtVuIeqTcyc3q1avN27/99hsuLi7mxwaDgcjISPz9/as0ONGwLd1/BYBJ3ZtipatwJeMt+7+E3DTwDIC291dRdELUbl26dOH48eN07NiRoKAgqa0RDUq5k5tx48YBoNFomD59epF9er0ef39//vOf/1RpcKLhupqcxY7ziQBM7nEPTVL52bDnU9N28AugvYckSYhazGg0cvjwYYKCgtDpdGi1WqZNmyZJjWiQyp3cGI2mBQpbtGjB/v37zcMIhagO4QeuohT0b92IZo3sK3+hw99DZgK4NoOOE6suQCFqkYyMDJYvX05UVBSJiYkMHz4cQBIb0WBVuM9NVFRUdcQhhJnBqAi/ObfNPdXaGPJh539N2/2eBZ2+CqITona5ePEiK1asICMjA71eT+PGjS0dkhAWV6mh4JmZmWzdupUrV66Ql5dXZN+zzz5bJYGJhmvH+USup+bgYqdneId7eKE+vhxSr4CDJ3SdVnUBClELGI1GtmzZwvbt21FK4e3tTVhYmNSqC0ElkpvDhw8zcuRIsrKyyMzMxN3dncTEROzt7fHy8pLkRtyzZTc7Eo/v6outXle5ixiNtxbI7PMk6O2qKDohLC8tLY3ly5dz+fJlALp3786IESPQ66V2UgioxDw3L7zwAmPGjCE5ORk7Ozv27NnD5cuX6d69O//+97+rI0bRgNzIyGXDyTjgHue2OfMLJJ4BG2fo+WgVRSdE7VBQUEBsbCzW1tZMnDiRMWPGSGIjxG0qXHNz5MgRPvvsM7RaLTqdjtzcXFq2bMm//vUvpk+fzoQJE6ojTtFArDx8jXyDonNTFwJ9nCt3EaVgx81am56zwNal7OOFqAOUUuYOwu7u7oSFheHm5kajRo0sHJkQtU+Fa270ej3am8Npvby8uHLF1ITg4uJCdHR01UYnGhSlFEv331oks9KitsG1g2Bla2qSEqKOS01NZdGiRVy8eNFc1rp1a0lshChFhZObrl27sn//fgAGDRrEvHnzWLx4Mc8//zwdO3as8gBFw3HoSgrn4zOw0+sY2+UeViourLXp+jA43sPMxkLUAmfOnGHBggVcvnyZtWvXmqflEEKUrsLJzTvvvIOPjw8Ab7/9Nm5ubvzpT38iISGBzz77rMoDFA1HYUfikZ18cLKtZP+Ba4fg4hbQ6KDfM1UXnBA1zGAw8Ntvv/HDDz+QnZ1NkyZNmDZtmrnmXAhRugr3uenRo4d528vLi3Xr1lVpQKJhysgtYM3vMQBM7XUPTVKFtTadJ4Nb8yqITIial5KSQnh4uHm9vj59+hAaGoqVVaVm7xCiwamyjwCHDh1i9OjRFT7v448/xt/fH1tbW3r37s2+ffvKPD4lJYWnnnoKHx8fbGxsaNu2Lb/88ktlwxa1xJqj18nKM9DS04Eezd0qd5GEs3BqjWm7//NVFpsQNSk1NZUFCxZw7do1bG1tmTp1KiNGjJDERogKqFBy89tvv/Hiiy/yyiuvmDu2nT59mnHjxtGzZ88KtwUvW7aMOXPm8Prrr3Po0CG6dOnC8OHDiY+PL/H4vLw8hg4dyqVLl4iIiODMmTN88cUX+Pr6Vui+ovYxdyTu4Vf5KeN3zgcUBIwGr4Aqi02ImuTs7Ey7du1o2rQpTzzxBAEB8rcsREVplFKqPAcuXLiQ2bNn4+7uTnJyMo0aNeL999/nmWeeYcqUKTz33HMEBgZW6Oa9e/emZ8+efPTRR4Bpxk0/Pz+eeeYZXn755WLHL1iwgPfee4/Tp09Xek6HtLQ0XFxcSE1Nxdm5kkONRZU6E5vO8PnbsNJq2D03BE8nm4pfJCUa/hsExgKYtQmadq/yOIWoLklJSdja2mJvb1pHLT8/3zzdhhDCpCLv3+Wuufnwww/55z//SWJiIj/++COJiYl88sknHDt2jAULFlQ4scnLy+PgwYOEhobeCkarJTQ0lN27d5d4zurVq+nbty9PPfUU3t7edOzYkXfeeQeDwVDqfXJzc0lLSyvyJWqXZTdrbUICvSqX2ADs+p8psWkxUBIbUaecOHGCzz77jJ9++onCz5p6vV4SGyHuQbmTmwsXLhAWFgbAhAkTsLKy4r333qNp06aVunFiYiIGgwFvb+8i5d7e3sTGxpZ4zsWLF4mIiMBgMPDLL7/w2muv8Z///Ie33nqr1Pu8++67uLi4mL/8/O6hs6qocrkFBlYevgrA1J7NKneRzEQ49K1pO3hOFUUmRPUqKChgzZo1hIeHk5ubS3Z2Nrm5uZYOS4h6odw91LKzs81VphqNBhsbG/OQ8JpiNBrx8vLi888/R6fT0b17d65du8Z7773H66+/XuI5c+fOZc6cW294aWlpkuDUIhtOxpGclU9jZ1sGtq3knDR7PoWCbGjSFVreV6XxCVEdbty4QXh4uPmD3IABAxg8eLAM8xaiilSo+/2XX36Jo6MjYPrUsWjRomIr0JZ34UwPDw90Oh1xcXFFyuPi4mjcuOSVoH18fIpV1wYGBhIbG0teXh7W1tbFzrGxscHGppJNHaLaFTZJhfVoik5biY7EOWmw7wvTdvAcqGxnZCFqyO+//86aNWvIy8vDwcGB8ePH07p1a0uHJUS9Uu7kplmzZnzxxRfmx40bN+a7774rcoxGoyl3cmNtbU337t2JjIxk3LhxgKlmJjIykqeffrrEc/r378+SJUswGo3mTzhnz57Fx8enxMRG1G7RSVnsOJ8IQFj3StamHfgKclPBo61plJQQtVh+fj6bNm0iLy8Pf39/Jk6ciJOTk6XDEqLeKXdyc+nSpSq/+Zw5c5g+fTo9evSgV69ezJ8/n8zMTGbOnAnAI488gq+vL++++y4Af/rTn/joo4947rnneOaZZzh37hzvvPNOuRMqUbuEH7yKUtC/dSOaNbKv+AXyc2DPJ6bt4BdAqvRFLafX6wkLC+PcuXMMHDhQmqGEqCYWnRVqypQpJCQkMG/ePGJjYwkKCmLdunXmTsZXrlwp8s/v5+fHb7/9xgsvvEDnzp3x9fXlueee46WXXrLUUxCVZDAqIg4ULpJZyY7ERxZDRhy4+EGnsCqMToiqc+TIEZRSdO3aFQBfX1+Zm0uIalbueW7qC5nnpnbYciaeGV/vx8VOz95XQrDVV3DYq6EA/tcNUi7D/f+C3o9XT6BCVFJeXh5r167l6NGjWFlZ8ac//UlW8RbiHlTk/Vvm8xYW8ePNWpvxXX0rntgAnFhpSmzsPUyrfwtRi8TFxREeHk5iYiIajYaBAwfi5lbJZUWEEBUmyY2ocTcyctlw0jRKbkrPSnQkNhpvLZDZ5wmwrkR/HSGqgVKKw4cP88svv1BQUICTkxMTJ07E39/f0qEJ0aBIciNq3MrD18g3KLo0dSHQpxJNg+d+g/iTYO0EPWdXfYBCVIJSilWrVnH06FEAWrduzfjx43FwcLBwZEI0PJXqqn/hwgX++te/8uCDD5oXufz11185ceJElQYn6h+llHmRzMmVqbVRCrbfrLXp+Uewc6264IS4BxqNBnd3d/MyMn/4wx8ksRHCQiqc3GzdupVOnTqxd+9eVqxYQUZGBgBHjx4tdZZgIQodupLM+fgM7PQ6xnZpUvELXN4JV/eBzgb6PFX1AQpRAUopsrOzzY8HDBjAY489RnBwcOVXtxdC3LMKJzcvv/wyb731Fhs2bCgycd6QIUPYs2dPlQYn6p/CGYlHdfbBybYSK7sX1tp0/QM4eZd9rBDVKCcnh4iICBYtWkR+fj5gWvy3tBnWhRA1p8J9bo4dO8aSJUuKlXt5eZGYmFglQYn6KSO3gDW/xwCV7Eh8/QhciASNDvrJxI3Ccq5fv054eDjJyclotVqio6Np2bKlpcMSQtxU4eTG1dWVmJgYWrRoUaT88OHDMjGVKNOao9fJyjPQ0tOBHs0rMSx2xwem7x0ngnuLso8Vohoopdi3bx/r16/HYDDg6urKpEmTaNq0qaVDE0LcpsLJzdSpU3nppZcIDw9Ho9FgNBrZuXMnL774Io888kh1xCjqicKOxFN6+FW8P0LieTj5k2k7+IUqjkyIu8vOzuann37i9OnTAAQEBPDAAw9gZ2dn4ciEEHeqcHLzzjvv8NRTT+Hn54fBYKB9+/YYDAYeeugh/vrXv1ZHjKIeOBObzpHoFKy0GiZ0q8Sn3J3zAQVt7wfv9lUdnhB3tXbtWk6fPo1Op2PYsGH06tVLOg0LUUtVOLmxtrbmiy++4LXXXuP48eNkZGTQtWtX2rRpUx3xiXqisCNxaKA3nk42FTs59RocXWraHjCniiMTonyGDh1KcnIyo0aNokmTSoz0E0LUmAonNzt27CA4OJhmzZrRrFklFzwUDUpugYEVh68ClexIvPsjMOZD82Dw61XF0QlRsqysLM6ePUtQUBAALi4uzJo1S2prhKgDKpzcDBkyBF9fXx588EGmTZtG+/bSRCDKtuFkHClZ+TR2tmVgW8+KnZx5Aw4uMm0PkL42omZcuXKFiIgI0tLSsLOzo127dgCS2AhRR1R4npvr16/zf//3f2zdupWOHTsSFBTEe++9x9WrV6sjPlEPFDZJhfVoik5bwTeHfZ9BfhY07gytQqohOiFuUUqxY8cOFi1aRFpaGo0aNcLFxcXSYQkhKqjCyY2HhwdPP/00O3fu5MKFC4SFhfHNN9/g7+/PkCFDqiNGUYdFJ2Wx47xp/qPJPSrYJJWbDns/M20PmAPyqVlUo8zMTBYvXszGjRsxGo106tSJxx57TCblE6IOuqeFM1u0aMHLL79Mly5deO2119i6dWtVxSXqifCDV1EK+rduhJ97BVfvPrgIclKgUWsIHFsd4QkBwKVLl1i+fDnp6elYWVkxcuRIunbtKs1QQtRRlU5udu7cyeLFi4mIiCAnJ4cHHniAd999typjE3WcwagIP3BzbpueFex8XpALuz82bfd/HrS6qg1OiNtkZGSQnp6Op6cnYWFheHl5WTokIcQ9qHByM3fuXJYuXcr169cZOnQoH374IQ888AD29hX8VC7qve3nEohJzcHVXs+w9hVcB+roD5AeA86+0HlK9QQoGjSllLlmpmPHjhgMBgIDA4usmSeEqJsqnNxs27aNP//5z0yePBkPD4/qiEnUE4UdiccF+WKrr0DNi6EAdsw3bfd9GqzkzUZUrYsXL7J+/XqmTZuGo6MjAF26dLFwVEKIqlLh5Gbnzp3VEYeoZxIzctl4Kg6oxNw2J1dBchTYuUP36VUfnGiwjEYjW7duZdu2bSil2LJlC6NHj7Z0WEKIKlau5Gb16tXcf//96PV6Vq9eXeaxY8dKx08BKw9dI9+g6NLUhUAf5/KfqNStWpveT4C1Q7XEJxqe9PR0li9fzqVLlwDo1q0bw4cPt2xQQohqUa7kZty4ccTGxuLl5cW4ceNKPU6j0WAwGKoqNlFHKaVYVtmOxOc2QNwxsHaEXrOrITrREJ0/f54VK1aQlZWFtbU1Y8aMoVOnTpYOSwhRTcqV3BiNxhK3hSjJoSvJnI/PwE6vY0wXn4qdvON90/fuM8DevcpjEw3PiRMnCA8PB6Bx48aEhYXRqFEjC0clhKhOFZ7E79tvvyU3N7dYeV5eHt9++22VBCXqtqX7TLU2ozr74GSrL/+Jl3fDld2gszZ1JBaiCrRu3ZpGjRrRs2dPZs2aJYmNEA1AhZObmTNnkpqaWqw8PT2dmTNnVklQou5Kz8lnze8xAEytaEfiwlqboIfAuYI1PkLc5urVqyilALCxseGxxx5j1KhRWFnd07ylQog6osLJze1zQ9zu6tWrsgaLYM3vMWTnG2jp6UD35m7lPzH2GJxbDxot9H+u+gIU9ZrBYGD9+vV8+eWX7Nmzx1xuY2NjwaiEEDWt3B9jCqci12g0hISEFPkEZDAYiIqKYsSIEdUSpKg7Cue2mdrTr2JT1+/4wPS9w3hwb1kNkYn6LiUlhYiICPMivunp6RaOSAhhKeVObgpHSR05coThw4ebJ74CsLa2xt/fn4kTJ1Z5gKLuOB2bxpHoFKy0GiZ0a1r+E29cgBMrTdvBL1RPcKJeO336NKtWrSInJwdbW1vGjRtHQECApcMSQlhIuZOb119/HQB/f3+mTJmCra1ttQUl6qbCWpvQQG88HCvQDLDzQ1BGaDMMGsvwXFF+BQUFbNiwgb179wLQtGlTJk2ahKurq2UDE0JYVIV7102fLjPGiuJyCwysPHwNgCm9KtCROC3GtI4UQPCcaohM1GcJCQns378fgH79+hESEoJOJ4usCtHQlSu5cXd35+zZs3h4eODm5lZmX4qkpKQqC07UHetPxJGSlY+Piy0D23iW/8TdH4EhD5r1heZ9qy9AUS/5+PgwcuRInJ2dadu2raXDEULUEuVKbj744AOcnJzM2xXqKCoahB9vzkgc1r0pOm05/z6ykuDA16ZtqbUR5VDYDNWtWze8vU0rzffo0cPCUQkhaptyJTe3N0XNmDGjumIRdVR0UhbbzyUCENajAk1S+76A/Ezw7gRthlZTdKK+uHHjBuHh4cTGxnLhwgWefPJJtNoKz2YhhGgAKvzKcOjQIY4dO2Z+/NNPPzFu3DheeeUV8vLyqjQ4UTeEHzQNvQ1u7YGfu335TsrLhL0LTNvBz4PUBooyHDt2jM8++4zY2FgcHBwYMWKEJDZCiFJV+NXh8ccf5+zZswBcvHiRKVOmYG9vT3h4OH/5y1+qPEBRuxmMivCbTVKTKzIj8cFvIDvJNKdNh/HVFJ2o6/Lz81m9ejXLly8nLy8Pf39/nnjiCVq3bm3p0IQQtViFk5uzZ88SFBQEQHh4OIMGDWLJkiUsWrSI5cuXV3V8opbbdi6BmNQcXO31DGvvXb6TCvJMHYnBNBuxVka3iOIyMjL44osvOHToEBqNhkGDBvHII4+Y+/8JIURpKjwUXCllXhl848aNjB49GgA/Pz8SExOrNjpR6/14c26b8V19sdWXM0n5fRmkXQMnH+jyYDVGJ+oye3t7HBwccHR0ZMKECbRsKTNXCyHKp8LJTY8ePXjrrbcIDQ1l69atfPrppwBERUWZRy+IhiExI5cNJ+MAmFLeJimj4dZSC32fAitZ80fckpeXh1arxcrKCq1Wa571/PYZ0YUQ4m4q3Cw1f/58Dh06xNNPP82rr75qbvuOiIigX79+VR6gqL1WHrpGgVHRxc+VgMbO5Tvp1GpIugC2rtB9RnWGJ+qY+Ph4vvjiC9atW2cuc3R0lMRGCFFhFa656dy5c5HRUoXee+89mRm0AVFKsXT/FQCmlHf4t1Kw/X3Tdu/HwUb6TgjT39Lhw4f55ZdfKCgoICcnhyFDhmBvX86Rd0IIcYcKJzeFDh48yKlTpwBo37493bp1q7KgRO136EoyFxIysdPrGNPFp3wnXYiE2N9Bbw+9n6jeAEWdkJuby9q1a/n9998BaN26NePHj5fERghxTyqc3MTHxzNlyhS2bt1qXpwuJSWFwYMHs3TpUjw9KzD1vqizlu4zdSQe1dkHJ1t9+U7afrOvTfcZYO9ePYGJOiM2Npbw8HBu3LiBVqtlyJAh9O/fX2ZAF0Lcswr3uXnmmWfIyMjgxIkTJCUlkZSUxPHjx0lLS+PZZ5+tjhhFLZOek8+a32MAmFrejsTR++DyDtDqoe/T1RidqAsKCgpYvHgxN27cwNnZmRkzZhAcHCyJjRCiSlS45mbdunVs3LiRwMBAc1n79u35+OOPGTZsWJUGJ2qnNb/HkJ1voJWnA92bu5XvpMK+Nl2mgotv9QUn6gQrKytGjx7NwYMHGTdunDRDCSGqVIWTG6PRiF5fvBlCr9eb578R9dvSm3PbTOnpV75P2nEn4eyvgAb6P1+tsYna6/r16+Tk5Jjnq2nXrh1t27aV2hohRJWrcLPUkCFDeO6557h+/bq57Nq1a7zwwguEhIRUaXCi9jkdm8bR6BSstBomdGtavpMK57Vp/wB4yLT5DY1Sir1797Jw4ULCw8NJTU0175PERghRHSpcc/PRRx8xduxY/P398fMz9beIjo6mY8eOfP/991UeoKhdlt2stQkN9MbDsRwT8CVFwfEI0/aAOdUYmaiNsrOzWb16tXlkZfPmzbG2trZwVEKI+q7CyY2fnx+HDh0iMjLS/IIVGBhIaGholQcnapfcAgMrD18DYEqvcnYk3vVfUEZoFQI+XaoxOlHbXL16lYiICFJSUtDpdAwbNoxevXpJbY0QotpVKLlZtmwZq1evJi8vj5CQEJ555pnqikvUQutPxJGSlY+Piy0D25RjyH96HBxebNqWWpsGQynFnj172LBhA0ajETc3N8LCwmjSpImlQxNCNBDlTm4+/fRTnnrqKdq0aYOdnR0rVqzgwoULvPfee9UZn6hFCpukwro3Ractx6fvPR+DIRea9oLm/as5OlFbaDQaEhMTMRqNdOjQgTFjxmBra2vpsIQQDUi5OxR/9NFHvP7665w5c4YjR47wzTff8Mknn1RnbKIWiU7KYsf5RDQaCCvPcgvZKbD/K9P2gDkgTRH1nlLKvD1ixAgmTJjApEmTJLERQtS4cic3Fy9eZPr06ebHDz30EAUFBcTExFRLYKJ2CT9gqrXp38oDP/dyzEmy/wvISwev9tBmeDVHJyxJKcWOHTtYsmSJOcHR6/V07txZ+tcIISyi3M1Subm5ODg4mB9rtVqsra3Jzs6ulsBE7WEwKsIPXgVMc9vcVV4W7PnUtB08B7QVnnFA1BGZmZmsXLmS8+fPA3D69OkiE3wKIYQlVKhD8WuvvVZkJtG8vDzefvttXFxczGXvv/9+1UUnaoVt5xKISc3B1V7PsA7edz/h8HeQdQPc/KHD+GqPT1jG5cuXiYiIID09HSsrK0aOHElAQIClwxJCiPInNwMHDuTMmTNFyvr168fFixfNj6UKun5adnORzPFdfbGx0pV9cEEe7Pyvabvfs6Cr9MLzopYyGo3s2LGDzZs3o5TC09OTsLAwvLy8LB2aEEIAFUhutmzZUo1hiNoqMSOXjafigHI2SR0Lh7Sr4OgNQX+o5uiEJaxdu5aDBw8CEBQUxMiRI2ViPiFErVIrOkN8/PHH+Pv7Y2trS+/evdm3b1+5zlu6dCkajYZx48ZVb4AN2IpDVykwKrr4uRLQ2Lnsg41G2DnftN3nSdDLKJn6qGfPntjZ2TF+/HjGjRsniY0QotaxeHKzbNky5syZw+uvv86hQ4fo0qULw4cPJz4+vszzLl26xIsvvsiAAQNqKNKGRyllnttmanlqbU6vgcSzYOsCPf5YzdGJmmI0GomOjjY/bty4MS+88AJdusiM00KI2sniyc3777/P7NmzmTlzJu3bt2fBggXY29vz1VdflXqOwWDgD3/4A2+++aZ5hWFR9Q5eTuZCQiZ2eh2jO/uUfbBSsONmZ/Kes8H2LrU8ok5IT0/n22+/ZdGiRVy7ds1cLrU1QojazKLJTV5eHgcPHiyyLpVWqyU0NJTdu3eXet7f/vY3vLy8ePTRR2sizAarsNZmdGcfnGz1ZR98cQtcPwxWdtDnT9UfnKh258+fZ8GCBVy6dAmdTkd6erqlQxJCiHKx6FCWxMREDAYD3t5Fhxd7e3tz+vTpEs/ZsWMHCxcu5MiRI+W6R25uLrm5uebHaWlplY63IUnPyWfN76YJGsvVkbiw1qbbI+DgUY2RiepmNBrZtGkTO3bsAEzNUGFhYTRq1MjCkQkhRPlUquZm+/btTJs2jb59+5qrqr/77jvzi2F1SU9P5+GHH+aLL77Aw6N8b6DvvvsuLi4u5i8/v3KuZt3A/Xw0hux8A608Heje3K3sg68ehKhtoLWCfrKYal2WmprKokWLzP/LPXv2ZNasWZLYCCHqlAonN8uXL2f48OHY2dlx+PBhc61Iamoq77zzToWu5eHhgU6nIy4urkh5XFwcjRs3Lnb8hQsXuHTpEmPGjMHKygorKyu+/fZbVq9ejZWVFRcuXCh2zty5c0lNTTV/3d4xUpRu2YHCjsTN7j5/UWGtTecp4CrJY1126tQprly5go2NDWFhYYwaNQorK5mrSAhRt1Q4uXnrrbdYsGABX3zxBXr9rX4Y/fv359ChQxW6lrW1Nd27dycyMtJcZjQaiYyMpG/fvsWODwgI4NixYxw5csT8NXbsWAYPHsyRI0dKrJWxsbHB2dm5yJco2+nYNI5Gp2Cl1TC+m2/ZB8efNo2SQgP9n6+J8EQ16t27N/379+fxxx+nQ4cOlg5HCCEqpcIfyc6cOcPAgQOLlbu4uJCSklLhAObMmcP06dPp0aMHvXr1Yv78+WRmZjJz5kwAHnnkEXx9fXn33XextbWlY8eORc53dXUFKFYuKq+wI/HQ9t54ONqUffCOD0zfA0eDZ9tqjkxUtZSUFDZv3syoUaOwtrZGo9EwdOhQS4clhBD3pMLJTePGjTl//jz+/v5Fynfs2FGpYdlTpkwhISGBefPmERsbS1BQEOvWrTN3Mr5y5QpaWXixxuQWGFh52NSPavLdOhInXzbNSAymBTJFnXL69GlWrVpFTk4O1tbWjBo1ytIhCSFElahwcjN79myee+45vvrqKzQaDdevX2f37t28+OKLvPbaa5UK4umnn+bpp58ucd/dln1YtGhRpe4pSrb+RBwpWfn4uNgysI1n2Qfv+h8oA7S8D3y71Uh84t4ZDAY2bNjAnj17APD19aV///4WjkoIIapOhZObl19+GaPRSEhICFlZWQwcOBAbGxtefPFFnnlGRsrUdYVNUmE9/NBpy+hInBFvWv0bpNamDklOTiY8PJzr168DpsVvQ0JC0OnusiCqEELUIRVObjQaDa+++ip//vOfOX/+PBkZGbRv3x5HR8fqiE/UoOikLHacT0SjgbDuTcs+eM+nUJADvt2hRfE+WKL2uXTpEj/88AO5ubnmtaHatpV+UkKI+qfSYzytra1p3759VcYiLCz85vDv4NYe+Lnbl35gTirs/9K0HTwH7jZUXNQKjRo1wsrKCm9vbyZOnIiLi4ulQxJCiGpR4eRm8ODBZc57smnTpnsKSFiGwagIP3gVgMk97tKReP9CyE0DzwBoN7IGohOVlZWVhb29KVF1cnJi5syZuLm5STOUEKJeq3ByExQUVORxfn4+R44c4fjx40yfPr2q4hI1bNu5BGJSc3C11zOsg3fpB+Znw55PTNvBL4CMZKu1jh07xpo1a3jggQfMtazlndlbCCHqsgonNx988EGJ5W+88QYZGRn3HJCwjGX7TE1S47v6YmNVxqf6w99DZgK4NIOOE2soOlER+fn5rFu3joMHDwJw9OhRaUIWQjQoVfaxe9q0aXz11VdVdTlRgxLSc9l4yrQERpmLZBryYdd/Tdv9nwXdXVYKFzUuMTGRL7/8koMHD6LRaBg0aBBTpkyxdFhCCFGjqmzRmN27d2Nra1tVlxM1aOXhqxQYFUF+rgQ0LmN5iuPLIeUKOHhC12k1F6Aol6NHj7JmzRry8/NxdHRkwoQJlZpYUwgh6roKJzcTJkwo8lgpRUxMDAcOHKj0JH7CcpRSLL05t02ZtTZG462lFvr8CfR2NRCdKK+YmBhWrlwJQIsWLZg4caJMzyCEaLAqnNzcOXxUq9XSrl07/va3vzFs2LAqC0zUjIOXk7mYkIm9tY4xXZqUfuDZXyHhNNg4Q89ZNRegKBcfHx/69euHjY0NAwYMkCVLhBANWoWSG4PBwMyZM+nUqRNubm7VFZOoQYW1NqM6+eBoU8qfg1Kw/X3Tds9HwVbmR7E0pRRHjx6lZcuW5pXu5cOFEEKYVOjjnU6nY9iwYZVa/VvUPuk5+az9PQaAqb3KaJK6tB2uHQArW+jzZA1FJ0qTm5vLypUrWbVqFRERERiNRkuHJIQQtUqFm6U6duzIxYsXadGiRXXEI2rQz0djyM430MrTgW7NyqiJK6y16ToNHL1qJjhRotjYWMLDw7lx4wZarZa2bduWOammEEI0RBVObt566y1efPFF/v73v9O9e3ccHByK7C+sIhe137L9VwCY2rNZ6W+Q1w/Dxc2g0UG/Z2swOnE7pRQHDx5k3bp1FBQU4OzszKRJk2jWrJmlQxNCiFqn3MnN3/72N/7v//6PkSNN0+2PHTu2yBuiUgqNRoPBYKj6KEWVOxWTxtGrqeh1GsZ38y39wMJam05h4Na8ZoITReTm5rJ69WpOnDgBQNu2bRk3bpx5WQUhhBBFlTu5efPNN3niiSfYvHlzdcYjasiymx2JQwO98XC0KfmghLNw6mfTdvDzNROYKEar1ZKQkIBWqyU0NJS+fftKU5QQQpSh3MmNUgqAQYMGVVswombk5BtYdeQacJe5bXbOBxS0GwVegTUSmzAp/H/TaDTo9XrCwsLIzc2ladOmFo5MCCFqvwr1uZFPi/XD+pNxpGTl08TFlgFtPEs+KCUafl9m2h4wp+aCE+Tk5PDTTz/RpEkTBgwYAICnZym/JyGEEMVUKLkpz8iMpKSkewpIVL8fbzZJTerhh05byu9z90dgLAD/AdC0Rw1G17Bdu3aN8PBwUlJSOH/+PF27dpWZhoUQooIqlNy8+eabxWYoFnVLdFIWO84notFAWPdSmjgyE+HgN6ZtqbWpEUop9uzZw8aNGzEYDLi5uREWFiaJjRBCVEKFkpupU6fi5SXznNRlPx4w1doEt/bAz72U0TZ7F0BBNvgEQcvBNRdcA5Wdnc2qVas4c+YMAO3bt2fs2LGyEK0QQlRSuZMb6W9T9xmMivADV4EyOhLnpMG+z03bA+aA/N6rlcFg4Msvv+TGjRtYWVkxfPhwevToIf9vQghxDyo8WkrUXdvOJhCbloObvZ6h7b1LPujg15CTCh5tIWBMzQbYAOl0Ovr06cOePXsICwujcePGlg5JCCHqvHInN7J+Td1XOLfN+K5NsbHSFT8gPwd2f2za7v88yMrS1SIrK4vMzEzzCKgePXoQFBSEXq+3cGRCCFE/VHj5BVE3JaTnsvFUHFBGk9TRJZARB85NTTMSiyp3+fJlIiIisLKy4vHHH8fW1tY8l40QQoiqIclNA7Hi0FUKjIogP1faNXYqfoChAHbMN233ewasrGs0vvpOKcX27dvZvHkzSik8PDzIysqSTsNCCFENJLlpAJRSLLs5SmpqabU2J1ZCymWwbwTdHqnB6Oq/jIwMVq5cyYULFwAICgpi5MiRWFtLAimEENVBkpsG4MDlZC4mZGJvrWN0lybFD1AKdnxg2u79J7CWBRmrSlRUFMuXLycjIwO9Xs+oUaMICgqydFhCCFGvSXLTABR2JB7d2QdHmxJ+5Wd/g/gTYO0IvWbVcHT12+7du8nIyMDLy4uwsDBZRkEIIWqAJDf1XHpOPmt/jwFK6UisFOx437Td449g51aD0dV/48aNY8eOHQwePFg6DQshRA2Rsb713M9HY8jON9Day5FuzUpIXC7vgui9oLOBvk/VfID1zIULF1i/fr35sb29PcOGDZPERgghapDU3NRzy/ZfAWBKD7+SZ70trLXp+gdwkgnkKstoNLJ582Z27NiBUgo/Pz8CAwMtHZYQQjRIktzUY6di0jh6NRW9TsP4br7FD4g5Cuc3gkYL/Z6t+QDribS0NJYvX87ly5cB06R8rVu3tnBUQgjRcElyU48VdiQe2t4bD0eb4gcUjpDqOBHcW9RgZPXHuXPnWLlyJVlZWdjY2DB27Fg6dOhg6bCEEKJBk+SmnsrJN7Dy8DUAJvcooSNx4nk4scq0HfxCzQVWj2zfvp3IyEgAmjRpwqRJk3B3d7dwVEIIISS5qafWn4wjNTufJi62DGhTwvDjnfMBBW1HgLfUNFSGj48PGo2GXr16MXToUKys5N9JCCFqA3k1rqcKOxJP6uGHTntHR+LUa3B0qWk7eE4NR1a3ZWZm4uDgAEDr1q158sknZe4aIYSoZWQoeD0UnZTFzvM30GggrHvT4gfs/hiM+dC8PzTrXfMB1kEGg4F169bxv//9j+TkZHO5JDZCCFH7SHJTD/