{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Dataset Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/cephyr/users/ribes/Alvis/PROTAC-Degradation-Predictor/notebooks\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Change directory to current file path (define __file__ first)\n",
    "os.chdir('/cephyr/users/ribes/Alvis/PROTAC-Degradation-Predictor/notebooks/')\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#83B8FE', '#FFA54C', '#94ED67', '#FF7FFF']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import colorsys\n",
    "\n",
    "def increase_saturation(hex_color, increase_by=0.3):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "    # Convert RGB to HSV\n",
    "    hsv = colorsys.rgb_to_hsv(rgb[0]/255, rgb[1]/255, rgb[2]/255)\n",
    "    # Increase saturation\n",
    "    new_saturation = min(hsv[1] + increase_by, 1)  # Ensure saturation doesn't exceed 1\n",
    "    # Convert back to RGB and then to hex\n",
    "    new_rgb = colorsys.hsv_to_rgb(hsv[0], new_saturation, hsv[2])\n",
    "    new_hex = '#' + ''.join(f'{int(c*255):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "def darken_color(hex_color, darkening_factor=1.0):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "\n",
    "    # Darken color\n",
    "    new_rgb = [(color * darkening_factor) for color in rgb]\n",
    "\n",
    "    # Convert RGB back to hex\n",
    "    new_hex = '#' + ''.join(f'{int(c):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "palette = [\n",
    "    '#D0E4FE', # blue\n",
    "    '#FFCC99', # orange\n",
    "    '#C4EDAF', # green\n",
    "    '#FFCCFF', # pink\n",
    "]\n",
    "\n",
    "\n",
    "# Adjusted palette\n",
    "palette = adjusted_palette = [increase_saturation(color) for color in palette]\n",
    "print(adjusted_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "palette = [\n",
    "    '#D0E4FE', # blue\n",
    "    '#FFCC99', # orange\n",
    "    '#C4EDAF', # green\n",
    "    '#FFCCFF', # pink\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <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>2136</th>\n",
       "      <td>2342</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...</td>\n",
       "      <td>VRVWHAZIBGEPEK-DPSJZEHMSA-N</td>\n",
       "      <td>1001.673</td>\n",
       "      <td>69</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD4 long in HEK293 cells after...</td>\n",
       "      <td>1000.374231</td>\n",
       "      <td>6.76</td>\n",
       "      <td>BRD4 long</td>\n",
       "      <td>MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>HEK293</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2137</th>\n",
       "      <td>2887</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...</td>\n",
       "      <td>FOOHAGZPIHCYKX-ZSFXBAAMSA-N</td>\n",
       "      <td>1161.359</td>\n",
       "      <td>82</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>1160.534044</td>\n",
       "      <td>6.81</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>2889</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...</td>\n",
       "      <td>RDCVMTUYWQXPEC-FSHOLZCKSA-N</td>\n",
       "      <td>1073.253</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>1072.481615</td>\n",
       "      <td>7.11</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>2890</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...</td>\n",
       "      <td>SLSLLSIRBMAERC-MGVZSLQJSA-N</td>\n",
       "      <td>1029.200</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>1028.455400</td>\n",
       "      <td>7.26</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2140</th>\n",
       "      <td>2891</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...</td>\n",
       "      <td>ASRIXACKPXMNKY-FCFVTTBASA-N</td>\n",
       "      <td>999.174</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>998.444835</td>\n",
       "      <td>7.31</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 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",
       "2136         2342  O60885  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "2137         2887  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...   \n",
       "2138         2889  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...   \n",
       "2139         2890  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...   \n",
       "2140         2891  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...   \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",
       "2136       VHL  InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...   \n",
       "2137       VHL  InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...   \n",
       "2138       VHL  InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...   \n",
       "2139       VHL  InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...   \n",
       "2140       VHL  InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...   \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",
       "2136  VRVWHAZIBGEPEK-DPSJZEHMSA-N          1001.673                69   \n",
       "2137  FOOHAGZPIHCYKX-ZSFXBAAMSA-N          1161.359                82   \n",
       "2138  RDCVMTUYWQXPEC-FSHOLZCKSA-N          1073.253                76   \n",
       "2139  SLSLLSIRBMAERC-MGVZSLQJSA-N          1029.200                73   \n",
       "2140  ASRIXACKPXMNKY-FCFVTTBASA-N           999.174                71   \n",
       "\n",
       "      Ring Count  Rotatable Bond Count  ...  Name  \\\n",
       "0             10                    24  ...   NaN   \n",
       "1             10                    25  ...   NaN   \n",
       "2             10                    26  ...   NaN   \n",
       "3             10                    27  ...   NaN   \n",
       "4             10                    28  ...   NaN   \n",
       "...          ...                   ...  ...   ...   \n",
       "2136           7                    20  ...   NaN   \n",
       "2137           7                    27  ...   NaN   \n",
       "2138           7                    21  ...   NaN   \n",
       "2139           7                    18  ...   NaN   \n",
       "2140           7                    16  ...   NaN   \n",
       "\n",
       "                                      Assay (DC50/Dmax)   Exact Mass XLogP3  \\\n",
       "0                                                   NaN          NaN    NaN   \n",
       "1                                                   NaN          NaN    NaN   \n",
       "2                                                   NaN          NaN    NaN   \n",
       "3                                                   NaN          NaN    NaN   \n",
       "4                                                   NaN          NaN    NaN   \n",
       "...                                                 ...          ...    ...   \n",
       "2136  Degradation of BRD4 long in HEK293 cells after...  1000.374231   6.76   \n",
       "2137  Degradation of FAK in A549 cells after 24 h tr...  1160.534044   6.81   \n",
       "2138  Degradation of FAK in A549 cells after 24 h tr...  1072.481615   7.11   \n",
       "2139  Degradation of FAK in A549 cells after 24 h tr...  1028.455400   7.26   \n",
       "2140  Degradation of FAK in A549 cells after 24 h tr...   998.444835   7.31   \n",
       "\n",
       "      Target (Parsed)                                       POI Sequence  \\\n",
       "0                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "1                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "2                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "3                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "4                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "...               ...                                                ...   \n",
       "2136        BRD4 long  MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...   \n",
       "2137              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
       "2138              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
       "2139              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
       "2140              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \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",
       "2136             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2137             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2138             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2139             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2140             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",
       "2136               HEK293        True  \n",
       "2137            A549 Cas9       False  \n",
       "2138            A549 Cas9       False  \n",
       "2139            A549 Cas9        True  \n",
       "2140            A549 Cas9        True  \n",
       "\n",
       "[2141 rows x 35 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_df = pd.read_csv('../data/PROTAC-Degradation-DB.csv')\n",
    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')\n",
    "protac_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of non-nan Dmax/DC50 values: Dmax (%)      812\n",
      "DC50 (nM)    1350\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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sWdK6du1qv/32m1e7d955xyR53s9nq1WrltWrVy/b50hLS/O8355//nnPZ2Pnzp1m5v3Zfv31123BggX24osvmsvlssGDB2d6PUqVKmWNGze2Tz/91ObPn2/btm2zRYsWmb+/v91yyy02ffp0mzt3rnXv3j3TWZpHHnnEChcubCNGjLDFixfbF198Ya+++qqNHj3a0yan9Rw/ftyqVatmwcHB9vrrr9v8+fPthRdeMF9fX2vdurXXa3DuGZPU1FQrWbKklSpVyiZMmOB5D1xzzTWZzga4f83OyS91Od2e2enWrZsVKlTIIiMjrVChQlaqVCl7/PHHbf/+/TmaPzs7d+60QoUK2V133WVmZs8//7xJsiVLlni1W7dunRUpUsTKlClj77zzji1cuNAmT55sHTt2tEOHDtnevXttyJAhns+1+720d+9eT/1nfwe98cYbJsl++uknr+eZM2eOSbLPP//czP76Vdb9a/LgwYMtKSnJPvjgAytVqpRVrlzZ64y1+72c1dm/rLz//vsWGhpqv//+u5lZtmdMzpabMyY//fSTSbIHH3wwR+2zktUZE/d7qWLFivbiiy9aUlKSjRgxwgICAuz+++/3mv+pp56ysWPH2ty5c23RokU2cuRIi4iIyNTugw8+MJfLZY0aNbIpU6bYggULbMyYMdazZ08zM/v555/tzjvvNEmebbty5UrPWdFzP0ft27e32NhYr19nzcz69+9v/v7+tm/fPjOzHO87zf56D0mybdu25fj1O3XqlB05csSWL19ulSpVsptvvtlOnz6dbfvs9llnc58N3rx5s/3jH/+wkiVL2o4dO3Jc09nOPpP61FNPma+vr/3444+e6VmdQejevbuNGzfOkpKSLCkpyf71r39ZUFCQ13eg2V+/ulepUsXOnDljCQkJdu2119rJkyfNLPt9WFxcXJ5+7c/JGZPTp0/byZMnbdu2bfbwww9bkSJFvI4F3c8fEhJiRYoUMV9fX893/fm2WUGdMcnJ8dbGjRvtpptusqioKK/PiVtutt253zN5ef+73X333VaoUKFM+xt/f3+77777MrXv0qWLSbJ58+aZWe6O2XIiX4OJmVlkZKRdf/31nsfuF+vsy1BOnTplJUqUMEn23Xffecbv37/ffHx87Omnn852+adPn7ZTp05Z06ZNrX379l7T9u3bZ6VLl7batWvbd999Z4ULF7Z77703R+t2oUu53F/AbsOGDTNJXqdW4+LizMfHx+uLw+yvg6oiRYpk2uivv/6610Fjr169rFixYuetM6f1bN68Oct233zzjUmy5557zjPu3IOCL7/80iTZG2+84TXvK6+8kuUH3MfHJ08BYOzYsSbJpk6d6jX+66+/znK8mXkObnbv3m1mfx0o3njjjSbJJFnr1q3t2LFj9tVXX1lQUFCmA5uLERcXZy6Xy9atW+c1vnnz5la0aFHP6c6cBJNOnTqZJNuzZ4+ZmQUGBlrdunVzXMuHH35or7zyis2ZM8cWLVpkr776qoWFhVlkZKTt2rXL0869zc69BMDMrEWLFlahQoXzPs+FLuWSZP/5z3+8xrdu3doqVqzoeex+Pc7e8blVqlTJatas6Qlobm3atLHo6GjPgUt8fLzdfvvt5601p/W4w9q57V577TWTZPPnz/eMO/eA6p///Ge274Fzg8mSJUvMx8cn004lKzndntkZMWKEjRgxwubPn2/z58+3gQMHWuHCha1SpUp2+PDhC86fnZdeeskk2dy5c83sr0sOXS5XpkssmjRpYsWKFfMEjayc71Kuc7+D9u3bZ/7+/l7fU2ZmHTt2tMjISM/7xf1jztn7F7P/e9+OGTPGM27SpEnm4+NjkyZNuuB679q1y0JDQ+3dd9/1jMvvYPLPf/7TcyCfV+cLJsOGDfNq27NnTwsMDPS6ZOdsGRkZdurUKfvwww/Nx8fH/vzzTzMzO3z4sBUtWtRuvvnmbOc1O/+lXOd+jj7//PNMn7XTp09bTEyM3XHHHZ5xOd13mpk98MAD5uPj4/lh8kJWrlzp2W+49x2HDh067zzZ7bPOFhAQ4FlmhQoVbNOmTTmqJytnB5N9+/ZZaGio1+tzoUub3Nv0pZdesvDwcK/t5w4mZmYLFiwwSZ4ferLbh1177bV27bXX5no9chJMKlas6HndoqOjbfny5Zna9OzZ08aPH29Lly61WbNmeX4YPN9xXkEEk9wcb+X0Uq4Lbbtzv2dy+/53O3DggAUGBlrLli0zTatRo4ZVqFDB6weEU6dOeX6knjJlipnl7pgtJ/I9mJQsWTJTMMkqLdWrV8+io6MzzR8dHe31wTP768ugZs2aXh94SVapUqVM83/99dfm6+trgYGBVqlSJTty5EiO1u1CwcS9Y3abO3euSbJVq1Z5xsXFxVnNmjUzLaNUqVLWtm1bO3XqlNewceNGr53nhx9+aJKsc+fONmvWrCyvN89pPe7rZb/99ttMy7j++uutTp06nsfnHhT079/fJHl+tXJzf1nl1we8Vq1aFh4enuleA/ebfNq0aZnmcb/Jzz7QPnPmjG3bts3zy2Z6erpdf/31nmvhP/30U6tcubIVL17cbrvttjz/ehUXF2fx8fGZxru3ybJly8wsZ8GkY8eOFxVMsvLNN99YoUKFvM6wuYNJVvd9tWjRwuuAPSsXCiZZfbafffZZCwwM9Dx2vx5PPfWUV7utW7eaJHv99dczfTbc71/3Tv2BBx6wgIAA++c//2mLFy/O8t6tnNbTsWNHCw4OznSQtWfPHpNk//znPz3jzj2gql279nnfA/l5/0RW2zM3Pv30U5NkI0aMyNP8Z86csbJly2b6Zbtx48ZWuHBhz7XER48eNR8fH3v44YfPu7zcBBMzszvuuMNKlSrlee4///zTAgIC7JlnnvG0ueeee6xYsWJ28uTJTO+hqKgo69ixY57WvU2bNtagQQOv90h+BhN3fe4Dw7w6XzDZsmWLV1t3ID/7u+C7776ztm3bWlhYmNe+9ex9ybx587wOQrKTm2DiXv8uXbp4xs2ePdsk2ezZsz3jcrrvzIsjR45YcnKyLV261N544w2Ljo62OnXqeF1Pf67s9llnW7Nmja1cudImT55sCQkJFhkZaRs2bMhTjefee+be/7m3TVbBZOHChda0aVMrWrRopm169rY/O5iY/bU/KFGihB06dChf7jE5W06CyYYNG+ybb76xTz75xJo2bWohISE5+j7t1atXph+5z1YQwSQ3x1vnCya52Xb5dY/JW2+9lek7xG3cuHEmyR577DHbtWuX7dixw3r06GE+Pj5ex2i5PWa7kHz9PyZHjx7V/v37FRMT4zW+cOHCmbq99Pf3V1hYWKZl+Pv7e+4RkP669v6xxx5TnTp19Nlnn2nVqlVKTk7WrbfequPHj2eav06dOqpSpYpOnDihxx57zOv+g4sRHh7u9TggIECSMtUQHR2dad49e/bof//7n/z8/LyGKlWqSJKni8KuXbtq/Pjx+u2333THHXeoZMmSqlOnjpKSknJdz/79+7OtJyYmxjM9K/v375evr2+m58jPXmN++OEHrV69Wvfee6+ndjf382ZV459//imXy6VixYp5xrlcLpUpU8bzvnPfb/TMM89oy5YtuueeezR8+HDt2rVLERERXj125VZWr4F73Ple03P99ttvCggI8HwGrrnmGm3bti3PdUlS7dq1VaFCBa8uLC/0Wmb1GcyNrD7bAQEBXp9ht3Pfi3v27JEk9evXL9Nno2fPnpL+77Px5ptv6p///KdmzZqlxo0bKywsTLfffru2bt2a63r279+vqKioTNcglyxZUr6+vhf8bJzvPZCfstqeudG+fXsFBwfnef5FixZp27Ztuuuuu3To0CEdPHhQBw8eVMeOHXXs2DHP/T0HDhxQRkZGjnqkyY0HHnhAv//+u+f7b+rUqUpPT/fqdnTPnj06ePCg/P39M72HUlNTM3X/mhOffvqp5s6dq2HDhiktLc2z3tJf97McPHgwy3swcmPOnDlKTU3Vgw8+eFHLOZ8L7SN27NihW265Rb///rveeOMNLVu2TMnJyZ57Rdzt3Pfq5Of29fX1VdeuXTVz5kzPaztx4kRFR0erZcuWnnY53XfmRXBwsGrVqqUGDRroiSee0MyZM/XNN9943SN7tvPts852ww03qG7durrnnnu0ePFimVmW94nmRZ8+fRQTE6P+/ftnOf3bb79VixYtJEnvv/++vv76ayUnJ2vgwIGSMh+vnO21117Tvn37LqqL4ItRpUoV1a5dW3feeafmzp2ruLi4HPXU596f5/V7Lj9czPGW28Vsu4sxbtw4lShRQv/4xz8yTXvggQf06quv6qOPPlLp0qV1zTXXaNOmTerXr58keXpnzO0x24Vc3B1Z55g9e7YyMjLy3L9yViZPnqxGjRpp7NixXuOzu9l00KBBWr9+vRISEvTiiy+qTZs2KleuXL7VcyFZ3XAVERGhatWq6ZVXXslynrOD3P3336/7779fR48e1VdffaVBgwapTZs2+umnnxQXF5fjOtxvlJSUlEw7lN27d5/3ZvDw8HCdPn1a+/fv99q55aWf/eyMGzdOkrLcMV977bUKCgrKssOC9evXe7p4zMqPP/6oV199VQsWLJCfn58WLFigKlWqePpqf/rpp1W9enUdOXIkT//zIqvXwD3u3AOB7Pz+++9as2aNGjZs6Lm5r2XLlho9erRWrVqlunXr5rouNzPzunmtatWqkv563SpXruwZf/r0ac/NygXl3M+G+z04YMCAbG/sd3exGRwcrMGDB2vw4MHas2ePvvzySz377LNq27attmzZkqs6wsPD9c0338jMvGrau3evTp8+fcHPxvneA/nt3O1ZkPO7P6MjRozQiBEjspz+yCOPKCwsTD4+PjnqVCM3WrZsqZiYGE2YMEEtW7bUhAkTVKdOHa/3sbvzj7lz52a5jJCQkFw/74YNG3T69OksP4fvv/++3n//fc2cOfOiupZ3dyjQtWvXPC/jYs2aNUtHjx7VjBkzvPYt5/7PIvcN3Pm9fe+//379+9//1rRp09SpUyd9/vnn6tOnj3x8fDxtcrPvvFi1atVSoUKFsu3k5Xz7rOy4O7DJr45jgoKClJiYqIcfflizZ8/ONH3atGny8/PTF1984bWPzOp/tJ2rRo0a6tKli0aMGKHWrVvnS7155evrqxtuuCFTJyhZsf9/Y/jFfE9erIs53nK7mG2XV2vXrtXatWvVt29f+fn5Zdnmn//8p/r06aOtW7cqJCREcXFxeuSRRxQcHKyEhARJF3fMlpV825I7duxQv379FBoaqkceeSS/FiuXy5Xp14kffvghy36Yk5KSNHToUD3//PNKSkpSaGioOnXqpJMnT17weQICAi5ZIm3Tpo02bNiga6+9VrVq1co0ZPXlGhwcrFatWmngwIE6efJkrv9Jk7tPcvf/H3BLTk7W5s2bz/s/PRo3bizpr/9/cbYpU6bkqobspKena/Lkyapdu7bi4+MzTff19VXbtm01Y8YMrwC6Y8cOLV68+Ly9Uz3yyCPq3r276tevL+mvL62jR496prv/qaD7yyy3Nm7cqO+//95r3JQpUxQSEqIbbrjhgvMfP35cDz74oE6fPu31q9dTTz2l4OBg9ezZM1PPOe56L9SzxapVq7R161avA6o6deooOjra0/uR26effqojR45csKev7M4M5oeKFSuqfPny+v7777P8XNSqVSvLA8vIyEh1795dXbp00Y8//qhjx47l6nmbNm2qI0eOZPrC//DDDz3Ts9O4ceNs3wP5LavtmRuffvqpjh07lqf5Dxw4oJkzZ+qmm27S4sWLMw333HOPkpOTtWHDBgUFBalhw4b65JNPzvsLdm7fSz4+PuratatmzZqlZcuWafXq1Zl6QmrTpo3279+vjIyMLN8/efnfEd27d89ynSXp9ttv1+LFi3XzzTfnerluqampmjNnjm6//fYc/5hxKbhD+dn7VzPT+++/79Wufv36Cg0N1TvvvHPe783cbt/rr79ederU0YQJEzRlyhSlp6dn6j0wL/vOvFq6dKnOnDmj6667LtO0C+2zsrNv3z7PgVl+eeCBB3T99dfr2Wef1ZkzZ7ymuVwu+fr6eoW748eP66OPPsrRsl9++WWdPHnS05uoU06cOKFVq1bl6HVzf29fzA96Fys3x1vZHWte7LbLC3fY7tGjx3nbBQQEKD4+XnFxcdqxY4emT5+uhx56yPN/si7mmC1LubkW7exeTVauXGnLli2zzz77zPr06WOhoaEWFhZmixYt8ponu/71z72+0e3caybdveq8+OKLtnDhQhszZoxFRUXZtdde63Wd3u7du61kyZLWuHFjzzXJK1euND8/P3vyyScvuG4NGza0kiVL2ueff27Jycme63Ozu68mq/9bkN2NaLt377a4uDirVKmSjRkzxhYuXGizZ8+2t99+22677TZPL0cPPvig9e7d26ZNm2ZLly616dOnW40aNSw0NNRzU2lu6nn44YfN5XJZnz59bN68efbuu+9ayZIlLTY21uv+kXOv787IyLAGDRpYQECADRky5IK9cuX25vdp06aZJHvvvfeybbN582YrUqSINWjQwObMmWMzZsyw+Ph4i4mJyfYG23Hjxll0dLTX/wHZuHGj+fj42AsvvGDz58+3evXq2U033eQ1n5SzXsXO7ZXryy+/9Nx85+4JzOz/7qno3bu3rVy50r7++mubM2eOvfLKK3bttdear6+vDR8+PNPy//e//1nhwoWtTJky9vrrr9vChQtt4cKFNnr0aKtZs6bVqFHD07ZatWo2bNgw+9///mdJSUn2yiuvWLFixSwmJibTTWYfffSRSbKHH37YFi9ebO+9954VK1bMmjdvfsF1Pnr0qAUFBdlNN91kixcvtuTkZM+9PNl9tt3XRZ/7emR1vfKiRYssICDAWrRoYVOmTLGlS5fazJkzbciQIXbnnXd62tWuXdteeuklmzVrli1dutTeeecdCw8P9+pVLKf1uHvlCgkJsREjRlhSUpINGjTI/Pz8LtgrV0pKipUoUSJTr1yxsbEXdfN7brbnuTegbt++3erXr29vvvmmzZkzx7788kvPfTVVqlTJdJ9dw4YNs70XwG306NEmyaZPn57l9B9++MEkWZ8+fczs/3rlKleunL333nu2aNEimzp1qnXp0sVzQ/Gvv/5qkuz222+3ZcuWWXJysud7KLv/pfTjjz+aJCtdurQFBQVl+h8/p0+ftlatWllYWJgNHjzYvvzyS1uwYIFNnDjRunXrZjNmzPC0zc3N71lRNveYJCcn2yeffGKffPKJxcbGWuXKlT2Ps7oR9dVXX8104/e53O/ZC11jf757TM69R9G973D32rN582bz9/e3Ro0aeb5jmzdvbuXLl8/03B988IFJsiZNmtjUqVNt0aJF9t5773m9Hu7lDxo0yFatWmXJycmWnp5uZtn/P6B3333Xs33P/b8IZjnfd5rl/Obf//3vf9auXTv74IMPLCkpyebMmWMvvfSShYWF2XXXXZfl/5G60D7r4MGDduONN9rIkSPtiy++sIULF9rYsWOtUqVKVrhw4Uz765x8Bs2y35YzZ8703Htw9jHHwoULTZLdeeedNn/+fJs6daolJCR4tunZPTZldwz25JNPepZ9MTe/L1myxPM5CAwMtEaNGnken70Pr1evng0dOtRmzZplixcvtgkTJljt2rXNx8fH0/ue2V//e+uOO+6w8ePH28KFC+2zzz6zzp07m6RMPbSZ/dWD3yeffOLpnfWuu+7yPP/Z9xHltre+s+c5t1eunBxvubfpmDFj7JtvvvEsI7fb7mJvfj9+/LgVL148y8+d2/r16y0xMdG++OILS0pKstdff90iIiKsVq1amTpVycsxW3byFEzcg7+/v5UsWdIaNmxoQ4YMyfLJLzaYpKenW79+/axUqVIWGBhoN9xwg82aNctrR3b69Glr2LChRUZGZrrB5t///rdJspkzZ5533datW2c33XSTFS5c2OtANT+CidlfN2A98cQTVrZsWfPz87OwsDBLSEiwgQMHeg4cJk2aZI0bN7bIyEjz9/e3mJgY69ixo/3www+e5eSmnoyMDHvttdesQoUK5ufnZxEREXbvvfd6fZmbZX1QcPDgQXvggQesWLFiVrhwYWvevLmnS7iL7S64efPmFhwcfMEeUFavXm1Nmza1woULW9GiRe3222+3n3/+Ocu2e/futbCwsCxv4Pr444+tfPnyVqRIEWvevLn9+uuvnmmHDx/2dDhwIe7t++mnn1qVKlXM39/fypQpk+nmYveBuHvw8fGx4sWLW0JCgvXp0yfLrnvdfvnlF+vZs6ddd911FhAQYEFBQVa5cmV7+umnvb6YOnfubNddd50FBwebn5+fxcXF2aOPPpptzxdTpkyxatWqmb+/v0VFRdkTTzyR496apk6dapUqVTI/Pz+v7Z8fwcTM7Pvvv7eOHTtayZIlzc/Pz6KioqxJkyb2zjvveNo8++yzVqtWLStevLgFBARYuXLl7KmnnsoUsHNSj9lfPQA++uijFh0dbb6+vhYXF2cDBgzIdFNrVgdUmzZtsubNm1tgYKCFhYVZjx497L///W+mz19uugvOzfY8t8vOP//809q3b29lypSxoKAg8/f3t/Lly1v//v2zPMhKSEiwqKio89ZTo0YNK1mypOfAMit169a1iIgIT5tNmzbZXXfdZeHh4ebv72/XXHONde/e3es1HTVqlJUtW9Zz86T7YOB8/+S1fv36JsnuueeeLKefOnXKXn/9datevboFBgZakSJFrFKlSvbII4/Y1q1bPe3ycgBytuyCibs3uKyGrJ6rQoUKVqZMmfP2cNW3b19zuVy2efPm89Z0McHE7K+DdPfrVqpUKXvmmWc8vTKeG4rmzJljDRs2tODgYCtcuLBVrlzZ6weZ9PR0e/DBB61EiRLmcrm8niu7YJKWlmZBQUEmyd5///0s1zEn+06znHeXunnzZrvzzjstLi7OAgMDPR3lPPPMM9l2r32hfdaJEyfswQcftOuvv97TlW3p0qXt3nvvzfL7PiefQbPz/+NV9+fi3GOO8ePHW8WKFT3fk0OHDvXcxJyTYPLHH394br6+mO6C3eErq+Hs91bfvn2tevXqFhoaar6+vhYVFWXt27e3r7/+2mt5K1eutKZNm1pUVJT5+flZ4cKF7cYbb7QxY8Zk6nbaXWt2z3/26+D+EebcDoXOJ6vjsJweb/3555925513WrFixTyfE7fcbLuL7S7Y/U+Wx48fn22bH3/80Ro0aGBhYWHm7+9v1113nT3//PPZdiqVm2O283GZXeL/3AJcxubMmaM2bdro+++/99yPkZ0yZcooPj5eX3zxRQFVB+Svw4cPKywsTKNGjdLjjz/udDnIRu3atRUXF6dPPvnE6VKQz/gMXl46duyobdu2KTk52elS8P/l683vwJVm8eLF6ty58wVDCXA1+Oqrr1SqVCk99NBDTpeCbBw6dEjff/+9Jk2a5HQpuAT4DF4+zExLlizJdG8InMUZEyCHOGMCAABw6RBMAAAAADjOuY6fAQAAAOD/I5gAAAAAcBzBBAAAAIDj6JXrb+jMmTPavXu3QkJCPP/9FwAA4HJgZjp8+LBiYmJUqBC/of+dEEz+hnbv3q3Y2FinywAAAMjWzp07Vbp0aafLQAEimPwNhYSESPrrA1+0aFGHqwEAAPg/hw4dUmxsrOd4BX8fBJO/IfflW0WLFiWYAACAyxKXm//9cOEeAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAc5+t0AQAAAH9HO3bs0L59+5wuI9ciIiJ0zTXXOF0GrkIEEwAAgAK2Y8cOVap0vY4fP+Z0KbkWFFRYW7ZsJpwg3xFMAAAACti+fft0/Pgx1XlgkIpGl3G6nBw7lLJd34wfrH379hFMkO8IJgAAAA4pGl1GYddUdLoM4LLAze8AAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMHEIUOHDpXL5VKfPn0848xMiYmJiomJUVBQkBo1aqSNGzd6zZeenq7evXsrIiJCwcHBateunXbt2lXA1QMAAAD5i2DigOTkZL333nuqVq2a1/hhw4ZpxIgReuutt5ScnKyoqCg1b95chw8f9rTp06ePZs6cqWnTpmn58uU6cuSI2rRpo4yMjIJeDQAAACDfEEwK2JEjR3TPPffo/fffV/HixT3jzUyjRo3SwIED1aFDB8XHx2vSpEk6duyYpkyZIklKS0vTuHHjNHz4cDVr1kw1a9bU5MmTtX79ei1YsMCpVQIAAAAuGsGkgD3++OO67bbb1KxZM6/x27ZtU2pqqlq0aOEZFxAQoIYNG2rFihWSpDVr1ujUqVNebWJiYhQfH+9pAwAAAFyJfJ0u4O9k2rRp+u6775ScnJxpWmpqqiQpMjLSa3xkZKR+++03Txt/f3+vMy3uNu75s5Kenq709HTP40OHDuV5HQAAAIBLgTMmBWTnzp168sknNXnyZAUGBmbbzuVyeT02s0zjznWhNkOHDlVoaKhniI2NzV3xAAAAwCVGMCkga9as0d69e5WQkCBfX1/5+vpq6dKlevPNN+Xr6+s5U3LumY+9e/d6pkVFRenkyZM6cOBAtm2yMmDAAKWlpXmGnTt35vPaAQAAABeHYFJAmjZtqvXr12vdunWeoVatWrrnnnu0bt06lStXTlFRUUpKSvLMc/LkSS1dulT169eXJCUkJMjPz8+rTUpKijZs2OBpk5WAgAAVLVrUawAAAAAuJ9xjUkBCQkIUHx/vNS44OFjh4eGe8X369NGQIUNUvnx5lS9fXkOGDFHhwoV19913S5JCQ0PVo0cP9e3bV+Hh4QoLC1O/fv1UtWrVTDfTAwAAAFcSgsllpH///jp+/Lh69uypAwcOqE6dOpo/f75CQkI8bUaOHClfX1917NhRx48fV9OmTTVx4kT5+Pg4WDkAAABwcQgmDlqyZInXY5fLpcTERCUmJmY7T2BgoEaPHq3Ro0df2uIAAACAAsQ9JgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHOfrdAG4eu3YsUP79u1zuoxci4iI0DXXXON0GQAAAH8rBBNcEjt27FClStfr+PFjTpeSa0FBhbVly2bCCQAAQAEimOCS2Ldvn44fP6Y6DwxS0egyTpeTY4dStuub8YO1b98+ggkAAEABIpjgkioaXUZh11R0ugwAAABc5rj5HQAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwK0NixY1WtWjUVLVpURYsWVb169fTll196pnfv3l0ul8trqFu3rtcy0tPT1bt3b0VERCg4OFjt2rXTrl27CnpVAAAAgHxFMClApUuX1quvvqrVq1dr9erVatKkif7xj39o48aNnja33nqrUlJSPMOcOXO8ltGnTx/NnDlT06ZN0/Lly3XkyBG1adNGGRkZBb06AAAAQL7xdbqAv5O2bdt6PX7llVc0duxYrVq1SlWqVJEkBQQEKCoqKsv509LSNG7cOH300Udq1qyZJGny5MmKjY3VggUL1LJly0u7AgAAAMAlwhkTh2RkZGjatGk6evSo6tWr5xm/ZMkSlSxZUhUqVNBDDz2kvXv3eqatWbNGp06dUosWLTzjYmJiFB8frxUrVhRo/QAAAEB+4oxJAVu/fr3q1aunEydOqEiRIpo5c6YqV64sSWrVqpXuuusuxcXFadu2bXrhhRfUpEkTrVmzRgEBAUpNTZW/v7+KFy/utczIyEilpqZm+5zp6elKT0/3PD506NClWTkAAAAgjwgmBaxixYpat26dDh48qM8++0zdunXT0qVLVblyZXXq1MnTLj4+XrVq1VJcXJxmz56tDh06ZLtMM5PL5cp2+tChQzV48OB8XQ8AAAAgP3EpVwHz9/fXddddp1q1amno0KGqXr263njjjSzbRkdHKy4uTlu3bpUkRUVF6eTJkzpw4IBXu7179yoyMjLb5xwwYIDS0tI8w86dO/NvhQAAAIB8QDBxmJl5XWZ1tv3792vnzp2Kjo6WJCUkJMjPz09JSUmeNikpKdqwYYPq16+f7XMEBAR4uih2DwAAAMDlhEu5CtBzzz2nVq1aKTY2VocPH9a0adO0ZMkSzZ07V0eOHFFiYqLuuOMORUdHa/v27XruuecUERGh9u3bS5JCQ0PVo0cP9e3bV+Hh4QoLC1O/fv1UtWpVTy9dAAAAwJWIYFKA9uzZo65duyolJUWhoaGqVq2a5s6dq+bNm+v48eNav369PvzwQx08eFDR0dFq3Lixpk+frpCQEM8yRo4cKV9fX3Xs2FHHjx9X06ZNNXHiRPn4+Di4ZgAAAMDFIZgUoHHjxmU7LSgoSPPmzbvgMgIDAzV69GiNHj06P0sDAAAAHMU9JgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMClAY8eOVbVq1VS0aFEVLVpU9erV05dffumZbmZKTExUTEyMgoKC1KhRI23cuNFrGenp6erdu7ciIiIUHBysdu3aadeuXQW9KgAAAEC+IpgUoNKlS+vVV1/V6tWrtXr1ajVp0kT/+Mc/POFj2LBhGjFihN566y0lJycrKipKzZs31+HDhz3L6NOnj2bOnKlp06Zp+fLlOnLkiNq0aaOMjAynVgsAAAC4aASTAtS2bVu1bt1aFSpUUIUKFfTKK6+oSJEiWrVqlcxMo0aN0sCBA9WhQwfFx8dr0qRJOnbsmKZMmSJJSktL07hx4zR8+HA1a9ZMNWvW1OTJk7V+/XotWLDA4bUDAAAA8o5g4pCMjAxNmzZNR48eVb169bRt2zalpqaqRYsWnjYBAQFq2LChVqxYIUlas2aNTp065dUmJiZG8fHxnjYAAADAlcjX6QL+btavX6969erpxIkTKlKkiGbOnKnKlSt7gkVkZKRX+8jISP3222+SpNTUVPn7+6t48eKZ2qSmpmb7nOnp6UpPT/c8PnToUH6tDgAAAJAvOGNSwCpWrKh169Zp1apVeuyxx9StWzdt2rTJM93lcnm1N7NM4851oTZDhw5VaGioZ4iNjb24lQAAAADyGcGkgPn7++u6665TrVq1NHToUFWvXl1vvPGGoqKiJCnTmY+9e/d6zqJERUXp5MmTOnDgQLZtsjJgwAClpaV5hp07d+bzWgEAAAAXh2DiMDNTenq6ypYtq6ioKCUlJXmmnTx5UkuXLlX9+vUlSQkJCfLz8/Nqk5KSog0bNnjaZCUgIMDTRbF7AAAAAC4n3GNSgJ577jm1atVKsbGxOnz4sKZNm6YlS5Zo7ty5crlc6tOnj4YMGaLy5curfPnyGjJkiAoXLqy7775bkhQaGqoePXqob9++Cg8PV1hYmPr166eqVauqWbNmDq8dAAAAkHcEkwK0Z88ede3aVSkpKQoNDVW1atU0d+5cNW/eXJLUv39/HT9+XD179tSBAwdUp04dzZ8/XyEhIZ5ljBw5Ur6+vurYsaOOHz+upk2bauLEifLx8XFqtQAAAICLRjApQOPGjTvvdJfLpcTERCUmJmbbJjAwUKNHj9bo0aPzuToAAADAOdxjAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gUoKFDh+rGG29USEiISpYsqdtvv10//vijV5vu3bvL5XJ5DXXr1vVqk56ert69eysiIkLBwcFq166ddu3aVZCrAgAAAOQrgkkBWrp0qR5//HGtWrVKSUlJOn36tFq0aKGjR496tbv11luVkpLiGebMmeM1vU+fPpo5c6amTZum5cuX68iRI2rTpo0yMjIKcnUAAACAfOPrdAF/J3PnzvV6PGHCBJUsWVJr1qxRgwYNPOMDAgIUFRWV5TLS0tI0btw4ffTRR2rWrJkkafLkyYqNjdWCBQvUsmXLS7cCAAAAwCXCGRMHpaWlSZLCwsK8xi9ZskQlS5ZUhQoV9NBDD2nv3r2eaWvWrNGpU6fUokULz7iYmBjFx8drxYoVWT5Penq6Dh065DUAAAAAlxOCiUPMTE8//bRuvvlmxcfHe8a3atVKH3/8sRYtWqThw4crOTlZTZo0UXp6uiQpNTVV/v7+Kl68uNfyIiMjlZqamuVzDR06VKGhoZ4hNjb20q0YAAAAkAdcyuWQXr166YcfftDy5cu9xnfq1Mnzd3x8vGrVqqW4uDjNnj1bHTp0yHZ5ZiaXy5XltAEDBujpp5/2PD506BDhBAAAAJcVzpg4oHfv3vr888+1ePFilS5d+rxto6OjFRcXp61bt0qSoqKidPLkSR04cMCr3d69exUZGZnlMgICAlS0aFGvAQAAALicEEwKkJmpV69emjFjhhYtWqSyZctecJ79+/dr586dio6OliQlJCTIz89PSUlJnjYpKSnasGGD6tevf8lqBwAAAC4lLuUqQI8//rimTJmi//73vwoJCfHcExIaGqqgoCAdOXJEiYmJuuOOOxQdHa3t27frueeeU0REhNq3b+9p26NHD/Xt21fh4eEKCwtTv379VLVqVU8vXQAAAMCVhmBSgMaOHStJatSokdf4CRMmqHv37vLx8dH69ev14Ycf6uDBg4qOjlbjxo01ffp0hYSEeNqPHDlSvr6+6tixo44fP66mTZtq4sSJ8vHxKcjVAQAAAPINwaQAmdl5pwcFBWnevHkXXE5gYKBGjx6t0aNH51dpAAAAgKO4xwQAAACA4wgmAAAAABxHMMmBcuXKaf/+/ZnGHzx4UOXKlXOgIgAAAODqQjDJge3btysjIyPT+PT0dP3+++8OVAQAAABcXbj5/Tw+//xzz9/z5s1TaGio53FGRoYWLlyoMmXKOFAZAAAAcHUhmJzH7bffLklyuVzq1q2b1zQ/Pz+VKVNGw4cPd6AyAAAA4OpCMDmPM2fOSJLKli2r5ORkRUREOFwRAAAAcHUimOTAtm3bnC4BAAAAuKoRTHJo4cKFWrhwofbu3es5k+I2fvx4h6oCAAAArg4EkxwYPHiwXnrpJdWqVUvR0dFyuVxOlwQAAABcVQgmOfDOO+9o4sSJ6tq1q9OlAAAAAFcl/o9JDpw8eVL169d3ugwAAADgqkUwyYEHH3xQU6ZMcboMAAAA4KrFpVw5cOLECb333ntasGCBqlWrJj8/P6/pI0aMcKgyAAAA4OpAMMmBH374QTVq1JAkbdiwwWsaN8IDAAAAF49gkgOLFy92ugQAAADgqsY9JgAAAAAcxxmTHGjcuPF5L9latGhRAVYDAAAAXH0IJjngvr/E7dSpU1q3bp02bNigbt26OVMUAAAAcBUhmOTAyJEjsxyfmJioI0eOFHA1AAAAwNWHe0wuwr333qvx48c7XQYAAABwxSOYXISVK1cqMDDQ6TIAAACAKx6XcuVAhw4dvB6bmVJSUrR69Wq98MILDlUFAAAAXD0IJjkQGhrq9bhQoUKqWLGiXnrpJbVo0cKhqgAAAICrB8EkByZMmOB0CQAAAMBVjWCSC2vWrNHmzZvlcrlUuXJl1axZ0+mSAAAAgKsCwSQH9u7dq86dO2vJkiUqVqyYzExpaWlq3Lixpk2bphIlSjhdIgAAAHBFo1euHOjdu7cOHTqkjRs36s8//9SBAwe0YcMGHTp0SE888YTT5QEAAABXPM6Y5MDcuXO1YMECXX/99Z5xlStX1ttvv83N7wAAAEA+4IxJDpw5c0Z+fn6Zxvv5+enMmTMOVAQAAABcXQgmOdCkSRM9+eST2r17t2fc77//rqeeekpNmzZ1sDIAAADg6kAwyYG33npLhw8fVpkyZXTttdfquuuuU9myZXX48GGNHj3a6fIAAACAKx73mORAbGysvvvuOyUlJWnLli0yM1WuXFnNmjVzujQAAADgqsAZk/NYtGiRKleurEOHDkmSmjdvrt69e+uJJ57QjTfeqCpVqmjZsmU5Xt7QoUN14403KiQkRCVLltTtt9+uH3/80auNmSkxMVExMTEKCgpSo0aNtHHjRq826enp6t27tyIiIhQcHKx27dpp165dF7/CAAAAgEMIJucxatQoPfTQQypatGimaaGhoXrkkUc0YsSIHC9v6dKlevzxx7Vq1SolJSXp9OnTatGihY4ePeppM2zYMI0YMUJvvfWWkpOTFRUVpebNm+vw4cOeNn369NHMmTM1bdo0LV++XEeOHFGbNm2UkZFxcSsMAAAAOIRgch7ff/+9br311mynt2jRQmvWrMnx8ubOnavu3burSpUqql69uiZMmKAdO3Z4lmFmGjVqlAYOHKgOHTooPj5ekyZN0rFjxzRlyhRJUlpamsaNG6fhw4erWbNmqlmzpiZPnqz169drwYIFF7fCAAAAgEMIJuexZ8+eLLsJdvP19dUff/yR5+WnpaVJksLCwiRJ27ZtU2pqqtf/RgkICFDDhg21YsUKSdKaNWt06tQprzYxMTGKj4/3tDlXenq6Dh065DUAAAAAlxOCyXmUKlVK69evz3b6Dz/8oOjo6Dwt28z09NNP6+abb1Z8fLwkKTU1VZIUGRnp1TYyMtIzLTU1Vf7+/ipevHi2bc41dOhQhYaGeobY2Ng81QwAAABcKgST82jdurVefPFFnThxItO048ePa9CgQWrTpk2elt2rVy/98MMPmjp1aqZpLpfL67GZZRp3rvO1GTBggNLS0jzDzp0781QzAAAAcKnQXfB5PP/885oxY4YqVKigXr16qWLFinK5XNq8ebPefvttZWRkaODAgblebu/evfX555/rq6++UunSpT3jo6KiJP11VuTsMzF79+71nEWJiorSyZMndeDAAa+zJnv37lX9+vWzfL6AgAAFBATkuk4AAACgoHDG5DwiIyO1YsUKxcfHa8CAAWrfvr1uv/12Pffcc4qPj9fXX3+d6bKr8zEz9erVSzNmzNCiRYtUtmxZr+lly5ZVVFSUkpKSPONOnjyppUuXekJHQkKC/Pz8vNqkpKRow4YN2QYTAAAA4HLHGZMLiIuL05w5c3TgwAH9/PPPMjOVL18+0z0eOfH4449rypQp+u9//6uQkBDPPSGhoaEKCgqSy+VSnz59NGTIEJUvX17ly5fXkCFDVLhwYd19992etj169FDfvn0VHh6usLAw9evXT1WrVuUfPgIAAOCKRTDJoeLFi+vGG2+8qGWMHTtWktSoUSOv8RMmTFD37t0lSf3799fx48fVs2dPHThwQHXq1NH8+fMVEhLiaT9y5Ej5+vqqY8eOOn78uJo2baqJEyfKx8fnouoDAAAAnEIwKUBmdsE2LpdLiYmJSkxMzLZNYGCgRo8erdGjR+djdQAAAIBzuMcEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmBeirr75S27ZtFRMTI5fLpVmzZnlN7969u1wul9dQt25drzbp6enq3bu3IiIiFBwcrHbt2mnXrl0FuBYAAABA/iOYFKCjR4+qevXqeuutt7Jtc+uttyolJcUzzJkzx2t6nz59NHPmTE2bNk3Lly/XkSNH1KZNG2VkZFzq8gEAAIBLxtfpAv5OWrVqpVatWp23TUBAgKKiorKclpaWpnHjxumjjz5Ss2bNJEmTJ09WbGysFixYoJYtW+Z7zQAAAEBB4IzJZWbJkiUqWbKkKlSooIceekh79+71TFuzZo1OnTqlFi1aeMbFxMQoPj5eK1asyHaZ6enpOnTokNcAAAAAXE4IJpeRVq1a6eOPP9aiRYs0fPhwJScnq0mTJkpPT5ckpaamyt/fX8WLF/eaLzIyUqmpqdkud+jQoQoNDfUMsbGxl3Q9AAAAgNziUq7LSKdOnTx/x8fHq1atWoqLi9Ps2bPVoUOHbOczM7lcrmynDxgwQE8//bTn8aFDhwgnAAAAuKxwxuQyFh0drbi4OG3dulWSFBUVpZMnT+rAgQNe7fbu3avIyMhslxMQEKCiRYt6DQAAAMDlhGByGdu/f7927typ6OhoSVJCQoL8/PyUlJTkaZOSkqINGzaofv36TpUJAAAAXDQu5SpAR44c0c8//+x5vG3bNq1bt05hYWEKCwtTYmKi7rjjDkVHR2v79u167rnnFBERofbt20uSQkND1aNHD/Xt21fh4eEKCwtTv379VLVqVU8vXQAAAMCViGBSgFavXq3GjRt7Hrvv++jWrZvGjh2r9evX68MPP9TBgwcVHR2txo0ba/r06QoJCfHMM3LkSPn6+qpjx446fvy4mjZtqokTJ8rHx6fA1wcAAADILwSTAtSoUSOZWbbT582bd8FlBAYGavTo0Ro9enR+lgYAAAA4intMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gUoC++uortW3bVjExMXK5XJo1a5bXdDNTYmKiYmJiFBQUpEaNGmnjxo1ebdLT09W7d29FREQoODhY7dq1065duwpwLQAAAID8RzApQEePHlX16tX11ltvZTl92LBhGjFihN566y0lJycrKipKzZs31+HDhz1t+vTpo5kzZ2ratGlavny5jhw5ojZt2igjI6OgVgMAAADId75OF/B30qpVK7Vq1SrLaWamUaNGaeDAgerQoYMkadKkSYqMjNSUKVP0yCOPKC0tTePGjdNHH32kZs2aSZImT56s2NhYLViwQC1btiywdQEAAADyE2dMLhPbtm1TamqqWrRo4RkXEBCghg0basWKFZKkNWvW6NSpU15tYmJiFB8f72mTlfT0dB06dMhrAAAAAC4nBJPLRGpqqiQpMjLSa3xkZKRnWmpqqvz9/VW8ePFs22Rl6NChCg0N9QyxsbH5XD0AAABwcQgmlxmXy+X12MwyjTvXhdoMGDBAaWlpnmHnzp35UisAAACQXwgml4moqChJynTmY+/evZ6zKFFRUTp58qQOHDiQbZusBAQEqGjRol4DAAAAcDkhmFwmypYtq6ioKCUlJXnGnTx5UkuXLlX9+vUlSQkJCfLz8/Nqk5KSog0bNnjaAAAAAFcieuUqQEeOHNHPP//sebxt2zatW7dOYWFhuuaaa9SnTx8NGTJE5cuXV/ny5TVkyBAVLlxYd999tyQpNDRUPXr0UN++fRUeHq6wsDD169dPVatW9fTSBQAAAFyJCCYFaPXq1WrcuLHn8dNPPy1J6tatmyZOnKj+/fvr+PHj6tmzpw4cOKA6depo/vz5CgkJ8cwzcuRI+fr6qmPHjjp+/LiaNm2qiRMnysfHp8DXBwAAAMgvBJMC1KhRI5lZttNdLpcSExOVmJiYbZvAwECNHj1ao0ePvgQVAgAAAM7gHhMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5hcZhITE+VyubyGqKgoz3QzU2JiomJiYhQUFKRGjRpp48aNDlYMAAAAXDyCyWWoSpUqSklJ8Qzr16/3TBs2bJhGjBiht956S8nJyYqKilLz5s11+PBhBysGAAAALg7B5DLk6+urqKgoz1CiRAlJf50tGTVqlAYOHKgOHTooPj5ekyZN0rFjxzRlyhSHqwYAAADyjmByGdq6datiYmJUtmxZde7cWb/++qskadu2bUpNTVWLFi08bQMCAtSwYUOtWLHCqXIBAACAi+brdAHwVqdOHX344YeqUKGC9uzZo5dffln169fXxo0blZqaKkmKjIz0micyMlK//fZbtstMT09Xenq65/GhQ4cuTfEAAABAHhFMLjOtWrXy/F21alXVq1dP1157rSZNmqS6detKklwul9c8ZpZp3NmGDh2qwYMHX5qCAQAAgHzApVyXueDgYFWtWlVbt2719M7lPnPitnfv3kxnUc42YMAApaWleYadO3de0poBAACA3CKYXObS09O1efNmRUdHq2zZsoqKilJSUpJn+smTJ7V06VLVr18/22UEBASoaNGiXgMAAABwOeFSrstMv3791LZtW11zzTXau3evXn75ZR06dEjdunWTy+VSnz59NGTIEJUvX17ly5fXkCFDVLhwYd19991Olw4AAADkGcHkMrNr1y516dJF+/btU4kSJVS3bl2tWrVKcXFxkqT+/fvr+PHj6tmzpw4cOKA6depo/vz5CgkJcbhyAAAAIO8IJpeZadOmnXe6y+VSYmKiEhMTC6YgAAAAoABwjwkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUyuUGPGjFHZsmUVGBiohIQELVu2zOmSAAAAgDwjmFyBpk+frj59+mjgwIFau3atbrnlFrVq1Uo7duxwujQAAAAgTwgmV6ARI0aoR48eevDBB3X99ddr1KhRio2N1dixY50uDQAAAMgTgskV5uTJk1qzZo1atGjhNb5FixZasWKFQ1UBAAAAF8fX6QKQO/v27VNGRoYiIyO9xkdGRio1NTXLedLT05Wenu55nJaWJkk6dOjQJavzyJEjkqQ/f/tRp9OPX7LnyW+HUv+6HG7NmjWedbhSFCpUSGfOnHG6jFy7Euu+EmuWqLugUXfBuRJr/vHHHyVdufvJI0eOXLLjCPdyzeySLB+XL4LJFcrlcnk9NrNM49yGDh2qwYMHZxofGxt7SWo725rJr17y57gUHn74YadLAAD8DVyp+8mGDRte8uc4fPiwQkNDL/nz4PJBMLnCREREyMfHJ9PZkb1792Y6i+I2YMAAPf30057HZ86c0Z9//qnw8PBsw8zFOnTokGJjY7Vz504VLVr0kjwHcodtcvlhm1xe2B6XH7bJ5acgtomZ6fDhw4qJibkky8fli2ByhfH391dCQoKSkpLUvn17z/ikpCT94x//yHKegIAABQQEeI0rVqzYpSzTo2jRouxMLjNsk8sP2+Tywva4/LBNLj+XeptwpuTviWByBXr66afVtWtX1apVS/Xq1dN7772nHTt26NFHH3W6NAAAACBPCCZXoE6dOmn//v166aWXlJKSovj4eM2ZM0dxcXFOlwYAAADkCcHkCtWzZ0/17NnT6TKyFRAQoEGDBmW6hAzOYZtcftgmlxe2x+WHbXL5YZvgUnIZfbEBAAAAcBj/YBEAAACA4wgmAAAAABxHMAEAAADgOIIJ8mzMmDEqW7asAgMDlZCQoGXLlp23/dKlS5WQkKDAwECVK1dO77zzTgFV+veRm20yY8YMNW/eXCVKlFDRokVVr149zZs3rwCr/XvI7efE7euvv5avr69q1KhxaQv8m8nt9khPT9fAgQMVFxengIAAXXvttRo/fnwBVfv3kNtt8vHHH6t69eoqXLiwoqOjdf/992v//v0FVO3V7auvvlLbtm0VExMjl8ulWbNmXXAe9u3ITwQT5Mn06dPVp08fDRw4UGvXrtUtt9yiVq1aaceOHVm237Ztm1q3bq1bbrlFa9eu1XPPPacnnnhCn332WQFXfvXK7Tb56quv1Lx5c82ZM0dr1qxR48aN1bZtW61du7aAK7965XabuKWlpem+++5T06ZNC6jSv4e8bI+OHTtq4cKFGjdunH788UdNnTpVlSpVKsCqr2653SbLly/Xfffdpx49emjjxo365JNPlJycrAcffLCAK786HT16VNWrV9dbb72Vo/bs25HvDMiD2rVr26OPPuo1rlKlSvbss89m2b5///5WqVIlr3GPPPKI1a1b95LV+HeT222SlcqVK9vgwYPzu7S/rbxuk06dOtnzzz9vgwYNsurVq1/CCv9ecrs9vvzySwsNDbX9+/cXRHl/S7ndJv/+97+tXLlyXuPefPNNK1269CWr8e9Kks2cOfO8bdi3I79xxgS5dvLkSa1Zs0YtWrTwGt+iRQutWLEiy3lWrlyZqX3Lli21evVqnTp16pLV+neRl21yrjNnzujw4cMKCwu7FCX+7eR1m0yYMEG//PKLBg0adKlL/FvJy/b4/PPPVatWLQ0bNkylSpVShQoV1K9fPx0/frwgSr7q5WWb1K9fX7t27dKcOXNkZtqzZ48+/fRT3XbbbQVRMs7Bvh35jX+wiFzbt2+fMjIyFBkZ6TU+MjJSqampWc6TmpqaZfvTp09r3759io6OvmT1/h3kZZuca/jw4Tp69Kg6dux4KUr828nLNtm6daueffZZLVu2TL6+fD3np7xsj19//VXLly9XYGCgZs6cqX379qlnz576888/uc8kH+Rlm9SvX18ff/yxOnXqpBMnTuj06dNq166dRo8eXRAl4xzs25HfOGOCPHO5XF6PzSzTuAu1z2o88i6328Rt6tSpSkxM1PTp01WyZMlLVd7fUk63SUZGhu6++24NHjxYFSpUKKjy/nZy8xk5c+aMXC6XPv74Y9WuXVutW7fWiBEjNHHiRM6a5KPcbJNNmzbpiSee0Isvvqg1a9Zo7ty52rZtmx599NGCKBVZYN+O/MRPcsi1iIgI+fj4ZPpFa+/evZl+OXGLiorKsr2vr6/Cw8MvWa1/F3nZJm7Tp09Xjx499Mknn6hZs2aXssy/ldxuk8OHD2v16tVau3atevXqJemvA2Mzk6+vr+bPn68mTZoUSO1Xo7x8RqKjo1WqVCmFhoZ6xl1//fUyM+3atUvly5e/pDVf7fKyTYYOHaqbbrpJzzzzjCSpWrVqCg4O1i233KKXX36ZX+gLGPt25DfOmCDX/P39lZCQoKSkJK/xSUlJql+/fpbz1KtXL1P7+fPnq1atWvLz87tktf5d5GWbSH+dKenevbumTJnCNdr5LLfbpGjRolq/fr3WrVvnGR599FFVrFhR69atU506dQqq9KtSXj4jN910k3bv3q0jR454xv30008qVKiQSpcufUnr/TvIyzY5duyYChXyPnTx8fGR9H+/1KPgsG9HvnPopntc4aZNm2Z+fn42btw427Rpk/Xp08eCg4Nt+/btZmb27LPPWteuXT3tf/31VytcuLA99dRTtmnTJhs3bpz5+fnZp59+6tQqXHVyu02mTJlivr6+9vbbb1tKSopnOHjwoFOrcNXJ7TY5F71y5a/cbo/Dhw9b6dKl7c4777SNGzfa0qVLrXz58vbggw86tQpXndxukwkTJpivr6+NGTPGfvnlF1u+fLnVqlXLateu7dQqXFUOHz5sa9eutbVr15okGzFihK1du9Z+++03M2PfjkuPYII8e/vtty0uLs78/f3thhtusKVLl3qmdevWzRo2bOjVfsmSJVazZk3z9/e3MmXK2NixYwu44qtfbrZJw4YNTVKmoVu3bgVf+FUst5+TsxFM8l9ut8fmzZutWbNmFhQUZKVLl7ann37ajh07VsBVX91yu03efPNNq1y5sgUFBVl0dLTdc889tmvXrgKu+uq0ePHi8+4X2LfjUnOZce4TAAAAgLO4xwQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBMDf3sSJE1WsWLECea6uXbtqyJAhBfJc+Pu68cYbNWPGDKfLAIBcIZgAuOKsWLFCPj4+uvXWW3M9b5kyZTRq1CivcZ06ddJPP/2UT9Vl74cfftDs2bPVu3dvz7hGjRrJ5XLJ5XIpICBApUqVUtu2bS/7g8r169erYcOGCgoKUqlSpfTSSy/JzLJtv2TJEs96njskJyfna22JiYmeZfv6+ioiIkINGjTQqFGjlJ6enqn9zz//rPvvv1+lS5dWQECAypYtqy5dumj16tWeNmXKlMlU97PPPuu1nB07dqht27YKDg5WRESEnnjiCZ08efK8tZ69/d1D586dc7x+7iEqKsqrzQsvvKBnn31WZ86cudDLBQCXDYIJgCvO+PHj1bt3by1fvlw7duy46OUFBQWpZMmS+VDZ+b311lu66667FBIS4jX+oYceUkpKin7++Wd99tlnqly5sjp37qyHH374kteUF4cOHVLz5s0VExOj5ORkjR49Wq+//rpGjBiR7Tz169dXSkqK1/Dggw+qTJkyqlWrVr7XWKVKFaWkpGjHjh1avHix7rrrLg0dOlT169fX4cOHPe1Wr16thIQE/fTTT3r33Xe1adMmzZw5U5UqVVLfvn29lvnSSy951f/88897pmVkZOi2227T0aNHtXz5ck2bNk2fffZZpmVkxb393cO7776b4/VzD+vXr/eafttttyktLU3z5s274LIA4LJhAHAFOXLkiIWEhNiWLVusU6dONnjw4Ext/vvf/1pCQoIFBARYeHi4tW/f3szMGjZsaJK8BjOzCRMmWGhoqJmZbdmyxSTZ5s2bvZY5fPhwi4uLszNnzpiZ2caNG61Vq1YWHBxsJUuWtHvvvdf++OOPbOvOyMiwYsWK2RdffOE1vmHDhvbkk09maj9+/HiTZElJSWZmtm3bNpNk06dPt5tvvtkCAwOtVq1a9uOPP9q3335rCQkJFhwcbC1btrS9e/d6lvPtt99as2bNLDw83IoWLWoNGjSwNWvWeKYvXrzY/Pz87KuvvvKMe/311y08PNx2796d5bqMGTPGQkND7cSJE55xQ4cOtZiYGM/rcyEnT560kiVL2ksvvZSj9m6DBg2y6tWr2zvvvGOlS5e2oKAgu/POO+3AgQOZ2pxr8+bN5u/vbwMHDjQzszNnzliVKlUsISHBMjIyMrU/e5lxcXE2cuTIbOuaM2eOFSpUyH7//XfPuKlTp1pAQIClpaVlO1922/98slu/c3Xv3t26du2aq2UDgJM4YwLgijJ9+nRVrFhRFStW1L333qsJEyZ4XUI0e/ZsdejQQbfddpvWrl2rhQsXen6RnzFjhkqXLu31y/e5KlasqISEBH388cde46dMmaK7775bLpdLKSkpatiwoWrUqKHVq1dr7ty52rNnjzp27Jht3T/88IMOHjyY47MD3bp1U/HixTNd0jVo0CA9//zz+u677+Tr66suXbqof//+euONN7Rs2TL98ssvevHFFz3tDx8+rG7dumnZsmVatWqVypcvr9atW3vOGjRq1Eh9+vRR165dlZaWpu+//14DBw7U+++/r+jo6CxrW7lypRo2bKiAgADPuJYtW2r37t3avn17jtbv888/1759+9S9e/cctT/bzz//rP/85z/63//+p7lz52rdunV6/PHHLzhfpUqV1KpVK89rum7dOm3cuFF9+/ZVoUKZd4fn3nf02muvKTw8XDVq1NArr7zidZnWypUrFR8fr5iYGM+4li1bKj09XWvWrDlvXR9//LEiIiJUpUoV9evXz+uMTna2bt2qmJgYlS1bVp07d9avv/6aqU3t2rW1bNmyCy4LAC4Xvk4XAAC5MW7cON17772SpFtvvVVHjhzRwoUL1axZM0nSK6+8os6dO2vw4MGeeapXry5JCgsLk4+Pj0JCQjJdk3+2e+65R2+99Zb+9a9/SZJ++uknrVmzRh9++KEkaezYsbrhhhu8bmIfP368YmNj9dNPP6lChQqZlrl9+3b5+Pjk+JKxQoUKqUKFCpkO9Pv166eWLVtKkp588kl16dJFCxcu1E033SRJ6tGjhyZOnOhp36RJE6/53333XRUvXlxLly5VmzZtJEkvv/yyFixYoIcfflgbN25U165d1b59+2xrS01NVZkyZbzGRUZGeqaVLVv2gus3btw4tWzZUrGxsRdse64TJ05o0qRJKl26tCRp9OjRuu222zR8+PDzblfpr3Ayf/58SX8d3LvHXciTTz6pG264QcWLF9e3336rAQMGaNu2bfrggw8k/bXe7tfArXjx4vL391dqamq2y73nnntUtmxZRUVFacOGDRowYIC+//57JSUlZTtPnTp19OGHH6pChQras2ePXn75ZdWvX18bN25UeHi4p12pUqW0Y8cOnTlzJsvgBQCXG4IJgCvGjz/+qG+//dbzi7evr686deqk8ePHe4LJunXr9NBDD13U83Tu3FnPPPOMVq1apbp16+rjjz9WjRo1VLlyZUnSmjVrtHjxYhUpUiTTvL/88kuWweT48eMKCAiQy+XKcR1mlql9tWrVPH+7D4SrVq3qNW7v3r2ex3v37tWLL76oRYsWac+ePcrIyNCxY8e87s3x9/fX5MmTVa1aNcXFxWXqHCAr59blPmuVk/XbtWuX5s2bp//85z8XbJuVa665xhNKJKlevXo6c+aMfvzxxwsGk7Nf09zU/NRTT3n+rlatmooXL64777zTcxYlu+VktQ3PdvZ7NT4+XuXLl1etWrX03Xff6YYbbshynlatWnn+rlq1qurVq6drr71WkyZN0tNPP+2ZFhQUpDNnzig9PV1BQUEXXEcAcBrBBMAVY9y4cTp9+rRKlSrlGWdm8vPz04EDB1S8ePF8OQCLjo5W48aNNWXKFNWtW1dTp07VI4884pl+5swZtW3bVq+99lqW82YlIiJCx44d08mTJ+Xv73/BGjIyMrR161bdeOONXuP9/Pw8f7sPeM8dd3ZPTN27d9cff/yhUaNGKS4uTgEBAapXr16m3qJWrFghSfrzzz/1559/Kjg4ONvaoqKiMp0FcIehc88aZGXChAkKDw9Xu3btLtg2J9yvQ04CxubNmz1ndNwBcvPmzapRo0aunrNu3bqS/rqsLDw8XFFRUfrmm2+82hw4cECnTp3K0WvidsMNN8jPz09bt27NNpicKzg4WFWrVvWcAXL7888/VbhwYUIJgCsG53YBXBFOnz6tDz/8UMOHD9e6des8w/fff6+4uDjPPSHVqlXTwoULs12Ov7+/MjIyLvh899xzj6ZPn66VK1fql19+8erC9YYbbtDGjRtVpkwZXXfddV5Ddgf07gPfTZs25Wh9J02apAMHDuiOO+7IUfvsLFu2TE888YRat26tKlWqKCAgQPv27fNq88svv+ipp57S+++/r7p16+q+++47bzez9erV01dffeUVbubPn6+YmJhMl3idy8w0YcIE3XfffV6BKjd27Nih3bt3ex6vXLnSc+nb+WzZskVz5871vKbus2DDhw/Pcn0PHjyY7bLWrl0r6f+CaL169bRhwwav+5bmz5+vgIAAJSQk5HjdNm7cqFOnTmUbcLOSnp6uzZs3Z5pnw4YNOQ43AHBZcO6+ewDIuZkzZ5q/v78dPHgw07TnnnvOatSoYWZ/9TJVqFAhe/HFF23Tpk32ww8/2GuvveZp27x5c2vXrp3t2rXL04vW2b1yuaWlpVlgYKBVr17dmjZt6jXt999/txIlStidd95p33zzjf3yyy82b948u//+++306dPZrsMNN9xgo0eP9hrXsGFDe+ihhywlJcV27txpq1atsv79+5ufn5899thjnnbuXrnWrl3rGbd48WKT5NV71LnrUqNGDWvevLlt2rTJVq1aZbfccosFBQV5epg6ffq01atXzzp06GBmZikpKRYREWHDhg3Ldj0OHjxokZGR1qVLF1u/fr3NmDHDihYtaq+//rqnzTfffGMVK1a0Xbt2ec27YMECk2SbNm3KdvnnM2jQIAsODrZmzZrZunXr7KuvvrIKFSpY586dvdpUqVLFUlJS7Pfff7cffvjB3nzzTStZsqTdeOONdvjwYa86Q0JC7KabbrLZs2fbL7/8Yt9//729/PLL1qBBAzMzW7FihY0YMcLWrl1rv/76q02fPt1iYmKsXbt2nuWcPn3a4uPjrWnTpvbdd9/ZggULrHTp0tarV69s1+Xnn3+2wYMHW3Jysm3bts1mz55tlSpVspo1a3q9j5o0aeL1vunbt68tWbLEfv31V1u1apW1adPGQkJCbPv27V7Lb9iwYa57PQMAJxFMAFwR2rRpY61bt85y2po1a0ySpxvczz77zGrUqGH+/v4WERHhOeg2M1u5cqVVq1bNAgICsuwu+Gx33XWXSbLx48dnmvbTTz9Z+/btrVixYhYUFGSVKlWyPn36nLe73Hfeecfq1q3rNe7sLoz9/f0tOjra2rRpYzNmzPBql9dg8t1331mtWrUsICDAypcvb5988olX17eDBw+26Oho27dvn2eeWbNmmb+/v9dzneuHH36wW265xQICAiwqKsoSExO91t1d27Zt27zm69Kli9WvXz/b5cbFxdmgQYOyne7uKnfMmDEWExNjgYGB1qFDB/vzzz+92rhfUx8fHwsLC7Obb77ZRo4c6dXFsduPP/5o9913n8XExJi/v7/FxcVZly5d7LvvvjOzv95fderUsdDQUAsMDLSKFSvaoEGD7OjRo17L+e233+y2226zoKAgCwsLs169emX5fG47duywBg0aWFhYmPn7+9u1115rTzzxhO3fv/+8r0mnTp0sOjra/Pz8LCYmxjp06GAbN270mmfXrl3m5+dnO3fuzPb5AeBy4zI7z7/qBQDkmxMnTqhixYqaNm2a6tWr53Q5l53jx48rLCxMc+bMUePGjbNsk5iYqFmzZmndunUFW9wV5plnnlFaWpree+89p0sBgBzj5ncAKCCBgYH68MMPM93jgb8sXbpUTZo0yTaUIOdKliypfv36OV0GAOQKZ0wAAFcMzpgAwNWLYAIAAADAcXQXDAAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHH/D7OltfSQ9zRLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:\n",
    "    \"\"\" Check if a PROTAC is active based on DC50 and Dmax.\t\n",
    "    Args:\n",
    "        DC50(float): DC50 in nM\n",
    "        Dmax(float): Dmax in %\n",
    "    Returns:\n",
    "        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN\n",
    "    \"\"\"\n",
    "    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan\n",
    "    Dmax = Dmax / 100\n",
    "    if pd.notnull(pDC50):\n",
    "        if pDC50 < pDC50_threshold:\n",
    "            return False\n",
    "    if pd.notnull(Dmax):\n",
    "        if Dmax < Dmax_threshold:\n",
    "            return False\n",
    "    if oring:\n",
    "        if pd.notnull(pDC50):\n",
    "            return True if pDC50 >= pDC50_threshold else False\n",
    "        elif pd.notnull(Dmax):\n",
    "            return True if Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "    else:\n",
    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
    "            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "print(f'Number of non-nan Dmax/DC50 values: {protac_df[[\"Dmax (%)\", \"DC50 (nM)\"]].count()}')\n",
    "\n",
    "# Add a column for a definition of activivity in which \n",
    "for Dmax_threshold in range(10):\n",
    "    for pDC50_threshold in [5 + 0.5 * i for i in range(10)]:\n",
    "        protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'] = protac_df.apply(\n",
    "            lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold * 0.1), axis=1\n",
    "        )\n",
    "        num_active = protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].value_counts()\n",
    "        num_nans = protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].isnull().sum()\n",
    "        total = len(protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'])\n",
    "        # If the number of active, i.e. num_active[True], is close to the number of inactive, i.e. num_active[False],\n",
    "        # then plot the histogram of the active values with different Dmax and pDC50 definitions\n",
    "        if abs(num_active[True] - num_active[False]) < 50:\n",
    "            sns.histplot(protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].dropna())\n",
    "            plt.title(f'Dmax threshold: {Dmax_threshold * 0.1:.0%}, pDC50 threshold: {round(pDC50_threshold, 1)}, Active: {num_active[True]}, Inactive: {num_active[False]}, NaN: {num_nans}, Total: {num_active[True] + num_active[False]}')\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "\n",
    "    # protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)})'] = protac_df.apply(\n",
    "    #     lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], Dmax_threshold=Dmax_threshold * 0.1, pDC50_threshold=6.5), axis=1\n",
    "    # )\n",
    "    # num_active = protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'].value_counts()\n",
    "    # num_inactive = protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'].isnull().sum()\n",
    "    # total = len(protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'])\n",
    "    # # Plot the KDE of the active values with different Dmax definitions\n",
    "    # plt.figure(figsize=(8, 6))\n",
    "    # sns.kdeplot(protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'].dropna(), shade=True)\n",
    "    # plt.title(f'Dmax threshold: {Dmax_threshold * 0.1:.0%}, Active: {num_active[True]}, Inactive: {num_active[False]}, NaN: {num_inactive}, Total: {total}')\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Active',\n",
       " 'Active - OR',\n",
       " 'Active (Dmax 0.0, pDC50 5.0)',\n",
       " 'Active (Dmax 0.0, pDC50 5.5)',\n",
       " 'Active (Dmax 0.0, pDC50 6.0)',\n",
       " 'Active (Dmax 0.0, pDC50 6.5)',\n",
       " 'Active (Dmax 0.0, pDC50 7.0)',\n",
       " 'Active (Dmax 0.0, pDC50 7.5)',\n",
       " 'Active (Dmax 0.0, pDC50 8.0)',\n",
       " 'Active (Dmax 0.0, pDC50 8.5)',\n",
       " 'Active (Dmax 0.0, pDC50 9.0)',\n",
       " 'Active (Dmax 0.0, pDC50 9.5)',\n",
       " 'Active (Dmax 0.1, pDC50 5.0)',\n",
       " 'Active (Dmax 0.1, pDC50 5.5)',\n",
       " 'Active (Dmax 0.1, pDC50 6.0)',\n",
       " 'Active (Dmax 0.1, pDC50 6.5)',\n",
       " 'Active (Dmax 0.1, pDC50 7.0)',\n",
       " 'Active (Dmax 0.1, pDC50 7.5)',\n",
       " 'Active (Dmax 0.1, pDC50 8.0)',\n",
       " 'Active (Dmax 0.1, pDC50 8.5)',\n",
       " 'Active (Dmax 0.1, pDC50 9.0)',\n",
       " 'Active (Dmax 0.1, pDC50 9.5)',\n",
       " 'Active (Dmax 0.2, pDC50 5.0)',\n",
       " 'Active (Dmax 0.2, pDC50 5.5)',\n",
       " 'Active (Dmax 0.2, pDC50 6.0)',\n",
       " 'Active (Dmax 0.2, pDC50 6.5)',\n",
       " 'Active (Dmax 0.2, pDC50 7.0)',\n",
       " 'Active (Dmax 0.2, pDC50 7.5)',\n",
       " 'Active (Dmax 0.2, pDC50 8.0)',\n",
       " 'Active (Dmax 0.2, pDC50 8.5)',\n",
       " 'Active (Dmax 0.2, pDC50 9.0)',\n",
       " 'Active (Dmax 0.2, pDC50 9.5)',\n",
       " 'Active (Dmax 0.3, pDC50 5.0)',\n",
       " 'Active (Dmax 0.3, pDC50 5.5)',\n",
       " 'Active (Dmax 0.3, pDC50 6.0)',\n",
       " 'Active (Dmax 0.3, pDC50 6.5)',\n",
       " 'Active (Dmax 0.3, pDC50 7.0)',\n",
       " 'Active (Dmax 0.3, pDC50 7.5)',\n",
       " 'Active (Dmax 0.3, pDC50 8.0)',\n",
       " 'Active (Dmax 0.3, pDC50 8.5)',\n",
       " 'Active (Dmax 0.3, pDC50 9.0)',\n",
       " 'Active (Dmax 0.3, pDC50 9.5)',\n",
       " 'Active (Dmax 0.4, pDC50 5.0)',\n",
       " 'Active (Dmax 0.4, pDC50 5.5)',\n",
       " 'Active (Dmax 0.4, pDC50 6.0)',\n",
       " 'Active (Dmax 0.4, pDC50 6.5)',\n",
       " 'Active (Dmax 0.4, pDC50 7.0)',\n",
       " 'Active (Dmax 0.4, pDC50 7.5)',\n",
       " 'Active (Dmax 0.4, pDC50 8.0)',\n",
       " 'Active (Dmax 0.4, pDC50 8.5)',\n",
       " 'Active (Dmax 0.4, pDC50 9.0)',\n",
       " 'Active (Dmax 0.4, pDC50 9.5)',\n",
       " 'Active (Dmax 0.5, pDC50 5.0)',\n",
       " 'Active (Dmax 0.5, pDC50 5.5)',\n",
       " 'Active (Dmax 0.5, pDC50 6.0)',\n",
       " 'Active (Dmax 0.5, pDC50 6.5)',\n",
       " 'Active (Dmax 0.5, pDC50 7.0)',\n",
       " 'Active (Dmax 0.5, pDC50 7.5)',\n",
       " 'Active (Dmax 0.5, pDC50 8.0)',\n",
       " 'Active (Dmax 0.5, pDC50 8.5)',\n",
       " 'Active (Dmax 0.5, pDC50 9.0)',\n",
       " 'Active (Dmax 0.5, pDC50 9.5)',\n",
       " 'Active (Dmax 0.6, pDC50 5.0)',\n",
       " 'Active (Dmax 0.6, pDC50 5.5)',\n",
       " 'Active (Dmax 0.6, pDC50 6.0)',\n",
       " 'Active (Dmax 0.6, pDC50 6.5)',\n",
       " 'Active (Dmax 0.6, pDC50 7.0)',\n",
       " 'Active (Dmax 0.6, pDC50 7.5)',\n",
       " 'Active (Dmax 0.6, pDC50 8.0)',\n",
       " 'Active (Dmax 0.6, pDC50 8.5)',\n",
       " 'Active (Dmax 0.6, pDC50 9.0)',\n",
       " 'Active (Dmax 0.6, pDC50 9.5)',\n",
       " 'Active (Dmax 0.7, pDC50 5.0)',\n",
       " 'Active (Dmax 0.7, pDC50 5.5)',\n",
       " 'Active (Dmax 0.7, pDC50 6.0)',\n",
       " 'Active (Dmax 0.7, pDC50 6.5)',\n",
       " 'Active (Dmax 0.7, pDC50 7.0)',\n",
       " 'Active (Dmax 0.7, pDC50 7.5)',\n",
       " 'Active (Dmax 0.7, pDC50 8.0)',\n",
       " 'Active (Dmax 0.7, pDC50 8.5)',\n",
       " 'Active (Dmax 0.7, pDC50 9.0)',\n",
       " 'Active (Dmax 0.7, pDC50 9.5)',\n",
       " 'Active (Dmax 0.8, pDC50 5.0)',\n",
       " 'Active (Dmax 0.8, pDC50 5.5)',\n",
       " 'Active (Dmax 0.8, pDC50 6.0)',\n",
       " 'Active (Dmax 0.8, pDC50 6.5)',\n",
       " 'Active (Dmax 0.8, pDC50 7.0)',\n",
       " 'Active (Dmax 0.8, pDC50 7.5)',\n",
       " 'Active (Dmax 0.8, pDC50 8.0)',\n",
       " 'Active (Dmax 0.8, pDC50 8.5)',\n",
       " 'Active (Dmax 0.8, pDC50 9.0)',\n",
       " 'Active (Dmax 0.8, pDC50 9.5)',\n",
       " 'Active (Dmax 0.9, pDC50 5.0)',\n",
       " 'Active (Dmax 0.9, pDC50 5.5)',\n",
       " 'Active (Dmax 0.9, pDC50 6.0)',\n",
       " 'Active (Dmax 0.9, pDC50 6.5)',\n",
       " 'Active (Dmax 0.9, pDC50 7.0)',\n",
       " 'Active (Dmax 0.9, pDC50 7.5)',\n",
       " 'Active (Dmax 0.9, pDC50 8.0)',\n",
       " 'Active (Dmax 0.9, pDC50 8.5)',\n",
       " 'Active (Dmax 0.9, pDC50 9.0)',\n",
       " 'Active (Dmax 0.9, pDC50 9.5)']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[c for c in protac_df.columns if 'Active' in c]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a PROTAC as active when Dmax >= 0.6 and pDC50 >= 6.0 seems to be a good compromise, resulting in a balanced amount of active and inactive entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique samples: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
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       "      <th></th>\n",
       "      <th>Active</th>\n",
       "      <th>Active - OR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
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       "      <th>793</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "</table>\n",
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      "text/plain": [
       "     Active Active - OR\n",
       "173   False       False\n",
       "793   False       False\n",
       "924    True        True\n",
       "1606  False       False\n",
       "1956  False       False\n",
       "1980  False       False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of active/inactive PROTACs in test set:\n",
      "False    0.666667\n",
      "True     0.333333\n",
      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n"
     ]
    },
    {
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       "  <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>Active (Dmax 0.9, pDC50 5.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 5.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 6.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 6.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 7.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 7.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 8.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 8.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 9.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 9.5)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>192</td>\n",
       "      <td>Q8IXJ6</td>\n",
       "      <td>Cc1cc(C)nc(SCC(=O)Nc2ncc(Cc3cccc(OCc4cn(CCCCNC...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C40H40N10O8S2/c1-23-15-24(2)44-40(43-...</td>\n",
       "      <td>GRYRXFYWVDWPQY-UHFFFAOYSA-N</td>\n",
       "      <td>852.956</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>1016</td>\n",
       "      <td>P10276</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](O)[C@H](N)Cc1ccccc1)C...</td>\n",
       "      <td>cIAP1</td>\n",
       "      <td>InChI=1S/C51H72N4O11/c1-34(2)27-42(55-48(60)46...</td>\n",
       "      <td>ZAOSGDCLGNWLSI-ACALULJJSA-N</td>\n",
       "      <td>917.154</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>1215</td>\n",
       "      <td>P62937</td>\n",
       "      <td>CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C/C=C/CCC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C89H147N15O16S/c1-29-63-83(115)97(22)...</td>\n",
       "      <td>RSBPUBFGFMTCCP-NIDZIXQMSA-N</td>\n",
       "      <td>1715.311</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>1782</td>\n",
       "      <td>O14744</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/C55H76N10O12S/c1-36(38-10-12-40(13-11...</td>\n",
       "      <td>XUJMNOQMXWVXQE-STHBVIMFSA-N</td>\n",
       "      <td>1101.338</td>\n",
       "      <td>78</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>2672</td>\n",
       "      <td>P14679</td>\n",
       "      <td>N[C@@H](Cc1ccc(O)c(O)c1)C(=O)NCCCCCNc1cccc2c1C...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C27H31N5O7/c28-17(13-15-7-9-20(33)21(...</td>\n",
       "      <td>GTUJRUVNUQLCDT-KKFHFHRHSA-N</td>\n",
       "      <td>537.573</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>2720</td>\n",
       "      <td>P07900</td>\n",
       "      <td>COc1c(C)cnc(Cn2cc(C#CCCNc3cccc4c3C(=O)N(C3CCC(...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C32H29ClN8O5/c1-16-13-36-21(17(2)26(1...</td>\n",
       "      <td>ZSERQSKFLSKTGE-UHFFFAOYSA-N</td>\n",
       "      <td>641.088</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Compound ID Uniprot                                             Smiles  \\\n",
       "173           192  Q8IXJ6  Cc1cc(C)nc(SCC(=O)Nc2ncc(Cc3cccc(OCc4cn(CCCCNC...   \n",
       "793          1016  P10276  CC(C)C[C@H](NC(=O)[C@@H](O)[C@H](N)Cc1ccccc1)C...   \n",
       "924          1215  P62937  CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C/C=C/CCC...   \n",
       "1606         1782  O14744  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "1956         2672  P14679  N[C@@H](Cc1ccc(O)c(O)c1)C(=O)NCCCCCNc1cccc2c1C...   \n",
       "1980         2720  P07900  COc1c(C)cnc(Cn2cc(C#CCCNc3cccc4c3C(=O)N(C3CCC(...   \n",
       "\n",
       "     E3 Ligase                                              InChI  \\\n",
       "173       CRBN  InChI=1S/C40H40N10O8S2/c1-23-15-24(2)44-40(43-...   \n",
       "793      cIAP1  InChI=1S/C51H72N4O11/c1-34(2)27-42(55-48(60)46...   \n",
       "924        VHL  InChI=1S/C89H147N15O16S/c1-29-63-83(115)97(22)...   \n",
       "1606       VHL  InChI=1S/C55H76N10O12S/c1-36(38-10-12-40(13-11...   \n",
       "1956      CRBN  InChI=1S/C27H31N5O7/c28-17(13-15-7-9-20(33)21(...   \n",
       "1980      CRBN  InChI=1S/C32H29ClN8O5/c1-16-13-36-21(17(2)26(1...   \n",
       "\n",
       "                        InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "173   GRYRXFYWVDWPQY-UHFFFAOYSA-N           852.956                60   \n",
       "793   ZAOSGDCLGNWLSI-ACALULJJSA-N           917.154                66   \n",
       "924   RSBPUBFGFMTCCP-NIDZIXQMSA-N          1715.311               121   \n",
       "1606  XUJMNOQMXWVXQE-STHBVIMFSA-N          1101.338                78   \n",
       "1956  GTUJRUVNUQLCDT-KKFHFHRHSA-N           537.573                39   \n",
       "1980  ZSERQSKFLSKTGE-UHFFFAOYSA-N           641.088                46   \n",
       "\n",
       "      Ring Count  Rotatable Bond Count  ...  Active (Dmax 0.9, pDC50 5.0)  \\\n",
       "173            7                    18  ...                         False   \n",
       "793            3                    28  ...                          True   \n",
       "924            4                    26  ...                          True   \n",
       "1606           7                    29  ...                         False   \n",
       "1956           4                    11  ...                         False   \n",
       "1980           6                     7  ...                         False   \n",
       "\n",
       "      Active (Dmax 0.9, pDC50 5.5)  Active (Dmax 0.9, pDC50 6.0)  \\\n",
       "173                          False                         False   \n",
       "793                          False                         False   \n",
       "924                           True                          True   \n",
       "1606                         False                         False   \n",
       "1956                         False                         False   \n",
       "1980                         False                         False   \n",
       "\n",
       "     Active (Dmax 0.9, pDC50 6.5)  Active (Dmax 0.9, pDC50 7.0)  \\\n",
       "173                         False                         False   \n",
       "793                         False                         False   \n",
       "924                          True                          True   \n",
       "1606                        False                         False   \n",
       "1956                        False                         False   \n",
       "1980                        False                         False   \n",
       "\n",
       "      Active (Dmax 0.9, pDC50 7.5)  Active (Dmax 0.9, pDC50 8.0)  \\\n",
       "173                          False                         False   \n",
       "793                          False                         False   \n",
       "924                           True                         False   \n",
       "1606                         False                         False   \n",
       "1956                         False                         False   \n",
       "1980                         False                         False   \n",
       "\n",
       "     Active (Dmax 0.9, pDC50 8.5) Active (Dmax 0.9, pDC50 9.0)  \\\n",
       "173                         False                        False   \n",
       "793                         False                        False   \n",
       "924                         False                        False   \n",
       "1606                        False                        False   \n",
       "1956                        False                        False   \n",
       "1980                        False                        False   \n",
       "\n",
       "     Active (Dmax 0.9, pDC50 9.5)  \n",
       "173                         False  \n",
       "793                         False  \n",
       "924                         False  \n",
       "1606                        False  \n",
       "1956                        False  \n",
       "1980                        False  \n",
       "\n",
       "[6 rows x 135 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "\n",
    "\n",
    "active_col = f'Active (Dmax 0.6, pDC50 6.0)'\n",
    "active_df = protac_df[protac_df[active_col].notna()]\n",
    "\n",
    "# Find the samples that:\n",
    "# * have their SMILES appearing only once in the dataframe\n",
    "# * have their Uniprot appearing only once in the dataframe\n",
    "# * have their (Smiles, Uniprot) pair appearing only once in the dataframe\n",
    "unique_smiles = active_df['Smiles'].value_counts() == 1\n",
    "unique_uniprot = active_df['Uniprot'].value_counts() == 1\n",
    "unique_smiles_uniprot = active_df.groupby(['Smiles', 'Uniprot']).size() == 1\n",
    "\n",
    "# Get the indices of the unique samples\n",
    "unique_smiles_idx = active_df['Smiles'].map(unique_smiles)\n",
    "unique_uniprot_idx = active_df['Uniprot'].map(unique_uniprot)\n",
    "unique_smiles_uniprot_idx = active_df.set_index(['Smiles', 'Uniprot']).index.map(unique_smiles_uniprot)\n",
    "\n",
    "# Cross the indices to get the unique samples\n",
    "unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx & unique_smiles_uniprot_idx].index\n",
    "# unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx].index\n",
    "test_df = active_df.loc[unique_samples]\n",
    "\n",
    "# Reporting\n",
    "print(f'Number of unique samples: {len(unique_samples)}')\n",
    "img = Draw.MolsToGridImage(\n",
    "    [Chem.MolFromSmiles(s) for s in test_df['Smiles']],\n",
    "    molsPerRow=5,\n",
    "    subImgSize=(400, 200),\n",
    "    legends=[f'{u}\\n({s})' for u, s in zip(test_df['Article DOI'], test_df['Database'])],\n",
    ")\n",
    "display(img)\n",
    "display(test_df[['Active', 'Active - OR']])\n",
    "print(f'Percentage of active/inactive PROTACs in test set:\\n{test_df[active_col].value_counts(normalize=True)}')\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               O60885\n",
       "Number of entries                                         55\n",
       "Number of entries per E3 ligase                            5\n",
       "Number of entries with same SMILES                        69\n",
       "Number of entries with same SMILES and not Uniprot        14\n",
       "Number of active entries                                  41\n",
       "Number of inactive entries                                14\n",
       "Name: 2, dtype: object"
      ]
     },
     "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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               P00533\n",
       "Number of entries                                         45\n",
       "Number of entries per E3 ligase                            4\n",
       "Number of entries with same SMILES                        45\n",
       "Number of entries with same SMILES and not Uniprot         0\n",
       "Number of active entries                                  22\n",
       "Number of inactive entries                                23\n",
       "Name: 3, dtype: object"
      ]
     },
     "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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               P10275\n",
       "Number of entries                                        107\n",
       "Number of entries per E3 ligase                            3\n",
       "Number of entries with same SMILES                       107\n",
       "Number of entries with same SMILES and not Uniprot         0\n",
       "Number of active entries                                  66\n",
       "Number of inactive entries                                41\n",
       "Name: 0, dtype: object"
      ]
     },
     "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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               O43353\n",
       "Number of entries                                          5\n",
       "Number of entries per E3 ligase                            3\n",
       "Number of entries with same SMILES                         5\n",
       "Number of entries with same SMILES and not Uniprot         0\n",
       "Number of active entries                                   5\n",
       "Number of inactive entries                                 0\n",
       "Name: 36, dtype: object"
      ]
     },
     "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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               P00520\n",
       "Number of entries                                         16\n",
       "Number of entries per E3 ligase                            3\n",
       "Number of entries with same SMILES                        17\n",
       "Number of entries with same SMILES and not Uniprot         1\n",
       "Number of active entries                                   1\n",
       "Number of inactive entries                                15\n",
       "Name: 16, dtype: object"
      ]
     },
     "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": [
    "# In active_df, for each uniprot ID, count the corresponding number of entries,\n",
    "# then count its corresponding number of entries per E3 ligase. The final dataframe\n",
    "# should have the following columns:\n",
    "# * Uniprot\n",
    "# * Number of entries\n",
    "# * Number of entries per E3 ligase type (e.g. CRL, VHL, etc.)\n",
    "test_candidate_df = active_df['Uniprot'].value_counts().reset_index()\n",
    "test_candidate_df.columns = ['Uniprot', 'Number of entries']\n",
    "test_candidate_df['Number of entries per E3 ligase'] = test_candidate_df['Uniprot'].map(active_df.groupby('Uniprot')['E3 Ligase'].nunique())\n",
    "# Sort by the number of entries per E3 ligase\n",
    "test_candidate_df = test_candidate_df.sort_values('Number of entries per E3 ligase', ascending=False)\n",
    "# Take the first row, then get all the SMILES associated to that Uniprot ID\n",
    "for row_idx in range(5):\n",
    "    uniprot_id = test_candidate_df['Uniprot'].iloc[row_idx]\n",
    "    smiles = active_df[active_df['Uniprot'] == uniprot_id]['Smiles']\n",
    "    # Get the entries in active_df that have the same SMILES but NOT the same Uniprot ID\n",
    "    uniprot_count = test_candidate_df.iloc[row_idx]\n",
    "    # uniprot_count['SMILES'] = smiles\n",
    "    uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
    "    uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
    "    # Get the number of active and inactive entries with the same UniProt ID\n",
    "    uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
    "    uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
    "    display(uniprot_count)\n",
    "    # Plot the distribution of E3 in active_df[active_df['Uniprot'] == uniprot_id]\n",
    "    sns.countplot(data=active_df[active_df['Uniprot'] == uniprot_id], x='E3 Ligase', hue=active_col)\n",
    "    plt.title(f'Distribution of E3 ligase for UniProt ID {uniprot_id}')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isolating _all_ entries with Uniprot ID (target) corresponding to P00533 seems to be a good addition to the test set. In fact, it has a balanced distribution of active and inactive entries, plus, the E3 ligase distribution is also quite balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution (in percentage) of E3 ligases\n",
    "warnings.filterwarnings('ignore')\n",
    "ax = sns.countplot(y='E3 Ligase', data=protac_df, order=protac_df['E3 Ligase'].value_counts().index)\n",
    "total = len(protac_df['E3 Ligase'])\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.2f}%'.format(100 * p.get_width() / total)\n",
    "    x = p.get_x() + p.get_width() + 0.02\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    ax.annotate(percentage, (x, y))\n",
    "# Set the x-axis to log scale\n",
    "plt.xscale('log')\n",
    "plt.title('Distribution of E3 Ligases')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('E3 Ligase')\n",
    "plt.grid(axis='x', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of entries: 20,594\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4b0cf1cf31f486eba7c1792295bbae0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading protein embeddings: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tid: Q07817, \tembeddings shape: (1024,), \tembeddings mean: -0.0005679130554199219\n",
      "\tid: P00533, \tembeddings shape: (1024,), \tembeddings mean: 0.001171112060546875\n",
      "\tid: Q9NWZ3, \tembeddings shape: (1024,), \tembeddings mean: 0.00041985511779785156\n",
      "\tid: P00519, \tembeddings shape: (1024,), \tembeddings mean: 0.0009603500366210938\n",
      "\tid: P11474, \tembeddings shape: (1024,), \tembeddings mean: -0.0018215179443359375\n",
      "\tid: Q16288, \tembeddings shape: (1024,), \tembeddings mean: 0.0010194778442382812\n",
      "\tid: O60674, \tembeddings shape: (1024,), \tembeddings mean: 0.0015687942504882812\n",
      "\tid: Q06187, \tembeddings shape: (1024,), \tembeddings mean: 0.0006914138793945312\n",
      "\tid: Q9UHD2, \tembeddings shape: (1024,), \tembeddings mean: 0.0012235641479492188\n",
      "\tid: Q8IXJ6, \tembeddings shape: (1024,), \tembeddings mean: -0.00042366981506347656\n",
      "KeyError for P31750\n",
      "KeyError for P00520\n",
      "KeyError for A8DG50\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "protein_embeddings = {}\n",
    "with h5py.File(\"../data/uniprot2embedding.h5\", \"r\") as file:\n",
    "    print(f\"number of entries: {len(file.items()):,}\")\n",
    "    uniprots = protac_df['Uniprot'].unique().tolist()\n",
    "    uniprots += protac_df['E3 Ligase Uniprot'].unique().tolist()\n",
    "    for i, sequence_id in tqdm(enumerate(uniprots), desc='Loading protein embeddings'):\n",
    "        try:\n",
    "            embedding = file[sequence_id][:]\n",
    "            protein_embeddings[sequence_id] = np.array(embedding)\n",
    "            if i < 10:\n",
    "                print(\n",
    "                    f\"\\tid: {sequence_id}, \"\n",
    "                    f\"\\tembeddings shape: {embedding.shape}, \"\n",
    "                    f\"\\tembeddings mean: {np.array(embedding).mean()}\"\n",
    "                )\n",
    "        except KeyError:\n",
    "            print(f'KeyError for {sequence_id}')\n",
    "            protein_embeddings[sequence_id] = np.zeros((1024,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the cosine similarity between E3 ligases embeddings\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "# Create a name mapping for the E3 ligases names and their uniprots\n",
    "e3_ligase_uniprot_mapping = protac_df[['E3 Ligase', 'E3 Ligase Uniprot']].drop_duplicates()\n",
    "e3_ligase_uniprot_mapping = e3_ligase_uniprot_mapping.set_index('E3 Ligase Uniprot').to_dict()['E3 Ligase']\n",
    "# Calculate the cosine similarity between E3 ligases embeddings\n",
    "e3_ligase_embeddings = {}\n",
    "for e3_ligase_uniprot in e3_ligase_uniprot_mapping.keys():\n",
    "    e3_ligase_embeddings[e3_ligase_uniprot] = protein_embeddings[e3_ligase_uniprot]\n",
    "e3_ligase_similarity = pd.DataFrame(cosine_similarity(list(e3_ligase_embeddings.values())), columns=e3_ligase_uniprot_mapping.values())\n",
    "# Set the index and columns of the cosine similarity dataframe as the E3 ligases names\n",
    "e3_ligase_similarity.index = e3_ligase_uniprot_mapping.values()\n",
    "# e3_ligase_similarity = e3_ligase_similarity.rename(columns=e3_ligase_uniprot_mapping)\n",
    "# Plot the cosine similarity between E3 ligases embeddings as it was a correlation matrix,\n",
    "# only the lower triangle is shown\n",
    "sns.heatmap(e3_ligase_similarity, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.yticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.title('Cosine Similarity between E3 Ligases Embeddings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use a sequence similarity tool to double check which E3s are similar to each others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add pDC50 column to the PROTAC dataframe\n",
    "protac_df['pDC50'] = protac_df['DC50 (nM)'].apply(lambda x: -1 * np.log10(x * 1e-9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'Uniprot', 'Smiles', 'E3 Ligase', 'InChI', 'InChI Key',\n",
       "       'Molecular Weight', 'Heavy Atom Count', 'Ring Count',\n",
       "       'Rotatable Bond Count',\n",
       "       ...\n",
       "       'Active (Dmax 0.9, pDC50 5.5)', 'Active (Dmax 0.9, pDC50 6.0)',\n",
       "       'Active (Dmax 0.9, pDC50 6.5)', 'Active (Dmax 0.9, pDC50 7.0)',\n",
       "       'Active (Dmax 0.9, pDC50 7.5)', 'Active (Dmax 0.9, pDC50 8.0)',\n",
       "       'Active (Dmax 0.9, pDC50 8.5)', 'Active (Dmax 0.9, pDC50 9.0)',\n",
       "       'Active (Dmax 0.9, pDC50 9.5)', 'pDC50'],\n",
       "      dtype='object', length=136)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "860"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:\n",
    "    \"\"\" Check if a PROTAC is active based on DC50 and Dmax.\t\n",
    "    Args:\n",
    "        DC50(float): DC50 in nM\n",
    "        Dmax(float): Dmax in %\n",
    "    Returns:\n",
    "        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN\n",
    "    \"\"\"\n",
    "    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan\n",
    "    Dmax = Dmax / 100\n",
    "    if pd.notnull(pDC50):\n",
    "        if pDC50 < pDC50_threshold:\n",
    "            return False\n",
    "    if pd.notnull(Dmax):\n",
    "        if Dmax < Dmax_threshold:\n",
    "            return False\n",
    "    if oring:\n",
    "        if pd.notnull(pDC50):\n",
    "            return True if pDC50 >= pDC50_threshold else False\n",
    "        elif pd.notnull(Dmax):\n",
    "            return True if Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "    else:\n",
    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
    "            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "\n",
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)',\n",
    "protac_df[active_col] = protac_df.apply(\n",
    "    lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1\n",
    ")\n",
    "tot_len = len(protac_df.dropna(subset=active_col))\n",
    "tot_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "E3 ligase &  E3 ligase (\\%) &  Unique PROTACs (\\% per E3) &  Unique targets (\\% per E3) &  Unique cell lines (\\% per E3) \\\\\n",
      "\\midrule\n",
      "      VHL &           43.8 &                       41.2 &                       58.0 &                          60.2 \\\\\n",
      "     CRBN &           50.7 &                       52.2 &                       77.0 &                          79.7 \\\\\n",
      "    Other &            5.5 &                        6.6 &                       15.0 &                          10.5 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E3 ligase</th>\n",
       "      <th>E3 ligase (%)</th>\n",
       "      <th>Unique PROTACs (% per E3)</th>\n",
       "      <th>Unique targets (% per E3)</th>\n",
       "      <th>Unique cell lines (% per E3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VHL</td>\n",
       "      <td>43.792464</td>\n",
       "      <td>41.164659</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.150376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CRBN</td>\n",
       "      <td>50.710315</td>\n",
       "      <td>52.208835</td>\n",
       "      <td>77.0</td>\n",
       "      <td>79.699248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other</td>\n",
       "      <td>5.497221</td>\n",
       "      <td>6.626506</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.526316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  E3 ligase  E3 ligase (%)  Unique PROTACs (% per E3)  \\\n",
       "0       VHL      43.792464                  41.164659   \n",
       "1      CRBN      50.710315                  52.208835   \n",
       "2     Other       5.497221                   6.626506   \n",
       "\n",
       "   Unique targets (% per E3)  Unique cell lines (% per E3)  \n",
       "0                       58.0                     60.150376  \n",
       "1                       77.0                     79.699248  \n",
       "2                       15.0                     10.526316  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "E3 ligase                       VHLCRBNOther\n",
       "E3 ligase (%)                          100.0\n",
       "Unique PROTACs (% per E3)              100.0\n",
       "Unique targets (% per E3)              150.0\n",
       "Unique cell lines (% per E3)       150.37594\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.\n",
      "NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.\n"
     ]
    }
   ],
   "source": [
    "# Plot the Dmax (%) and DC50 (nM) distributions side by side\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "# ax_idx = 0\n",
    "# sns.histplot(protac_df['pDC50'], bins=20, ax=axes[ax_idx], kde=False, color=palette[0])\n",
    "# # axes[ax_idx].set_title('pDC50 Distribution')\n",
    "# axes[ax_idx].set_xlabel('$pDC_{50}$ [$-log_{10}(M)$]')\n",
    "# axes[ax_idx].set_ylabel('Count')\n",
    "# axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# ax_idx = 1\n",
    "# sns.histplot(protac_df['Dmax (%)'], bins=20, ax=axes[ax_idx], kde=False, color=palette[1])\n",
    "# # axes[ax_idx].set_title('Dmax (%) Distribution')\n",
    "# axes[ax_idx].set_xlabel('$D_{max}$ [%]')\n",
    "# # Remove y-axis\n",
    "# axes[ax_idx].set_ylabel('')\n",
    "# axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Plot the Dmax (%) and DC50 (nM) distributions in one plot, in axes[ax_idx == 1]\n",
    "ax_idx = 0\n",
    "# sns.kdeplot(protac_df['pDC50'] * 10, ax=axes[ax_idx], color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5)\n",
    "# sns.kdeplot(protac_df['Dmax (%)'], ax=axes[ax_idx], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5)\n",
    "sns.histplot(protac_df['pDC50'] * 10, ax=axes[ax_idx], color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5, kde=True)\n",
    "sns.histplot(protac_df['Dmax (%)'], ax=axes[ax_idx], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5, kde=True)\n",
    "axes[ax_idx].set_xlabel('')\n",
    "axes[ax_idx].set_ylabel('')\n",
    "axes[ax_idx].legend(loc='upper left')\n",
    "axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "\n",
    "\n",
    "# # Plot the E3 ligase distribution\n",
    "# ax_idx = 3\n",
    "# sns.countplot(y='E3 Ligase', data=protac_df, ax=axes[ax_idx], order=protac_df['E3 Ligase'].value_counts().index, color=palette[2])\n",
    "# axes[ax_idx].set_xscale('log')\n",
    "# axes[ax_idx].set_xlabel('Count')\n",
    "# axes[ax_idx].set_ylabel('E3 Ligase')\n",
    "# axes[ax_idx].grid(axis='x', alpha=0.5)\n",
    "\n",
    "# Create a new dataframe for which, for each E3 ligase name, we have:\n",
    "# - the percentage of unique PROTACs associated to it\n",
    "# - The percentage of unique POI associated to it\n",
    "# - The percentage of unique cell lines associated to it\n",
    "tmp = protac_df[protac_df[active_col].notna()].copy()\n",
    "tmp['E3 ligase'] = tmp['E3 Ligase'].apply(lambda x: x if x == 'VHL' or x == 'CRBN' else 'Other')\n",
    "e3_ligase_stats = pd.DataFrame()\n",
    "e3_ligase_stats['E3 ligase'] = tmp['E3 ligase'].unique()\n",
    "e3_ligase_stats['E3 ligase (%)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * len(tmp[tmp['E3 ligase'] == x]) / len(tmp['E3 ligase'])\n",
    ")\n",
    "e3_ligase_stats['Unique PROTACs (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Smiles'].nunique() / tmp['Smiles'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique targets (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Uniprot'].nunique() / tmp['Uniprot'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique cell lines (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Cell Line Identifier'].nunique() / tmp['Cell Line Identifier'].nunique()\n",
    ")\n",
    "\n",
    "print(e3_ligase_stats.round(1).to_latex(index=False))\n",
    "display(e3_ligase_stats)\n",
    "display(e3_ligase_stats.sum(axis=0))\n",
    "\n",
    "# stacked Plot the distribution of PROTACs, POI and cell lines associated to each E3 ligase\n",
    "ax_idx = 1\n",
    "e3_ligase_stats.plot.barh(x='E3 ligase', y=['E3 ligase (%)', 'Unique PROTACs (% per E3)', 'Unique targets (% per E3)', 'Unique cell lines (% per E3)'],\n",
    "    stacked=True,\n",
    "    ax=axes[ax_idx],\n",
    "    color=adjusted_palette,\n",
    "    grid=False,\n",
    ")\n",
    "axes[ax_idx].set_xlabel('Percentage (stacked)')\n",
    "axes[ax_idx].set_ylabel('')\n",
    "axes[ax_idx].legend()\n",
    "# Set the x-axis to log scale\n",
    "axes[ax_idx].grid(axis='x', alpha=0.3)\n",
    "# For 'VHL' and 'CRBN' E3 ligases, show the percentage of PROTACs, POI and cell lines associated to them\n",
    "# for i, e3_ligase in enumerate(['VHL', 'CRBN']):\n",
    "#     axes[ax_idx].text(\n",
    "#         0.5, i, f'{e3_ligase}\\n'\n",
    "#         f'{e3_ligase_stats.loc[e3_ligase_stats[\"E3 Ligase\"] == e3_ligase, \"PROTACs (% per E3)\"].values[0]:.1f}%',\n",
    "#         ha='center', va='center', color='black'\n",
    "#     )\n",
    "# Put the percentages on top of the bars if the bar corresponding to the E3 ligases 'VHL' and 'CRBN'\n",
    "for i, p in enumerate(axes[ax_idx].patches):\n",
    "    if p.get_width() < 20:\n",
    "        continue\n",
    "    percentage = '{:.1f}%'.format(p.get_width())\n",
    "    x = p.get_x() + p.get_width() / 2\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    axes[ax_idx].annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "# # Plot the number of active and inactive PROTACs\n",
    "# ax_idx = 2\n",
    "# sns.countplot(x=active_col, data=protac_df, ax=axes[ax_idx], palette=palette[2:])\n",
    "# # Change the x-axis labels to 'Inactive' and 'Active'\n",
    "# axes[ax_idx].set_xticklabels(['Inactive', 'Active'])\n",
    "# axes[ax_idx].set_xlabel('')\n",
    "# axes[ax_idx].set_ylabel('')\n",
    "# axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "# # Put the percentages on top of the bars\n",
    "# total = len(protac_df[protac_df[active_col].notna()])\n",
    "# for p in axes[ax_idx].patches:\n",
    "#     percentage = '{:.1f}%'.format(100 * p.get_height() / total)\n",
    "#     x = p.get_x() + p.get_width() / 2\n",
    "#     y = p.get_height() + 0.02\n",
    "#     axes[ax_idx].annotate(percentage, (x, y), ha='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('dataset_distributions.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "print('NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.')\n",
    "print('NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRBN      53.39\n",
       "VHL       40.12\n",
       "IAP        2.80\n",
       "MDM2       1.26\n",
       "cIAP1      0.98\n",
       "XIAP       0.93\n",
       "FEM1B      0.37\n",
       "Ubr1       0.09\n",
       "RNF114     0.05\n",
       "Name: E3 Ligase, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename E3 ligase Mdm2 to MDM2\n",
    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Mdm2', 'MDM2')\n",
    "# Percentage of each E3 ligase in the dataset\n",
    "e3_ligase_percentage = protac_df['E3 Ligase'].value_counts(normalize=True) * 100\n",
    "e3_ligase_percentage.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrr}\n",
      "\\toprule\n",
      "E3 Ligase &  E3 Ligase (\\%) &  Active (\\%) &  Inactive (\\%) \\\\\n",
      "\\midrule\n",
      "     CRBN &          53.39 &       49.49 &         50.51 \\\\\n",
      "    FEM1B &           0.37 &       50.00 &         50.00 \\\\\n",
      "      IAP &           2.80 &       10.00 &         90.00 \\\\\n",
      "     MDM2 &           1.26 &       16.67 &         83.33 \\\\\n",
      "     Ubr1 &           0.09 &       50.00 &         50.00 \\\\\n",
      "      VHL &          40.12 &       56.09 &         43.91 \\\\\n",
      "    cIAP1 &           0.98 &        9.09 &         90.91 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "\n",