14cx2p4NYe+LnfsZRCVhIcXGTallqbcklOTuarr75iz5495OTkcO7cOUuHJIQQogzSLFXPGIyK8ANXgVJmJN77GeRnQuPO0DqkhqOre06ePMnq1avJycnBzs6OcePG0a5dO0uHJYQQogyS3NQz284mEJuWg5u9nqHtvYvuzM2AvQtM2wPmQEmT+gkACgoKWL9+Pfv27QPAz8+PSZMm4eLiYuHIhBBC3I0kN/XM0psdicd3bYqNla7ozoOLICcFGrWGwLE1HltdsnfvXnNiExwczODBg9HpdHc5SwghRG0gyU09kpCeS+SpeKCEJqmCXNj9kWm7/3OglTfqsvTu3ZuoqCh69+5NmzZtLB2OEEKICpAOxfXIikNXKTAqgvxcadfYqejOoz9Aegw4NYHOUy0TYC2Wn5/Prl27MBqNAFhZWTFt2jRJbIQQog6Smpt6QillXm5h6p21NkYD7PzQtN3vabCyruHoarfExETCw8OJi4sjJyeHIUOGWDokIYQQ90CSm3riwOVkLiZmYm+tY3SXJkV3nlwFSRfBzg26TbdIfLXV0aNHWbt2LXl5eTg6OuLv72/pkIQQQtwjSW7qiaX7TLU2ozv74Ghz269VKdh+c4HM3k+AjaMFoqt98vLy+PXXXzl8+DAALVq0YOLEiTg6ys9HCCHqOklu6oG0nHx+ORYDwJSezYruPL8R4o6B3gF6PWaB6GqfhIQEfvzxRxISEtBoNNx3330MGDAArVa6oAkhRH0gyU098PPR62TnG2jt5Ui3Zq5Fd25/3/S9x0ywlxWrwdQ/KSUlBScnJyZOnChNUUIIUc9IclMP/HhbR2LN7RPzXdkDV3aBzhr6PmWh6GoHo9Forpnx8vJiypQp+Pj4mBfBFEIIUX9IPXwdd/J6GkevpqLXaRjf1bfozsJamy4PgnOT4ic3ELGxsXz66adcuXLFXNa6dWtJbIQQop6S5KaOK1wkc2h7bxo52tzaEXsMzv0GGq1p0r4GSCnFgQMH+PLLL0lISGD9+vUopSwdlhBCiGomzVJ1WE6+gZWHrwEldCTecXOEVPtx0KhVzQZWC+Tm5vLzzz9z/PhxANq0acP48eOLNtsJIYSolyS5qcN+OxFLanY+TVxsCW7tcWvHjQtwYqVpO/gFywRnQTExMYSHh5OUlIRWqyU0NJS+fftKYiOEEA2EJDd1WGGTVFgPP3Ta2964d/0XlBFaDwWfzhaKzjLi4+P58ssvMRgMuLi4MGnSJPz8/O5+ohBCiHpDkps66sqNLHaev4FGA2E9mt7akRYDR5aYtgfMsUxwFuTp6Unbtm0xGo2MGzcOOzs7S4ckhBCihtWKDsUff/wx/v7+2Nra0rt3b/bt21fqsV988QUDBgzAzc0NNzc3QkNDyzy+vgo/aKq1CW7tQVM3+1s79nwMhjzw6wPN+1koupp1/fp1cnJyANBoNEyYMIGpU6dKYiOEEA2UxZObZcuWMWfOHF5//XUOHTpEly5dGD58OPHx8SUev2XLFh588EE2b97M7t278fPzY9iwYVy7dq2GI7ecAoOR8ANXAZh6e0fi7GQ48LVpuwHU2iil2L17NwsXLuTnn382j4TS6/XSv0YIIRowiyc377//PrNnz2bmzJm0b9+eBQsWYG9vz1dffVXi8YsXL+bJJ58kKCiIgIAAvvzyS4xGI5GRkTUcueVsO5dAbFoObvZ6Qtt73dqx7wvIywDvjtBmmOUCrAHZ2dksXbqU3377DYPBgFIKg8Fg6bCEEELUAhbtc5OXl8fBgweZO3euuaxwdMvu3bvLdY2srCzy8/Nxd284Swssuzkj8YRuTbGx0pkK8zJhz6em7eAXoB7XXERHRxMREUFqaio6nY4RI0bQo0cPqa0RQggBWDi5SUxMxGAw4O3tXaTc29ub06dPl+saL730Ek2aNCE0NLTE/bm5ueTm5pofp6WlVT7gWiAhPZfIU6Ymuyk9bxsFdOhbyE4CtxamuW3qIaUUu3btIjIyEqPRiLu7O2FhYfj4+Fg6NCGEELVInR4t9Y9//IOlS5eyZcsWbG1tSzzm3Xff5c0336zhyKrPikNXKTAqujZzpa23k6mwIA92/c+03f850NXpX2upcnJy2LNnD0ajkU6dOjF69GhsbGzufqIQQogGxaLvgh4eHuh0OuLi4oqUx8XF0bhx4zLP/fe//80//vEPNm7cSOfOpc/lMnfuXObMudW5Ni0trc7Oe6KUMjdJTelx23P4fRmkXQPHxhD0kIWiq352dnZMmjSJxMREunXrJs1QQgghSmTRDsXW1tZ07969SGfgws7Bffv2LfW8f/3rX/z9739n3bp19OjRo8x72NjY4OzsXOSrrtp/KZmLiZnYW+sY3eXmQphGA+ycb9ru+xRY1Z+aDKUU27Zt4/fffzeXNW/enO7du0tiI4QQolQWb7+YM2cO06dPp0ePHvTq1Yv58+eTmZnJzJkzAXjkkUfw9fXl3XffBeCf//wn8+bNY8mSJfj7+xMbGwuAo6Mjjo6OFnseNaGw1mZM5yY42tz81Z36GW6cB1tX6DHTcsFVsYyMDFauXMmFCxfQ6/X4+/vX6cRUCCFEzbF4cjNlyhQSEhKYN28esbGxBAUFsW7dOnMn4ytXrqDV3qpg+vTTT8nLy2PSpElFrvP666/zxhtv1GToNSotJ5+1x64DMLmwI7FSsON903avx8DGyULRVa2oqCiWL19ORkYGer2ekSNH4uRUP56bEEKI6qdRhTOfNRBpaWm4uLiQmppap2oCFu+9zKsrj9PGy5H1Lww0Ncucj4TvJ4DeHp4/Dg6NLB3mPTEajWzbto2tW7eilMLLy4uwsDA8PT0tHZoQQggLq8j7t8VrbkT5mDsS9/S71d9kxwem792m14vE5vvvv+fixYsAdOvWjfvvvx+9Xm/hyIQQQtQ1ktzUASevp/H71VT0Og3ju/qaCqP3w6XtoNVDv6ctG2AV0Gq1NGnShKtXrzJ69OgyR8AJIYQQZZHkpg748YCp1mZoe28aOd4cDVXY16bLFHBpWsqZtZvRaCQ7OxsHBwcABg8eTLdu3RrUbNNCCCGqniQ3tVxOvoGVh02Lgk4pXCQz7iSc+QXQQP/nLRbbvUhLSyMiIgKDwcAf//hHdDodOp1OEhshhBD3TJKbWu63E7GkZufj62pHcGsPU2FhX5v2Y8GjjeWCq6Rz586xcuVKsrKysLGxIT4+XpZQEEIIUWUkuanlCjsST+reFJ1WA8mX4Phy087gOaWfWAsZDAY2bdrEzp07AfDx8SEsLExqa4QQQlQpSW5qsSs3sth14QYaDYT1uNmvZud/QRmg1RBoEmTR+CoiJSWFiIgIrl69CkDv3r0ZOnQoVlbyJyiEEKJqyTtLLVbYkTi4tQdN3ewhPQ4Of2/aWcdqbVavXs3Vq1extbXlgQceIDAw0NIhCSGEqKckuamlCgxGIg6aajmmFnYk3vMJGHKhaU/wD7ZgdBU3evRo1qxZw5gxY3Bzc7N0OEIIIeoxiy6cKUq37VwCsWk5uDtYE9reC7JTYP9C087gOVDLF45MTk7m0KFD5sfu7u488sgjktgIIYSodlJzU0st3Wdqkhrf1RcbKx3s+hLy0sGrPbQdYeHoynby5ElWr15Nbm4urq6utGzZ0tIhCSGEaEAkuamF4tNz2HQ6HjAtt0BeFuz51LQz+AXQ1s4Kt4KCAtavX8++ffsA8PPzk5FQQgghapwkN7XQikPXKDAqujZzpa23E+z9DLISwbU5dJhg6fBKlJSURHh4ODExMQD079+fIUOGoNPpLByZEEKIhkaSm1pGKcWPN+e2mdrTDwz5sOt/pp39nwVd7fuVnThxwtwMZW9vz/jx42nTpu5NLiiEEKJ+qH3vlA3c/kvJXEzMxN5ax6jOTeDYj5AaDQ5eEDTN0uGVKC8vj9zcXJo3b87EiRPvuhS9EEIIUZ0kualllu6/AsCYzk1w1Gthx3zTjr5Pgt7WcoHdwWg0or3Z9ycoKAhra2sCAwPNZUIIIYSlyDtRLZKWk88vx0x9Vqb08oMzayHxDNi4QI9HLRzdLUePHuXTTz8lKysLAI1GQ4cOHSSxEUIIUSvIu1EtsvrIdXLyjbTxcqRrUxfY/r5pR69ZYGv5pp68vDx++uknVq5cSUJCAnv37rV0SEIIIUQx0ixVixQutzClpx+aS9vg+iGwsoXef7JwZBAfH094eDgJCQloNBoGDRrEwIEDLR2WEEIIUYwkN7XEyetp/H41Fb1Ow4RuTSHiedOObtPB0dNicSmlOHLkCL/88gv5+fk4OjoyceJEWrRoYbGYhBBCiLJIclNLFNbaDGvfGPeUYxC1FbRW0O8Zi8a1f/9+fvnlFwBatWrF+PHjcXR0tGhMQgghRFkkuakFcvINrDhkWiRzck8/2P68aUenyeDqZ7nAgM6dO7Nnzx66du1KcHAwmlq+ppUQQgghyU0t8NuJWNJyCvB1tSPYJRFOrwE0EPx8jceilOLixYu0bNkSjUaDra0tTz75JFZW8qcihBCibpDRUrXAspszEof1aIpu14emwoBR4NmuRuPIzc1l+fLlfPfdd0VW9JbERgghRF0i71oWdvlGJrsu3ECjgaltgUXhph0D5tRoHDExMYSHh5OUlIRWqyU/P79