    "# Percentage of True/False in active_col per E3 ligase\n",
    "tmp = protac_df.groupby('E3 Ligase')[active_col].value_counts(normalize=True).unstack().fillna(0).round(4) * 100\n",
    "# Rename the columns\n",
    "tmp.columns = ['Inactive (%)', 'Active (%)']\n",
    "tmp.reset_index(inplace=True)\n",
    "# Add a column with the percentage of each E3 ligase in the dataset\n",
    "tmp['E3 Ligase (%)'] = tmp['E3 Ligase'].map(e3_ligase_percentage)\n",
    "tmp = tmp.round(2)\n",
    "print(tmp[['E3 Ligase', 'E3 Ligase (%)', 'Active (%)', 'Inactive (%)']].to_latex(index=False, bold_rows=True))\n",
    "# Plot tmp as a countplot of E3 ligases percentages, with each bar showing the percentage of active and inactive PROTACs\n",
    "# Percentage of True/False in active_col per E3 ligase\n",
    "tmp = protac_df.groupby('E3 Ligase')[active_col].value_counts(normalize=True).unstack().fillna(0).round(4) * 100\n",
    "# Rename the columns\n",
    "tmp.columns = ['Inactive (%)', 'Active (%)']\n",
    "tmp.reset_index(inplace=True)\n",
    "# Add a column with the percentage of each E3 ligase in the dataset\n",
    "tmp['E3 Ligase (%)'] = tmp['E3 Ligase'].map(e3_ligase_percentage)\n",
    "tmp = tmp.round(2)\n",
    "\n",
    "# Sort the rows according to the E3 ligase percentage\n",
    "tmp = tmp.sort_values('E3 Ligase (%)', ascending=False)\n",
    "\n",
    "# Create the bottom bar which is the 'Inactive (%)', we will stack 'Active (%)' on top of it\n",
    "ax_inactive = plt.bar(tmp['E3 Ligase'], tmp['E3 Ligase (%)'] * tmp['Inactive (%)'] / 100, color=adjusted_palette[0], label='Inactive (%)')\n",
    "# The bottom parameter is set to 'Inactive (%)' so that 'Active (%)' starts where 'Inactive (%)' ends\n",
    "ax_active = plt.bar(tmp['E3 Ligase'], tmp['E3 Ligase (%)'] * tmp['Active (%)'] / 100, bottom=tmp['E3 Ligase (%)'] * tmp['Inactive (%)'] / 100, color=adjusted_palette[1], label='Active (%)')\n",
    "\n",
    "# Add the value of column E3 ligase (%) on top of the bars Active (%)\n",
    "for i, p in enumerate(ax_active):\n",
    "    percentage = tmp['E3 Ligase (%)'].iloc[i]\n",
    "    if percentage < 3:\n",
    "        continue\n",
    "    percentage = tmp['Active (%)'].iloc[i]\n",
    "    percentage = f'{percentage:.1f}%'\n",
    "    x = p.get_x() + p.get_width() / 2\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    plt.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "for i, p in enumerate(ax_inactive):\n",
    "    percentage = tmp['E3 Ligase (%)'].iloc[i]\n",
    "    if percentage < 3:\n",
    "        continue\n",
    "    percentage = tmp['Inactive (%)'].iloc[i]\n",
    "    percentage = f'{percentage:.1f}%'\n",
    "    x = p.get_x() + p.get_width() / 2\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    plt.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "for i, (active_p, ax_inactive_p) in enumerate(zip(ax_active, ax_inactive)):\n",
    "    percentage = tmp['E3 Ligase (%)'].iloc[i]\n",
    "    percentage = f'{percentage:.1f}%'\n",
    "    x = active_p.get_x() + active_p.get_width() / 2\n",
    "    y = active_p.get_y() + active_p.get_height() + 1\n",
    "    plt.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "# Set y-axis labels as percentages\n",
    "for ax in [ax_active, ax_inactive]:\n",
    "    plt.gca().set_yticklabels([f'{int(y)}%' for y in plt.gca().get_yticks()])\n",
    "\n",
    "plt.ylabel('')\n",
    "plt.xlabel('E3 Ligase')\n",
    "# plt.title('Percentage of Active/Inactive PROTACs per E3 Ligase')\n",
    "# Set legend below the plot\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
    "\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.savefig('active_inactive_per_e3_ligase.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TZ4sMhLvIBPA2Q9HH4PN0zbalyynYiYiI9GVNVfDB3YERsHFZbMm5hp0VjV27D5sDsqcGQl5zNRQvD0yjIr2Ogp2IiEhf5W6AD++B+lKIToWZv8Jri+mefUVEQ/YZgalP6oqhcmP37EeOi4KdiIhIX+T3wae/DdxJIjIRZt4PzuTu3aczJTAgA2DfusAdLKRXUbATERHpi1Y/B6UFgSlMpt8DsRk9s9+EQRA3ADCh+LNAwJReQ8FORESkr9n+Hmx+PfD8tPmB0a89xTAg89TA9XauWtj3Zc/tW9qkYCciItKXVO2ElU8Hno+6DLJP7/kabA7of2rg+f7NgQEc0iso2ImIiPQVnkZY+hD43JA5HkZeGrpaYvsH7juLGTglrFGyvYKCnYiISF9gmvD57wITETtT4LSfBEaohlL6WDCsgduO1ewKbS0CKNiJiIj0DTvfh6JPA0Fqys8Ct/oKtYjowD1oAcrWaOLiXkDBTkREpLerK4WVzwSej74cUnJDW8/hkodDRGzgrhSVm0JdzQlPwU5ERKQ38/vgs4cD92rtNwLyLg51RS1ZrJA2OvB8/6ZAwJOQ6XCw+/jjjzn//PPJzMzEMAz+9a9/tXj/6quvxjCMFo9Jkya1aONyuZg3bx4pKSlER0dzwQUXUFxcfFwfREREJCwVvhboCbM7YfL8QJDqbeKyIDIJ/F6o0B0pQqnDwa6hoYExY8bwxBNPHLXNN77xDUpLS4OPt956q8X7t9xyC4sWLeKVV15h6dKl1NfXM2fOHHw+TXIoIiISVL0b1r0ceJ5/feC2Yb2RYXzVa3dga+BWZxISto6uMHv2bGbPnn3MNg6Hg/T09Fbfq6mp4dlnn+XFF1/krLPOAuCll14iKyuL9957j3POOaejJYmISB9SVFREZWVlh9dLSUkhOzu7Gyrqpfw+WP5ooBcscwIMmhHqio4tJh2cqdBYHui16z8h1BWdkDoc7Nrjo48+IjU1lYSEBKZNm8YDDzxAamrgr4yCggI8Hg+zZs0Kts/MzGTkyJEsW7as1WDncrlwuVzB17W1tQD4/X78fn93fAQREekGe/bsYeTIkTQ2NnZ4XafTyfr168nKyuqGynqhwtewHNiGaY/GnHBjYLqTdswVZ5omFosFE/D36NRyBqSOwrLrfczqHZj9RgROH7fBhEC9pqnf6UfRkePS5cFu9uzZXHLJJeTk5LBz507uvvtuZs6cSUFBAQ6Hg7KyMiIiIkhMTGyxXlpaGmVlZa1uc+HChdx7771HLK+oqKC5WRdpioj0FcXFxeTl5XHJD35Camb7A1r53j38/blHKC4uxuFwdGOFvYOlaT/91r0CQM2Q79Bc54O68nat29zcTH5+Ps2WWMqbevh6PEsGSY5+RLgqaNi3lfrkcW2u0myJDdTb3Ex5efs+44mmrq6u3W27PNhdeulXs2CPHDmS8ePHk5OTw3/+8x8uvvjoI3lM08QwjFbfu/POO5k/f37wdW1tLVlZWfTr14+4uF4wj4+IiLRLSUkJBQUFXHfXUHJOHtvu9dxEUlBQQGRkZPAMUDgzlj6L4Xdj9htJ3OhvEneU34+tOXSMI/3fJDUqpRurPIq0PCiqILpuC86MPLBGHLN5ib/uhPradkZkZGS723bLqdjDZWRkkJOTw9atWwFIT0/H7XZTVVXVoteuvLycyZMnt7oNh8PR6l9oFosFi0UztoiI9BWGYRw8rWRgdOiuCYH1DMMI/5/7ewugeDkYFowJN2BYO9brdugYG4Cl/Xmw68RmQmQCRnM1xoEtX01gfBQGnDhf207qyHHp9iO4f/9+9uzZQ0ZGBgD5+fnY7XYWL14cbFNaWsr69euPGuxEREROCD4PrPx94PnwCyAhJ7T1dIZhQEpe4Pn+LYHBH9JjOtxjV19fz7Zt24Kvd+7cyZo1a0hKSiIpKYkFCxbwrW99i4yMDHbt2sXPf/5zUlJSuOiiiwCIj4/nmmuu4dZbbyU5OZmkpCRuu+02Ro0aFRwlKyIickIqfA3qSyEyEUZ9L9TVdF58Nuz7EjwNULMbEk8KdUUnjA4Hu5UrVzJjxldDrg9d+3bVVVfx9NNPs27dOv785z9TXV1NRkYGM2bM4NVXXyU2Nja4ziOPPILNZuM73/kOTU1NnHnmmTz//PNYO9jdLCIiEjYaymHD3wLPx/2wXSNKey3DAslDA/eP3b8FEgYHevKk23U42E2fPh3zGMOt33nnnTa3ERkZyeOPP87jjz/e0d2LiIiEp4I/gs8duCYtZ1qoqzl+iSfBvnXQXA2NFb13cuUw0+2DJ0RERMJBZydWbo+4+q0MKf4MEwuF0VNpXr2609sqLCzswsqOgzUCEgfBgW2wf7OCXQ9RsBMREWlDUVEReXm5NDY2dfm2I2wW1v96GqRH88hb27j1L+d2yXbr6uq7ZDvHJWlYINjVloC7HiJiQl1R2FOwExERaUNlZSWNjU289OBN5A3O7NJtp/lL6G/uwYOdmXO+Q8H5x/er+a1P1nL3k3/vHRP4R8ZDdDo0lMGB7ZA+JtQVhT0FOxERkXbKG5zJuLxBXbdBdwNsXQGAfcB4TkkYeNybLNy597i30aWShgSCXfUOSBsVGFgh3UZHV0REJFTKVoHpA2c/iO+Dc9a1R1x/sEWCtzlwSla6lYKdiIhIKNSVQm0xYEDm+PCdDsSwBKY7Aajaduy2ctwU7ERERHqa3welBYHnycMgMiGk5XS7pIMTFNeXBQZRSLdRsBMREelplZvAXRc4RZk6KtTVdL+ImMAgCggMopBuo2AnIiLSk9wNULEh8Dx9LFjtoa2npxzqtaveCaY/tLWEMQU7ERGRnnQiDJhoTWz/wKTF3iao3xfqasKWgp2IiEhPOVEGTLTGYv0qyFbvCG0tYUzBTkREQsY0od4F1U1Q0wS+cD5Dd6INmGhN4sE5AGtLAvfFlS6nCYpFRKTH+bCxrQIq6sF7WJgzgPgoyEqEhKiQldc99p9gAyZaE5kEjnhw1UBNUWDyYulS6rETEZEeY5ow4bwb2O8YQWltINRZLeC0g8MGJoHeu3V7YWMZeHyhrriLuBug/AQcMPF1hgEJB3vtqneGtpYwpR47ERHplKKiIiorK9vd3m/C0uI4Lpj3FCYQ64CcpEAPneXgpWZNHiiphrJa2N8QOE07Ir1byu9ZZatPzAETrUkYCPvWQmMluOpCXU3YUbATEZEOKyoqIi8vj8bGxna1t1isfPtnLzFq2qX4fT4czbsZM3jwEWMHouwwpB+kx0FhGTR74cu9EGc4u+FT9JC6Uqjdwwk5YKI19iiISQtMVlyzG4gOdUVhRcFOREQ6rLKyksbGRu763xfJGZJ3zLYmUGvLptmWgun38fJ9F3HVtXMxjMFHXSfGAWMHwPpSqHNBVcQQ0gaO7OJP0QM0YKJ18QMDwa56F5gnh7qasKJgJyIinZYzJI9hI8Yds01xNZTvDzz3FH/K5s//DdfObXPbNiuMzIQNpVDbbOOK+/5Ns6+mC6ruQS0GTPTBYNpd4gbAXiu464iyNIS6mrCiwRMiItJtqptg58FQNzgF/HXFHVrfZoGT08HqbyYhNZuC2sH4/GY3VNoNjhgwERHaenoTqx3i+gOQZLb/Ok1pm4KdiIh0C48PNh+8wUBqLGTGdW47diskeLbRWLufGm80H2zpuhq7lQZMHNvBY5Jo7g8OnpHjp2AnIiLdYnsluH0HB0SkHN+YAZvpZtHDPwTg892wtbyX99ppwETbYjLAGkEEHqafnBzqasKGgp2IiHS5yvrA5MMAw1MDc9Udr03L32RgVDkAb6yH2uZeGu40YKJ9LFaIywLgssn9Q1xM+FCwExGRLuXzw46D19VlJUBsZNdte3j0XtLjAvPdLfoS/GYvDHcaMNF+iYOpMNJ45oOiUFcSNhTsRESkS+2pApc3cCeJrMSu3bbVMLl4NERYA/tZ3ttuXtBiwMQpGjDRFmcKeyyD+GJ7dagrCRsKdiIi0mUa3YHpTSAwCrYrTsF+XVK0wTkHp85bsg3K63pRr12LARMDQ12NnIAU7EREpEuYZmDAhAkkOiG5G28WMToThvUDnwmvr6N3TIFSt1cDJiTkFOxERKRL7G8IzFtnACcd5yjYthiGwbkjAiNu99XBJ9u7b1/t4vfC3kMDJoZrwISEjIKdiIgcN7/51UTEAxIDgau7xTgMZh+8G9WnO2FvTQh77So2gqcebFEaMCEhpWAnIiLHrawWmr2ByYSzEnpuvyenG4xID5wGfn0deHwhCHeuWqgsDDzPyA/cVUEkRBTsRETkuPj8UFQVeJ6d2D0DJo7lG3kQExE4FfzR1p7dN6YJe1eC6Q9MuBs3oIcLEGlJwU5ERI5LSU3g9mGRNkjv5G3DjkdUhMF5B89+fr4bdh/owV67miJo2AeGJdBbpwETEmIKdiIi0ml+rMHpTXKSCNk9P4f2Mzjl4M0L3lwPLm8PhDufG8pWBZ73GwGO2O7fp0gbFOxERKTTGmzp+PwQHQH9YkJby9m5EB8ZGJn7/uYe2OG+L8HbDBGxkJLXAzsUaZuCnYiIdEpcSn8arf0AGJgU+rOQDpvB+QdPya4qhu2V3dhr11gBBw5e0Jc5PnDfU5FewBbqAkREpG+accUCMCzERQYmJO4JhYWFbbYZGNWfXU2pvLbazdTETWSkJpKdnd11Rfh9UPJF4HnCIIhJ77ptixwnBTsREemweq+DcWdfDcCg5O7vrdtfUQYYXHHFFW22tTuiuOnJVaQMGM5v/7aW/z55I4WFhV0X7io2BqY4sTogfWzXbFOkiyjYiYhIh21uyMRiteLwVRPXA3dZqK+tBkxu/sXjjBl/Wpvt3YZJlWlyyplXUrjsX3zyySfk5XXsOriUlJQjwmCk2QiVGwMvMseDzdGhbYp0NwU7ERHpkJJqk33uBPw+HzHevUBCj+27f85Qho0Y1662u/bDnmq4YN7/ceNNp1B3oLRD+3I6nS16+iwG5Ph3AH6I7Q9xWR2sXqT7KdiJiEi7mabJBwfHDKx5/8/MnjomtAUdQ3YS7C6tIjqhH7c8vYIBjjLae8Z497ZCHrjtSiorK4PB7qazBxJNPVhsgd66UI8WEWmFgp2IiLTbjv2w+wBY8PPBS/cye+proS7pqCwGeIqXYmZOIzI+k4iETAYld25bEZ5qFl6aG3iRdgrYe2i0iEgHaboTERFpF9M0+fBgb112VCU15UWhLagdTHcd/3rkGgCKqwO3Hev4RvzklC4iJtJGPbGQNKRLaxTpSgp2IiLSLoX7oKwWIqxwknNfqMtptw1L/4lRtxuAzeXQ5OngBjb/m9jGXTQ0e9ltOUmnYKVXU7ATEZE2+fwmHx3srZs0EBwWb0jr6SijeguxDvD5obAM/P72redwVcDaFwD46V8LcRmR3VilyPFTsBMRkTatKYEDjeCMgIkDQ11NxxmY5KWDzQINbthe2fY6VovBwNJF4HNT6zyJp9/b3f2FihwnBTsRETkmj8/kk+2B52cMDty6qy9y2CA3LfC8rA721R67/R0XnER0cwnYo9md8c3uL1CkCyjYiYjIMX2xG+pdEB8FY/v41G2JTshODDzfVgkNrtbbJftK+eVFwwIvxv8Ijz2+ZwoUOU4KdiIiclR1LpNPdwSeTx8CNkvf7K07XHYiJEaB34SN+8Dra/m+4Xcz3bUIu81CdUweDJwRmkJFOkHBTkREjurDLeD2Qf94GJkR6mq6hmHA8LTAqdlmD2ypANP86v2Tiv5EklnOvhoXRelzNApW+hQFOxERaVVJtcmXewPPZ+WCEUYBx26FvDQwCMxtt7cmsDzlwDL6l78FwPefXoPXFhO6IkU6QcFORESOYJom724KPB+dCf0TwifUHRIbCYNTAs93HgBfXTnDdj4OwFr76by7riKE1Yl0joKdiIgcYV0plNQEJiOeMSzU1XSfjDhIcoLh95K77X+x+xqojR7GCvvMUJcm0ikKdiIi0kKzx+SDzYHnpw+GWEf49dYdYhgwtB9Mb/4rmZ5NuA0nhUNuwzSsoS5NpFNsoS5ARER6l/c2Q7070JPVFycj7qjUhjWMbvwHAG9Gz8XqSwf2hrYokU5SsBMRkaCd+03WlASezxkZHtObHIvDVUHe9v+Hgcmm2HMojJhCRDnE64SW9FH6zhURESBwh4n/bAg8z8+C7MTwDnWG383J2x4iwltDvXMQZUOuJdIWmN6lwRYmc7vICUfBTkREAFiyDaqbAqNFZ4bxgAkATJOhu39PXMNWPNZYNgy9E8Pu4KR+gbcbramkDx4T2hpFOkHBTkREKK42+XxX4Pm5J/fd+8G2V0bFO2RULMbEQuGQ22h2pAOB6wpTogHD4IJ5T7eYuFikL+hwsPv44485//zzyczMxDAM/vWvf7V43zRNFixYQGZmJlFRUUyfPp0NGza0aONyuZg3bx4pKSlER0dzwQUXUFxcfFwfREREOqfZY/KvL8EkcHeJof3CO9TF1W1iyO5nANg54Aqq4se2eH9wChimj6y8SRQ1J4eiRJFO63Cwa2hoYMyYMTzxxBOtvv+b3/yGhx9+mCeeeIIVK1aQnp7O2WefTV1dXbDNLbfcwqJFi3jllVdYunQp9fX1zJkzB5/P1+o2RUSke5imyX83Bk7BxkfBN/JCXVH3inAf4ORtD2ExvVQkTmZPxreOaOOwQYw3MCp2c0MmDW5120nf0eFRsbNnz2b27NmtvmeaJo8++ih33XUXF198MQAvvPACaWlpvPzyy1x//fXU1NTw7LPP8uKLL3LWWWcB8NJLL5GVlcV7773HOeeccxwfR0REOuLLvbChLDCf20WjIdIevr11Fp+LEVsfxOE5QENUFpsH/89R7wMb5atgy+5KMoaM5dMdkNLDtYp0VpdOd7Jz507KysqYNWtWcJnD4WDatGksW7aM66+/noKCAjweT4s2mZmZjBw5kmXLlrUa7FwuFy6XK/i6trYWAL/fj9/v78qPICJywtjfAG8XAhhMPckkMw78/vb1TpmmicViAUxMs2M/h0Oznp/cHf+PuIYteKyxrB9yJ15LJBxlWwYmi5+7i+8/8F8KikzOSLBjsVgwgXYeohAyDtZq9IFaA5cAWCwWTNPU7/Sj6Mhx6dJgV1ZWBkBaWlqL5WlpaezevTvYJiIigsTExCPaHFr/6xYuXMi99957xPKKigqam5u7onQRkROKx2fwxrZEPD47GdFuTnJWUV7e/vWbm5vJz88ngma89e1fMd5pJT8/n5gIf4+uNytyGf2q1uA3bKwdcAN1XhscYzsRNJNgOUCCtZpqXwKb6lLJz8+n2RJLeVPvviuFNSaF/Px8/JFJvb5WgGZLbODYNjdT3pFvwhPI4ZeztaVbJig2vta1bZrmEcu+7lht7rzzTubPnx98XVtbS1ZWFv369SMuLu74CxYROYGYJrz2JVQ1G0RHmHx7nJ3YyNQObaOkpISCggLcRGKLaf+6NY0+CgoKuMht6bH18uPKybesAWDToHnUJ5/e5i8/N4HPtzCmjM9qEijz9mNPZTOR/jpSo3r3iVlffSUFBQVYmqeQGtX7r10v8ddRUFBAZGQkqakd+z48UURGRra7bZcGu/T0wHDxsrIyMjK+mtyxvLw82IuXnp6O2+2mqqqqRa9deXk5kydPbnW7DocDh8NxxHKLxXKwi11ERNrrk+0mm8vBasAlYw3inR2/rs4wjIOnhwwMo2M/h3tyvaERpSy4agQAO/tfRkXKDNr3aQOfLzGiidw02LTPYOaV92Kwkd5/M47AKU0Dsw/UCgaBr61hGPqdfhQdOS5degQHDRpEeno6ixcvDi5zu90sWbIkGNry8/Ox2+0t2pSWlrJ+/fqjBjsREekaW8pNlmwLPP/GyTAgoQ/85u+k6IYdXJrwKTarhXXe4RRlXtqp7UwfAmCSN/lCqqz9u7RGka7W4R67+vp6tm3bFny9c+dO1qxZQ1JSEtnZ2dxyyy08+OCDDB06lKFDh/Lggw/idDq57LLLAIiPj+eaa67h1ltvJTk5maSkJG677TZGjRoVHCUrIiJdr7I+MF8dwPgsGDsgfENdVPNeRm/+JREWLx9urOSLgTcyqY1Lgo4mJcZgQGQlxc0pbIucwpl80cXVinSdDge7lStXMmPGjODrQ9e+XXXVVTz//PPcfvvtNDU1cdNNN1FVVcXEiRN59913iY2NDa7zyCOPYLPZ+M53vkNTUxNnnnkmzz//PFZr77/IU0SkL2r2mPxtdeA+qNmJcHZuqCvqPhHu/YzedA8R3hr2ehK48OG3ueN3x/f75STnPooaEqmwn0SZZyvp9qouqlaka3U42E2fPh3zGPdYMQyDBQsWsGDBgqO2iYyM5PHHH+fxxx/v6O5FRKSD/KbJoi/hQCPERcK3xoC1L1x81Qk2Ty2jN99DpLucRkcGL5RPpLbppU5vr7CwEICiLYWsX7eW0TMu4/3KQcxkY6vtnU4n8fHxnd6fyPHqllGxIiISGkVFRVRWVrZYtqk+gx1N6VjwMypyC5s3NLV43+VytTpA7VgOBZ7exOJrYtSW+4hu2oPLnsyXuffRsPv9Tm1rf0UZYHDFFVcEl6UNGsXoGZexwz+ErX97B2p3H7Ge3W5n7s03K9xJyCjYiYiEiaKiIvLy8mhsbAwuGzn1O1z681cAeOWhK7jro1eOWM8wjGOeiTmW+vr6zhXbxQy/m5FbHwxOQPxl7r24HGltr3gU9bXVgMnNv3icMeNPY/f2TTxw6xVE1WygKX4Egy7+LSObF7dYp+ZAJR+/u4jGxkYFOwkZBTsRkTBRWVlJY2Mjd/3vi+QMycNjRHEgYjgATu8+5t1yG9xyW4t1ln/0X/706N3BANNeh9Zrbgr9JPGHQl1i7Vp8lkjWDf8ljVHZXbLt/jlDGTZi3Fevm1ayLX4EJREjmBCzHafRdIy1RXqegp2ISJjJGZLHoNxxrC4GvJAYBSMy0jCMI3uwdm/fBBwZYNpyaL1QM/weRmx9iKSaVfgsDtYNu4e6mGHdtr8YVzH9jHIqzFQKfbnk21Z3275EOkPBTkSkHVq7dq09UlJSyM7umt6j9jKBzfvA5YVIGwxPO+q97vu0QKhbSHLNSnyWCNYPu5uauJHdvt+R1o186E1lm28Io6zriDC83b5PkfZSsBMRaUNr1661l9PppLCwsEfDXYMtg4YmsBhwcjrYw3AmKcPv4eRtvw6EOiOC9UPvpjpudI/sO9Oylzijhloznh2+weTatvTIfkXaQ8FORKQNX792rb12byvkgduupLKysseC3dAJs2mwBW7pOKQfRHdssGufcCjUpVR/EQh1w35BdfyYntu/AbnWzXzhPZVNvuEMs27FYnRu8IlIV1OwExFpp5wheR26Dq2nNfoi+PbtLwKQEQdpsW2s0AfZ8DByy/0k1a7Gb9jZMOwuquNP6fE6Blt2soZTqCeWvf5MBlhLerwGkdbobrsiImHA7zdZU5uDMzYJm7+BwSmhrqjrxURaudTxJkm1q4MDJarix4akFpvhY4g1cHvNTb7hIalBpDUKdiIiYeCTHVDtjaG5oYYEz07C7cYSkYab9+6cRLZ1L16rky+H39ujp19bM8y6BQM/ZWY61X7NWye9g4KdiEgfV1RlsnR74Pkbv7sBq+kObUFdzO6p5pqkD5g4JJEm08Ha3PupjT051GURYzSSZSkG1GsnvYeCnYhIH9bkMfnXl4EpTvo79rNuyauhLqlLRTaXccrGO8iwV1NW3cxLzRdTHz0k1GUF5Vo3A7DTPxC3ERniakQU7ERE+rT/boTaZkh0woiY4lCX06ViGrYztvB2nK69VPmcTP3VZ1SayaEuq4V+RgWJxgF82Ci2jwp1OSIKdiIifdXGMpONZYHpNy4aDTaLP9QldZmEmjWMKfw5EZ5q6p2D+P3+s9la1hDqso4QmPokMI9dUcQYMPRrVUJL34EiIn1Qg8vk7Y2B56cPgsz48BktkVq5hFFb7sPmb6IqbjRrch+k3h8V6rKOKseymwhcNFniMbKnhbocOcEp2ImI9DGmafLWRmj0QGoMnHFSqCvqOgNK/0Xejv+HxfRSnnQG64b9Ep8tOtRlHVNg6pPA6BVjxBUhrkZOdAp2IiJ9zIZS2FweuGXYBaPAGg5zm5h+Bhc9y0l7/gRAcdoFFJ50K6bFHuLC2meYdSuYfiwDTqeapFCXIycwBTsRkT6k3mXydmHg+RknQXpc3w91ht9D7vaHySp7HYDtWT9ge/Y1fep6tRijgVTvDgA2ckpoi5ETmm4pJiLSR5imyX83QrMX0mNh8qBQV3T89hdv5tSyp0nz78CPhSWOb7LtQA4cWN2iXWnxTgBKdm9ly4b231ajq9bbvX1Tm+vkuFdTbh/CNk7G5d+Kw+Jt9/5EuoqCnYhIL1RUVERlZWWLZaWuBDbXDsLAZIhtM2vXNLV4v7CwsCdLPG41e9YzaPlcBuTEUd/s5eJHV7J43RvHXOfJ++d1al9dtV5TY/1R2yb7ijCrtuNJPIkvmwYxIXprp/YpcjwU7EREepmioiLy8vJobGwMLnPGJTPvmQ3EJMAHL/2KX7y04Kjr19cfPXz0FolGNdPLXiIuJ44Gr4XV/jH8z9zT+Z+jtC8u2sbqZR8yYdps0jMGtHs/XbXeh5+v5eE//QO3q/mo6xiAf8NfsE65h5UNwxjv3IrR98+USx+jYCci0stUVlbS2NjIXf/7IjlD8gCosQ+k2ZqEzd/Epd8+n+9+e84R6y3/6L/86dG7aW46evjoDSYMTuD7kf/AaTazrayBotjJDB6aR9Yx1nFST1lhJMMHZpKV0/5z0F213raive1az9z6OvYpP2O/L46d7nQGO8ravU+RrqBgJyLSS+UMyWPYiHHsb4B9B/PByKwoYiPHttq+PdeBhdrQiL3cedcknEYzFZYMJt/7An/67cxQl9V1PA0MZSMbGcvKhqEKdtLj+s6QIxGRE5DXB9sqAs8HJEBsH74daVrF+1yR+AnRkTZ2+LL4d+TVVNS6Q11Wl8tjDQBbXP2p8vbuOfgk/CjYiYj0Yjv2g9sHUXbITgx1NZ1kmmTt/Tu5Ox/Dapi8uLSYv7vm4DUcoa6sWyRQxeCIUsBgVePQUJcjJxidihUR6aVclliq6wLPh/YDazv/FK/eX86WDava1ba7pxExTD+nud9msPcLAP5bmsFV//dvbrprB3W1Ve3eX18zPnoLO9wZrG4czNTYddgNX6hLkhOEgp2ISC8UERVDrT0bgMx4iO/ArVIfvec63G5Xh/bXHdOIOOwWXrzxFEZMzMTvN/nJSxv53Tv/PmK9Y00h0lcNcZQSb62nxhfD+qYcxjp3hLokOUEo2ImI9ELnXPtb/IaDSBsM7OAdqtxuF4/8/EaG5GS22ba7phGx4WVC1BaSrXX4TIM17iGcffEk8sZ/tV5h0f42pxDpqyyGyXjnVt6vG8vKhmGcErVDU59Ij1CwExHpZfa54jn1vOuBjp2CPdyQnExGDmt7eo/umEbE6mskbf8SIrx1+A0bFclnkOxII/lr69V7wvsy71OcO1hSN4p93kT2eFLIjqhseyWR4xTe/6tERPqYumaTL+sCp2Cd3n0kOENcUAfZPTVkVL5HhLcGryWS0pQzaXakhbqskIiyuBkZtRuAlQ3DQlyNnCgU7EREegnTNHljPXhMG6XbVhPjbd+kuL2Fw11JeuX72HyNeGyxlKWchcfeV4fydo3x0VsA2NScRZ2vAxdKinSSgp2ISC/x+W7YuR8s+Pn7ry/HwAx1Se0W1VxCWuWHWE03Lnsypcln4bXFhLqskEu3V5NlL8ePhYLGIaEuR04ACnYiIr3A7gMmHwQ6d8iLKaZiT++/i8QhMQ3bST2wFAs+Gh2ZlCXPwG8NzznqOuPU6M0ArGwYituvS9uleynYiYiEWHWTyT/XgN+Ek9MhO3J/qEtqt2xLCSk1KzAwqXMOojxpCqZF4eVwwyNLSLTW0Ww6WNM0ONTlSJhTsBMRCSG31+Tvq6HRA+mxMGcEfWJaDAOT88elkWMNXAdYHTOC/fGngqFfK19nMUwmRRcCsLwhF5/ZB77A0mfpf6CISIiYpsmb62FfHURHwCVjIcLW+3/pG34voyKLyB8Uj2lCZfx4quNG9Y1EGiKjnbuItjRT64tmY3N2qMuRMKZgJyISAqZp8t5mKNwHFgO+fQrER/X+YGTxuUjb/yEptno8Pj+FviHUR2tQQFvsho8JzsC1dp/V52H2nXEx0sco2ImIhMDH2wOjYAHOGwFZib0/1Nm89WRUvkekZz8e08qfPylmv3liT2fSEfnR27AbHsq9iexwZYS6HAlTusJVRKQHmabJR9vg04O3Dp2VC2P69/5QF+GpInX/Emz+ZrxWJwW1mezZX4im3T1SRWXFUd8bxpdsIJ8Pq07CyargcqfTSXx8fE+UJ2FOwU5EpIf4TZPFm2BFUeD1zGFwak7vD3WRrrLAdCamF7ctgX3JU2ms2R7qsnqdpsZ6AF57bdHRG0W/gfW771JmzeKZN9diln4OgN1uZ+7NNyvcyXFTsBMR6QFur8nr62BzeeD1ObkwoQ+EuujGXaRUf4GBn6aI1IPTmUSEuqxeye1qBuDUqbNJyxxw1HYbfBsoso4lZfaDTGx8hdoDlXz87iIaGxsV7OS4KdiJiHSzeq+DPy2HygawWuCbo+Dk9N4f6uLqN5FUuwaAhsgsKhIngWENbVF9QGxCEsn9jn4N3XhzFyXuUVTZ+uNOHkf8YadkRY6XBk+IiHSjEWd8m0+rh1PZADEOuGJ8Hwh1pklizepgqKuJHkZF4mSFui7iNJoYat0GwBrv6D504zjpC9RjJyLSDfwm1NkG8N27/obPhJxEuGgMxDh6e6jz0a/qc6KbAxcCHog7hdro4ZqjrouNtG5gq28IB8xkym0nhbocCSPqsRMR6WIuL3xZAo22VAAGR+3j8vG9P9QZfjdp+5cQ3VyEiUFFwiRqY3IV6rpBpOEi1xqY126rYzKgYyxdQ8FORKQLVTfC6j1Q5wLD9PKXBd8kN2YvFkvv/sXtwEVG5ftEucvxGzb2JU2jwTkw1GWFtZOthdhxU2dNxRh6QajLkTChYCci0kVKa2FdKXj8gVuEJbk3sWn5m6Euq00ZCQ5OsRUS4a3Ba4mkLOVMmiPTQ11W2HMYbkZaNwBgmXgrbjTaWI6fgp2IyHEyTdi5H7YdnJc2NQbG9Aeb6Q5tYe2QYq3lB9OyiDA8uG3xlKacjduuu0n0lFzrZpy+KgxnKmuYGOpyJAwo2ImIHAe/Hzbtg+LqwOvsRBiWGpjWpLeLrd/MqMg9RNgsVPnjKE05C58tOtRlnVCshp8814cAbGAc+72xIa5I+ro+8KNHRKR38ptQuC8wP51BINDlJPWBsQamn6SaApJrV2MYsHJHNRt8QzEt9lBXdkJK9e7EX/QRfqy8WzsOU/OfyHFQsBMR6QTThE1lcKARLAaMyIC0PtDZYvg9pB5YSlzDVgC2udL49+pyTP06CCn/Zw9hwcd2VyZbXf1DXY70YfqfLCLSQaYJ2yph/8FQd3I6JDpDXVXbbN56Mirfx+naix8r5YmnU+RJCXVZAlCzi5EUAPBWzXia/BpIIZ2jCYpFRDqopAbKagPPh6e1HeoKCws7tP2Otm8Ph6uc1KpPsfpd+CwOypPOwBWRAlR3+b6kc8aynBJrLvt9cbxdk89FiZ+FuiTpgxTsREQ6oKoxMAIWYHAypBxjrMH+ijLA4IorrujUvurr6zu1XgumSWzjNpJqVmFg4rInUp44RYMkeiEbXi5I+Izn95/NhuaB5DYVkxe1J9RlSR+jYCci0k4+7GzaF3ieFguZ8cduX19bDZjc/IvHGTP+tHbvZ/lH/+VPj95Nc1Nzp2sFwPSRXFNAbOOOQD1ROeyPn4Bp0Y/+3qp/xAEmx2zk0/qR/Ld2PFkRFcRYj/P7QE4o+t8tItIOhsVCTcRAvH6IccCQlPaPfu2fM5RhI8a1e1+7t2/qZJVfiTICd5JweA5gAlVxY6iN1u3B+oKpMRvY2tyfcm8i/6k5le8kfqwvm7SbBk+IiLTDGZfcjscSi9WA3DSw9OKfnnPGpnKGcx0OzwF8hp3ypGnUxuQp1PURVsPPNxOWY8XHVld/Pq0/OdQlSR/S5T+aFixYgGEYLR7p6V/dmsY0TRYsWEBmZiZRUVFMnz6dDRs2dHUZIiJdptYbycwr7wXgpBSI6qXTvVnw8+vv5fHmbacSYfhw2ZMo7XcOTZEZoS5NOijNXs034lcC8FH9aLY162so7dMtf3OOGDGC0tLS4GPdunXB937zm9/w8MMP88QTT7BixQrS09M5++yzqaur645SRESOi99v8mVdDlabHYevmtReOledw1XONUkfcPuckwDY4U6nNOVMvLaYEFcmnTXWuYOxzm2Awb+qJ3PAq6+ltK1bgp3NZiM9PT346NevHxDorXv00Ue56667uPjiixk5ciQvvPACjY2NvPzyy91RiojIcfl8N9R6nTTVVRHrKep9ZzNNk7SK9xm/bh45EZXUNHr41qMr2ejOAcMa6urkOJ0TV0B/eyXNZgT/qDoDl1+Xxsuxdct3yNatW8nMzMThcDBx4kQefPBBBg8ezM6dOykrK2PWrFnBtg6Hg2nTprFs2TKuv/76VrfncrlwuVzB17W1gQmk/H4/fr+/Oz6CiPSAPXv2UFlZGeoyjqrZb+PjAyMAK2//8TZuuPEmTLNjP3MsFgtgdst6dk81w3c9RUr1FwAUuZM58+5/sqO8kauvpV23pjIBDAsm7WvfleuBcfBzGm1uI5R1BtZru9bj3pcZuE3d4Sz4uThhKX/afw7l3gT+XnUG30lcgs041vdToFYT44jt9UYmge930zT1O/0oOnJcujzYTZw4kT//+c8MGzaMffv2cf/99zN58mQ2bNhAWVkZAGlpaS3WSUtLY/fu3Ufd5sKFC7n33nuPWF5RUUFzs4aBi/RFFRUV3HTTjbhc7lCXclSnXHQ32eNGc2DPeqxVG3DVlOKtb//tnuKdVvLz84mJ8OOtL+/S9frVriKv7CUifPX4sbK93wW8vj2SxKyN5GcBEbHUetvusfNFJBLXfzgeW1y72nflelHxPvLz87HGJLW5jVDWWeu1EhWf0matnd1XsxFNXP/hVPudWJpaW8/NWVFL+U/DdHa50/nHgdOYEbX8qL3H1phArf7IJMpb3V7v0myJJT8/n+bmZsrL2///5ETSkcvVujzYzZ49O/h81KhRnHbaaZx00km88MILTJo0CQDja9+Npmkesexwd955J/Pnzw++rq2tJSsri379+hEXF9fFn0BEekJJSQnLln3Gn++/kdzBve/C8BprOsti5wCQs+8fPLZyJbMqaxkZk9r+bTT6KCgo4CK3BVsXrRfp2seQoj+SUr0CgPqogRQO/jENzkFUr3uFgoLAbalwX0Ccre3bhVW6q6gt2YzdO444W79219gV6zXVVFJQUIDvW6cTZ/P12jrjbP3aVWtn9+UxG6gt2UyC5QxSo1qfHDGVSpwRS3m1aio7PNkk2puZFVtAbW0NTY2NLevYs42CggLqykbgHxDV6vainE7i49uYiLGHlPjrKCgoIDIyktTU9v8/OZFERka2u223n6yPjo5m1KhRbN26lQsvvBCAsrIyMjK++kFeXl5+RC/e4RwOBw6H44jlFovlYNe4iPQ1hmHg9/vJG5zBuLxBoS6nBdOEF/afBR4YGbWTqPja4KkQw+jYz5zAesZxr2f4PWSVvkb23r9jNd34DSvF6Rexq//3MC12jBbrgUH7ZjcxAEx/u9t35Xpw6NSb2eY2QllnYL22az3ufRmBew8fzUmRZXwzYTmLqidT0DgMi7uOFX+4Gq/H06LduqLA9+sHH3xA4eplrW7Lbrcz9+abe0W4Mwh83xqGod/pR9GR49Ltwc7lclFYWMgZZ5zBoEGDSE9PZ/HixYwdOxYAt9vNkiVL+PWvf93dpYiItMvG5myKPf2wG15mxq4lpHfsNE2Sago4afcfcLpKAaiOHcXWgdfTGJUdysokBEZEFdHod/BO7XhWePPxj7mJM5KKSEj6qnfW9skqXlv5V8ZOnsGEMSOP2EbNgUo+fncRjY2NvSLYSdfq8mB32223cf7555OdnU15eTn3338/tbW1XHXVVRiGwS233MKDDz7I0KFDGTp0KA8++CBOp5PLLrusq0sREekwj2nl/bpTAJgcvZE4a1PIasm2FHNK4R3E1xcC4LInsiPrh5QnT9VkwyewCdFb8ZsWFteNwzLuRva5Pmdw7Pbgt0RMXAIAsbEJJPfrfZc5SPfq8mBXXFzM9773PSorK+nXrx+TJk1i+fLl5OTkAHD77bfT1NTETTfdRFVVFRMnTuTdd98lNraXTg4lIieU5fW51PqiibM0MCnm+G/t1RkD7JW8e8dEzo78F9SD37BTknYeuzMvxWeLDklN0rtMjNlMXV0Ny5nBDsdEHL5YxlnXKO9L1we7V1555ZjvG4bBggULWLBgQVfvWkTkuNT6oljWELh905lxa7Abx76gv0uZPpKrV9B/33+YlrwWkvvhMy2UpX2DosxLcEck91wt0ieMYDWfLv0E65R7KPSdjM+0McG2MtRlSYhppkMRkYM+qBuDx7QxwF7ByZFFPbJPm6eWjIp3ySz/L5HuCgB8psHzS3bjmvgLRgz8do/UIX2TufFlRuZPYn3U2WzxD8PjtWOG9qpQCTEFOxERoNidzPqmwOjcWXGruvWUltXbQHL1CvpVLSOpugCLGRjV6LHFUtpvFn/70sVP/3ADD56q6ZykbVmedcTHRrPMexo7/YOw5v0Qq/0PoS5LQkTBTkROeKYJi2vHATA6ageZEQe6eg84XPtIqF1Hv6rPSKxZjcX0Bt+tc55ESdocKpKn4Lc4qF6tWyxKxwyy7saGl0+8U/CljOWyexbhNz4MdVkSAgp2EjJFRUW9+nZSh0tJSSE7W1NLhKv1zTmUeFKIMDzMiF173Nuz+F1ENZUwIWobL900ljmRLxC/9skWbRoj+1ORdDoVSafTEDVQo1zluGVZS5hhLOH95tMZNmE2++qH4jZXEWF4215ZwoaCnYREUVEReXm5NDaGbiqJjnA6oygs3KRw11NME0wf+L1g+g97+A6+d/A1AMZhoeiw54Yl8MA4+NxodZnbb+OD2lMAOD1mI7HWNm5TaJpY/M1EeKqJ8FQF/410leNsLsbZtIdI1z4MTMbHA6f3B+rxG1bqnSdxIH4cFUmnB+agU5iTLpZhKSNq/RNUDbkWYobwvieRmfYPcRhH3rqvorKiw9t39qI7VkjrFOwkJCorK2lsbOKlB28ib3BmqMs5psIde7ni509RWVmpYNcRpgneJnDVgav2sH9rwV1LVtkOXpk3jiG+Qti+Ffwe8HkD//q9HLw1ereLAObyKn6s2Kr8XwVAvgqDU+ObKH/6bOJj/k7EimOP/D/EY41hT6OTF/+zlKHn/pSs0y/Hb23/bYFEOstau4M//Wwmcx9dwn5bMos9Z3Gm/QOijMAfLU2N9QC89tqiDm+7N92xQlqnYCchlTc4s9fdTkpa4feBux7cdQcDWh24a4/y/LAA5z/6KaB+wKWTMoEaOFbH7aFetq8/OLy3ywwEycOfm/6Dz/0tX7fCihcrXvC3+jZRVoiKcwBfTX/iszhw2xODD1dEEo2RA2iMyqIxagAeWwKL3/wrD/zzTzz4jSz6K9RJGzrag3as9qXbVpOx9QkO5P0P1WYC73rO5kz7+8QYjbhdgYB36tTZpGUOaPf+dMeKvkHBTqQj/D7wucDTBF5XoEfK2xx47vcePFV48BSi33/Y64MPAvf/DJyCO9QzxFH+Pdgu2PawdY5YZhDoYSLwLxxWhw/MQ/UcfH1ouc8VqN/T9NVn8Rz6TE1fLXc30OkeNIsdIuMhIhYcceAI/Ft6oIGFjzzJ/Ku+ycCszEA7qy3wr8UOFlvg0ZWnK02Tw8Pem9Xj2dacQZZ9H9+K/wTj0HscFgZNk8/WbOLaB17iBz/9XybP+g4+SyR+i0OnUqVLHE8PGgRuzdmaCNc+ZkW8x3vumdSZsbzrPpuz7B8E349NSNKdKcKQgp2cuEwzcNrvUHg5FGh8LvC5D4YeN3m+OoofP4uMzb+CTSf4Rch258GAdjCktfr8UIA7+LC2HoBKV63i8Xd+ytU/6MfAuPb3GhyXw8LxHncKa90ng8VkSuLnGPaYo65W69vFxpJ6qn0xeOwJPVOrnDA624NWvGsbq5d/iNd79J9LsUY9syIW875nJrVmPO96zmK4fftx1yy9l4KdhK9D13i56w8+GgL/ehq+CnJm23cWiAL6J0UGersObRoDvyUCnyUCv2HHb4nANKyYhgUTS+BfwwrB54HlwYBjmoHeoYNb+/pr41DP0sFlh782Dn02Dq1zaP3AujabFUdEBBhWsFgP9nodem4Fw3bwXyvYHGCLBFsU2KMOe37w30PPD4U2S3j8yPCbBu/U5ANwStR20u1VIa5IpOM9aDVV7ZtVINpoYpb9Pd73zKTKTGRD2o+g39LOlim9XHj8lJYTl+kHTyO4ag5elN/wVZDzNBw2cvIYLPaDoeZQsHEEepmsEWB18HnhHm5+6GX2N7ipa/LS4PLR7GnHdkNEI3jbtrrxJMq8STgMN9Nj14W6HJFuF2m4OMv+Hh96ZlBpTcF63vNU1/2TrFAXJl1OwU76Br/vsIvya766ON9V10avmwH2aIiIhoiYwMPuDDwO9VK10Qu1raqYgl01PHH7ZZw27uSu/VxdTCN429boj+DDutEATI/9kpi2pjcRCRMOw8OZ9g/4b20+tZEnsS7pMpL9S8m0lIa6NOlCCnbSu5hmoKetubrlw13PUS/eNywtL8y3x3wV5OzOrwYkHKchWakawRsGPqgdQ7PpIM1WRb5zW6jLEelRdsNLXvnzfMaZkD2djzxTmWL7lGxrcahLky6iYCch43RYcZp1cGBbyxDn97S+gsV+2EX58V89j4jusvAm4a3EncyapiEAfCN+JRajZ+bKE+lNrKYX/+J5pF3+KhVRJ/OJdwozjCXquQsTCnbS/UwTGiuhajtU7YTqXZxctom6P34Di38D7P1ae8MSCGyRCYGH4+C/tkhNL9FOhYWFoS6hTT1do980eLs2MGBidNQOsiL6xu3sRLqF30NezWs4o53s9g9kiecMzrR/QKpF/y/6OgU76VqmCY0VcGB7oCfuwLbAc1dNi2aRABYDD3bsMclfhbjIxMBpVYs1BMX3faWV1RjAFVdcEepS2q2urr5H9rO8IZdSTzIOw83MLrgfrEhfZ2Ay2bYcjzeCvf5MPvRMZ5b9PRIt1aEuTY6Dgp10nmlCw76DIe5gkKvaHhjU8HWGBeKzIXEwJAxia6WH0+dcwdvP3MO4gbpuratU1zViQp8Y6PHWJ2u5+8m/09zc/YMXKjxxLKkbBcCsuFUaMCFykNXwM9X2Ce97ZlBhpvK+ZwbnRCwm1uiZP7ik6ynYhZmioiIqK7unK93uqcXZXEJ0UzHO5r04m0ux+Y+8F5SJhSZHKo2RmTRGZtAYmUmTIw3TYg80aILCokIqalufLV2OX18Y6FG48+vn4LuH3zR4s2YiPqwMcZQwOmpnj+xXpK+wGT5m2Jew2HMWVWYi77lnck7EYpzGse71J72Vgl0YKSoqIi8vl8bG4//PGGm3MG5QPJOGJDLxpAQmDUkkOyXqiHZur591e2op2FkTfKwvrsPVznneeuo0nJy4ljfksteTgsNwc278Cl2mKdKKCMPDTPuHvOs5mzozNtBzZ19MhHGUwWzSaynYhZHKykoaG5t46cGbyBuc2aF17aaLGLOOaOqINutx0njYnRACTKAJJ41GDA1E02hE0xzhxBhiYfwQGN+B/fXkaTg5cZV74oOnYM+OW0WcVT0QIkcTZTRzpv193nHPosZMYInnDGbaP8Jq9N4J2eVICnZhKG9w5rFPw5lmYLLfhgpoLA/862k4sp0tEqKSwZkCUckYUUk4rXacQMpx1thTp+HkxOX22/hn1en4sHKSYy9jdApWpE0xRiMz7B/xruds9pnpLPdOZLLtM/V09yEKdicKdz3Ul0H9vkCY8369p8yAqMRAiHOmQFTKwcl99b9Z+h7ThP/UTGC/L55YSyMXxC/Xt7JIOyVZqplqX8qHnmns9A8i2tfAKbYvQ12WtJOCXbjyuaGh/GCYKz1454bDGJZAb1x0Kjj7BcKc1R6aWkU6oaamhsbGxlbfK2Q0GxiIgZ+p/jeoLd/LobHaXp8Xm7VjP/qqq6uOs1qRviXTUspE2xcs905ivW8k0UYDSWgC475AwS5cmCaRrnJ+dv5JDPOth8LPaXkLLgOcyRCTDtFpgVCnueKkj6qpqeGJJ5/E42nlwu6UEVi/ORfDCt7l/8vrX/6pxduGEejR64jSqkAPd9NRgqRIOBpi3UGDGc063yi+8E4g37on1CVJOyjY9WV+H1RshOLPoeQLTq4v5aHv5gEHe+ciYgNB7lCYU4+chInGxkY8Hg9TZ11EfNJXV3w2GbF8Fv09XJYIUj3bGHeyFePk64LvF+/axurlH3Lq1NmkZQ5o9/6WLP0MPizC5XJ16ecQ6e1GW9dRb8aw0z+I1c4LIPmlUJckbVCw62t8HihdDUWfwN6VLU6x+g0b/11Vwqhxk8geOipwD1WRMBaflEJyvwwA3KadZZ6zcZmxxBvVTI9eTURMRov2NVWBOR5jE5KC67WHMzYWgIrSPWzZsKrd65UWBwZslOzeypYN7R9ydLzriXQVw4BJts9p8kRRRjrWbzxDPa+Huiw5BgW7NnTnhL/tZvqIbdxFYu06EuoKsfm/GvjgtTqpiR5GTexwVhR7+e7//oCCV75JtkKdnEB8poUlnqnUmAlE0chM+0ddOv/WgZp6DOBvz/6Gvz37mw6v/+T98zq1386uB9DUqDkipWtYDT9T7Z/wVtMM6qNTeYeLyPJ/RKRFc9z1Rgp2x9CVE/52lGHA5KGJfPe0/lwyMYO0eEfwvb1Vzfxt+V7+uaKMZVsO4P/a9UKa9FdOJH7TYJn3NPaZadjxMNP+EdFG114LV9/owgRuu+Icpp0xpd3rFRdtY/WyD5kwbTbpGe0/9Xs86/3ttX/zYeF+3C7NESldJ8LwML7xNT60XEh1dCr/qJrC95KWaI67XkjB7hiOZ8LfzrKbLpLNCpLNChx8dT2PFxtVRhJVRgr1ybFMnWMwdU7LdTXpr5xo/Fj41DuZ3f6cwAhY+8fdegPzrLQkRg5r/63anNRTVhjJ8IGZZOX0zHqJ0bqWVrpHlFmH7+3rifrWq+xyp/NG9UQuTNAcd72Ngl07tDnh7/Hy+6CuBKp2BKYnOTSa1WKDuCyIz8YWk04/w0K/Y2xGk/7KCcViZ3XU+ZT7c7DgY4rtUzIs+0JdlUh421/ITN5kMRexoXkgMXXNnBW7WuGuF1GwC6XmGqjaDtW7wHfYaDtnP0g8CeKzAuFORFrwYsMy6wnK7UOw4GOa7RP6W/WHjUhPGMBuzk9YzuvVk/m8IZcYSxOnxWwKdVlykFJDTzP9gd65/Vuh4bDeBVsUJAyCxMHgiA1dfSK9XI3Pyb+5FEt2GhbTw4yIj9VTJ9LDRkXtpsEXxXt1Y3m/bizRlmZGO3eFuixBwa7neJvhwHao2gaeQxd2GxCbCUknQUxG4G4QIn3Yse4G8XVVVdVA4K4ONTU1xMfHt7nOHncK/6g6gwYiMZsOcKr/HTIiOzjbsIh0iUkxm6j3R7K8IY9/10wk2uLipEjdnSLUFOy6W+N+2L8FaosCvXUAVkfgVGvSEM01J2HjmHeDaMW6osBNvj744EO2rV/J3JtvPmq4M00oaBzCu7Xj8GMliQrKF32XxDnnAu2fj05EutaZsWuo90Wxvnkg/6iewmVJH5IVEeIpwk5wCnbdwTQDp1srN0FjxVfLo5IhaSjEZ+t2XhJ2jnY3iKOxfbKK11b+leGjxlNbvJ7GxsZWg12dL4p/15zKdldgZHpuZBGnNr/On+pLuvwziEjHGAacn/A5jVUOdrgy+OuB6Vye9AH9Iw6EurQTloJdV/J7AwMhKjeBuy6wzLAEglzSsMC9WkXC3OF3gziWmLgEIHBXh9pW3jdN2Niczds142kyHVjxMTNuLac6N1NWpolRRXoLq+HnksRPeOXANHa70/jrgRlcnvwBGfaqUJd2QlKw6wreZjiwNTAg4tDoVos9cKo1eRjYnaGtT6SPKffE805tPrvdaQBk2PdzQfxy+tlbi4AiEmp2w8eliR/z8oHpFHv68fL+GVyR/AFp9upQl3bCUbA7Hq5aqNwM1TvB9AWW2aMheXhgdKtVE4WKdES9L5JP609mZeNQTCzY8DI5ZiOnx2zEamiQhEhvFmHx8r2kj3j5wAxKPCm8uP9Mvpv0EQMi9oe6tBOKgl1HmSY0VkJlYeA6ukOikiAlD+IGaHSrSEdFZ/AZM9hSPgrvwR9LuZFFnBW7hgRbQ4iLE5H2chwMd68cmEaxpx9/OTCD7yR+wiCHpiTqKQp27WX6obY4cP1c02F/fcT2h5TcwKTCmnpbpEOSModQN/QHWE8/jY0Eerj72yuZHvulfhGI9FGRFg+XJX3I36vOYKc7g1cOTOPixE8ZHqkBTz1Bwa4N0Q4r/fxlsGUdeA72HBiWwGTCKbngiAttgSJ9UJU/gebcH/DjP/wOl9WKAWRQxMykbQyM2Ke/kUR6sYrKirYbAdP4G37OZTdD+UfVFM6OW80E5xb9/+5mCnbHkF75EUW/O5Mkcxd4AGtEYHRr8lCwRYa6PJE+p9KfzDrfCEr8A6AfWAD7/jU0fbKQcy+cSIZDc9KJ9FZNjfUAvPbaovavZDyDZcovseR9h3dr89nvjeWcuFVYdM1st1GwOwa7t46kmAiaiSQyc2Sgl073bhXpsHJ/P770jqTMPBTcTGwVq3jsnmv45WVjaSpfC0wMZYki0ga3qxmAU6fOJi1zQLvXqz6wmqXLd2Gd9FMKGodR5YvlooRPibJo2qLuoJRyDPuSTufae57ml7fNZVzS4FCXI9LnVPhTWOsdFQx0Bn4GWXYywrqRDze9TdnOL4GxoS1SRDokNiGpXXNVHs589w+cOSmXJZzLDlcGf6z8Bt9K+JRMTWTc5RTsjsEdkcQbBfv4pS4IEOmQCn8yX3pHU3pYoDvJsp2Rto3EGBrlKnIiGsg2BqW8xz+qTqfaF8sL+89iVtwqYGeoSwsrCnYi0nVi+rM6ag5lnuHAoUC3g5G2DQp0Iie4isoK+rGROWznY86hiCH8t3YCidYInPGPUlhY2KJ9SkoK2dnZIaq271KwE5Hj5vbbWMlkrN+5mTKbI3jKdZRtPbEKdCIntNYHXTyOMfqHWE79CVWxY5j3f+u4/7EfsWn5m8EWTqeTwsJChbsOUrATkU4zTVjXNJAP68ZQhxPDBkneIk6LWk+ipbpD22rvFAodbSsioXWsQRc1ja+wwnoWMYkZXL7gdSJ9+4n1lLBn2zoeuO1KKisrFew6SMFORDql2J3Mu7Xj2OtJASCWaqrf/QWnThpEYnT7L6x2Nwd+6HdoCoVD67rdHV5HREKjtUEXyUD9lhd48gsrZ3z7NpqtyXjtyfQbnoBh0V2cOkPBTkQ6pNYXxQe1p7C+eSAAEYaHKTEbyK5bwh93vYcx6boObc/rDUx50JEpFIp3bWP18g/xer0d2peI9D4WfLz77M+54ILzccfm0uCGOns2P3pkGQfc0aEur89RsBORdnH7rXzWkMdn9XkH7+dqMiZqBzNivyTG2kxpne+4tt+RKRRqqiqPa18i0vtEmI2MGAB7a2BnpY8Bw09leQ1UFpjMGAZpsZqhoj0U7ETkmEwTvmwaxEd1o6nzOwHIspczK34VGfaqEFcnIuHEMKB/AtSWbOA/iz9l0vk3sK3SYFsl5KWZnDYIMuMV8I5FwU5EWmWasMudxgd1Yyj1JAOQYK3nzNg15Ebu0f0eRaTbWPHy7ydv5vbvT6PCcTIby6BwX+AxMMlk0kAYnAIW/SA6goKdiBxht6sfS+pHU+ROBb66ju7U6M3YDH+IqxORE0W0zcUZYwymDDb5bBdsKIVdBwKPuEgY09/klP4QH6WAd4iCnYgE7ffG8lbNBHa70wCw4mOscztTYjYQY20OcXUicqJKjTX45iiYPsTki93w5V6obYZPtgceAxJMctMgNw0STvCQp2AnIkEOw0OJOxkLPsY6dzA5ZiPx1sZQlyUiAgR65s7OhRlDTTaVw+pi2H0AiqsDj/c2Q3K0yaBkGJQE2YkQFXFiBT0FOxEJirE2c2HCZ8Q078baWE5jI7Q31mnSYBHpal+/zdjXjbDCSUk2ytwJ7HMlsN8Tw/4Gg/0NsLIo0CYxyiQzHjLioV80JMdAfCQYYXp9XkiD3VNPPcVvf/tbSktLGTFiBI8++ihnnHFGKEsSOeFluDfwxNNP4vF4OrW+Jg0WkeO1v6IMMLjiiis6tF5UTCLDJ5zDzx/6PZXuWPY3QFVT4LGh7Kt2dmugZy/ZGbhWLy4S4qK+eu60993gF7Jg9+qrr3LLLbfw1FNPcfrpp/P73/+e2bNns3HjRt0+RCSEGhsb8Xg8TJ11EfFJKe1eT5MGi0hXqa+tBkxu/sXjjBl/WrvX272tkAduu5KTbD/lklPH0eQ22VsbmBtvXx1U1sOBRvD4oKw28GiNBT8Oi4cIizfwMLxfPT/42nHweUZKPIMHDug1QTBkwe7hhx/mmmuu4dprrwXg0Ucf5Z133uHpp59m4cKFoSpLRA6KT0pp94TBoEmDRaTr9c8ZyrAR4zq83tdP4UYDg4HBTvBHQaPPQYMvkgafg2a/naoGP4Xbi4lN7k9sUgZ+LDT5HTT5HW3uy6z0c6VlDwNzekenVEiCndvtpqCggDvuuKPF8lmzZrFs2bIj2rtcLlwuV/B1TU0NANXV1fj93Tf1Ql1dHYZhULBxN3WNvX9EYOHOUgzDYN22vURGH/u6hFBTrd2jK2qtrDxAUWUzH3++mpjYHe1er6KshKLKZj5buZ5Nu8rabL9h01YMw2DTjmK8Ne1frzP7Ot71tu8OHNeN23cTteTTbt/f8axXWe/BMAxWbdhMY3Pbp8VDUeOh9baWVrW71lDWuWlXWfD79Vi1hrrGQ9qqtbfUecju0koMw2DLhlU0NdYBULS9EMMw2LXlS6Kj2w5Xh2xctRzDsHDllVe2e53DXXTlXPoPHIZhj8Jij8KwRmLYAg+LzRF8Hng4wOqguaGO3c49JMTHdWqf7VFbG+haNE2z7cZmCJSUlJiA+emnn7ZY/sADD5jDhg07ov0vf/lLE9BDDz300EMPPfQ4YR979uxpM2OFdPDE189Hm6bZ6jnqO++8k/nz5wdf+/1+Dhw4QHJycree066trSUrK4s9e/YQF9d9SVyOpGMfOjr2oaNjHzo69qGjY9820zSpq6sjMzOzzbYhCXYpKSlYrVbKylp2yZaXl5OWlnZEe4fDgcPRsis2ISGhO0tsIS4uTt9sIaJjHzo69qGjYx86Ovaho2N/bPHx8e1qZ+nmOloVERFBfn4+ixcvbrF88eLFTJ48ORQliYiIiPR5ITsVO3/+fK688krGjx/PaaedxjPPPENRURE33HBDqEoSERER6dNCFuwuvfRS9u/fz3333UdpaSkjR47krbfeIicnJ1QlHcHhcPDLX/7yiNPA0v107ENHxz50dOxDR8c+dHTsu5Zhmu0ZOysiIiIivV1IrrETERERka6nYCciIiISJhTsRERERMKEgp2IiIhImFCwExEREQkTCnbH8NRTTzFo0CAiIyPJz8/nk08+CXVJYWXhwoVMmDCB2NhYUlNTufDCC9m8eXOLNqZpsmDBAjIzM4mKimL69Ols2LAhRBWHr4ULF2IYBrfccktwmY599ykpKeGKK64gOTkZp9PJKaecQkFBQfB9Hfvu4fV6+cUvfsGgQYOIiopi8ODB3Hffffj9/mAbHfuu8fHHH3P++eeTmZmJYRj861//avF+e46zy+Vi3rx5pKSkEB0dzQUXXEBxcXEPfoo+qs27yZ6gXnnlFdNut5t/+MMfzI0bN5o//vGPzejoaHP37t2hLi1snHPOOeZzzz1nrl+/3lyzZo153nnnmdnZ2WZ9fX2wzUMPPWTGxsaa//znP81169aZl156qZmRkWHW1taGsPLw8sUXX5gDBw40R48ebf74xz8OLtex7x4HDhwwc3JyzKuvvtr8/PPPzZ07d5rvvfeeuW3btmAbHfvucf/995vJycnmv//9b3Pnzp3m3//+dzMmJsZ89NFHg2107LvGW2+9Zd51113mP//5TxMwFy1a1OL99hznG264wezfv7+5ePFic9WqVeaMGTPMMWPGmF6vt4c/Td+iYHcUp556qnnDDTe0WJabm2vecccdIaoo/JWXl5uAuWTJEtM0TdPv95vp6enmQw89FGzT3NxsxsfHm//3f/8XqjLDSl1dnTl06FBz8eLF5rRp04LBTse++/zsZz8zp0yZctT3dey7z3nnnWf+8Ic/bLHs4osvNq+44grTNHXsu8vXg117jnN1dbVpt9vNV155JdimpKTEtFgs5ttvv91jtfdFOhXbCrfbTUFBAbNmzWqxfNasWSxbtixEVYW/mpoaAJKSkgDYuXMnZWVlLb4ODoeDadOm6evQRW6++WbOO+88zjrrrBbLdey7zxtvvMH48eO55JJLSE1NZezYsfzhD38Ivq9j332mTJnC+++/z5YtWwBYu3YtS5cu5dxzzwV07HtKe45zQUEBHo+nRZvMzExGjhypr0UbQnZLsd6ssrISn89HWlpai+VpaWmUlZWFqKrwZpom8+fPZ8qUKYwcORIgeKxb+zrs3r27x2sMN6+88gqrVq1ixYoVR7ynY999duzYwdNPP838+fP5+c9/zhdffMH//M//4HA4+P73v69j341+9rOfUVNTQ25uLlarFZ/PxwMPPMD3vvc9QN/3PaU9x7msrIyIiAgSExOPaKPfw8emYHcMhmG0eG2a5hHLpGvMnTuXL7/8kqVLlx7xnr4OXW/Pnj38+Mc/5t133yUyMvKo7XTsu57f72f8+PE8+OCDAIwdO5YNGzbw9NNP8/3vfz/YTse+67366qu89NJLvPzyy4wYMYI1a9Zwyy23kJmZyVVXXRVsp2PfMzpznPW1aJtOxbYiJSUFq9V6xF8F5eXlR/yFIcdv3rx5vPHGG3z44YcMGDAguDw9PR1AX4duUFBQQHl5Ofn5+dhsNmw2G0uWLOF3v/sdNpsteHx17LteRkYGJ598cotleXl5FBUVAfq+704//elPueOOO/jud7/LqFGjuPLKK/nJT37CwoULAR37ntKe45yeno7b7aaqquqobaR1CnatiIiIID8/n8WLF7dYvnjxYiZPnhyiqsKPaZrMnTuX1157jQ8++IBBgwa1eH/QoEGkp6e3+Dq43W6WLFmir8NxOvPMM1m3bh1r1qwJPsaPH8/ll1/OmjVrGDx4sI59Nzn99NOPmNZny5Yt5OTkAPq+706NjY1YLC1/7Vmt1uB0Jzr2PaM9xzk/Px+73d6iTWlpKevXr9fXoi0hG7bRyx2a7uTZZ581N27caN5yyy1mdHS0uWvXrlCXFjZuvPFGMz4+3vzoo4/M0tLS4KOxsTHY5qGHHjLj4+PN1157zVy3bp35ve99T1MPdJPDR8Wapo59d/niiy9Mm81mPvDAA+bWrVvNv/zlL6bT6TRfeumlYBsd++5x1VVXmf379w9Od/Laa6+ZKSkp5u233x5so2PfNerq6szVq1ebq1evNgHz4YcfNlevXh2cMqw9x/mGG24wBwwYYL733nvmqlWrzJkzZ2q6k3ZQsDuGJ5980szJyTEjIiLMcePGBafhkK4BtPp47rnngm38fr/5y1/+0kxPTzcdDoc5depUc926daErOox9Pdjp2HefN9980xw5cqTpcDjM3Nxc85lnnmnxvo5996itrTV//OMfm9nZ2WZkZKQ5ePBg86677jJdLlewjY591/jwww9b/fl+1VVXmabZvuPc1NRkzp0710xKSjKjoqLMOXPmmEVFRSH4NH2LYZqmGZq+QhERERHpSrrGTkRERCRMKNiJiIiIhAkFOxEREZEwoWAnIiIiEiYU7ERERETChIKdiIiISJhQsBMREREJEwp2IiIiImFCwU5EREQkTCjYiYiIiIQJBTsRERGRMKFgJyIiIhImFOxEREREwoSCnYiIiEiYULATERERCRMKdiIiIiJhQsFOREREJEwo2ImIiIiECQU7ERERkTChYCciIiISJhTsRERERMKELdQFiLTF5/Ph8XhCXYaISK8UERGBxaJ+GglQsJNeyzRNysrKqK6uDnUpIiK9lsViYdCgQURERIS6FOkFDNM0zVAXIdKa0tJSqqurSU1Nxel0YhhGqEsSEelV/H4/e/fuxW63k52drZ+Toh476Z18Pl8w1CUnJ4e6HBGRXqtfv37s3bsXr9eL3W4PdTkSYjopL73SoWvqnE5niCsREendDp2C9fl8Ia5EegMFO+nVdFpBROTY9HNSDqdgJyIiIhImFOxEREREwoSCnYiIiEiY0KhY6XOKioqorKzskX2lpKSQnZ3dI/sKtenTp7NkyRIAVq9ezSmnnBLagtrh1ltvZcuWLbz55pshrWP//v3k5eXxxRdfMHDgwE5t49vf/jaTJ09m/vz5wWVXX301L7zwAgCLFi3iwgsv7IJqO6Yn/7/BifV/TqRbmCK9UFNTk7lx40azqampxfLdu3ebTqfTBHrk4XQ6zd27d3f55zvjjDOC+7BarWZ6erp54YUXmp9++mmr7VeuXGleeumlZkZGhulwOMzBgwebP/jBD8zNmzd3WU3Tpk0zr7vuOrO0tNT0eDyd2saSJUvMOXPmmBkZGSZgLlq0qNV2Tz75pDlw4EDT4XCY48aNMz/++ONO7W/mzJnmL37xi06t25VuvfVW84c//OERyw99ne+7774Wy/1+v3nqqaeagHn33Xebpmmaa9euNZOSksyamppgu+rqarO0tPSYx7I7Bf6/RfXY/7fA/7mobvk/15OmTZsW/DyrV68+rm1dddVVwW0d7XvgaD8v5cSkHjvpUyorK2lsbOSu/32RnCF53bqv3dsKeeC2K6msrOzSHgTTNFmzZg0PPfQQV111Fc3NzezatYunn36aqVOn8sYbb3DuuecG2//xj3/khhtu4JprruG1114jIyOD7du387vf/Y5nn32WX//6111Wm9PpJD09vdPrNzQ0MGbMGH7wgx/wrW99q9U2r776KrfccgtPPfUUp59+Or///e+ZPXs2Gzdu7PBxXrt2LTfddFOn6+0KTU1NPPvss7z11lstlh/6Oufk5LBu3boW773wwgvs3bsXgHHjxgEwevRoBg4cyF/+8hduvPFGAOLj44mPj++BT9G6wP+3Jl568CbyBmd2+/4Kd+zlip8/1an/c1OnTuWTTz4BwGq10q9fPyZNmsRPf/pTJk+e3B3lHtN1113HfffdR0pKSnDZX/7yF+644w4aGhq45ppr+O1vfxt8b9euXcyaNYuVK1cSFxcXXP7YY4/x0EMPkZGR0aP1S9+lYCd9Us6QPIaNGBfqMlq1b98+0tPTefTRR3n++ecpLCzkpJNO4ve//z1Tpkxh69at1NXVMXXq1GCIGjhwINOnT2fmzJncddddwWC3dOlSrr/+ep544ongL3uAnJwcZs6cyYEDB0LyGY9m9uzZzJ49+5htHn74Ya655hquvfZaAB599FHeeecdnn76aRYuXNjufe3Zs4f9+/e3OGW8fv16br/9dpYuXYrT6eTyyy9n4cKFLW61tGHDBm688UZWrFjB8OHDefLJJ5kyZQpr1qxhzJgxHfvAwH//+19sNhunnXZai+WHvs7z58/n1VdfDS6vq6vjzjvv5Nprr+X+++8nPz8/+N4FF1zAX//61xZf694gb3Am4/IGhbqMo+roH0s94et/JFVWVnLttdfy/PPPM3jwYM477zymT5/OeeedB8CNN97IQw891CLUQejDvfQ9Gjwh0sVWr14NwFNPPcUjjzzC2rVrGThwIJdffjl+v5+CggKsVmurIeLss89m3bp1+P1+AObPn8+0adOO+os+KSmp+z5IN3C73RQUFDBr1qwWy2fNmsWyZcs6tK01a9YQGxvL4MGDgcBxnzx5MuPGjWPVqlW8+uqr/PWvf23Ro7lhwwYmTZrEGWecwerVq7nnnnv49re/jd1uJy+vcz3AH3/8MePHjz9ieUFBAZGRkXzve99j69atuFwuAH71q19xyimnkJGRQUpKCllZWcF1Tj31VL744otgW2mfr/+xdOgPpVdffZWpU6dy1113hbpEduzYQXx8PJdeeikTJkxgxowZbNy4EYCXX36ZiIgILr744hBXKeFAwU6ki61duxa73c7bb7/N9OnTGT58OPfddx9FRUWUlJSwatUqcnNzW72rht1ux2q1YrFYKCwsZMWKFdx8881t7tNms3HKKadwyimnBHvCAP79738zfPhwhg4dyh//+Mcu/ZydUVlZic/nIy0trcXytLQ0ysrKjmj/2GOPHdETdsihHrZDk7Ned911XHnlldx///0MGTKEadOmcd111/Hvf/87uM7cuXM599xzeeCBB8jNzeXiiy/mtNNO4+STTw726l100UUkJiby7W9/u8X+jnYsd+3aRWbmkacpV61axejRoxk2bBjR0dEUFhaydevWYOBftWpVi946gP79++NyuVo9FnJ0HfljKVSGDh1KY2Mjq1ev5sCBA6xYsYLRo0dz4MAB7rnnHp544omQ1ifhQ8FOpIutWbOGiy++mEGDvjp15XA4gs8LCgqC11V93ZYtW8jNzQUCwQA44pd/axISElizZg1r1qwJhg6v18v8+fP54IMPWLVqFb/+9a87fOp2wYIFGIZxzMfKlSs7tE04cqZ80zRbnT2/oqKCrVu3trqNNWvWBE/Dbtq0iYKCAubNm9eiTURERLD3a9euXXz00Ufcc889Ldo4HI4WgeB//ud/+POf/9yizbGOZVNTE5GRkUfUV1BQQH5+PoZhMHr0aNavX89PfvITfvSjH5Gbm9vq90FUVBQAjY2NrX5maV17/1gKpcTERF544QW+//3vc+qpp/L973+fc845h9tuu4158+axc+dOxo4dy8iRI/nHP/4R0lqlb1OwE+lihweOQ1atWkVKSgr9+/dn9erVrQa7pqYmXn/99eCgg0O/3GNiYjpVxxdffMGIESPo378/sbGxnHvuubzzzjsd2sbcuXMpLCw85mPkyJHt3l5KSgpWq/WIHqny8vIjevEA7r///qNOtXH4cd6wYQN2u51hw4a1aLNx40ZGjRoFBHpSIyIiGDFiRIs2hYWFLb5eM2bMIDY2tkWbYx3LlJQUqqqqjqjv8K/zmDFjeOyxx/jiiy/45S9/idvtZsOGDUd8HxwKi/369Wv1M0vr2vvHUqhddNFFrFu3jm3btrFgwQI++ugj1q1bx3XXXcd3v/tdHn30Uf75z39yzTXXUF5eHupypY9SsBPpQk1NTWzdurXFzbj9fj+PPfYYV111Fbt27aK6uvqIX0J+v58bb7wRm80W7HU6FJgOjfRrbV+H1NbWkp+fz5QpU4Jz0e3du5f+/fsH2wwYMICSkpIOfZ6UlBRyc3OP+Witt+poIiIiyM/PZ/HixS2WL168uEMjF+vq6ti5c2cwkMXGxuLz+fB4PME2RUVF/OMf/+Cyyy4DAiMlvV4vzc3NwTZLlixh7dq1bQ6aONaxHDt2bPBaqUN27NhBdXV1sLf1lFNOYeXKlTzwwAPEx8ezbt06PB7PEb2x69evZ8CAAS1GUkrb2vvH0uzZs5k/fz6TJk0iNzeXFStWcMEFF5CTk8MzzzwTXO/FF19k4sSJjBo1igsuuAC32w3AxIkTgz3UV111FU8//XSna3a5XNx00038/ve/Z9u2bXi9XqZNm8bw4cMZNmwYn3/+eae3LSc2jYqVPmn3tsJeuY9169ZhGAYvvfQSM2fOJCEhgXvuuYfq6mp+8YtfBANNRkYGZWVl1NbWUlBQwO9+9zt2797Nm2++SWJiIgCnnXYas2bN4qabbqK+vp7TTjsNv9/PihUr+L//+z+efvrpYPg7dJ3X+vXrOe+881i3bh2maR5RX3ffLLy+vp5t27YFX+/cuZM1a9aQlJQUnL5i/vz5XHnllYwfP57TTjuNZ555hqKiIm644YZ272fNmjVYrdbg5584cSJJSUnccccdzJs3j127djFv3jwuueSS4Cjd/Px87HY7P/3pT/nJT37Cxo0bueWWWwDanIz5WMfynHPO4c4776Sqqir4tSsoKCAiIiJY31VXXcWFF15IcnIyEOjBTUxMbHG6HgIh/usDS3qDwh17e+1+DoXo9vyxtH79ei699FIefvhhvv/97/Ozn/2MN998k61bt3LjjTfyox/9CIBzzz2XK6+8EoAf/vCHfPLJJ5x55pncfffdPPjgg5x++unExMQc1+jlX/3qV8yePZtx48axevVqvF5v8D2Px9Pij0ORjlCwkz4lJSUFp9PJA7dd2SP7czqdHeo9WbNmDbm5udxxxx18+9vfprq6mjlz5vDZZ5+RkJAQvG5u2LBhWK1W4uPjyc3NZc6cOdx4441HjHJ94403eOSRR/jNb37Djh07cDgcDBkyhPPPP5+TTz452O7QxfsjR47k5JNPZsuWLfTv379FD11xcTETJ048nsPRppUrVzJjxozg60N3Ubjqqqt4/vnnAbj00kvZv38/9913H6WlpYwcOZK33nqLnJycdu9n7dq15ObmBq9djI+P5/XXX+fHP/4xv//978nIyOC6667jpz/9aXCdjIwM/vSnP3HHHXfw3HPPMWvWLH7wgx/w/PPPtzm6+FjHctSoUYwfP56//e1vXH/99UAguI0cORK73Q4ErvM6/Pto1apVjB07tsU+mpubWbRoUYdPl3enwP+3KK74+VM9tk+nM6pD/+cKCgqAtv9YqqmpISIigquvvhqAyMhIfvzjHxMdHY3D4QhOKWKaJs888wyvvfYabreboqIirrnmGgDmzJnDL37xC+rr64+Yt7AjNmzYwKuvvsqaNWsAyM3NxWKx8Oyzz5Kens6mTZuYMGFCp7cvJzYFO+lTsrOzKSws7LW3FFu7di2jRo3i8ssv5/LLLz/i/YULF3ZorjaHw8Edd9zBHXfccdQ2VVVVOJ1OHA4HxcXFbNy4kcGDBxMXF8f69espKSkhLi6Ot95664iBA11t+vTprfZufd1NN910XBMLz507l7lz57ZYNnnyZFasWHHM9S677LLgqVm/38+MGTO45JJL2tzfqaeeesxjeffdd3Pbbbdx3XXXYbFY2vw6t3YK79lnn2XixIlMmjSpzXp6SuD/26ZefUux9v6xtH79+hZhad26ddx3333B54d6V59//nm2bdvGxx9/TFRUFDk5OcE/or744guqq6sZNmwYNlvnfn2apsmPfvQjHnnkEaKjo4HAoJnnn3+em2++GZfLxRNPPNHi1L9IRyjYSZ+TnZ3da+8luWbNGs4///we3WdhYSHXX389FosFwzB47LHHgr/M/t//+3/MmDEDv9/P7bffHjwVeDRPPfUUf/zjH/nss8+Cgw7Cxccff0xFRQVjx46lsrKS3/72t+zatYtFixa1aHfOOeewatUqGhoaGDBgAIsWLWLChAnHPJbnnnsuW7dupaSkpMW8dB1ht9t5/PHHWyy74YYbeOmllzq1va7Sm/+/Qfv/WFq/fn3we9o0zeBE4l9/b8OGDUyePJmoqCgee+wx/H4/iYmJlJSUcO211/Lhhx9y8cUXU1hY2Km5Dw3D4NNPPz1i+Zw5c5gzZ06HtydyhFDdy0zkWPrivQ/9fr8ZGxtr/uc//wl1KZ1SXFxsbt261dy6davpcrlCXU6X+9vf/mYOHjzYdDgcZnZ2tvnDH/7QLCsrC3VZx7Rv377g16S+vj7U5fRp8+bNM19//XXTNE1zx44d5owZM4LvXXTRRWZBQYFpmqa5Zs0aMycnx5w6dap59913m7NnzzYbGxvNSZMmmUuWLDFN0zRffvll87LLLjvqvqZNm2ba7XYzOjra/PLLL4+r7uuvv96Mjo7WvWKl3QzTbMd5E5Ee1tzczM6dOxk0aFCHRl2KiIRaSUlJcNR6dnZ2i1vadVR5eTm1tbVA4DrCQ6dvD6efl3I4nYoVERHpQl15fVxqaiqpqaldtj0Jf5rHTkRERCRMKNiJiIiIhAkFO+nVdAmoiMix6eekHE7BTnqlQxO76mboIiLHduiWZ1arNcSVSG+gwRPSK1mtVhISEoI3wnY6nd1+OywRkb7G7/dTUVGB0+ns9KTJEl70XSC91qHJQw+FOxEROZLFYiE7O1t//AoAmsdOej2fz4fH4wl1GSIivVJERAQWi66skgAFOxEREZEwoYgvIiIiEiYU7ERERETChIKdiIiISJhQsBMREREJEwp2IiIiImFCwU5EREQkTCjYiYiIiISJ/w9fsXD0Q9SSLgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "E3 ligase &  E3 ligase (\\%) &  Unique PROTACs (\\% per E3) &  Unique targets (\\% per E3) &  Unique cell lines (\\% per E3) \\\\\n",
      "\\midrule\n",
      "      VHL &            0.5 &                        0.4 &                        0.3 &                           0.3 \\\\\n",
      "    Other &            0.1 &                        0.0 &                        0.0 &                           0.0 \\\\\n",
      "     CRBN &            0.5 &                        0.5 &                        0.3 &                           0.3 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E3 ligase</th>\n",
       "      <th>E3 ligase (%)</th>\n",
       "      <th>Unique PROTACs (% per E3)</th>\n",
       "      <th>Unique targets (% per E3)</th>\n",
       "      <th>Unique cell lines (% per E3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VHL</td>\n",
       "      <td>0.487209</td>\n",
       "      <td>0.441536</td>\n",
       "      <td>0.338235</td>\n",
       "      <td>0.263736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>0.059302</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CRBN</td>\n",
       "      <td>0.453488</td>\n",
       "      <td>0.481675</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  E3 ligase  E3 ligase (%)  Unique PROTACs (% per E3)  \\\n",
       "0       VHL       0.487209                   0.441536   \n",
       "1     Other       0.059302                   0.000000   \n",
       "2      CRBN       0.453488                   0.481675   \n",
       "\n",
       "   Unique targets (% per E3)  Unique cell lines (% per E3)  \n",
       "0                   0.338235                      0.263736  \n",
       "1                   0.000000                      0.000000  \n",
       "2                   0.264706                      0.285714  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "E3 ligase                       VHLOtherCRBN\n",
       "E3 ligase (%)                            1.0\n",
       "Unique PROTACs (% per E3)           0.923211\n",
       "Unique targets (% per E3)           0.602941\n",
       "Unique cell lines (% per E3)        0.549451\n",
       "dtype: object"
      ]
     },
     "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.\n",
      "NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.\n"
     ]
    }
   ],
   "source": [
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# sns.kdeplot(protac_df['pDC50'] * 10, ax=axes[ax_idx], color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5)\n",
    "# sns.kdeplot(protac_df['Dmax (%)'], ax=axes[ax_idx], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5)\n",
    "sns.histplot(protac_df['pDC50'] * 10, color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5, kde=True, ax=ax)\n",
    "sns.histplot(protac_df['Dmax (%)'], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5, kde=True, ax=ax)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "# plt.legend(loc='upper left')\n",
    "# Set legend below the plot\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.savefig('dc50_dmax_distributions.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# Create a new dataframe for which, for each E3 ligase name, we have:\n",
    "# - the percentage of unique PROTACs associated to it\n",
    "# - The percentage of unique POI associated to it\n",
    "# - The percentage of unique cell lines associated to it\n",
    "tmp = protac_df[protac_df[active_col].notna()].copy()\n",
    "tmp['E3 ligase'] = tmp['E3 Ligase'].apply(lambda x: x if x == 'VHL' or x == 'CRBN' else 'Other')\n",
    "e3_ligase_stats = pd.DataFrame()\n",
    "e3_ligase_stats['E3 ligase'] = tmp['E3 ligase'].unique()\n",
    "\n",
    "e3_ligase_stats['E3 ligase (%)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: len(tmp[tmp['E3 ligase'] == x]) / len(tmp['E3 ligase'])\n",
    ")\n",
    "\n",
    "def get_unique_per_e3(df: pd.DataFrame, e3: str, column: str) -> pd.DataFrame:\n",
    "    \"\"\" Get the unique number of entries per E3 ligase which are NOT in the other E3.\n",
    "\n",
    "    Args:\n",
    "        df(pd.DataFrame): The dataframe containing the data\n",
    "        e3(str): The E3 ligase name\n",
    "        column(str): The column name to count the unique entries\n",
    "    Returns:\n",
    "        pd.DataFrame: A dataframe containing the unique number of entries per E3 ligase\n",
    "    \"\"\"\n",
    "    e3_df = df[df['E3 Ligase'] == e3]\n",
    "    other_e3_df = df[df['E3 Ligase'] != e3]\n",
    "    e3_unique = e3_df[~e3_df[column].isin(other_e3_df[column])][column].nunique()\n",
    "    return e3_unique\n",
    "\n",
    "e3_ligase_stats['Unique PROTACs (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    # lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Smiles'].nunique() / tmp['Smiles'].nunique()\n",
    "    lambda x: get_unique_per_e3(tmp, x, 'Smiles') / tmp['Smiles'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique targets (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    # lambda x: tmp[tmp['E3 ligase'] == x]['Uniprot'].nunique() / tmp['Uniprot'].nunique()\n",
    "    lambda x: get_unique_per_e3(tmp, x, 'Uniprot') / tmp['Uniprot'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique cell lines (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    # lambda x: tmp[tmp['E3 ligase'] == x]['Cell Line Identifier'].nunique() / tmp['Cell Line Identifier'].nunique()\n",
    "    lambda x: get_unique_per_e3(tmp, x, 'Cell Line Identifier') / tmp['Cell Line Identifier'].nunique()\n",
    ")\n",
    "\n",
    "\n",
    "print(e3_ligase_stats.round(1).to_latex(index=False))\n",
    "display(e3_ligase_stats)\n",
    "display(e3_ligase_stats.sum(axis=0))\n",
    "\n",
    "# Sort the e3_ligase_stats as: CRBN, VHL, Other\n",
    "e3_ligase_stats = e3_ligase_stats.sort_values('E3 ligase', key=lambda x: x.map({'CRBN': 0, 'VHL': 1, 'Other': 2}))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# stacked Plot the distribution of PROTACs, POI and cell lines associated to each E3 ligase\n",
    "e3_ligase_stats.plot.bar(\n",
    "    x='E3 ligase',\n",
    "    # y=['E3 ligase (%)', 'Unique PROTACs (% per E3)', 'Unique targets (% per E3)', 'Unique cell lines (% per E3)'],\n",
    "    y=['Unique PROTACs (% per E3)', 'Unique targets (% per E3)', 'Unique cell lines (% per E3)'],\n",
    "    stacked=True,\n",
    "    ax=ax,\n",
    "    color=adjusted_palette,\n",
    "    grid=False,\n",
    ")\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "# Set the y-axis to log scale\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "# Put the percentages on top of the bars if the bar corresponding to the E3 ligases 'VHL' and 'CRBN'\n",
    "for i, p in enumerate(ax.patches):\n",
    "    if p.get_height() < 0.20 and p.get_height() > 0:\n",
    "        percentage = f'{p.get_height() * 100:.1f}%'\n",
    "        if percentage == '0.0%':\n",
    "            continue\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = p.get_y() + p.get_height() / 2 + 0.08\n",
    "        ax.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "    else:\n",
    "        percentage = f'{p.get_height() * 100:.1f}%'\n",
    "        if percentage == '0.0%':\n",
    "            continue\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = p.get_y() + p.get_height() / 2\n",
    "        ax.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "# Set the x-axis to percentage\n",
    "ax.yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "\n",
    "# Set x-axis labels to orientation 90 degrees\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n",
    "\n",
    "# Set the legend below the plot and outside the plot in 4 columns\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('e3_distributions.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print('NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.')\n",
    "print('NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting CV Scores and Ablation Study Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#83B8FE', '#FFA54C', '#94ED67', '#FF7FFF']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import colorsys\n",
    "\n",
    "def increase_saturation(hex_color, increase_by=0.3):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "    # Convert RGB to HSV\n",
    "    hsv = colorsys.rgb_to_hsv(rgb[0]/255, rgb[1]/255, rgb[2]/255)\n",
    "    # Increase saturation\n",
    "    new_saturation = min(hsv[1] + increase_by, 1)  # Ensure saturation doesn't exceed 1\n",
    "    # Convert back to RGB and then to hex\n",
    "    new_rgb = colorsys.hsv_to_rgb(hsv[0], new_saturation, hsv[2])\n",
    "    new_hex = '#' + ''.join(f'{int(c*255):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "def darken_color(hex_color, darkening_factor=1.0):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "\n",
    "    # Darken color\n",
    "    new_rgb = [(color * darkening_factor) for color in rgb]\n",
    "\n",
    "    # Convert RGB back to hex\n",
    "    new_hex = '#' + ''.join(f'{int(c):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "palette = [\n",
    "    '#D0E4FE', # blue\n",
    "    '#FFCC99', # orange\n",
    "    '#C4EDAF', # green\n",
    "    '#FFCCFF', # pink\n",
    "]\n",
    "\n",
    "\n",
    "# Adjusted palette\n",
    "palette = adjusted_palette = [increase_saturation(color) for color in palette]\n",
    "print(adjusted_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>group_type</th>\n",
       "      <th>train_len</th>\n",
       "      <th>val_len</th>\n",
       "      <th>train_perc</th>\n",
       "      <th>val_perc</th>\n",
       "      <th>train_active_perc</th>\n",
       "      <th>train_inactive_perc</th>\n",
       "      <th>val_active_perc</th>\n",
       "      <th>val_inactive_perc</th>\n",
       "      <th>...</th>\n",
       "      <th>hparam_batch_size</th>\n",
       "      <th>hparam_learning_rate</th>\n",
       "      <th>hparam_join_embeddings</th>\n",
       "      <th>hparam_smote_k_neighbors</th>\n",
       "      <th>hparam_use_smote</th>\n",
       "      <th>hparam_apply_scaling</th>\n",
       "      <th>hparam_dropout</th>\n",
       "      <th>disabled_embeddings</th>\n",
       "      <th>train_unique_groups</th>\n",
       "      <th>val_unique_groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
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       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled poi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>616</td>\n",
       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>616</td>\n",
       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled smiles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>616</td>\n",
       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled e3 cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>30</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled poi</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled cell</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled smiles</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled e3 cell</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled poi e3 cell</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    fold group_type  train_len  val_len  train_perc  val_perc  \\\n",
       "0      0     random        616      155    0.798962  0.201038   \n",
       "1      0     random        616      155    0.798962  0.201038   \n",
       "2      0     random        616      155    0.798962  0.201038   \n",
       "3      0     random        616      155    0.798962  0.201038   \n",
       "4      0     random        616      155    0.798962  0.201038   \n",
       "..   ...        ...        ...      ...         ...       ...   \n",
       "30     4   tanimoto        661      111    0.856218  0.143782   \n",
       "31     4   tanimoto        661      111    0.856218  0.143782   \n",
       "32     4   tanimoto        661      111    0.856218  0.143782   \n",
       "33     4   tanimoto        661      111    0.856218  0.143782   \n",
       "34     4   tanimoto        661      111    0.856218  0.143782   \n",
       "\n",
       "    train_active_perc  train_inactive_perc  val_active_perc  \\\n",
       "0            0.514610             0.485390         0.516129   \n",
       "1            0.514610             0.485390         0.516129   \n",
       "2            0.514610             0.485390         0.516129   \n",
       "3            0.514610             0.485390         0.516129   \n",
       "4            0.514610             0.485390         0.516129   \n",
       "..                ...                  ...              ...   \n",
       "30           0.521936             0.478064         0.468468   \n",
       "31           0.521936             0.478064         0.468468   \n",
       "32           0.521936             0.478064         0.468468   \n",
       "33           0.521936             0.478064         0.468468   \n",
       "34           0.521936             0.478064         0.468468   \n",
       "\n",
       "    val_inactive_perc  ...  hparam_batch_size  hparam_learning_rate  \\\n",
       "0            0.483871  ...                 32              0.000056   \n",
       "1            0.483871  ...                 32              0.000056   \n",
       "2            0.483871  ...                 32              0.000056   \n",
       "3            0.483871  ...                 32              0.000056   \n",
       "4            0.483871  ...                 32              0.000056   \n",
       "..                ...  ...                ...                   ...   \n",
       "30           0.531532  ...                 16              0.000019   \n",
       "31           0.531532  ...                 16              0.000019   \n",
       "32           0.531532  ...                 16              0.000019   \n",
       "33           0.531532  ...                 16              0.000019   \n",
       "34           0.531532  ...                 16              0.000019   \n",
       "\n",
       "    hparam_join_embeddings  hparam_smote_k_neighbors  hparam_use_smote  \\\n",
       "0                   concat                         6              True   \n",
       "1                   concat                         6              True   \n",
       "2                   concat                         6              True   \n",
       "3                   concat                         6              True   \n",
       "4                   concat                         6              True   \n",
       "..                     ...                       ...               ...   \n",
       "30               beginning                         5             False   \n",
       "31               beginning                         5             False   \n",
       "32               beginning                         5             False   \n",
       "33               beginning                         5             False   \n",
       "34               beginning                         5             False   \n",
       "\n",
       "    hparam_apply_scaling  hparam_dropout   disabled_embeddings  \\\n",
       "0                   True        0.119461                   NaN   \n",
       "1                   True        0.119461          disabled poi   \n",
       "2                   True        0.119461         disabled cell   \n",
       "3                   True        0.119461       disabled smiles   \n",
       "4                   True        0.119461      disabled e3 cell   \n",
       "..                   ...             ...                   ...   \n",
       "30                  True        0.217163          disabled poi   \n",
       "31                  True        0.217163         disabled cell   \n",
       "32                  True        0.217163       disabled smiles   \n",
       "33                  True        0.217163      disabled e3 cell   \n",
       "34                  True        0.217163  disabled poi e3 cell   \n",
       "\n",
       "    train_unique_groups  val_unique_groups  \n",
       "0                   NaN                NaN  \n",
       "1                   NaN                NaN  \n",
       "2                   NaN                NaN  \n",
       "3                   NaN                NaN  \n",
       "4                   NaN                NaN  \n",
       "..                  ...                ...  \n",
       "30                 47.0                8.0  \n",
       "31                 47.0                8.0  \n",
       "32                 47.0                8.0  \n",
       "33                 47.0                8.0  \n",
       "34                 47.0                8.0  \n",
       "\n",
       "[140 rows x 43 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>dummy_val_acc</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_roc_auc</th>\n",
       "      <th>dummy_test_acc</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <td>0.515</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tanimoto</th>\n",
       "      <td>0.555</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniprot</th>\n",
       "      <td>0.580</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dummy_val_acc  val_acc  val_roc_auc  dummy_test_acc  test_acc  \\\n",
       "group_type                                                                  \n",
       "random              0.515    0.846        0.905           0.535     0.714   \n",
       "tanimoto            0.555    0.775        0.852           0.529     0.715   \n",
       "uniprot             0.580    0.628        0.654           0.541     0.584   \n",
       "\n",
       "            test_roc_auc  \n",
       "group_type                \n",
       "random             0.779  \n",
       "tanimoto           0.840  \n",
       "uniprot            0.592  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_show = [\n",
    "    # 'val_active_perc',\n",
    "    # 'val_inactive_perc',\n",
    "    'dummy_val_acc',\n",
    "    'val_acc',\n",
    "    'val_roc_auc',\n",
    "    # 'test_active_perc',\n",
    "    # 'test_inactive_perc',\n",
    "    'dummy_test_acc',\n",
    "    'test_acc',\n",
    "    'test_roc_auc',\n",
    "]\n",
    "\n",
    "report = pd.read_csv('../reports/cv_report_hparam_search_5-splits_Active_Dmax_0.6_pDC50_6.0_test_split_0.1.csv')\n",
    "report_tanimoto = pd.read_csv('../reports/cv_report_hparam_search_5-splits_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv')\n",
    "report = pd.concat([report, report_tanimoto])\n",
    "\n",
    "# report = pd.read_csv('../reports/cv_report_hparam_search_5-splits_Active_Dmax_0.6_pDC50_6.0_test_split_0.2.csv')\n",
    "report.columns = [c.replace('split_type', 'group_type') for c in report.columns]\n",
    "\n",
    "display(report)\n",
    "\n",
    "# Remove group_type e3_ligase\n",
    "report = report[report['group_type'] != 'e3_ligase']\n",
    "\n",
    "report['dummy_val_acc'] = report[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
    "report['dummy_test_acc'] = report[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
    "\n",
    "tmp = report[report['disabled_embeddings'].isna()]\n",
    "# Suppress future warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# tmp.groupby(['group_type', 'active']).mean().round(3)[cols_to_show]\n",
    "tmp.groupby(['group_type',]).mean().round(3)[cols_to_show]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "857"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:\n",
    "    \"\"\" Check if a PROTAC is active based on DC50 and Dmax.\t\n",
    "    Args:\n",
    "        DC50(float): DC50 in nM\n",
    "        Dmax(float): Dmax in %\n",
    "    Returns:\n",
    "        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN\n",
    "    \"\"\"\n",
    "    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan\n",
    "    Dmax = Dmax / 100\n",
    "    if pd.notnull(pDC50):\n",
    "        if pDC50 < pDC50_threshold:\n",
    "            return False\n",
    "    if pd.notnull(Dmax):\n",
    "        if Dmax < Dmax_threshold:\n",
    "            return False\n",
    "    if oring:\n",
    "        if pd.notnull(pDC50):\n",
    "            return True if pDC50 >= pDC50_threshold else False\n",
    "        elif pd.notnull(Dmax):\n",
    "            return True if Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "    else:\n",
    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
    "            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "\n",
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "pDC50_threshold = 6.0\n",
    "Dmax_threshold = 0.6\n",
    "protac_df[active_col] = protac_df.apply(\n",
    "    lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1\n",
    ")\n",
    "tot_len = len(protac_df.dropna(subset=active_col))\n",
    "tot_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of active PROTACs: 437 (50.99%)\n",
      "Number of inactive PROTACs: 420 (49.01%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True     437\n",
       "False    420\n",
       "Name: Active (Dmax 0.6, pDC50 6.0), dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = protac_df.dropna(subset=active_col)\n",
    "print(f'Number of active PROTACs: {len(tmp[tmp[active_col] == True])} ({100 * len(tmp[tmp[active_col] == True]) / len(tmp):.2f}%)')\n",
    "print(f'Number of inactive PROTACs: {len(tmp[tmp[active_col] == False])} ({100 * len(tmp[tmp[active_col] == False]) / len(tmp):.2f}%)')\n",
    "tmp[active_col].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2141\n",
      "812 0.3792620270901448\n",
      "1350 0.6305464736104625\n"
     ]
    }
   ],
   "source": [
    "print(len(protac_df))\n",
    "print(len(protac_df.dropna(subset='Dmax (%)')), len(protac_df.dropna(subset='Dmax (%)')) / len(protac_df))\n",
    "print(len(protac_df.dropna(subset='DC50 (nM)')), len(protac_df.dropna(subset='DC50 (nM)')) / len(protac_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "\n",
    "# Display train and test set informations\n",
    "cols_to_show = [\n",
    "    'fold',\n",
    "    'group_type',\n",
    "    'train_len',\n",
    "    'train_perc',\n",
    "    'val_len',\n",
    "    'val_perc',\n",
    "    'test_len',\n",
    "    'test_perc',\n",
    "    'train_active_perc',\n",
    "    'val_active_perc',\n",
    "    'test_active_perc',\n",
    "    'train_leaking_uniprot_perc',\n",
    "    'train_leaking_smiles_perc',\n",
    "    # 'train_unique_groups',\n",
    "    # 'val_unique_groups',\n",
    "]\n",
    "report['test_len'] = tot_len - report['train_len'] - report['val_len']\n",
    "report['train_perc'] = report['train_len'] / tot_len\n",
    "report['val_perc'] = report['val_len'] / tot_len\n",
    "report['test_perc'] = report['test_len'] / tot_len\n",
    "\n",
    "tmp = report[report['group_type'] != 'e3_ligase'].groupby('group_type').mean().round(3).copy()\n",
    "# tmp = report[cols_to_show].groupby('group_type').mean().round(3).copy()\n",
    "\n",
    "# \"Collapse\" group_type into another column along side the others\n",
    "tmp = tmp.reset_index()\n",
    "# Display the columns with '_perc' in their name as percentages\n",
    "for c in tmp.columns:\n",
    "    if '_perc' in c:\n",
    "        tmp[c] *= 100\n",
    "# Plot a stacked barplot of the train/val/test percentages\n",
    "# fig, ax = plt.subplots() #figsize=(8, 6))\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "tmp.plot.bar(x='group_type', y=['train_perc', 'val_perc', 'test_perc'], stacked=True, ax=ax, color=palette, grid=False)\n",
    "ax.set_xlabel('')\n",
    "# ax.set_xlabel('Split Type')\n",
    "# ax.set_ylabel('Percentage')\n",
    "# ax.set_title('Train/Validation/Test Split')\n",
    "\n",
    "# Write the train/val/test len inside the stacked bars\n",
    "for i, p in enumerate(ax.patches):\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy()\n",
    "    ax.text(x + width / 2, y + height / 2, f'{round(height / 100 * tot_len)} ({height/100:.1%})', ha='center', va='center')\n",
    "\n",
    "# Rename the legend labels\n",
    "ax.legend(['Train set', 'Validation set', 'Test set'])\n",
    "# Rename x-axis labels\n",
    "ax.set_xticklabels(['Standard split', 'Similarity split', 'Target split'])\n",
    "# Set x ticks to 90 degree orientation\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "\n",
    "# Set the y-axis labels to percentage\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "plt.tight_layout()\n",
    "plt.savefig('train_val_test_split.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fold', 'group_type', 'train_len', 'val_len', 'train_perc', 'val_perc',\n",
       "       'train_active_perc', 'train_inactive_perc', 'val_active_perc',\n",
       "       'val_inactive_perc', 'test_active_perc', 'test_inactive_perc',\n",
       "       'num_leaking_uniprot', 'num_leaking_smiles',\n",
       "       'train_leaking_uniprot_perc', 'train_leaking_smiles_perc', 'val_loss',\n",
       "       'val_acc', 'val_f1_score', 'val_hp_metric', 'val_opt_score',\n",
       "       'val_precision', 'val_recall', 'val_roc_auc', 'test_loss', 'test_acc',\n",
       "       'test_f1_score', 'test_hp_metric', 'test_opt_score', 'test_precision',\n",
       "       'test_recall', 'test_roc_auc', 'hparam_hidden_dim', 'hparam_batch_size',\n",
       "       'hparam_learning_rate', 'hparam_join_embeddings',\n",
       "       'hparam_smote_k_neighbors', 'hparam_use_smote', 'hparam_apply_scaling',\n",
       "       'hparam_dropout', 'disabled_embeddings', 'train_unique_groups',\n",
       "       'val_unique_groups', 'dummy_val_acc', 'dummy_test_acc', 'test_len',\n",
       "       'test_perc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "add dummy score bar, but the description of it can go in the text only, not in the legend\n",
    "\n",
    "replace uniprot-wise to target-wise\n",
    "\n",
    "remove the title, it's redundant because it goes into the caption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llr}\n",
      "\\toprule\n",
      "       CV-Groups &        Metric &  Score \\\\\n",
      "\\midrule\n",
      "     Dummy model & Test Accuracy &  0.535 \\\\\n",
      "     Dummy model &  Test ROC AUC &  0.500 \\\\\n",
      "     Dummy model &  Val Accuracy &  0.516 \\\\\n",
      "     Dummy model &   Val ROC AUC &  0.500 \\\\\n",
      "Similarity split & Test Accuracy &  0.715 \\\\\n",
      "Similarity split &  Test ROC AUC &  0.840 \\\\\n",
      "Similarity split &  Val Accuracy &  0.775 \\\\\n",
      "Similarity split &   Val ROC AUC &  0.852 \\\\\n",
      "  Standard split & Test Accuracy &  0.714 \\\\\n",
      "  Standard split &  Test ROC AUC &  0.779 \\\\\n",
      "  Standard split &  Val Accuracy &  0.846 \\\\\n",
      "  Standard split &   Val ROC AUC &  0.905 \\\\\n",
      "    Target split & Test Accuracy &  0.584 \\\\\n",
      "    Target split &  Test ROC AUC &  0.592 \\\\\n",
      "    Target split &  Val Accuracy &  0.628 \\\\\n",
      "    Target split &   Val ROC AUC &  0.654 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CV-Groups</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dummy model</td>\n",
       "      <td>Test Accuracy</td>\n",
       "      <td>0.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dummy model</td>\n",
       "      <td>Test ROC AUC</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dummy model</td>\n",
       "      <td>Val Accuracy</td>\n",
       "      <td>0.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dummy model</td>\n",
       "      <td>Val ROC AUC</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Similarity split</td>\n",
       "      <td>Test Accuracy</td>\n",
       "      <td>0.715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Similarity split</td>\n",
       "      <td>Test ROC AUC</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Similarity split</td>\n",
       "      <td>Val Accuracy</td>\n",
       "      <td>0.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Similarity split</td>\n",
       "      <td>Val ROC AUC</td>\n",
       "      <td>0.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Standard split</td>\n",
       "      <td>Test Accuracy</td>\n",
       "      <td>0.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Standard split</td>\n",
       "      <td>Test ROC AUC</td>\n",
       "      <td>0.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Standard split</td>\n",
       "      <td>Val Accuracy</td>\n",
       "      <td>0.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Standard split</td>\n",
       "      <td>Val ROC AUC</td>\n",
       "      <td>0.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Target split</td>\n",
       "      <td>Test Accuracy</td>\n",
       "      <td>0.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Target split</td>\n",
       "      <td>Test ROC AUC</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Target split</td>\n",
       "      <td>Val Accuracy</td>\n",
       "      <td>0.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Target split</td>\n",
       "      <td>Val ROC AUC</td>\n",
       "      <td>0.654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CV-Groups         Metric  Score\n",
       "0        Dummy model  Test Accuracy  0.535\n",
       "1        Dummy model   Test ROC AUC  0.500\n",
       "2        Dummy model   Val Accuracy  0.516\n",
       "3        Dummy model    Val ROC AUC  0.500\n",
       "4   Similarity split  Test Accuracy  0.715\n",
       "5   Similarity split   Test ROC AUC  0.840\n",
       "6   Similarity split   Val Accuracy  0.775\n",
       "7   Similarity split    Val ROC AUC  0.852\n",
       "8     Standard split  Test Accuracy  0.714\n",
       "9     Standard split   Test ROC AUC  0.779\n",
       "10    Standard split   Val Accuracy  0.846\n",
       "11    Standard split    Val ROC AUC  0.905\n",
       "12      Target split  Test Accuracy  0.584\n",
       "13      Target split   Test ROC AUC  0.592\n",
       "14      Target split   Val Accuracy  0.628\n",
       "15      Target split    Val ROC AUC  0.654"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{rllr}\n",
      "\\toprule\n",
      " Fold &        CV-Groups &        Metric &    Score \\\\\n",
      "\\midrule\n",
      "  0.0 &   Standard split &  Val Accuracy & 0.838710 \\\\\n",
      "  1.0 &   Standard split &  Val Accuracy & 0.824675 \\\\\n",
      "  2.0 &   Standard split &  Val Accuracy & 0.850649 \\\\\n",
      "  3.0 &   Standard split &  Val Accuracy & 0.857143 \\\\\n",
      "  4.0 &   Standard split &  Val Accuracy & 0.857143 \\\\\n",
      "  0.0 &     Target split &  Val Accuracy & 0.646226 \\\\\n",
      "  1.0 &     Target split &  Val Accuracy & 0.882759 \\\\\n",
      "  2.0 &     Target split &  Val Accuracy & 0.554545 \\\\\n",
      "  3.0 &     Target split &  Val Accuracy & 0.561947 \\\\\n",
      "  4.0 &     Target split &  Val Accuracy & 0.493671 \\\\\n",
      "  0.0 & Similarity split &  Val Accuracy & 0.760000 \\\\\n",
      "  1.0 & Similarity split &  Val Accuracy & 0.767857 \\\\\n",
      "  2.0 & Similarity split &  Val Accuracy & 0.765306 \\\\\n",
      "  3.0 & Similarity split &  Val Accuracy & 0.732955 \\\\\n",
      "  4.0 & Similarity split &  Val Accuracy & 0.846847 \\\\\n",
      "  0.0 &   Standard split &   Val ROC AUC & 0.903500 \\\\\n",
      "  1.0 &   Standard split &   Val ROC AUC & 0.909797 \\\\\n",
      "  2.0 &   Standard split &   Val ROC AUC & 0.900253 \\\\\n",
      "  3.0 &   Standard split &   Val ROC AUC & 0.902785 \\\\\n",
      "  4.0 &   Standard split &   Val ROC AUC & 0.906667 \\\\\n",
      "  0.0 &     Target split &   Val ROC AUC & 0.740495 \\\\\n",
      "  1.0 &     Target split &   Val ROC AUC & 0.914657 \\\\\n",
      "  2.0 &     Target split &   Val ROC AUC & 0.613922 \\\\\n",
      "  3.0 &     Target split &   Val ROC AUC & 0.570508 \\\\\n",
      "  4.0 &     Target split &   Val ROC AUC & 0.431061 \\\\\n",
      "  0.0 & Similarity split &   Val ROC AUC & 0.837271 \\\\\n",
      "  1.0 & Similarity split &   Val ROC AUC & 0.856459 \\\\\n",
      "  2.0 & Similarity split &   Val ROC AUC & 0.825875 \\\\\n",
      "  3.0 & Similarity split &   Val ROC AUC & 0.824699 \\\\\n",
      "  4.0 & Similarity split &   Val ROC AUC & 0.918188 \\\\\n",
      "  0.0 &   Standard split & Test Accuracy & 0.732558 \\\\\n",
      "  1.0 &   Standard split & Test Accuracy & 0.697674 \\\\\n",
      "  2.0 &   Standard split & Test Accuracy & 0.709302 \\\\\n",
      "  3.0 &   Standard split & Test Accuracy & 0.720930 \\\\\n",
      "  4.0 &   Standard split & Test Accuracy & 0.709302 \\\\\n",
      "  0.0 &     Target split & Test Accuracy & 0.611765 \\\\\n",
      "  1.0 &     Target split & Test Accuracy & 0.647059 \\\\\n",
      "  2.0 &     Target split & Test Accuracy & 0.611765 \\\\\n",
      "  3.0 &     Target split & Test Accuracy & 0.505882 \\\\\n",
      "  4.0 &     Target split & Test Accuracy & 0.541176 \\\\\n",
      "  0.0 & Similarity split & Test Accuracy & 0.705882 \\\\\n",
      "  1.0 & Similarity split & Test Accuracy & 0.717647 \\\\\n",
      "  2.0 & Similarity split & Test Accuracy & 0.729412 \\\\\n",
      "  3.0 & Similarity split & Test Accuracy & 0.705882 \\\\\n",
      "  4.0 & Similarity split & Test Accuracy & 0.717647 \\\\\n",
      "  0.0 &   Standard split &  Test ROC AUC & 0.791304 \\\\\n",
      "  1.0 &   Standard split &  Test ROC AUC & 0.764130 \\\\\n",
      "  2.0 &   Standard split &  Test ROC AUC & 0.772826 \\\\\n",
      "  3.0 &   Standard split &  Test ROC AUC & 0.764674 \\\\\n",
      "  4.0 &   Standard split &  Test ROC AUC & 0.801087 \\\\\n",
      "  0.0 &     Target split &  Test ROC AUC & 0.571349 \\\\\n",
      "  1.0 &     Target split &  Test ROC AUC & 0.743590 \\\\\n",
      "  2.0 &     Target split &  Test ROC AUC & 0.652174 \\\\\n",
      "  3.0 &     Target split &  Test ROC AUC & 0.448718 \\\\\n",
      "  4.0 &     Target split &  Test ROC AUC & 0.542921 \\\\\n",
      "  0.0 & Similarity split &  Test ROC AUC & 0.803333 \\\\\n",
      "  1.0 & Similarity split &  Test ROC AUC & 0.861111 \\\\\n",
      "  2.0 & Similarity split &  Test ROC AUC & 0.855556 \\\\\n",
      "  3.0 & Similarity split &  Test ROC AUC & 0.842778 \\\\\n",
      "  4.0 & Similarity split &  Test ROC AUC & 0.838333 \\\\\n",
      "  NaN &      Dummy model &  Val Accuracy & 0.516094 \\\\\n",
      "  NaN &      Dummy model &   Val ROC AUC & 0.500000 \\\\\n",
      "  NaN &      Dummy model & Test Accuracy & 0.535157 \\\\\n",
      "  NaN &      Dummy model &  Test ROC AUC & 0.500000 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "Plotting value: 0.8456640124320984 -> 84.6%\n",
      "Plotting value: 0.9046003818511963 -> 0.90\n",
      "Plotting value: 0.7139534711837768 -> 71.4%\n",
      "Plotting value: 0.778804337978363 -> 0.78\n",
      "Plotting value: 0.627829658985138 -> 62.8%\n",
      "Plotting value: 0.6541285753250122 -> 0.65\n",
      "Plotting value: 0.5835294365882874 -> 58.4%\n",
      "Plotting value: 0.5917502701282501 -> 0.59\n",
      "Plotting value: 0.774592924118042 -> 77.5%\n",
      "Plotting value: 0.8524984359741211 -> 0.85\n",
      "Plotting value: 0.7152941346168518 -> 71.5%\n",
      "Plotting value: 0.8402222156524658 -> 0.84\n",
      "Plotting value: 0.516094026565312 -> 51.6%\n",
      "Plotting value: 0.5 -> 0.50\n",
      "Plotting value: 0.53515731874145 -> 53.5%\n",
      "Plotting value: 0.5 -> 0.50\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.set_style(\"whitegrid\")\n",
    "\n",
    "\n",
    "def plot_report(report_df, title=None):\n",
    "    n_splits = 5\n",
    "    tmp = report_df.copy()[[\n",
    "        'fold', 'group_type',\n",
    "        'val_acc', 'val_roc_auc', # 'val_precision', 'val_recall', 'val_f1_score',\n",
    "        'test_acc', 'test_roc_auc', # 'test_precision', 'test_recall', 'test_f1_score',\n",
    "    ]]\n",
    "    # Rename columns\n",
    "    tmp.columns = [\n",
    "        'Fold', 'CV-Groups',\n",
    "        'Val Accuracy', 'Val ROC AUC', # 'Val Precision', 'Val Recall', 'Val F1 score',\n",
    "        'Test Accuracy', 'Test ROC AUC', # 'Test Precision', 'Test Recall', 'Test F1 score',\n",
    "    ]\n",
    "    # Remove all entries with 'CV-Groups' == 'e3_ligase'\n",
    "    tmp = tmp[tmp['CV-Groups'] != 'e3_ligase']\n",
    "    # Plot aggregated mean value of all metrics in one bar plot\n",
    "    tmp = tmp.melt(id_vars=['Fold', 'CV-Groups'], var_name='Metric', value_name='Score')\n",
    "    # Rename elements in 'CV-Groups' column\n",
    "    group2name = {\n",
    "        'random': 'Standard split',\n",
    "        'uniprot': 'Target split',\n",
    "        'tanimoto': 'Similarity split',\n",
    "    }\n",
    "\n",
    "    tmp['CV-Groups'] = tmp['CV-Groups'].map(group2name)\n",
    "    # Draw one horizontal line for each CV-group representing the max value of inactive PROTACs percentage across the folds\n",
    "    dummy_val_acc = 0\n",
    "    dummy_test_acc = 0\n",
    "    for i, group in enumerate(group2name.keys()):\n",
    "        # Get the majority class in group_df\n",
    "        group_df = report_df[report_df['group_type'] == group]\n",
    "        \n",
    "        major_col = 'inactive' if group_df['val_inactive_perc'].mean() > 0.5 else 'active'\n",
    "        dummy_val_acc += group_df[f'val_{major_col}_perc'].mean()\n",
    "\n",
    "        major_col = 'inactive' if group_df['test_inactive_perc'].mean() > 0.5 else 'active'\n",
    "        dummy_test_acc += group_df[f'test_{major_col}_perc'].mean()\n",
    "\n",
    "    #     # plt.axhline(group_df[f'val_{major_col}_perc'].max(), color=f'C{i}', linestyle='-.', label=f'Max val {major_col} (%) for {group2name[group]}')\n",
    "    #     plt.axhline(group_df[f'val_{major_col}_perc'].mean(), color=f'C{i}', linestyle='--', label=f'Mean val {major_col} (%) for {group2name[group].split(\" (\")[0]}')\n",
    "    # plt.axhline(report_df[f'test_{major_col}_perc'].max(), color='black', linestyle=':', label=f'Max test {major_col} (%)')\n",
    "    \n",
    "    dummy_val_acc /= len(group2name)\n",
    "    dummy_test_acc /= len(group2name)\n",
    "\n",
    "    # create a dummy model dataframe:\n",
    "    # The \"Metric\" column shall have: 'Val Accuracy', 'Val ROC AUC', 'Test Accuracy', 'Test ROC AUC'\n",
    "    # The \"CV-Groups\" column shall have: 'Dummy model'\n",
    "    # The \"Score\" column shall have the following values:\n",
    "    # its val and test ROC AUC is 0.5,\n",
    "    # its val accuracy is the max value of val_active_perc and val_inactive_perc per group,\n",
    "    # and its test accuracy is the max value of test_active_perc and test_inactive_perc per group.\n",
    "    # The \"Fold\" column shall have the same value for all rows, e.g. 0\n",
    "    dummy_model = pd.DataFrame({\n",
    "        'Metric': ['Val Accuracy', 'Val ROC AUC', 'Test Accuracy', 'Test ROC AUC'],\n",
    "        'CV-Groups': 'Dummy model',\n",
    "        'Score': [\n",
    "            dummy_val_acc,\n",
    "            0.5,\n",
    "            dummy_test_acc,\n",
    "            0.5,\n",
    "        ],\n",
    "    })\n",
    "\n",
    "    # Append the dummy model dataframe to the tmp dataframe\n",
    "    tmp = pd.concat([tmp, dummy_model])\n",
    "\n",
    "    summary_df = tmp.groupby(['CV-Groups', 'Metric']).mean().round(3).reset_index().drop('Fold', axis=1)\n",
    "    # Print summary_df to Latex\n",
    "    print(summary_df.to_latex(index=False))\n",
    "    display(summary_df)\n",
    "\n",
    "    # Setup the plot size\n",
    "    # plt.figure(figsize=(8, 4)) # Original\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    \n",
    "    # Plot the bar plot\n",
    "    sns.barplot(data=tmp,\n",
    "        x='Metric',\n",
    "        y='Score',\n",
    "        hue='CV-Groups',\n",
    "        errorbar=('sd', 1),\n",
    "        # Lighten the color of the error bars\n",
    "        errcolor='0.5',\n",
    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
    "    )\n",
    "\n",
    "    print(tmp.to_latex(index=False))\n",
    "\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "\n",
    "    plt.grid(axis='y', alpha=0.4)\n",
    "    # Set y-axis to end at 1.0\n",
    "    plt.ylim(0, 1.0)\n",
    "    # Make the y-axis as percentage\n",
    "    plt.gca().yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    # Plot the legend below the x-axis, outside the plot, and divided in two columns\n",
    "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=4)\n",
    "\n",
    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
    "    for i, p in enumerate(plt.gca().patches):\n",
    "        # TODO: For some reasons, there are 4 additional rectangles being\n",
    "        # plotted... I suspect it's because the dummy_df doesn't have the same\n",