G7y9EXWYwGOR/RogqYG1tXSVzpklyY2HhB0x9bQa08aTx8S/AWAAtBoFv9xq5v1KK/fv389tvv2EwGHBxcWHSpEn4+Vm2r48QdYFSitjYWFJSUiwdihD1glarpUWLFlhbW9/TdSS5saACg5Hwg6YmqYc72sP6b007aqjWJicnh9WrV3Py5EkA2rVrx7hx47Czs6uR+wtR1xUmNl5eXtjb20uHeyHugdFo5Pr168TExNCsWbN7+n+S5MaCtp1LIC4tF3cHawanLoeCHGjSzVRzUwPi4uI4deoUOp2OoUOH0rt3b3lxFqKcDAaDObFp1KiRpcMRol7w9PTk+vXrFBQUoNfrK30dSW4saOk+U63N1M6uWB1YaCoc8H9QQwlG8+bNGTlyJE2aNMHX17dG7ilEfVHYx8be3t7CkQhRfxQ2RxkMhntKbmS0lIXEp+cQeToegD/aREJuKngGQLuR1XbP7Oxsli9fTmJiormsZ8+ektgIcQ+ktlOIqlNV/09Sc2MhKw5dw2BU9PGzw+PYzVqb/s9DNa2sHR0dTUREBKmpqSQlJTFr1ix5URZCCFEvSc2NBSil+PHm3DZ/9joAmfHg0gw6TaqWe+3cuZOvv/6a1NRU3N3dGT16tCQ2Qoha5cyZMzRu3Jj09HRLh9JgnDx5kqZNm5KZmWnpUKqcJDcWsP9SMhcTM3G2hq5XvzMV9nsGdJVvXyxJVlYWS5YsYcOGDRiNRjp27Mjjjz+Oj49Pld5HCFG3zJgxA41Gg0ajQa/X4+3tzdChQ/nqq68wGo1FjvX390ej0bBnz54i5c8//zz33Xef+fEbb7yBRqPhiSeeKHLckSNH0Gg0XLp0qcyY5s6dyzPPPIOTk1OxfQEBAdjY2BAbG1tsn7+/P/Pnzy9W/sYbbxAUFFSkLDY2lmeeeYaWLVtiY2ODn58fY8aMITIysszY7lV4eDgBAQHY2trSqVMn83p9pbn993P7V4cOHUo8/h//+AcajYbnn3++SHlOTg5PPfUUjRo1Mi96HBcXZ97fvn17+vTpw/vvv3/Pz7G2keTGApbuvwLAK81Ook29AvYe0HVald4jKSmJBQsWcO7cOaysrBgzZgwTJ07ExsamSu8jhKibRowYQUxMDJcuXeLXX39l8ODBPPfcc4wePZqCgoIix9ra2vLSSy/d9Zq2trYsXLiQc+fOVSiWK1eusGbNGmbMmFFs344dO8jOzmbSpEl88803Fbru7S5dukT37t3ZtGkT7733HseOHWPdunUMHjyYp556qtLXvZtdu3bx4IMP8uijj3L48GHGjRvHuHHjOH78eKnnfPjhh8TExJi/oqOjcXd3JywsrNix+/fv57PPPqNz587F9r3wwgv8/PPPhIeHs3XrVq5fv86ECROKHDNz5kw+/fTTYr/zuk763NSwtJx8fjkWgwYjD2T8aCrs8yewrtoRFy4uLri4uGBtbU1YWBje3t5Ven0hRHFKKbLzDRa5t51eV6HmZhsbGxo3bgyAr68v3bp1o0+fPoSEhLBo0SJmzZplPvaxxx5jwYIF/PLLL4wcWfqgh3bt2uHl5cWrr77Kjz/+WO5YfvzxR7p06VLi4IaFCxfy0EMPMWjQIJ577rlyJVklefLJJ9FoNOzbtw8HBwdzeYcOHfjjH/9YqWuWx4cffsiIESP485//DMDf//53NmzYwEcffcSCBQtKPKfw9bvQqlWrSE5OZubMmUWOy8jI4A9/+ANffPEFb731VpF9qampLFy4kCVLljBkyBAAvv76awIDA9mzZw99+vQBYOjQoSQlJbF161ZCQkKq7HlbmiQ3NWz1kevk5Bt5xP0UdilnwdoJes66+4nlkJmZia2tLTqdDp1Ox+TJk7GxsbnnmR6FEOWTnW+g/bzfLHLvk38bjr31vb2kDxkyhC5durBixYoiyU2LFi144oknmDt3LiNGjChzevx//OMf9OzZkwMHDtCjR49y3Xf79u0lHpuenk54eDh79+4lICCA1NRUtm/fzoABAyr0vJKSkli3bh1vv/12kcSmkKura6nnLl68mMcff7zM6//666+lxrR7927mzCk6Mevw4cNZtWrVXeMutHDhQkJDQ2nevHmR8qeeeopRo0YRGhpaLLk5ePAg+fn5hIaGmssCAgJo1qwZu3fvNic31tbWBAUFsX37dkluROWZFslUPGW12lTQ81Gwc73n60ZFRbF8+XI6d+7MsGHDAEpsuxZCiLIEBATw+++/Fyv/61//ytdff83ixYt5+OGHSz2/W7duTJ48mZdeeqncfVkuX75cYnKzdOlS2rRpY+5rMnXqVBYuXFjh5Ob8+fMopQgICKjQeQBjx46ld+/eZR5T1nQasbGxxWrOvb29S+w/VJLr16/z66+/smTJkiLlS5cu5dChQ+zfv7/U+1pbWxdL3Eq6d5MmTbh8+XK54qkrJLmpQSeup3LsWir9rU7jnXYMrGyh77219RqNRrZt28bWrVtRSnH+/HkGDx58T5MfCSEqx06v4+Tfhlvs3lVBKVVi85anpycvvvgi8+bNY8qUKWVe46233iIwMJD169fj5eV113tmZ2dja2tbrPyrr75i2rRb/RGnTZvGoEGD+N///lehD29KqXIfeycnJyeLflD85ptvcHV1Zdy4ceay6OhonnvuOTZs2FDiz62i7OzsyMrKuufr1CbSobgGFQ7/ftX5V1NB12ngePd//NKkp6fz3XffsWXLFpRSdO3aldmzZ0tiI4SFaDQa7K2tLPJVVdM7nDp1ihYtWpS4b86cOWRnZ/PJJ5+UeY1WrVoxe/ZsXn755XIlFh4eHiQnJxcpO3nyJHv27OEvf/kLVlZWWFlZ0adPH7Kysli6dKn5OGdnZ1JTU4tdMyUlxdxvpU2bNmg0Gk6fPn3XWO60ePFiHB0dy/zavn17qec3bty4yAglMC19U9jfqSxKKb766isefvjhIt0LDh48SHx8PN26dTP/bLZu3cp///tfrKysMBgMNG7cmLy8vGKLupZ076SkJDw9Pcvx06g7pOamhuTkG1h5+BodNRdpn3UANDro92ylr3fhwgVWrFhBZmYm1tbWjBo1ii5dulRhxEKIhmbTpk0cO3aMF154ocT9jo6OvPbaa7zxxhuMHTu2zGvNmzePVq1aFUlEStO1a1fzAr6FFi5cyMCBA/n444+LlH/99dcsXLiQ2bNnA6ZOzAcPHix2zUOHDtGuXTsA3N3dGT58OB9//DHPPvtssX43KSkppfa7uddmqb59+xIZGVlkmPaGDRvo27dvmdcE2Lp1K+fPn+fRRx8tUh4SEsKxY8eKlM2cOZOAgABeeukldDod3bt3R6/XExkZycSJEwHTXEJXrlwpdu/jx48zaVLVz7NmUaqBSU1NVYBKTU2t0fuuOnxVNX9pjdr05nClXndWavnsSl8rOztbvfvuu+r1119Xn3zyiUpISKjCSIUQ5ZGdna1OnjypsrOzLR1KhU2fPl2NGDFCxcTEqKtXr6qDBw+qt99+Wzk6OqrRo0ergoIC87HNmzdXH3zwgflxXl6eatWqlbK1tVWDBg0yl7/++uuqS5cuRe7z2muvKVtbWwWoqKioUuNZvXq18vLyMt83Ly9PeXp6qk8//bTYsSdPnlSAOn78uFJKqZ07dyqtVqveeustdfLkSXXs2DH1yiuvKCsrK3Xs2DHzeRcuXFCNGzdW7du3VxEREers2bPq5MmT6sMPP1QBAQEV+OlVzM6dO5WVlZX697//rU6dOqVef/11pdfri8T28ssvq4cffrjYudOmTVO9e/cu130GDRqknnvuuSJlTzzxhGrWrJnatGmTOnDggOrbt6/q27dvkWOioqKURqNRly5dqviTqwZl/V9V5P1bmqVqyNJ90bTSXOM+482JsIJL/mRUHra2towePZru3bsza9YsPDw8qihKIURDsW7dOnx8fPD392fEiBFs3ryZ//73v/z000/odKX339Hr9fz9738nJyfnrvd48cUXcXR0vOtx999/P1ZWVmzcuBGA1atXc+PGDcaPH1/s2MDAQAIDA1m40LRsTb9+/fj111/59ddf6d+/P/fddx+7du0iMjKSjh07ms9r2bIlhw4dYvDgwfzf//0fHTt2ZOjQoURGRvLpp5/eNcbK6tevH0uWLOHzzz+nS5cuREREsGrVqiKxxcTEcOXKlSLnpaamsnz58mK1NhXxwQcfMHr0aCZOnMjAgQNp3LgxK1asKHLMDz/8wLBhw4qNxKrrNErdQ0+rOigtLQ0XFxdSU1NxdnaukXtevpHJoPe28C/9Z0zWbTUtjvngDxW6RuFkfKW1hQshalZOTg5RUVG0aNGiSjp1NnQff/wxq1ev5rffLDOUviHKy8ujTZs2LFmyhP79+1s6HKDs/6uKvH9Ln5sa8OOBaHy4wQTdDlNB8JyyT7iNwWBg06ZN7Ny5E0dHR5544olyfRISQoi65PHHHyclJYX09HSZxqKGXLlyhVdeeaXWJDZVSZKbalZgMBJx8CqPW63FCgP4DwC/nuU6NzU1lYiICKKjTaOs2rdvL58QhRD1kpWVFa+++qqlw2hQWrduTevWrS0dRrWQ5KaabT2bQF5aAg/abjYVlLOvzZkzZ1i1apV5/oexY8fSvn37aoxUCCGEqB8kualmy/ZHM8PqN+zIBZ8u0GpImccbjUY2bNjA7t27AdMQw0mTJuHm5lYT4QohhBB1niQ31Sg+PYc9py/znv5mB7kB/wd3mWhLo9GQmZkJQJ8+fRg6dGiZIxeEEEIIUZQkN9Vo+cFrTNFsxEWTBY3aQMCYUo81Go1otVo0Gg2jRo2iU6dOtGnTpgajFUIIIeoHmeemmiilWLn/ArOtfjEVBD8PJaykW1BQwC+//MKyZcvM05Tb2NhIYiOEEEJUktTcVJN9UUn0SFmHlz4Fo1MTtJ0mFzsmKSmJ8PBwYmJiANOwvPo2kZIQQghR0yS5qSbh+6N4RvczANr+z4KVdZH9x48f5+effyY3Nxd7e3vGjRsniY0QQghRBaRZqhqkZuejTqyiuTaeAhs36PaIeV9+fj5r1qwhIiKC3NxcmjVrxhNPPEHbtm0tGLEQQjQsZ86coXHjxqSnp1s6lAYjLy8Pf39/Dhw4UO33kuSmGqw+co1Z/ASAru+fwPrWCrQREREcOHAAjUbDgAEDmDFjRo0tAyGEEAaDgX79+jFhwoQi5ampqfj5+RWbSG/58uUMGTIENzc37OzsaNeuHX/84x85fPiw+ZhFixah0WjMX46OjnTv3r3YOkbV7b777iuy+nZZ5s6dyzPPPFPibMgBAQHY2NgQGxtbbJ+/vz/z588vVv7GG28QFBRUpCw2NpZnnnmGli1bYmNjg5+fH2PGjCEyMrJcMVZWeHg4AQEB2Nra0qlTJ3755Zcyj58xY0aR31/hV4cOHczHpKen8/zzz9O8eXPs7Ozo168f+/fvL3KduLg4ZsyYQZMmTbC3t2fEiBGcO3fOvN/a2poXX3yRl156qWqfcAkkuakGF3etIFB7hTydPZrejxXZN2DAAJydnZk2bRohISFoS+hkLIQQ1UWn07Fo0SLWrVvH4sWLzeXPPPMM7u7uvP766+ayl156iSlTphAUFMTq1as5c+YMS5YsoWXLlsydO7fIdZ2dnYmJiSEmJobDhw8zfPhwJk+ezJkzZ2rsuZXXlStXWLNmDTNmzCi2b8eOHWRnZzNp0iS++eabSt/j0qVLdO/enU2bNvHee+9x7Ngx1q1bx+DBg3nqqafuIfqy7dq1iwcffJBHH32Uw4cPM27cOMaNG8fx48dLPefDDz80/+5iYmKIjo7G3d2dsLAw8zGzZs1iw4YNfPfddxw7doxhw4YRGhrKtWvXANMgmnHjxnHx4kV++uknDh8+TPPmzQkNDTVPbwLwhz/8gR07dnDixIlq+xkUBtSgVGTJ9Mo4fi1F7Xuth1KvO6usNXNVXl6eioqKKnJMfn5+tdxbCFFzsrOz1cmTJ1V2dvatQqNRqdwMy3wZjRWK/8MPP1Rubm7q+vXratWqVUqv16sjR46Y9+/evVsB6sMPPyzxfONt9/v666+Vi4tLkf0Gg0Hp9Xr1448/msuSkpLUww8/rFxdXZWdnZ0aMWKEOnv2bJHzIiIiVPv27ZW1tbVq3ry5+ve//11k/8cff6xat26tbGxslJeXl5o4caJSSqnp06croMjXna+9hd577z3Vo0ePEvfNmDFDvfzyy+rXX39Vbdu2Lba/efPm6oMPPihW/vrrr6suXbqYH99///3K19dXZWRkFDs2OTm5xHtXhcmTJ6tRo0YVKevdu7d6/PHHy32NlStXKo1Goy5duqSUUiorK0vpdDq1Zs2a/2/vzuOiKtv/gX9mBmYYdjdkFQMEl9xQITBDjYJKRVukVAIlNYHoJU8WXyVxyaVSknxQwxTUSFBfbk8qlAs9gFuiqAWCCLgkaKWAijgwc/3+8Md5GhnQQRlivN6v1/wx97nPOde5BpiLc9/nHLV+7u7uNGfOHCIiKiwsJAD066+/CsuVSiV16dKF1q5dq7beiBEjKCYmRuO+Nf5e/X/afH/zhOIn7Oih/yBUXIQ6kSFq+kxE2tq1uHHjBqZOnYquXbsCuP8MFcaYHqqrARbbts2+Z19VGwJ/mA8++AA7duxAUFAQzp49i7lz56J///7C8s2bN8PU1BRhYWEa1xc1c0NSpVKJjRs3AgDc3d2F9pCQEJw/fx67d++Gubk5PvnkE7z66qvIz8+HoaEhcnNzMX78eMybNw+BgYE4fPgwwsLC0KlTJ4SEhODEiROIjIzEpk2b4O3tjRs3biArKwvA/bMPRUVFePbZZ7FgwQIAQJcuXTTGl5WVhcGDBzdqv3XrFrZu3Ypjx46hZ8+eqKqqQlZWFoYNG/aQbKq7ceMG0tPTsWjRIpiYNP5MLC0tm1w3JSUF06dPb3b7+/btazKmI0eOICpK/eHMfn5+2Llz50PjbrBu3Tr4+voKF7nU19dDqVQ2erahXC5Hdvb9B0Lfu3cPANT6iMViyGQyZGdn47333hPaPTw8hM+ttfwjxkQSEhLQvXt3GBkZwdPTE8ePH2+2v7bjibpSW6eE6/lvAQDFHUdgTcpOXL9+HUZGRsIHzxhj/wQikQirV6/GgQMH0LVrV0RHR6stLyoqgpOTk9o/Y3FxcTA1NRVeVVVVwrKqqiqhXSqVYsaMGUhMTISzszMACEXNt99+i2HDhqF///5ISUnB77//LnzxxsXF4cUXX8Snn34KV1dXhISEICIiAl9++SWA+8NJJiYmGDVqFBwdHTFw4EBERkYCACwsLCCVSmFsbAxra2tYW1s3eXf3ixcvwta2cRGampqKHj16oE+fPpBIJHj77bexbt06rXNbXFwMIkLPnj21XnfMmDHIy8tr9qWpMGtQUVEh/CPdoGvXrhrnD2ly9epV7Nu3T60YMTMzg5eXFxYuXIirV69CqVTiu+++w5EjR4RbmfTs2RPdunXD//3f/+HmzZtQKBT4/PPPceXKFaFPA1tbW1y8ePFRU9IibX4KIS0tDVFRUVizZg08PT2xYsUK+Pn5obCwEFZWVo36N4wnLlmyBKNGjcL333+PsWPH4uTJk3j22Wfb4Aj+52jOQQzHKShJhIw/bVEnqoOzszPGjRsHU1PTNo2NMaYDhsb3z6C01b61tH79ehgbG6O0tBRXrlxB9+7dm+0/ZcoUjBkzBseOHcOkSZOEG48C978AT548CQCoqanB/v378f7776NTp04YPXo0CgoKYGBgAE9PT2GdTp06wc3NDQUFBQCAgoICBAQEqO1z6NChWLFiBZRKJV566SU4OjrCyckJ/v7+8Pf3x7hx42BsrN2xNzyQWFM+Jk2aJLyfNGkSfHx8sHLlSo0Tj5vy97xoy8zMTKt9PWkbNmyApaUlxo4dq9a+adMmTJkyBXZ2dpBIJHB3d8c777yD3NxcAIChoSG2b9+O0NBQdOzYERKJBL6+vnjllVca5UMul6OmpqZVj6PNz9zExcVh6tSpmDx5Mnr37o01a9bA2NgY69ev19g/Pj4e/v7+mDVrFnr16oWFCxfC3d0d//73v3UceWMG2csAAP+944gqcUeMHDkSkyZN4sKGsaeFSHR/aKgtXg95bt2DDh8+jK+++go//PADPDw8EBoaqvYl1KNHD5SUlKCurk5os7S0hIuLC+zs7BptTywWw8XFBS4uLujXrx+ioqIwfPhwfP755y3P5wMaCqjNmzfDxsZGGEqrrKzUajudO3fGzZs31dry8/Nx9OhRfPzxxzAwMICBgQGee+451NTUIDU1Vehnbm6udsaqQWVlJSwsLADcz51IJMK5c+e0PsaUlBS1s2OaXs0N6VhbW+PatWtqbdeuXYO1tfVD901EWL9+PYKCgiCVqt+bzdnZGT///DNu376Ny5cv4/jx46irq4OTk5PQZ9CgQcjLy0NlZSXKy8uRnp6Ov/76S60PcH/YrqkhwyelTYsbhUKB3Nxc+Pr6Cm1isRi+vr7CU7EfdOTIEbX+wP3xxKb637t3D9XV1Wqv1vD7hbPwqjsGAMi3eBEhISF44YUXmh2XZoyxtlBTU4OQkBDMmDEDI0aMwLp163D8+HGsWbNG6PPOO+/g9u3bWLVqVYv3I5FIcPfuXQBAr169UF9fj2PHjgnL//rrLxQWFqJ3795Cn5ycHLVt5OTkwNXVVRhiMjAwgK+vL7744gucOXMGZWVlOHjwIID7lxorlcqHxjVw4EDk5+erta1btw4vvPACTp8+rTYEFBUVpTY05ebmJpyt+LuTJ08K9yvr2LEj/Pz8kJCQoHalUIPmirHHHZby8vJqdKn5Tz/9BC8vrybXafDzzz+juLgYoaGhTfYxMTGBjY0Nbt68iYyMjEZn2oD7Q4RdunTB+fPnceLEiUZ9fv31VwwcOPCh8TyWh045bkW///47AaDDhw+rtc+aNYs8PDw0rmNoaEjff/+9WltCQgJZWVlp7B8bG9toBj1a4Wqp05nb6XqsIx2ZM0Tj7HjGmH5p7qqOf7rIyEhycXGhO3fuCG1r1qwhU1NTtSuM/vWvf5FEIqGZM2dSVlYWlZWV0ZEjR2jSpEkkEomEv6NJSUlkbm5O5eXlVF5eTiUlJfTNN9+QRCKh+fPnC9sLCAig3r17U1ZWFuXl5ZG/vz+5uLiQQqEgIqLc3FwSi8W0YMECKiwspOTkZJLL5ZSUlERERP/5z38oPj6eTp06RWVlZbRq1SoSi8XCFTpTp06lIUOGUGlpKf3xxx+kVCo1Hv/u3bvJysqK6uvriYhIoVBQly5daPXq1Y365ufnq10FlJOTQ2KxmD777DPKz8+ns2fP0uzZs8nAwIDOnj0rrHfhwgWytram3r1707Zt26ioqIjy8/MpPj6eevbsqe1H9shycnLIwMCAli1bRgUFBRQbG0uGhoZqsUVHR1NQUFCjdSdNmkSenp4at5uenk779u2jkpIS+vHHH6l///7k6ekpfHZERFu2bKFDhw7RhQsXaOfOneTo6Eivv/56o205OjrSxo0bNe7nSV0tpffFTW1tLVVVVQmvy5cvt9ql4HW1NfTH1bInvl3G2D9Pey1uMjMzSSKRUFZWVqNlL7/8Mo0cOVLtMu+0tDQaPnw4WVhYkKGhIdnb29OECRPo6NGjQp+kpCS1fx5lMhm5urrSokWLhAKC6H+XgltYWJBcLic/P78mLwU3NDSkbt260Zdffiksy8rKIh8fH+rQoQPJ5XLq168fpaWlCcsLCwvpueeeI7lc3uyl4HV1dWRra0vp6enCPsViMVVUVGjs36tXL5o5c6bwPiMjg4YOHUodOnSgTp060fDhw+nnn39utN7Vq1cpPDycHB0dSSqVkp2dHY0ZM4YOHTqkcT9PypYtW8jV1ZWkUin16dOH9uzZo7Y8ODiYfHx81NoqKytJLpdTYmKixm2mpaWRk5MTSaVSsra2pvDwcKqsrFTrEx8fT/b29sJnFxMTQ/fu3VPrc/jwYbK0tKSamhqN+3lSxY2I6DFmPj0mhUIBY2NjbNu2TW3yUnBwMCorK7Fr165G63Tr1g1RUVFqd6GMjY3Fzp07cfr06Yfus7q6GhYWFqiqquI7AzPGWqy2thalpaV45plnNE5OZf9sCQkJ2L17NzIyMto6lKdKYGAg+vfvj9mzZ2tc3tzvlTbf320650YqlWLQoEFq44MqlQoHDhxocnzwccYTGWOMMQCYPn06XnjhBX62lA4pFAr07dsXM2fObPV9tfml4FFRUQgODsbgwYPh4eGBFStW4M6dO5g8eTIA4N1334WdnR2WLFkCAPjwww/h4+OD5cuX47XXXkNqaipOnDiBxMTEtjwMxhhj7YiBgUGj52ix1iWVShETE6OTfbV5cRMYGIg//vgDc+fORUVFBQYMGID09HThJkSXLl1Se/6St7c3vv/+e8TExGD27Nno0aMHdu7c2eb3uGGMMcbYP0ObzrlpCzznhjH2JPCcG8aePL2Yc8MYY+3dU/b/IWOt6kn9PnFxwxhjLWBoaAgArX4becaeJgqFAgCafC7Yo2rzOTeMMdYeSSQSWFpa4vr16wAAY2NjviM5Y49BpVLhjz/+gLGxsdoDW1uCixvGGGuhhuf1NBQ4jLHHIxaL0a1bt8f+R4GLG8YYayGRSAQbGxtYWVmpPWCSMdYyUqlU7QrpluLihjHGHpNEInnsOQKMsSeHJxQzxhhjTK9wccMYY4wxvcLFDWOMMcb0ylM356bhBkHV1dVtHAljjDHGHlXD9/aj3OjvqStuGp4A6+Dg0MaRMMYYY0xbt27dgoWFRbN9nrpnS6lUKly9ehVmZmZP/IZb1dXVcHBwwOXLl/m5Va2I86wbnGfd4DzrDudaN1orz0SEW7duwdbW9qGXiz91Z27EYjHs7e1bdR/m5ub8i6MDnGfd4DzrBudZdzjXutEaeX7YGZsGPKGYMcYYY3qFixvGGGOM6RUubp4gmUyG2NhYyGSytg5Fr3GedYPzrBucZ93hXOvGPyHPT92EYsYYY4zpNz5zwxhjjDG9wsUNY4wxxvQKFzeMMcYY0ytc3DDGGGNMr3Bxo6WEhAR0794dRkZG8PT0xPHjx5vtv3XrVvTs2RNGRkbo27cv9u7dq6NI2zdt8rx27VoMGzYMHTp0QIcOHeDr6/vQz4Xdp+3Pc4PU1FSIRCKMHTu2dQPUE9rmubKyEuHh4bCxsYFMJoOrqyv/7XgE2uZ5xYoVcHNzg1wuh4ODA2bOnIna2lodRds+/fe//8Xo0aNha2sLkUiEnTt3PnSdzMxMuLu7QyaTwcXFBcnJya0eJ4g9stTUVJJKpbR+/Xr67bffaOrUqWRpaUnXrl3T2D8nJ4ckEgl98cUXlJ+fTzExMWRoaEhnz57VceTti7Z5njBhAiUkJNCpU6eooKCAQkJCyMLCgq5cuaLjyNsXbfPcoLS0lOzs7GjYsGEUEBCgm2DbMW3zfO/ePRo8eDC9+uqrlJ2dTaWlpZSZmUl5eXk6jrx90TbPKSkpJJPJKCUlhUpLSykjI4NsbGxo5syZOo68fdm7dy/NmTOHtm/fTgBox44dzfYvKSkhY2NjioqKovz8fFq5ciVJJBJKT09v1Ti5uNGCh4cHhYeHC++VSiXZ2trSkiVLNPYfP348vfbaa2ptnp6eNH369FaNs73TNs8Pqq+vJzMzM9qwYUNrhagXWpLn+vp68vb2pm+//ZaCg4O5uHkE2uZ59erV5OTkRAqFQlch6gVt8xweHk4jR45Ua4uKiqKhQ4e2apz65FGKm48//pj69Omj1hYYGEh+fn6tGBkRD0s9IoVCgdzcXPj6+gptYrEYvr6+OHLkiMZ1jhw5otYfAPz8/Jrsz1qW5wfV1NSgrq4OHTt2bK0w272W5nnBggWwsrJCaGioLsJs91qS5927d8PLywvh4eHo2rUrnn32WSxevBhKpVJXYbc7Lcmzt7c3cnNzhaGrkpIS7N27F6+++qpOYn5atNX34FP34MyW+vPPP6FUKtG1a1e19q5du+LcuXMa16moqNDYv6KiotXibO9akucHffLJJ7C1tW30C8X+pyV5zs7Oxrp165CXl6eDCPVDS/JcUlKCgwcPYuLEidi7dy+Ki4sRFhaGuro6xMbG6iLsdqcleZ4wYQL+/PNPPP/88yAi1NfX4/3338fs2bN1EfJTo6nvwerqaty9exdyubxV9stnbpheWbp0KVJTU7Fjxw4YGRm1dTh649atWwgKCsLatWvRuXPntg5Hr6lUKlhZWSExMRGDBg1CYGAg5syZgzVr1rR1aHolMzMTixcvxqpVq3Dy5Els374de/bswcKFC9s6NPYE8JmbR9S5c2dIJBJcu3ZNrf3atWuwtrbWuI61tbVW/VnL8txg2bJlWLp0Kfbv349+/fq1ZpjtnrZ5vnDhAsrKyjB69GihTaVSAQAMDAxQWFgIZ2fn1g26HWrJz7ONjQ0MDQ0hkUiEtl69eqGiogIKhQJSqbRVY26PWpLnTz/9FEFBQXjvvfcAAH379sWdO3cwbdo0zJkzB2Ix/+//JDT1PWhubt5qZ20APnPzyKRSKQYNGoQDBw4IbSqVCgcOHICXl5fGdby8vNT6A8BPP/3UZH/WsjwDwBdffIGFCxciPT0dgwcP1kWo7Zq2ee7ZsyfOnj2LvLw84TVmzBiMGDECeXl5cHBw0GX47UZLfp6HDh2K4uJioXgEgKKiItjY2HBh04SW5LmmpqZRAdNQUBI/cvGJabPvwVadrqxnUlNTSSaTUXJyMuXn59O0adPI0tKSKioqiIgoKCiIoqOjhf45OTlkYGBAy5Yto4KCAoqNjeVLwR+BtnleunQpSaVS2rZtG5WXlwuvW7dutdUhtAva5vlBfLXUo9E2z5cuXSIzMzOKiIigwsJC+uGHH8jKyoo+++yztjqEdkHbPMfGxpKZmRlt3ryZSkpK6McffyRnZ2caP358Wx1Cu3Dr1i06deoUnTp1igBQXFwcnTp1ii5evEhERNHR0RQUFCT0b7gUfNasWVRQUEAJCQl8Kfg/0cqVK6lbt24klUrJw8ODjh49Kizz8fGh4OBgtf5btmwhV1dXkkql1KdPH9qzZ4+OI26ftMmzo6MjAWj0io2N1X3g7Yy2P89/x8XNo9M2z4cPHyZPT0+SyWTk5OREixYtovr6eh1H3f5ok+e6ujqaN28eOTs7k5GRETk4OFBYWBjdvHlT94G3I4cOHdL497Yht8HBweTj49NonQEDBpBUKiUnJydKSkpq9ThFRHz+jTHGGGP6g+fcMMYYY0yvcHHDGGOMMb3CxQ1jjDHG9AoXN4wxxhjTK1zcMMYYY0yvcHHDGGOMMb3CxQ1jjDHG9AoXN4wxNcnJybC0tGzrMFpMJBJh586dzfYJCQnB2LFjdRIPY0z3uLhhTA+FhIRAJBI1ehUXF7d1aEhOThbiEYvFsLe3x+TJk3H9+vUnsv3y8nK88sorAICysjKIRCLk5eWp9YmPj0dycvIT2V9T5s2bJxynRCKBg4MDpk2bhhs3bmi1HS7EGNMePxWcMT3l7++PpKQktbYuXbq0UTTqzM3NUVhYCJVKhdOnT2Py5Mm4evUqMjIyHnvbD3t6PABYWFg89n4eRZ8+fbB//34olUoUFBRgypQpqKqqQlpamk72z9jTis/cMKanZDIZrK2t1V4SiQRxcXHo27cvTExM4ODggLCwMNy+fbvJ7Zw+fRojRoyAmZkZzM3NMWjQIJw4cUJYnp2djWHDhkEul8PBwQGRkZG4c+dOs7GJRCJYW1vD1tYWr7zyCiIjI7F//37cvXsXKpUKCxYsgL29PWQyGQYMGID09HRhXYVCgYiICNjY2MDIyAiOjo5YsmSJ2rYbhqWeeeYZAMDAgQMhEokwfPhwAOpnQxITE2Fra6v2FG4ACAgIwJQpU4T3u3btgru7O4yMjODk