    "        # shape as the df containing all the evaluation data...\n",
    "        if p.get_height() < 0.01:\n",
    "            continue\n",
    "        if i % 2 == 0:\n",
    "            value = '{:.1f}%'.format(100 * p.get_height())\n",
    "        else:\n",
    "            value = '{:.2f}'.format(p.get_height())\n",
    "        \n",
    "        print(f'Plotting value: {p.get_height()} -> {value}')\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = 0.4 # p.get_height() - p.get_height() / 2\n",
    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # Remove axis labels\n",
    "    plt.xlabel('')\n",
    "    plt.ylabel('')\n",
    "    plt.savefig('genlife_poster_performance.pdf', bbox_inches='tight')\n",
    "    plt.savefig('stefano_performance_plot.pdf', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "tmp = report[report['disabled_embeddings'].isna()]\n",
    "plot_report(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'disabled poi', 'disabled cell', 'disabled smiles',\n",
       "       'disabled e3 cell', 'disabled poi e3 cell', 'disabled e3'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report['disabled_embeddings'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ablation study:\n",
    "\n",
    "- one single embedding/branch active at the time\n",
    "- just the cell and no other embeddings -> hopefully the performance is bad\n",
    "- just the SMILES branch and no other embeddings\n",
    "\n",
    "**TODO**: Ordinal encoding for the cell line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'disabled poi', 'disabled cell', 'disabled smiles',\n",
       "       'disabled e3 cell', 'disabled poi e3 cell', 'disabled e3'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report['disabled_embeddings'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllr}\n",
      "\\toprule\n",
      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
      "\\midrule\n",
      "0  &                             All embeddings enabled &        Test Accuracy &  0.714 \\\\\n",
      "1  &                             All embeddings enabled &  Validation Accuracy &  0.846 \\\\\n",
      "2  &                            Disabled E3 information &        Test Accuracy &  0.700 \\\\\n",
      "3  &                            Disabled E3 information &  Validation Accuracy &  0.786 \\\\\n",
      "4  &                          Disabled cell information &        Test Accuracy &  0.670 \\\\\n",
      "5  &                          Disabled cell information &  Validation Accuracy &  0.776 \\\\\n",
      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.647 \\\\\n",
      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.730 \\\\\n",
      "8  &                      Disabled compound information &        Test Accuracy &  0.663 \\\\\n",
      "9  &                      Disabled compound information &  Validation Accuracy &  0.799 \\\\\n",
      "10 &                        Disabled target information &        Test Accuracy &  0.686 \\\\\n",
      "11 &                        Disabled target information &  Validation Accuracy &  0.763 \\\\\n",
      "12 &                                        Dummy model &        Test Accuracy &  0.535 \\\\\n",
      "13 &                                        Dummy model &  Validation Accuracy &  0.515 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
      "  sns.barplot(data=final_df,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllr}\n",
      "\\toprule\n",
      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
      "\\midrule\n",
      "0  &                             All embeddings enabled &        Test Accuracy &  0.478 \\\\\n",
      "1  &                             All embeddings enabled &  Validation Accuracy &  0.806 \\\\\n",
      "2  &                            Disabled E3 information &        Test Accuracy &  0.506 \\\\\n",
      "3  &                            Disabled E3 information &  Validation Accuracy &  0.784 \\\\\n",
      "4  &                          Disabled cell information &        Test Accuracy &  0.396 \\\\\n",
      "5  &                          Disabled cell information &  Validation Accuracy &  0.753 \\\\\n",
      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.439 \\\\\n",
      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.700 \\\\\n",
      "8  &                      Disabled compound information &        Test Accuracy &  0.580 \\\\\n",
      "9  &                      Disabled compound information &  Validation Accuracy &  0.774 \\\\\n",
      "10 &                        Disabled target information &        Test Accuracy &  0.443 \\\\\n",
      "11 &                        Disabled target information &  Validation Accuracy &  0.764 \\\\\n",
      "12 &                                        Dummy model &        Test Accuracy &  0.824 \\\\\n",
      "13 &                                        Dummy model &  Validation Accuracy &  0.531 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
      "  sns.barplot(data=final_df,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllr}\n",
      "\\toprule\n",
      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
      "\\midrule\n",
      "0  &                             All embeddings enabled &        Test Accuracy &  0.584 \\\\\n",
      "1  &                             All embeddings enabled &  Validation Accuracy &  0.628 \\\\\n",
      "2  &                            Disabled E3 information &        Test Accuracy &  0.508 \\\\\n",
      "3  &                            Disabled E3 information &  Validation Accuracy &  0.598 \\\\\n",
      "4  &                          Disabled cell information &        Test Accuracy &  0.489 \\\\\n",
      "5  &                          Disabled cell information &  Validation Accuracy &  0.580 \\\\\n",
      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.501 \\\\\n",
      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.507 \\\\\n",
      "8  &                      Disabled compound information &        Test Accuracy &  0.600 \\\\\n",
      "9  &                      Disabled compound information &  Validation Accuracy &  0.641 \\\\\n",
      "10 &                        Disabled target information &        Test Accuracy &  0.471 \\\\\n",
      "11 &                        Disabled target information &  Validation Accuracy &  0.541 \\\\\n",
      "12 &                                        Dummy model &        Test Accuracy &  0.541 \\\\\n",
      "13 &                                        Dummy model &  Validation Accuracy &  0.580 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
      "  sns.barplot(data=final_df,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllr}\n",
      "\\toprule\n",
      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
      "\\midrule\n",
      "0  &                             All embeddings enabled &        Test Accuracy &  0.715 \\\\\n",
      "1  &                             All embeddings enabled &  Validation Accuracy &  0.775 \\\\\n",
      "2  &                            Disabled E3 information &        Test Accuracy &  0.666 \\\\\n",
      "3  &                            Disabled E3 information &  Validation Accuracy &  0.750 \\\\\n",
      "4  &                          Disabled cell information &        Test Accuracy &  0.687 \\\\\n",
      "5  &                          Disabled cell information &  Validation Accuracy &  0.704 \\\\\n",
      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.614 \\\\\n",
      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.618 \\\\\n",
      "8  &                      Disabled compound information &        Test Accuracy &  0.706 \\\\\n",
      "9  &                      Disabled compound information &  Validation Accuracy &  0.773 \\\\\n",
      "10 &                        Disabled target information &        Test Accuracy &  0.718 \\\\\n",
      "11 &                        Disabled target information &  Validation Accuracy &  0.719 \\\\\n",
      "12 &                                        Dummy model &        Test Accuracy &  0.529 \\\\\n",
      "13 &                                        Dummy model &  Validation Accuracy &  0.555 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
      "  sns.barplot(data=final_df,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "ablation_study_combinations = [\n",
    "    'disabled smiles',\n",
    "    'disabled poi',\n",
    "    'disabled e3',\n",
    "    'disabled cell',\n",
    "    'disabled poi e3 smiles',\n",
    "    'disabled poi e3 cell',\n",
    "]\n",
    "\n",
    "for group in report['group_type'].unique():    \n",
    "    baseline = report[report['disabled_embeddings'].isna()].copy()\n",
    "    baseline = baseline[baseline['group_type'] == group]\n",
    "    baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
    "    metrics_to_show = ['val_acc', 'test_acc']\n",
    "    baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "\n",
    "    ablation_dfs = []\n",
    "    for disabled_embeddings in ablation_study_combinations:\n",
    "        if pd.isnull(disabled_embeddings):\n",
    "            continue\n",
    "        tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
    "        tmp = tmp[tmp['group_type'] == group]\n",
    "        tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "        ablation_dfs.append(tmp)\n",
    "    ablation_df = pd.concat(ablation_dfs)\n",
    "\n",
    "    dummy_val_df = pd.DataFrame()\n",
    "    tmp = report[report['group_type'] == group]\n",
    "    dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
    "    dummy_val_df['metric'] = metrics_to_show[0]\n",
    "    dummy_val_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "    dummy_test_df = pd.DataFrame()\n",
    "    dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
    "    dummy_test_df['metric'] = metrics_to_show[1]\n",
    "    dummy_test_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "    dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
    "\n",
    "    final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
    "\n",
    "    final_df['metric'] = final_df['metric'].map({\n",
    "        'val_acc': 'Validation Accuracy',\n",
    "        'test_acc': 'Test Accuracy',\n",
    "        'val_roc_auc': 'Val ROC-AUC',\n",
    "        'test_roc_auc': 'Test ROC-AUC',\n",
    "    })\n",
    "\n",
    "    \n",
    "\n",
    "    # final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
    "    #     'all embeddings enabled': 'All embeddings',\n",
    "    #     'dummy': 'Dummy model',\n",
    "    #     'disabled smiles': 'E3, Cell, Target',\n",
    "    #     'disabled poi e3 smiles': 'Cell only',\n",
    "    #     'disabled poi e3 cell': 'SMILES only',\n",
    "    #     'disabled poi': 'SMILES, E3, Cell',\n",
    "    #     'disabled e3': 'SMILES, Cell, Target',\n",
    "    #     'disabled cell': 'SMILES, E3, Target',\n",
    "    # })\n",
    "    final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
    "        'all embeddings enabled': 'All embeddings enabled',\n",
    "        'dummy': 'Dummy model',\n",
    "        'disabled smiles': 'Disabled compound information',\n",
    "        'disabled e3': 'Disabled E3 information',\n",
    "        'disabled poi': 'Disabled target information',\n",
    "        'disabled cell': 'Disabled cell information',\n",
    "        'disabled poi e3 smiles': 'Disabled compound, E3, and target info\\n(only cell information left)',\n",
    "        'disabled poi e3 cell': 'Disabled cell, E3, and target info\\n(only compound information left)',\n",
    "    })\n",
    "\n",
    "    # Print final_df to latex\n",
    "    tmp  = final_df.groupby(['disabled_embeddings', 'metric']).mean().round(3)\n",
    "    # Remove fold column to tmp\n",
    "    tmp = tmp.reset_index().drop('fold', axis=1)\n",
    "    print(tmp.to_latex())\n",
    "\n",
    "    # fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    sns.barplot(data=final_df,\n",
    "        y='disabled_embeddings',\n",
    "        x='score',\n",
    "        hue='metric',\n",
    "        ax=ax,\n",
    "        errorbar=('sd', 1),\n",
    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
    "        saturation=1,\n",
    "    )\n",
    "\n",
    "    # ax.set_title(f'{group.replace(\"random\", \"standard\")} CV split')\n",
    "    ax.grid(axis='x', alpha=0.5)\n",
    "    ax.tick_params(axis='y', rotation=0)\n",
    "    ax.set_xlim(0, 1.0)\n",
    "    ax.xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xlabel('')\n",
    "    # Set the legend outside the plot and below\n",
    "    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=2)\n",
    "\n",
    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
    "    for i, p in enumerate(plt.gca().patches):\n",
    "        value = '{:.1f}%'.format(100 * p.get_width())\n",
    "        y = p.get_y() + p.get_height() / 2\n",
    "        x = 0.4 # p.get_height() - p.get_height() / 2\n",
    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, alpha=0.8)\n",
    "\n",
    "    plt.savefig(f'ablation_study_{group}.pdf', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of times the scaling was applied:     95.0%\n",
      "Number of times the scaling was not applied: 5.0%\n",
      "hparam_hidden_dim:\n",
      "512    9\n",
      "256    6\n",
      "768    5\n",
      "Name: hparam_hidden_dim, dtype: int64\n",
      "\n",
      "hparam_batch_size:\n",
      "32    10\n",
      "16     6\n",
      "8      4\n",
      "Name: hparam_batch_size, dtype: int64\n",
      "\n",
      "hparam_learning_rate:\n",
      "0.000056    1\n",
      "0.000037    1\n",
      "0.000014    1\n",
      "0.000025    1\n",
      "0.000010    1\n",
      "0.000015    1\n",
      "0.000014    1\n",
      "0.000012    1\n",
      "0.000012    1\n",
      "0.000029    1\n",
      "0.000454    1\n",
      "0.000049    1\n",
      "0.000013    1\n",
      "0.000014    1\n",
      "0.000013    1\n",
      "0.000020    1\n",
      "0.000029    1\n",
      "0.000019    1\n",
      "0.000024    1\n",
      "0.000014    1\n",
      "Name: hparam_learning_rate, dtype: int64\n",
      "\n",
      "hparam_join_embeddings:\n",
      "sum          9\n",
      "beginning    7\n",
      "concat       4\n",
      "Name: hparam_join_embeddings, dtype: int64\n",
      "\n",
      "hparam_smote_k_neighbors:\n",
      "6     4\n",
      "3     4\n",
      "14    4\n",
      "5     3\n",
      "10    2\n",
      "9     1\n",
      "7     1\n",
      "8     1\n",
      "Name: hparam_smote_k_neighbors, dtype: int64\n",
      "\n",
      "hparam_use_smote:\n",
      "True     10\n",
      "False    10\n",
      "Name: hparam_use_smote, dtype: int64\n",
      "\n",
      "hparam_apply_scaling:\n",
      "True     19\n",
      "False     1\n",
      "Name: hparam_apply_scaling, dtype: int64\n",
      "\n",
      "hparam_dropout:\n",
      "0.119461    1\n",
      "0.277674    1\n",
      "0.459840    1\n",
      "0.124534    1\n",
      "0.445686    1\n",
      "0.134516    1\n",
      "0.109033    1\n",
      "0.194294    1\n",
      "0.277930    1\n",
      "0.269190    1\n",
      "0.125423    1\n",
      "0.169210    1\n",
      "0.212074    1\n",
      "0.176164    1\n",
      "0.219126    1\n",
      "0.226225    1\n",
      "0.152720    1\n",
      "0.326619    1\n",
      "0.128565    1\n",
      "0.446376    1\n",
      "Name: hparam_dropout, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# [c for c in report.columns if 'hparam' in c]\n",
    "# display(report[report['hparam_apply_scaling']].groupby(['fold', 'group_type'])[['val_acc', 'test_acc']].mean().round(3))\n",
    "# display(report[~report['hparam_apply_scaling']].groupby(['fold', 'group_type'])[['val_acc', 'test_acc']].mean().round(3))\n",
    "\n",
    "# Count the number of times the scaling was applied and its percentage\n",
    "scaling_counts = report['hparam_apply_scaling'].value_counts()\n",
    "scaling_counts = scaling_counts / scaling_counts.sum()\n",
    "print(f'Number of times the scaling was applied:     {scaling_counts[True]:.1%}')\n",
    "print(f'Number of times the scaling was not applied: {scaling_counts[False]:.1%}')\n",
    "\n",
    "# Count the number and percentage of occurance of the join_embeddings column\n",
    "join_embeddings_counts = report['hparam_join_embeddings'].value_counts()\n",
    "join_embeddings_counts = join_embeddings_counts / join_embeddings_counts.sum()\n",
    "join_embeddings_counts\n",
    "# print(f'Number of times the embeddings were joined: {join_embeddings_counts[True]:.1%}')\n",
    "# print(f'Number of times the embeddings were not joined: {join_embeddings_counts[False]:.1%}')\n",
    "\n",
    "\n",
    "# For each hparam, print some relevant statistics\n",
    "hparam_cols = [c for c in report.columns if 'hparam' in c]\n",
    "for hparam in hparam_cols:\n",
    "    print(f'{hparam}:')\n",
    "    print(report[hparam].value_counts() // 6)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../reports/study_Active_Dmax_0.6_pDC50_6.0_random_fold_0_test_split_0.2.pkl\n",
      "{'hidden_dim': 512, 'batch_size': 16, 'learning_rate': 1.7221700002340547e-05, 'join_embeddings': 'sum', 'smote_k_neighbors': 14, 'use_smote': False, 'apply_scaling': True, 'dropout': 0.15240823317125454}\n",
      "OrderedDict([('learning_rate', 0.42893372832121324), ('smote_k_neighbors', 0.30085702206051634), ('join_embeddings', 0.0975632989422997), ('batch_size', 0.05369329248321026), ('dropout', 0.05078700222041663), ('hidden_dim', 0.03849912063219203), ('use_smote', 0.01543507014670634), ('apply_scaling', 0.014231465193445552)])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import joblib\n",
    "import optuna\n",
    "\n",
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "active_name = active_col.replace(' ', '_').replace('(', '').replace(')', '').replace(',', '')\n",
    "test_split = 0.2\n",
    "cv_n_splits = 5\n",
    "\n",
    "for split_type in report.group_type.unique():\n",
    "    for k in range(cv_n_splits):\n",
    "        study_filename = f'../reports/study_{active_name}_{split_type}_fold_{k}_test_split_{test_split}.pkl'\n",
    "        study = joblib.load(study_filename)\n",
    "        print(f'{study_filename}')\n",
    "        print(f'{study.best_params}')\n",
    "        print(optuna.importance.get_param_importances(study))\n",
    "        ax = optuna.visualization.matplotlib.plot_param_importances(study)\n",
    "        plt.show()\n",
    "        break\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Older Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'active'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'active'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[50], line 20\u001b[0m\n\u001b[0;32m     18\u001b[0m baseline \u001b[38;5;241m=\u001b[39m report[report[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misna()]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m     19\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgroup_type\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m group]\n\u001b[1;32m---> 20\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[\u001b[43mbaseline\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mactive\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m==\u001b[39m active_def]\n\u001b[0;32m     21\u001b[0m baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall embeddings enabled\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Melt accross folds and get acc and roc_auc\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\frame.py:3804\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3804\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3806\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3810\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'active'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make two plots side by side, one for active - or, one for active - and.\n",
    "# On the y-axis put the val and test accuracy (different hue).\n",
    "# On the x-axis put the baseline (all embeddings on) with all the disabled embeddings combinations next.\n",
    "\n",
    "ablation_study_combinations = [\n",
    "    'disabled smiles',\n",
    "    'disabled poi',\n",
    "    'disabled e3',\n",
    "    'disabled cell',\n",
    "    'disabled poi e3 smiles',\n",
    "    'disabled poi e3 cell',\n",
    "]\n",
    "\n",
    "for group in report['group_type'].unique():\n",
    "    # Set the two plots side by side\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    for i, active_def in enumerate(['and', 'or']):\n",
    "        baseline = report[report['disabled_embeddings'].isna()].copy()\n",
    "        baseline = baseline[baseline['group_type'] == group]\n",
    "        baseline = baseline[baseline['active'] == active_def]\n",
    "        baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
    "        # Melt accross folds and get acc and roc_auc\n",
    "        metrics_to_show = ['val_acc', 'test_acc']\n",
    "        # metrics_to_show = ['val_roc_auc', 'test_roc_auc']\n",
    "\n",
    "        baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "\n",
    "        ablation_dfs = []\n",
    "        for disabled_embeddings in ablation_study_combinations:\n",
    "            if pd.isnull(disabled_embeddings):\n",
    "                continue\n",
    "            tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
    "            tmp = tmp[tmp['group_type'] == group]\n",
    "            tmp = tmp[tmp['active'] == active_def]\n",
    "            # Melt accross folds and get acc and roc_auc\n",
    "            tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "            ablation_dfs.append(tmp)\n",
    "        ablation_df = pd.concat(ablation_dfs)\n",
    "\n",
    "        # Create a dummy df with the same structure as the baseline df\n",
    "        # Score is the max between the val_active_perc and val_inactive_perc\n",
    "        dummy_val_df = pd.DataFrame()\n",
    "        tmp = report[report['group_type'] == group]\n",
    "        tmp = tmp[tmp['active'] == active_def]\n",
    "        dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
    "        # dummy_val_df['score'] = 0.5\n",
    "        dummy_val_df['metric'] = metrics_to_show[0]\n",
    "        dummy_val_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "        dummy_test_df = pd.DataFrame()\n",
    "        dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
    "        # dummy_test_df['score'] = 0.5\n",
    "        dummy_test_df['metric'] = metrics_to_show[1]\n",
    "        dummy_test_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "        dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
    "\n",
    "        final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
    "\n",
    "        # Rename 'val_acc' to 'Val Accuracy' and 'test_acc' to 'Test Accuracy'\n",
    "        final_df['metric'] = final_df['metric'].map({\n",
    "            'val_acc': 'Val Accuracy',\n",
    "            'test_acc': 'Test Accuracy',\n",
    "            'val_roc_auc': 'Val ROC-AUC',\n",
    "            'test_roc_auc': 'Test ROC-AUC',\n",
    "        })\n",
    "        # Map 'all embeddings enabled' to 'Baseline', then turn disabled into the remaining embeddings\n",
    "        final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
    "            'all embeddings enabled': 'All embeddings',\n",
    "            'dummy': 'Dummy model',\n",
    "            'disabled smiles': 'E3, Cell, Target',\n",
    "            'disabled poi e3 smiles': 'Cell only',\n",
    "            'disabled poi e3 cell': 'SMILES only',\n",
    "            'disabled poi': 'SMILES, E3, Cell',\n",
    "            'disabled e3': 'SMILES, Cell, Target',\n",
    "            'disabled cell': 'SMILES, E3, Target',\n",
    "        })\n",
    "\n",
    "        # display(dummy_df)\n",
    "        # display(baseline)\n",
    "        # display(ablation_df)\n",
    "        # display(final_df)\n",
    "\n",
    "        # Plot\n",
    "        sns.barplot(data=final_df,\n",
    "            x='disabled_embeddings',\n",
    "            y='score',\n",
    "            hue='metric',\n",
    "            ax=ax[i],\n",
    "            errorbar=('sd', 1),\n",
    "            # palette=sns.color_palette(\"tab10\", 8),\n",
    "            palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
    "            # Set brightness of the colors\n",
    "            saturation=1,\n",
    "        )\n",
    "        # Add a bar for the dummy accuracy for the val and test set\n",
    "\n",
    "\n",
    "        ax[i].set_title(f'Active - {active_def.upper()} definition, {group.replace(\"random\", \"standard\")} CV split')\n",
    "        # Set legend outside the plot on the right, just on the second plot, the first one will be hidden\n",
    "        if i > 0:\n",
    "            ax[i].legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Metric')\n",
    "        else:\n",
    "            # Disable the legend for the first plot\n",
    "            ax[i].legend().set_visible(False)\n",
    "\n",
    "        ax[i].grid(axis='y', alpha=0.5)\n",
    "        # Rotate x-axis labels to 90 degrees\n",
    "        ax[i].tick_params(axis='x', rotation=90)\n",
    "        # Set y-axis to end at 1.0 and make the y-axis as percentage\n",
    "        ax[i].set_ylim(0, 1.0)\n",
    "        ax[i].yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "        # Remove axis labels\n",
    "        ax[i].set_xlabel('')\n",
    "        ax[i].set_ylabel('')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'active'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'active'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[41], line 12\u001b[0m\n\u001b[0;32m     10\u001b[0m baseline \u001b[38;5;241m=\u001b[39m report[report[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misna()]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m     11\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgroup_type\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m group]\n\u001b[1;32m---> 12\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[\u001b[43mbaseline\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mactive\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m==\u001b[39m active_def]\n\u001b[0;32m     13\u001b[0m baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall embeddings enabled\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     14\u001b[0m metrics_to_show \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_acc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest_acc\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\frame.py:3804\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3804\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3806\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3810\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'active'"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "active_def = 'or'\n",
    "\n",
    "for group in report['group_type'].unique():\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    baseline = report[report['disabled_embeddings'].isna()].copy()\n",
    "    baseline = baseline[baseline['group_type'] == group]\n",
    "    baseline = baseline[baseline['active'] == active_def]\n",
    "    baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
    "    metrics_to_show = ['val_acc', 'test_acc']\n",
    "    baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "\n",
    "    ablation_dfs = []\n",
    "    for disabled_embeddings in ablation_study_combinations:\n",
    "        if pd.isnull(disabled_embeddings):\n",
    "            continue\n",
    "        tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
    "        tmp = tmp[tmp['group_type'] == group]\n",
    "        tmp = tmp[tmp['active'] == active_def]\n",
    "        tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "        ablation_dfs.append(tmp)\n",
    "    ablation_df = pd.concat(ablation_dfs)\n",
    "\n",
    "    dummy_val_df = pd.DataFrame()\n",
    "    tmp = report[report['group_type'] == group]\n",
    "    tmp = tmp[tmp['active'] == active_def]\n",
    "    dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
    "    dummy_val_df['metric'] = metrics_to_show[0]\n",
    "    dummy_val_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "    dummy_test_df = pd.DataFrame()\n",
    "    dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
    "    dummy_test_df['metric'] = metrics_to_show[1]\n",
    "    dummy_test_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "    dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
    "\n",
    "    final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
    "\n",
    "    final_df['metric'] = final_df['metric'].map({\n",
    "        'val_acc': 'Val Accuracy',\n",
    "        'test_acc': 'Test Accuracy',\n",
    "        'val_roc_auc': 'Val ROC-AUC',\n",
    "        'test_roc_auc': 'Test ROC-AUC',\n",
    "    })\n",
    "\n",
    "    final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
    "        'all embeddings enabled': 'All embeddings',\n",
    "        'dummy': 'Dummy model',\n",
    "        'disabled smiles': 'E3, Cell, Target',\n",
    "        'disabled poi e3 smiles': 'Cell only',\n",
    "        'disabled poi e3 cell': 'SMILES only',\n",
    "        'disabled poi': 'SMILES, E3, Cell',\n",
    "        'disabled e3': 'SMILES, Cell, Target',\n",
    "        'disabled cell': 'SMILES, E3, Target',\n",
    "    })\n",
    "\n",
    "    sns.barplot(data=final_df,\n",
    "        x='disabled_embeddings',\n",
    "        y='score',\n",
    "        hue='metric',\n",
    "        ax=ax,\n",
    "        errorbar=('sd', 1),\n",
    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
    "        saturation=1,\n",
    "    )\n",
    "\n",
    "    ax.set_title(f'Active - {active_def.upper()} definition, {group.replace(\"random\", \"standard\")} CV split')\n",
    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Metric')\n",
    "    ax.grid(axis='y', alpha=0.5)\n",
    "    ax.tick_params(axis='x', rotation=90)\n",
    "    ax.set_ylim(0, 1.0)\n",
    "    ax.yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "\n",
    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
    "    for i, p in enumerate(plt.gca().patches):\n",
    "        value = '{:.1f}%'.format(100 * p.get_height())\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = 0.4 # p.get_height() - p.get_height() / 2\n",
    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "active_def = 'or'\n",
    "\n",
    "for group in report['group_type'].unique():\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    \n",
    "    baseline = report[report['disabled_embeddings'].isna()].copy()\n",
    "    baseline = baseline[baseline['group_type'] == group]\n",
    "    baseline = baseline[baseline['active'] == active_def]\n",
    "    baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
    "    metrics_to_show = ['val_acc', 'test_acc']\n",
    "    baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "\n",
    "    ablation_dfs = []\n",
    "    for disabled_embeddings in ablation_study_combinations:\n",
    "        if pd.isnull(disabled_embeddings):\n",
    "            continue\n",
    "        tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
    "        tmp = tmp[tmp['group_type'] == group]\n",
    "        tmp = tmp[tmp['active'] == active_def]\n",
    "        tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
    "        ablation_dfs.append(tmp)\n",
    "    ablation_df = pd.concat(ablation_dfs)\n",
    "\n",
    "    dummy_val_df = pd.DataFrame()\n",
    "    tmp = report[report['group_type'] == group]\n",
    "    tmp = tmp[tmp['active'] == active_def]\n",
    "    dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
    "    dummy_val_df['metric'] = metrics_to_show[0]\n",
    "    dummy_val_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "    dummy_test_df = pd.DataFrame()\n",
    "    dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
    "    dummy_test_df['metric'] = metrics_to_show[1]\n",
    "    dummy_test_df['disabled_embeddings'] = 'dummy'\n",
    "\n",
    "    dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
    "\n",
    "    final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
    "\n",
    "    final_df['metric'] = final_df['metric'].map({\n",
    "        'val_acc': 'Val Accuracy',\n",
    "        'test_acc': 'Test Accuracy',\n",
    "        'val_roc_auc': 'Val ROC-AUC',\n",
    "        'test_roc_auc': 'Test ROC-AUC',\n",
    "    })\n",
    "\n",
    "    final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
    "        'all embeddings enabled': 'All embeddings',\n",
    "        'dummy': 'Dummy model',\n",
    "        'disabled smiles': 'E3, Cell, Target',\n",
    "        'disabled poi e3 smiles': 'Cell only',\n",
    "        'disabled poi e3 cell': 'SMILES only',\n",
    "        'disabled poi': 'SMILES, E3, Cell',\n",
    "        'disabled e3': 'SMILES, Cell, Target',\n",
    "        'disabled cell': 'SMILES, E3, Target',\n",
    "    })\n",
    "\n",
    "    sns.barplot(data=final_df,\n",
    "        y='disabled_embeddings',\n",
    "        x='score',\n",
    "        hue='metric',\n",
    "        ax=ax,\n",
    "        errorbar=('sd', 1),\n",
    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
    "        saturation=1,\n",
    "    )\n",
    "\n",
    "    ax.set_title(f'Active - {active_def.upper()} definition, {group.replace(\"random\", \"standard\")} CV split')\n",
    "    ax.grid(axis='x', alpha=0.5)\n",
    "    ax.tick_params(axis='y', rotation=0)\n",
    "    ax.set_xlim(0, 1.0)\n",
    "    ax.xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xlabel('')\n",
    "    # Set the legend outside the plot and below\n",
    "    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=2)\n",
    "\n",
    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
    "    for i, p in enumerate(plt.gca().patches):\n",
    "        value = '{:.1f}%'.format(100 * p.get_width())\n",
    "        y = p.get_y() + p.get_height() / 2\n",
    "        x = 0.4 # p.get_height() - p.get_height() / 2\n",
    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, alpha=0.8)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bcS6ZmZkKCQnRsGHDzttv+vTpSk1NdSwXFxcrMjLSpVoBAKiJFi1aSJIj4OD8QkJCHN+Zqzwabpo1ayZvb+9KozQFBQWVRnPOZozRkiVLNGrUKPn6+p63r5+fn/z8/GpdLwAANWWz2RQREaHmzZvr1KlTni6nTvPx8anViM0ZHg03vr6+iouLU1ZWloYPH+5oz8rK0q233nrebT/99FPt2rVLycnJF7tMAABqzdvb2y1/cePCPH5ZKjU1VaNGjVJ8fLwSEhK0aNEi5efna/z48ZJOX1Lav3+/li1b5rTd4sWLdfXVVys2NtYTZQMAgDrK4+EmKSlJRUVFmjNnjux2u2JjY7Vq1SrHr5/sdnule94cO3ZMK1eu1IIFCzxRMgAAqMM8Hm4kKSUlRSkpKVWuy8zMrNQWHBzMz+oAAECVPH4TPwAAAHci3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEupE+EmIyND0dHR8vf3V1xcnDZs2HDe/iUlJZoxY4aioqLk5+en9u3ba8mSJZeoWgAAUJc18HQBK1as0KRJk5SRkaG+ffvq5Zdf1uDBg7V9+3a1adOmym3uvPNOHTp0SIsXL1aHDh1UUFCgsrKyS1w5AACoizwebtLT05WcnKxx48ZJkubPn681a9Zo4cKFSktLq9R/9erV+vTTT7Vnzx41bdpUktS2bdtLWTIAAKjDPHpZqrS0VDk5OUpMTHRqT0xMVHZ2dpXbvP/++4qPj9dTTz2lVq1aqWPHjpoyZYr+9a9/nfM4JSUlKi4udnoBAABr8ujIzeHDh1VeXq7w8HCn9vDwcB08eLDKbfbs2aONGzfK399ff/3rX3X48GGlpKToyJEj55x3k5aWptmzZ7u9fgAAUPfUiQnFNpvNadkYU6ntjIqKCtlsNr3++uvq1auXhgwZovT0dGVmZp5z9Gb69Ok6duyY47Vv3z63fwYAAFA3eHTkplmzZvL29q40SlNQUFBpNOeMiIgItWrVSsHBwY62mJgYGWP0448/6oorrqi0jZ+fn/z8/NxbPAAAqJM8OnLj6+uruLg4ZWVlObVnZWWpT58+VW7Tt29fHThwQCdOnHC0ff/99/Ly8lLr1q0var0AAKDu8/hlqdTUVL366qtasmSJ8vLy9PDDDys/P1/jx4+XdPqS0ujRox39R4wYodDQUN17773avn27PvvsM02dOlVjx45Vw4YNPfUxAABAHeHxn4InJSWpqKhIc+bMkd1uV2xsrFatWqWoqChJkt1uV35+vqN/o0aNlJWVpYceekjx8fEKDQ3VnXfeqSeeeMJTHwEAANQhHg83kpSSkqKUlJQq12VmZlZq69y5c6VLWQAAAFIduCwFAADgToQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKS7dofizzz5Tnz591KCB8+ZlZWXKzs7Wtdde65biAFRf3NRlHjt20NETjn8p2Y+e8GgtkpTz9OgLdwJgWS6N3Fx33XU6cuRIpfZjx47puuuuq3VRAAAArnIp3BhjZLPZKrUXFRUpMDCw1kUBAAC4qkaXpW677TZJks1m05gxY+Tn5+dYV15ern/+85/q06ePeysEAACogRqFm+DgYEmnR24aN26shg0bOtb5+vqqd+/euu+++9xbIQAAQA3UKNwsXbpUktS2bVtNmTKFS1AAAKDOcenXUjNnznR3HQBgCRMnTlRhYaEkKSwsTAsWLPBwRcDlx6VwEx0dXeWE4jP27NnjckEAUJ8VFhbq0KFDni4DuKy5FG4mTZrktHzq1Cnl5uZq9erVmjp1qjvqAgAAcIlL4WbixIlVtr/44ovaunVrrQoCAACoDbc+fmHw4MFauXKlO3cJAABQI24NN++8846aNm3qzl0CAADUiEuXpXr27Ok0odgYo4MHD6qwsFAZGRluKw4AAKCmXAo3w4YNc1r28vJSWFiYBgwYoM6dO7ujLgAAAJdwnxsAAGApLoUb6fSzpN577z3l5eXJZrOpS5cuGjp0qLy9vd1ZHwAAQI24FG527dqlIUOGaP/+/erUqZOMMfr+++8VGRmpv//972rfvr276wQAAKgWl34tNWHCBLVv31779u3Tl19+qdzcXOXn5ys6OloTJkxwd40AAADV5tLIzaeffqrNmzc7/ew7NDRUc+fOVd++fd1WHACgfoubusxjxw46esLxL3j70RMerSXn6dEeO/blyKWRGz8/Px0/frxS+4kTJ+Tr61vrogAAAFzlUri5+eab9bvf/U5ffPGFjDEyxmjz5s0aP368hg4d6u4aAQAAqs2lcPPcc8+pffv2SkhIkL+/v/z9/dW3b1916NBBCxYscHeNAAAA1ebSnJuQkBD97W9/086dO5WXlydJ6tKlizp06ODW4gAAAGqqVs+WuuKKK3TLLbfolltuqVWwycjIUHR0tPz9/RUXF6cNGzacs+/69etls9kqvb777juXjw8AAKzD5XCzePFixcbGOi5LxcbG6tVXX63xflasWKFJkyZpxowZys3NVb9+/TR48GDl5+efd7sdO3bIbrc7XldccYWrHwUAAFiIS5elHnvsMc2bN08PPfSQEhISJEmbNm3Sww8/rB9++EFPPPFEtfeVnp6u5ORkjRs3TpI0f/58rVmzRgsXLlRaWto5t2vevLlCQkKqdYySkhKVlJQ4louLi6tdHwAAqF9cGrlZuHChXnnlFaWlpWno0KEaOnSo0tLStGjRIr300kvV3k9paalycnKUmJjo1J6YmKjs7OzzbtuzZ09FRETohhtu0CeffHLevmlpaQoODna8IiMjq10jAACoX1wKN+Xl5YqPj6/UHhcXp7Kysmrv5/DhwyovL1d4eLhTe3h4uA4ePFjlNhEREVq0aJFWrlypd999V506ddINN9ygzz777JzHmT59uo4dO+Z47du3r9o1AgCA+sWly1J33323Fi5cqPT0dKf2RYsWaeTIkTXen81mc1o2xlRqO6NTp07q1KmTYzkhIUH79u3TM888o2uvvbbKbfz8/OTn51fjugAAQP3j8lPBFy9erLVr16p3796SpM2bN2vfvn0aPXq0UlNTHf3ODkC/1qxZM3l7e1capSkoKKg0mnM+vXv31muvvVbDTwAAAKzIpXDzzTff6KqrrpIk7d69W5IUFhamsLAwffPNN45+5xp9OcPX11dxcXHKysrS8OHDHe1ZWVm69dZbq11Pbm6uIiIiavIRAACARbkUbi40gbcmUlNTNWrUKMXHxyshIUGLFi1Sfn6+xo8fL+n0fJn9+/dr2bLTDzybP3++2rZtq65du6q0tFSvvfaaVq5cqZUrV7qtJgAAUH+5fFnqjB9//FE2m02tWrVyafukpCQVFRVpzpw5stvtio2N1apVqxQVFSVJstvtTve8KS0t1ZQpU7R//341bNhQXbt21d///ncNGTKkth8FAABYgEvhpqKiQk888YSeffZZnThxQpLUuHFjTZ48WTNmzJCXV81+hJWSkqKUlJQq12VmZjotT5s2TdOmTXOlbAAAcBlwKdzMmDFDixcv1ty5c9W3b18ZY/T5559r1qxZ+uWXX/S///u/7q4TAACgWlwKN3/+85/16quvaujQoY627t27q1WrVkpJSSHcAAAAj3HpJn5HjhxR586dK7V37txZR44cqXVRAAAArnIp3HTv3l0vvPBCpfYXXnhB3bt3r3VRAAAArnLpstRTTz2l3/72t/roo4+UkJAgm82m7Oxs7du3T6tWrXJ3jQAAANXmUrjp37+/vv/+e7344ov67rvvZIzRbbfdppSUFLVs2dLdNQIAABdNnDhRhYWFkk7fcHfBggUerujiq3G4OXXqlBITE/Xyyy8zcRgAgDqusLBQhw4d8nQZl1SN59z4+Pjom2++ueCjFQAAADzBpQnFo0eP1uLFi91dCwAAQK25NOemtLRUr776qrKyshQfH6/AwECn9ed7EjgAAMDFVOungn///fdO67hcBQAAPMnjTwUHAABwJ5fm3AAAANRV1R65ue2226q903fffdelYgAAAGqr2iM3wcHBjldQUJDWrVunrVu3Otbn5ORo3bp1Cg4OviiFAgAAVEe1R26WLl3qeP/II4/ozjvv1EsvvSRvb29JUnl5uVJSUhQUFOT+KgEAAKrJpTk3S5Ys0ZQpUxzBRpK8vb2VmpqqJUuWuK04AACAmnIp3JSVlSkvL69Se15enioqKmpdFAAAgKtc+in4vffeq7Fjx2rXrl3q3bu3JGnz5s2aO3eu7r33XrcWCAAAUBMuhZtnnnlGLVq00Lx582S32yVJERERmjZtmiZPnuzWAgEAAGrCpXDj5eWladOmadq0aSouLpakKicSf/7554qPj5efn1/tqgQAAKimWt/ELygo6Jy/kBo8eLD2799f20MAAABU20W9Q7Ex5mLuHgAAoBIevwAAACyFcAMAACyFcAMAACzlooYbm812MXcPAABQCROKAQCApbh0n5vqOn78+MXcPQAAQCUuhZuioiI9/vjj+uSTT1RQUFDpeVJHjhxxS3EAAAA15VK4ufvuu7V7924lJycrPDycuTUAAKDOcCncbNy4URs3blT37t3dUkRGRoaefvpp2e12de3aVfPnz1e/fv0uuN3nn3+u/v37KzY2Vtu2bXNLLQAAoH5zaUJx586d9a9//cstBaxYsUKTJk3SjBkzlJubq379+mnw4MHKz88/73bHjh3T6NGjdcMNN7ilDgAAYA0uhZuMjAzNmDFDn376qYqKilRcXOz0qon09HQlJydr3LhxiomJ0fz58xUZGamFCxeed7v7779fI0aMUEJCgisfAQAAWJRL4SYkJETHjh3T9ddfr+bNm6tJkyZq0qSJQkJC1KRJk2rvp7S0VDk5OUpMTHRqT0xMVHZ29jm3W7p0qXbv3q2ZM2dW6zglJSW1CmAAAKD+cGnOzciRI+Xr66vly5fXakLx4cOHVV5ervDwcKf28PBwHTx4sMptdu7cqUcffVQbNmxQgwbVKz8tLU2zZ892qUYAAFC/uBRuvvnmG+Xm5qpTp05uKeLscGSMqTIwlZeXa8SIEZo9e7Y6duxY7f1Pnz5dqampjuXi4mJFRka6XjAAAKizXAo38fHx2rdvX63DTbNmzeTt7V1plKagoKDSaI50+qaAW7duVW5urh588EFJUkVFhYwxatCggdauXavrr7++0nZ+fn7y8/OrVa0AAKB+cCncPPTQQ5o4caKmTp2qK6+8Uj4+Pk7ru3XrVq39+Pr6Ki4uTllZWRo+fLijPSsrS7feemul/kFBQfr666+d2jIyMvTxxx/rnXfeUXR0tAufBgAAWIlL4SYpKUmSNHbsWEebzWZzXE4qLy+v9r5SU1M1atQoxcfHKyEhQYsWLVJ+fr7Gjx8v6fQlpf3792vZsmXy8vJSbGys0/bNmzeXv79/pXYAAHB5cinc7N27120FJCUlqaioSHPmzJHdbldsbKxWrVqlqKgoSZLdbr/gPW8AAADOcCncnAke7pKSkqKUlJQq12VmZp5321mzZmnWrFlurQcAANRftXoq+Pbt25Wfn6/S0lKn9qFDh9aqKAAAAFe5FG727Nmj4cOH6+uvv3bMtZH+85Pumsy5AQAAcCeX7lA8ceJERUdH69ChQwoICNC3336rzz77TPHx8Vq/fr2bSwQAAKg+l0ZuNm3apI8//lhhYWHy8vKSl5eXrrnmGqWlpWnChAnKzc11d50AAADV4tLITXl5uRo1aiTp9I34Dhw4IOn0ROMdO3a4rzoAAIAacmnkJjY2Vv/85z/Vrl07XX311Xrqqafk6+urRYsWqV27du6uEQAAoNpcCjd/+MMf9PPPP0uSnnjiCd18883q16+fQkNDtWLFCrcWCAAAUBMuhZtBgwY53rdr107bt2/XkSNH1KRJE5efEA4AAOAONZ5zU1ZWpgYNGuibb75xam/atCnBBgAAeFyNw02DBg0UFRXFvWwAAECd5PKcm+nTp+u1115T06ZN3V0TUC0TJ05UYWGhJCksLEwLFizwcEUAgLrApXDz3HPPadeuXWrZsqWioqIUGBjotP7LL790S3HA+RQWFurQoUOeLgMAUMe4FG6GDRvm5jIAAADcw6VwM3PmTHfXAQAA4BYu3aEYAACgrnJp5Ka8vFzz5s3TW2+9pfz8fJWWljqtP3LkiFuKAwAAqCmXRm5mz56t9PR03XnnnTp27JhSU1N12223ycvLS7NmzXJziQAAANXnUrh5/fXX9corr2jKlClq0KCB7rrrLr366qt6/PHHtXnzZnfXCAAAUG0uhZuDBw/qyiuvlCQ1atRIx44dkyTdfPPN+vvf/+6+6gAAAGrIpTk3rVu3lt1uV5s2bdShQwetXbtWV111lbZs2SI/Pz931wgAQL2WP+dKjx277KdQSd7/fn/Ao7W0efzrS3Icl0Zuhg8frnXr1kk6fZfYxx57TFdccYVGjx6tsWPHurVAAACAmnBp5Gbu3LmO9//1X/+l1q1bKzs7Wx06dNDQoUPdVhwAAEBNuRRuzta7d2/17t3bHbsCAACoFZfDzY4dO/T8888rLy9PNptNnTt31kMPPaROnTq5sz4AqDHmN5x2qeY3AHWNS3Nu3nnnHcXGxionJ0fdu3dXt27d9OWXXyo2NlZvv/22u2sEAACoNpdGbqZNm6bp06drzpw5Tu0zZ87UI488ojvuuMMtxQEAANSUy/e5GT16dKX2u+++WwcPHqx1UQAAAK5yKdwMGDBAGzZsqNS+ceNG9evXr9ZFAQAAuMqly1JDhw7VI488opycHMevpDZv3qy3335bs2fP1vvvv+/UFwAA4FJxKdykpKRIkjIyMpSRkVHlOkmy2WwqLy+vRXkAAAA141K4qaiocHcdAAAAbuHyfW7WrVundevWqaCgwCns2Gw2LV682C3FAQAA1JRLE4pnz56txMRErVu3TocPH9bRo0cdryNHjtR4fxkZGYqOjpa/v7/i4uKqnKx8xsaNG9W3b1+FhoaqYcOG6ty5s+bNm+fKxwAAABbk0sjNSy+9pMzMTI0aNarWBaxYsUKTJk1SRkaG+vbtq5dfflmDBw/W9u3b1aZNm0r9AwMD9eCDD6pbt24KDAzUxo0bdf/99yswMFC/+93val0PAACo31wauSktLVWfPn3cUkB6erqSk5M1btw4xcTEaP78+YqMjNTChQur7N+zZ0/ddddd6tq1q9q2bau7775bgwYNOu9oT0lJiYqLi51eAADAmlwKN+PGjdPy5ctrffDS0lLl5OQoMTHRqT0xMVHZ2dnV2kdubq6ys7PVv3//c/ZJS0tTcHCw4xUZGVmrugEAQN1V7ctSqampjvcVFRVatGiRPvroI3Xr1k0+Pj5OfdPT06u1z8OHD6u8vFzh4eFO7eHh4Re803Hr1q1VWFiosrIyzZo1S+PGjTtn3+nTpzvVX1xcXK8DzsSJE1VYWChJCgsL04IFCzxcEQAAdUe1w01ubq7Tco8ePSRJ33zzjVO7zWarcRFnb2OMueB+NmzYoBMnTmjz5s169NFH1aFDB911111V9vXz85Ofn1+N66qrCgsLdejQIU+XAQBAnVTtcPPJJ5+4/eDNmjWTt7d3pVGagoKCSqM5Z4uOjpYkXXnllTp06JBmzZp1znADAAAuHy7NuXEXX19fxcXFKSsry6k9KyurRhOWjTEqKSlxd3kAqqnCJ1AVvv9++QR6uhwAlzmXb+LnLqmpqRo1apTi4+OVkJCgRYsWKT8/X+PHj5d0er7M/v37tWzZMknSiy++qDZt2qhz586STt/35plnntFDDz3ksc8AXO5OdBrs6RIAwMHj4SYpKUlFRUWaM2eO7Ha7YmNjtWrVKkVFRUmS7Ha78vPzHf0rKio0ffp07d27Vw0aNFD79u01d+5c3X///Z76CAAAoA7xeLiRTj9s89cP3Py1zMxMp+WHHnqIURoAAHBOHp1zAwAA4G6EGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCl14g7FAAC4268f4soDXS8vhBsAgCXxQNfLF5elAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApfBsKdRK/pwrPXbssp9CJXn/+/0Bj9bS5vGvPXZsAIAzRm4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClcJ8bF8VNXeaxYwcdPeFIpfajJzxay18be+zQAABUqU6M3GRkZCg6Olr+/v6Ki4vThg0bztn33Xff1cCBAxUWFqagoCAlJCRozZo1l7BaAABQl3k83KxYsUKTJk3SjBkzlJubq379+mnw4MHKz8+vsv9nn32mgQMHatWqVcrJydF1112nW265Rbm5uZe4cgAA6r6mfuUK/ferqV+5p8u5JDx+WSo9PV3JyckaN26cJGn+/Plas2aNFi5cqLS0tEr958+f77T8pz/9SX/729/0wQcfqGfPnpeiZAAA6o3/6fmTp0u45Dw6clNaWqqcnBwlJiY6tScmJio7O7ta+6ioqNDx48fVtGnTc/YpKSlRcXGx0wsAAFiTR8PN4cOHVV5ervDwcKf28PBwHTx4sFr7ePbZZ/Xzzz/rzjvvPGeftLQ0BQcHO16RkZG1qhsAANRdHp9zI0k2m81p2RhTqa0qb7zxhmbNmqUVK1aoefPm5+w3ffp0HTt2zPHat29frWsGAAB1k0fn3DRr1kze3t6VRmkKCgoqjeacbcWKFUpOTtbbb7+tG2+88bx9/fz85OfnV+t6AQBA3efRkRtfX1/FxcUpKyvLqT0rK0t9+vQ553ZvvPGGxowZo+XLl+u3v/3txS4TAADUIx7/tVRqaqpGjRql+Ph4JSQkaNGiRcrPz9f48eMlnb6ktH//fi1bdvpGdW+88YZGjx6tBQsWqHfv3o5Rn4YNGyo4ONhjnwMAANQNHg83SUlJKioq0pw5c2S32xUbG6tVq1YpKipKkmS3253uefPyyy+rrKxMv//97/X73//e0X7PPfcoMzPzUpcPAADqGI+HG0lKSUlRSkpKlevODizr16+/+AUBAIB6q078WgoAAMBdCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSGni6ANRchU9gle8BAADhpl460Wmwp0sAAKDO4rIUAACwFMINAACwFMINAACwFMINAACwFCYUA4AbNfUrr/I9gEuHcAMAbvQ/PX/ydAnAZa9OXJbKyMhQdHS0/P39FRcXpw0bNpyzr91u14gRI9SpUyd5eXlp0qRJl65QAABQ53k83KxYsUKTJk3SjBkzlJubq379+mnw4MHKz8+vsn9JSYnCwsI0Y8YMde/e/RJXCwAA6jqPh5v09HQlJydr3LhxiomJ0fz58xUZGamFCxdW2b9t27ZasGCBRo8ereDg4Godo6SkRMXFxU4vAABgTR4NN6WlpcrJyVFiYqJTe2JiorKzs912nLS0NAUHBztekZGRbts3PKepX7lC//1i4iYA4AyPTig+fPiwysvLFR4e7tQeHh6ugwcPuu0406dPV2pqqmO5uLiYgGMBTNwEAFSlTvxaymazOS0bYyq11Yafn5/8/Pzctj8AAFB3efSyVLNmzeTt7V1plKagoKDSaA4AAEB1eDTc+Pr6Ki4uTllZWU7tWVlZ6tOnj4eqAgAA9ZnHL0ulpqZq1KhRio+PV0JCghYtWqT8/HyNHz9e0un5Mvv379eyZcsc22zbtk2SdOLECRUWFmrbtm3y9fVVly5dPPERAABAHeLxcJOUlKSioiLNmTNHdrtdsbGxWrVqlaKioiSdvmnf2fe86dmzp+N9Tk6Oli9frqioKP3www+XsnQAAFAHeTzcSFJKSopSUlKqXJeZmVmpzRhzkSsCAAD1lcdv4gcAAOBOhBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGApdSLcZGRkKDo6Wv7+/oqLi9OGDRvO2//TTz9VXFyc/P391a5dO7300kuXqFIAAFDXeTzcrFixQpMmTdKMGTOUm5urfv36afDgwcrPz6+y/969ezVkyBD169dPubm5+p//+R9NmDBBK1euvMSVAwCAusjj4SY9PV3JyckaN26cYmJiNH/+fEVGRmrhwoVV9n/ppZfUpk0bzZ8/XzExMRo3bpzGjh2rZ5555hJXDgAA6qIGnjx4aWmpcnJy9Oijjzq1JyYmKjs7u8ptNm3apMTERKe2QYMGafHixTp16pR8fHwqbVNSUqKSkhLH8rFjxyRJxcXFLtdeXvIvl7e1kuM+5Z4uoU6ozbnkLpyT/8F5eRrnZd3BOXlabc/JM9sbY87bz6Ph5vDhwyovL1d4eLhTe3h4uA4ePFjlNgcPHqyyf1lZmQ4fPqyIiIhK26SlpWn27NmV2iMjI2tRPSQp1tMF1BVpwZ6uAL/CeflvnJd1Bufkv7npnDx+/LiCg8+9L4+GmzNsNpvTsjGmUtuF+lfVfsb06dOVmprqWK6oqNCRI0cUGhp63uPg/IqLixUZGal9+/YpKCjI0+UAkjgvUfdwTrqPMUbHjx9Xy5Ytz9vPo+GmWbNm8vb2rjRKU1BQUGl05owWLVpU2b9BgwYKDQ2tchs/Pz/5+fk5tYWEhLheOJwEBQXxPyzqHM5L1DWck+5xvhGbMzw6odjX11dxcXHKyspyas/KylKfPn2q3CYhIaFS/7Vr1yo+Pr7K+TYAAODy4vFfS6WmpurVV1/VkiVLlJeXp4cfflj5+fkaP368pNOXlEaPHu3oP378eP3f//2fUlNTlZeXpyVLlmjx4sWaMmWKpz4CAACoQzw+5yYpKUlFRUWaM2eO7Ha7YmNjtWrVKkVFRUmS7Ha70z1voqOjtWrVKj388MN68cUX1bJlSz333HO6/fbbPfURLlt+fn6aOXNmpUt+gCdxXqKu4Zy89GzmQr+nAgAAqEc8flkKAADAnQg3AADAUgg3AADAUgg3qLUxY8Zo2LBhni4Dl5H169fLZrPpp59+8nQpgCSpbdu2mj9/vqfLwL8RbgDUO3369JHdbq/Wzbxqa9asWerRo8dFPw4ujQEDBmjSpElu3++WLVv0u9/9zu37PZvNZtN777130Y9T3xFuLhOlpaWeLgFwG19fX7Vo0aJWj085deqUGyvC5S4sLEwBAQGeLgP/RrixqAEDBujBBx9UamqqmjVrpoEDByo9PV1XXnmlAgMDFRkZqZSUFJ04ccKxTWZmpkJCQrRmzRrFxMSoUaNGuummm2S32x19ysvLlZqaqpCQEIWGhmratGmVns5aUlKiCRMmqHnz5vL399c111yjLVu2ONafuaSwZs0a9ezZUw0bNtT111+vgoICffjhh4qJiVFQUJDuuusunTx58uJ/WbjkqhrC79Gjh2bNmiXp9L9OX331VQ0fPlwBAQG64oor9P777zv6nn1Z6sy5+95776ljx47y9/fXwIEDtW/fPsc2Z0ZglixZonbt2snPz0/GGOXn5+vWW29Vo0aNFBQUpDvvvFOHDh1y7Hf27Nn66quvZLPZZLPZlJmZeTG/GlxEY8aM0aeffqoFCxY4/nvu3r1bycnJio6OVsOGDdWpUyctWLCg0nbDhg3TM888o4iICIWGhur3v/+9U0A++5y22Wx6+eWXdfPNNysgIEAxMTHatGmTdu3apQEDBigwMFAJCQnavXu307EWLlyo9u3by9fXV506ddJf/vIXp2NI0vDhw2Wz2RzLF9rusmRgSf379zeNGjUyU6dONd99953Jy8sz8+bNMx9//LHZs2ePWbdunenUqZN54IEHHNssXbrU+Pj4mBtvvNFs2bLF5OTkmJiYGDNixAhHnyeffNIEBwebd955x2zfvt0kJyebxo0bm1tvvdXRZ8KECaZly5Zm1apV5ttvvzX33HOPadKkiSkqKjLGGPPJJ58YSaZ3795m48aN5ssvvzQdOnQw/fv3N4mJiebLL780n332mQkNDTVz5869ZN8ZLp2oqCgzb948p7bu3bubmTNnGmOMkWRat25tli9fbnbu3GkmTJhgGjVqVOkcOnr0qDHmP+dufHy8yc7ONlu3bjW9evUyffr0cex/5syZJjAw0AwaNMh8+eWX5quvvjIVFRWmZ8+e5pprrjFbt241mzdvNldddZXp37+/McaYkydPmsmTJ5uuXbsau91u7Ha7OXny5MX+enCR/PTTTyYhIcHcd999jv+ev/zyi3n88cfNP/7xD7Nnzx7z2muvmYCAALNixQrHdvfcc48JCgoy48ePN3l5eeaDDz4wAQEBZtGiRY4+Z5/TkkyrVq3MihUrzI4dO8ywYcNM27ZtzfXXX29Wr15ttm/fbnr37m1uuukmxzbvvvuu8fHxMS+++KLZsWOHefbZZ423t7f5+OOPjTHGFBQUGElm6dKlxm63m4KCgmptdzki3FhU//79TY8ePc7b56233jKhoaGO5aVLlxpJZteuXY62F1980YSHhzuWIyIinALHqVOnTOvWrR3h5sSJE8bHx8e8/vrrjj6lpaWmZcuW5qmnnjLG/Ocvpo8++sjRJy0tzUgyu3fvdrTdf//9ZtCgQTX85KgPqhNu/vCHPzjWnThxwthsNvPhhx8aY6oON5LM5s2bHdvk5eUZSeaLL74wxpwONz4+Po6/EIwxZu3atcbb29vk5+c72r799lsjyfzjH/9wbNe9e3d3fXR4WP/+/c3EiRPP2yclJcXcfvvtjuV77rnHREVFmbKyMkfbHXfcYZKSkhzLVYWbX5/DmzZtMpLM4sWLHW1vvPGG8ff3dyz36dPH3HfffU613HHHHWbIkCFO+/3rX//q1Kc6211uuCxlYfHx8U7Ln3zyiQYOHKhWrVqpcePGGj16tIqKivTzzz87+gQEBKh9+/aO5YiICBUUFEiSjh07JrvdroSEBMf6Bg0aOB1n9+7dOnXqlPr27eto8/HxUa9evZSXl+dUT7du3Rzvw8PDFRAQoHbt2jm1nTk2Lj+/Pj8CAwPVuHHj854PZ5+LnTt3VkhIiNN5FxUVpbCwMMdyXl6eIiMjFRkZ6Wjr0qVLpe1gbS+99JLi4+MVFhamRo0a6ZVXXnF67I8kde3aVd7e3o7lX//ZeC5n/xknSVdeeaVT2y+//KLi4mJJp8/HX//ZKUl9+/a94Lno6nZWRrixsMDAQMf7//u//9OQIUMUGxurlStXKicnRy+++KIk54mVZz9Z3WazVZpTcz5n+p490dMYU6nt18ey2WxVHruioqLax0b94eXlVem8OnuCryvnQ1UTjH/d9uv/J6Sqz8vztcN63nrrLT388MMaO3as1q5dq23btunee++t9CMMV87Hs/+MO1fbr/dTnT87q+LqdlZFuLlMbN26VWVlZXr22WfVu3dvdezYUQcOHKjRPoKDgxUREaHNmzc72srKypSTk+NY7tChg3x9fbVx40ZH26lTp7R161bFxMTU/oPAEsLCwpwmqhcXF2vv3r212mdZWZm2bt3qWN6xY4d++uknde7c+ZzbdOnSRfn5+U4Tj7dv365jx445zldfX1+Vl5fXqjbUHWf/99ywYYP69OmjlJQU9ezZUx06dKg0yfdSiYmJcfqzU5Kys7Od/uz08fGpdD5WZ7vLjcefCo5Lo3379iorK9Pzzz+vW265RZ9//rleeumlGu9n4sSJmjt3rq644grFxMQoPT3d6UZqgYGBeuCBBzR16lQ1bdpUbdq00VNPPaWTJ08qOTnZjZ8I9dn111+vzMxM3XLLLWrSpIkee+wxpyF/V/j4+Oihhx7Sc889Jx8fHz344IPq3bu3evXqdc5tbrzxRnXr1k0jR47U/PnzVVZWppSUFPXv399xiatt27bau3evtm3bptatW6tx48Y83bkea9u2rb744gv98MMPatSokTp06KBly5ZpzZo1io6O1l/+8hdt2bJF0dHRl7y2qVOn6s4779RVV12lG264QR988IHeffddffTRR071r1u3Tn379pWfn5+aNGlSre0uN4zcXCZ69Oih9PR0Pfnkk4qNjdXrr7+utLS0Gu9n8uTJGj16tMaMGaOEhAQ1btxYw4cPd+ozd+5c3X777Ro1apSuuuoq7dq1S2vWrFGTJk3c9XFQz02fPl3XXnutbr75Zg0ZMkTDhg1zmuvlioCAAD3yyCMaMWKEEhIS1LBhQ7355pvn3ebMDdGaNGmia6+9VjfeeKPatWunFStWOPrcfvvtuummm3TdddcpLCxMb7zxRq3qhGdNmTJF3t7e6tKli8LCwnTTTTfptttuU1JSkq6++moVFRUpJSXFI7UNGzZMCxYs0NNPP62uXbvq5Zdf1tKlSzVgwABHn2effVZZWVmKjIxUz549q73d5cZmajKhAgDqoMzMTE2aNInHMQCQxMgNAACwGMINAACwFC5LAQAAS2HkBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBsBlZ/369bLZbNzRGLAowg2AS6a0tNTTJQC4DBBuALjs+PHjGjlypAIDAxUREaF58+ZpwIABmjRpkqTTTzB+4oknNGbMGAUHB+u+++6TJK1cuVJdu3aVn5+f2rZtq2effdZpv2ceaPlrISEhyszMlCT98MMPstlsevPNN9WnTx/5+/ura9euWr9+/QVr/uGHH3TddddJkpo0aSKbzaYxY8Zo2bJlCg0NVUlJiVP/22+/XaNHj5YkzZo1Sz169NDLL7+syMhIBQQE6I477qg0ArR06VLFxMTI399fnTt3VkZGRjW+TQBuYwDARePGjTNRUVHmo48+Ml9//bUZPny4ady4sZk4caIxxpioqCgTFBRknn76abNz506zc+dOs3XrVuPl5WXmzJljduzYYZYuXWoaNmxoli5d6tivJPPXv/7V6VjBwcGOPnv37jWSTOvWrc0777xjtm/fbsaNG2caN25sDh8+fN6ay8rKzMqVK40ks2PHDmO3281PP/1kTp48aYKDg81bb73l6FtYWGh8fX3Nxx9/bIwxZubMmSYwMNBcf/31Jjc313z66aemQ4cOZsSIEY5tFi1aZCIiIszKlSvNnj17zMqVK03Tpk1NZmam6180gBoh3ABwSXFxsfHx8TFvv/22o+2nn34yAQEBTuFm2LBhTtuNGDHCDBw40Klt6tSppkuXLo7l6oabuXPnOtafOnXKtG7d2jz55JMXrP2TTz4xkszRo0ed2h944AEzePBgx/L8+fNNu3btTEVFhTHmdLjx9vY2+/btc/T58MMPjZeXl7Hb7cYYYyIjI83y5cud9vvHP/7RJCQkXLAuAO7BZSkALtmzZ49OnTqlXr16OdqCg4PVqVMnp37x8fFOy3l5eerbt69TW9++fbVz506Vl5fXqIaEhATH+wYNGig+Pl55eXk12sev3XfffVq7dq32798v6fTlpTFjxshmszn6tGnTRq1bt3aqoaKiQjt27FBhYaH27dun5ORkNWrUyPF64okntHv3bpfrAlAzDTxdAID6yfz7mbu//ov/1+1nBAYGVlp/oW1sNlultlOnTlWrrrP3XRM9e/ZU9+7dtWzZMg0aNEhff/21Pvjgg2odz2azqaKiQpL0yiuv6Oqrr3bq5+3t7XJdAGqGkRsALmnfvr18fHz0j3/8w9FWXFysnTt3nne7Ll26aOPGjU5t2dnZ6tixoyMAhIWFyW63O9bv3LlTJ0+erLSvzZs3O96XlZUpJydHnTt3vmDtvr6+klTlSNG4ceO0dOlSLVmyRDfeeKMiIyOd1ufn5+vAgQOO5U2bNsnLy0sdO3ZUeHi4WrVqpT179qhDhw5Or+jo6AvWBcA9GLkB4JLGjRvrnnvu0dSpU9W0aVM1b95cM2fOlJeX13lHTyZPnqzf/OY3+uMf/6ikpCRt2rRJL7zwgtMviq6//nq98MIL6t27tyoqKvTII4/Ix8en0r5efPFFXXHFFYqJidG8efN09OhRjR079oK1R0VFyWaz6f/9v/+nIUOGqGHDhmrUqJEkaeTIkZoyZYpeeeUVLVu2rNK2/v7+uueee/TMM8+ouLhYEyZM0J133qkWLVpIOv2LqgkTJigoKEiDBw9WSUmJtm7dqqNHjyo1NfWCtQFwA4/O+AFQrxUXF5sRI0aYgIAA06JFC5Oenm569eplHn30UWPM6QnF8+bNq7TdO++8Y7p06WJ8fHxMmzZtzNNPP+20fv/+/SYxMdEEBgaaK664wqxatarKCcXLly83V199tfH19TUxMTFm3bp11a59zpw5pkWLFsZms5l77rnHad2oUaNM06ZNzS+//OLUPnPmTNO9e3eTkZFhWrZsafz9/c1tt91mjhw54tTv9ddfNz169DC+vr6mSZMm5tprrzXvvvtutWsDUDs2Y866sA0ALvr555/VqlUrPfvss0pOTr5ox/nhhx8UHR2t3Nxc9ejRw+37HzhwoGJiYvTcc885tc+aNUvvvfeetm3b5vZjAnAfLksBcFlubq6+++479erVS8eOHdOcOXMkSbfeequHK3PNkSNHtHbtWn388cd64YUXPF0OABcRbgDUyjPPPKMdO3bI19dXcXFx2rBhg5o1a+bRmsaPH6/XXnutynV33323XnrppSrXXXXVVTp69KiefPLJSj9pB1B/cFkKgOUUFBSouLi4ynVBQUFq3rz5Ja4IwKVEuAEAAJbCfW4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAICl/H9URD+E3JhZAQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = report[report['disabled_embeddings'].isna()]\n",
    "# Barplot the dropout of the different groups\n",
    "sns.barplot(data=tmp, x='group_type', y='hparam_dropout', hue='active', errorbar=('sd', 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fold', 'group_type', 'train_len', 'val_len', 'train_perc', 'val_perc',\n",
       "       'train_active_perc', 'train_inactive_perc', 'val_active_perc',\n",
       "       'val_inactive_perc', 'test_active_perc', 'test_inactive_perc',\n",
       "       'num_leaking_uniprot', 'num_leaking_smiles', 'disabled_embeddings',\n",
       "       'val_loss', 'val_acc', 'val_f1_score', 'val_hp_metric', 'val_opt_score',\n",
       "       'val_precision', 'val_recall', 'val_roc_auc', 'test_loss', 'test_acc',\n",
       "       'test_f1_score', 'test_hp_metric', 'test_opt_score', 'test_precision',\n",
       "       'test_recall', 'test_roc_auc', 'hparam_hidden_dim', 'hparam_batch_size',\n",
       "       'hparam_learning_rate', 'hparam_dropout', 'hparam_join_embeddings',\n",
       "       'hparam_smote_k_neighbors', 'train_unique_groups', 'val_unique_groups',\n",
       "       'active', 'dummy_val_acc', 'dummy_test_acc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = report[report['disabled_embeddings'].isna()]\n",
    "# Barplot the dropout of the different groups\n",
    "sns.barplot(data=tmp, x='group_type', y='hparam_learning_rate', hue='active', errorbar=('sd', 1))\n",
    "# Set y-axis to log scale\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}