5IT58+ejvr6+2eM0MDCAtbU17Ozs4Ovri7feegs//fSTsFypVCI0NBTPPPMM5HI53NzcEB8fLyyfN28eNmzYgF27dglngTIzMwEAly9fxvjx42FpaYmOHTsiICAAZWVlzcbD2NOCixvGnjJisRhff/01fvvtN2zYsAEHDx7Exx9/3GT/iRMnwt7eHr/88gtyc3MRHR0NQ0NDAMCFCxfg7++PN954A2fOnEFaWhqys7MRERGhVUxyuRwqlQr19fWIj4/H8uXLsWzZMpw5cwZ+fn4YM2YMzp8/DwD4+uuvsXv3bmzZsgWFhYVISUlB9+7dNW73+PHjAID9+/ejvLwc27dvb9Tnrbfewl9//YVDhw4JbTdu3EB6ejomTpwIAMjKysK7776LDz/8EPn5+fjmm2+QnJyMRYsWPfIxlpWVISMjA1KpVGhTqVSwt7fH1q1bkZ+fj7lz52L27NnYsmULAOCjjz7C+PHj4e/vj/LycpSXl8Pb2xt1dXXw8/ODmZkZsrKykJOTA1NTU/j7+0OhUDxyTIzprVZ/NCdjTOeCg4NJIpGQiYmJ8HrzzTc19t26dSt16tRJeJ+UlEQWFhbCezMzM0pOTta4bmhoKE2bNk2tLSsri8RiMd29e1fjOg9uv6ioiFxdXWnw4MFERGRra0uLFi1SW2fIkCEUFhZGREQffPABjRw5klQqlcbtA6AdO3YQEVFpaSkBoFOnTqn1efCJ5gEBATRlyhTh/TfffEO2trakVCqJiOjFF1+kxYsXq21j06ZNZGNjozEGIqLY2FgSi8VkYmJCRkZGwtOT4+LimlyHiCg8PJzeeOONJmNt2Lebm5taDu7du0dyuZwyMjKa3T5jTwOec8OYnhoxYgRWr14tvDcxMQFw/yzGkiVLcO7cOVRXV6O+vh61tbWoqamBsbFxo+1ERUXhvffew6ZNm4ShFWdnZwD3h6zOnDmDlJQUoT8RQaVSobS0FL169dIYW1VVFUxNTaFSqVBbW4vnn38e3377Laqrq3H16lUMHTpUrf/QoUNx+vRpAPeHlF566SW4ubnB398fo0aNwssvv/xYuZo4cSKmTp2KVatWQSaTISUlBW+//TbEYrFwnDk5OWpnapRKZbN5AwA3Nzfs3r0btbW1+O6775CXl4cPPvhArU9CQgLWr1+PS5cu4e7du1AoFBgwYECz8Z4+fRrFxcUwMzNTa6+trcWFCxdakAHG9AsXN4zpKRMTE7i4uKi1lZWVYdSoUZgxYwYWLVqEjh07Ijs7G6GhoVAoFBq/pOfNm4cJEyZgz5492LdvH2JjY5Gamopx48bh9u3bmD59OiIjIxut161btyZjMzMzw8mTJyEWi2FjYwO5XA4AqK6ufuhxubu7o7S0FPv27cP+/fsxfvx4+Pr6Ytu2bQ9dtymjR48GEWHPnj0YMmQIsrKy8NVXXwnLb9++jfnz5+P1119vtK6RkVGT25VKpcJnsHTpUrz22muYP38+Fi5cCABITU3FRx99hOXLl8PLywtmZmb48ssvcezYsWbjvX37NgYNGqRWVDb4p0waZ6wtcXHD2FMkNzcXKpUKy5cvF85KNMzvaI6rqytcXV0xc+ZMvPPOO0hKSsK4cePg7u6O/Pz8RkXUw4jFYo3rmJubw9bWFjk5OfDx8RHac3Jy4OHhodYvMDAQgYGBePPNN+Hv748bN26gY8eOattrmN+iVCqbjcfIyAivv/46UlJSUFxcDDc3N7i7uwvL3d3dUVhYqPVxPigmJgYjR47EjBkzhOP09vZGWFiY0OfBMy9SqbRR/O7u7khLS4OVlRXMzc0fKybG9BFPKGbsKeLi4oK6ujqsXLkSJSUl2LRpE9asWdNk/7t37yIiIgKZmZm4ePEicnJy8MsvvwjDTZ988gkOHz6MiIgI5OXl4fz589i1a5fWE4r/btasWfj888+RlpaGwsJCREdHIy8vDx9++CEAIC4uDps3b8a5c+dQVFSErVu3wtraWuONB62srCCXy5Geno5r166hqqqqyf1OnDgRe/bswfr164WJxA3mzp2LjRs3Yv78+fjtt99QUFCA1NRUxMTEaHVsXl5e6NevHxYvXgwA6NGjB06cOIGMjAwUFRXh008/xS+//KK2Tvfu3XHmzBkUFhbizz//RF1dHSZOnIjOnTsjICAAWVlZKC0tRWZmJiIjI3HlyhWtYmJML7X1pB/G2JOnaRJqg7i4OLKxsSG5XE5+fn60ceNGAkA3b94kIvUJv/fu3aO3336bHBwcSCqVkq2tLUVERKhNFj5+/Di99NJLZGpqSiYmJtSvX79GE4L/7sEJxQ9SKpU0b948srOzI0NDQ+rfvz/t27dPWJ6YmEgDBgwgExMTMjc3pxdffJFOnjwpLMffJhQTEa1du5YcHBxILBaTj49Pk/lRKpVkY2NDAOjChQuN4kpPTydvb2+Sy+Vkbm5OHh4elJiY2ORxxMbGUv/+/Ru1b968mWQyGV26dIlqa2spJCSELCwsyNLSkmbMmEHR0dFq612/fl3ILwA6dOgQERGVl5fTu+++S507dyaZTEZOTk40depUqqqqajImxp4WIiKiti2vGGOMMcaeHB6WYowxxphe4eKGMcYYY3qFixvGGGOM6RUubhhjjDGmV7i4YYwxxphe4eKGMcYYY3qFixvGGGOM6RUubhhjjDGmV7i4YYwxxphe4eKGMcYYY3qFixvGGGOM6RUubhhjjDGmV/4fN/6oT/8JWCEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC-AUC curves for DNN and XGBoost models\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['dnn_mean'])\n",
    "plt.plot(fpr, tpr, label=f'DNN (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"dnn_mean\"]):.3f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--', alpha=0.5)\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['xgb_mean'])\n",
    "plt.plot(fpr, tpr, label=f'XGBoost (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"xgb_mean\"]):.3f})')\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC-AUC Curve (mean)')\n",
    "plt.show()\n",
    "\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['dnn_majority_vote'])\n",
    "plt.plot(fpr, tpr, label=f'DNN (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"dnn_majority_vote\"]):.3f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--', alpha=0.5)\n",
    "fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df['xgb_majority_vote'])\n",
    "plt.plot(fpr, tpr, label=f'XGBoost (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[\"xgb_majority_vote\"]):.3f})')\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC-AUC Curve (majority_vote)')\n",
    "plt.show()\n",
    "\n",
    "for i in range(3):\n",
    "    fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df[f'dnn_model_n{i}'])\n",
    "    plt.plot(fpr, tpr, label=f'DNN (model n.{i}) (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[f\"dnn_model_n{i}\"]):.3f})')\n",
    "    fpr, tpr, _ = roc_curve(result_df['Active'].astype(int), result_df[f'xgb_model_n{i}'])\n",
    "    plt.plot(fpr, tpr, label=f'XGBoost (model n.{i}) (AUC = {roc_auc_score(result_df[\"Active\"].astype(int), result_df[f\"xgb_model_n{i}\"]):.3f})', linestyle='dashed')\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title(f'ROC-AUC Curve (models)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}