diff --git "a/notebooks/plotting_dragradation_activity_performance.ipynb" "b/notebooks/plotting_dragradation_activity_performance.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/plotting_dragradation_activity_performance.ipynb"
@@ -0,0 +1,4350 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting Dataset Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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": 1,
+   "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": 2,
+   "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": 2,
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "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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+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "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",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    .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>Active</th>\n",
+       "      <th>Active - OR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>793</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": {
+      "image/png": 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CIKDn8jioq6rg9wB3GGirYfnem+i44DQuLWkPXa3S/9cYaKvh6jIvuTItDVUAwOPn+fhi/QVEjG6KehY68F12Bl4NTeHb7GX4GbshEYv6N4CBjnrFD/QtfPzxx0hMTJQrGzZsGBo0aICvvvoK9erVg5WVFWJiYtCsWTMAQH5+PmJjY7F48eIK68e8efPQpUsXNG/eHPHx8SgsLJQtKygogFQqfc3aREREVeutZuzKe6+xgIAAmJiYwM7ODlu3bpVbtn37dnTv3v1tulWtZL0oxKC18Vj/hRuMdf8NU0lp2Th1/RlChzeGe30jONvoYe2wxsjKK8SWk/df26ZEAlgZacm9it18mA1DHXX0a2MD9/pG6NDQFJfuvZwFjDp+DxpqKujlbl05g30L+vr6cHV1lXvp6urC1NQUrq6uss/ZggULsGvXLvzzzz8YOnQodHR0MHDgQFk7fn5+mDlzpux9fn4+EhISkJCQgPz8fNy7dw8JCQm4fv26Qh8uXryIbdu2Ye7cuQCABg0aQEVFBeHh4fjzzz9x5coVuLu7V/7OICIiKqdyz9i96V5jERERcHJyQnBwMDp16oSrV69CX18fu3fvRlRUFKKjo5GUlIRhw4ahU6dOMDU1xbNnzzBr1iwcOHDgrQdWXYyL+Ae+TS3Q0dUcwb8lycrzCooAAFrq/2ZrVRUJNNRUcOzqU3zRwa7UNrNeSFFn4gFIiwQ0rWOAeX2c0ayuIQDA0UoXOXlSxKdkoI6ZNuJuZmC4ly2eZuUjcMdVHJrVppJGWvmmT5+O3Nxc+Pv7Iz09Ha1atUJ0dDT09f+dCb19+zZUVP7dp/fv35fN8AHAsmXLsGzZMrRv3x6HDx+WlQuCgFGjRuH777+Hrq4uAEBbWxsREREYN24c8vLyEBISglq1alX+QImIiMpJIgiCoOxKWVlZaN68OdauXYvg4GA0bdoUK1asgCAIsLGxweTJk/HVV18BeHnOkqWlJRYvXozRo0djyZIlOH/+vGymztLSEnv27IG7uztGjRqFhg0bYsqUKUoPJDMzE4aGhsjIyKjcm8lGlX02cevJe5j/23XEzfsAWhqq8Ao+gaZ1DLHicxcUFBbBceoheNQ3wg8jGkNXUw3L/7qJmduuwLuxOfbPaFVim6eS0nH9QTYa2xogM7cAK/cl46+/H+Lvhe3gaKUHANgVl4rAX68ht0CKwW1rIegzZwwP+xtN7PTRrI4hJv10EQVSAUG9HNG7lc2bBzJwd5nHTERERBVLmYxTrhm7V+81FhwcLCt/073GRo8ejSZNmiAsLAzp6em4efMmcnNz4eDggGPHjuH8+fMIDQ0tUx/y8vKQl5cne1/8yCepVFrJ50GV7ej1nSe5mLTpIvbOaAN1DXVIAQiQQIAEUqhARU0F2ye7Y1RYAkxGRUNVRYKPXc3QucnLKy6lpWzH3dEU7o6msvetnczgPisWq/bfwoohjQEAPdxroYf7vzNLBy49RuKd51g5pDGcAw7g5/EtYGWohTaBR9C2gTksDBVv3Cvnbffnthp4g99+O6u6B0REJBLK5Bqlg93b3mvMx8cHgwcPhru7O7S1tREZGQldXV2MHTsWERERCA0NxerVq2FmZoawsDC4uLiU2I+FCxdizpw5CuU3btyAnp6essMqO/WmZar2v9tX8DAzHx7fHJGVSYsEHL3yBGuik/F31CwYOKlg6zIvPM95gYJCKUwMdNHv6x/hUs8GSWXcDgA4OtzF3w8zS1wnv6AQoyPCsHhCHxx8ooK8IlXYuL28+a+dzRX8lmKEDi2dX7+BpKTXL38TJcYiGm+7z4iIiP5fVtbr75jxKqWCXUXdaywoKAhBQUFy7zt27Ah1dXUEBwcjMTERe/bsgZ+fH86dO1fiNmbOnImAgADZ+8zMTNja2qJ+/fqVeyj2fEKZqlk1KMSHi7zkyr4IS4CztR6mdXdAA+kFoDiAq798Jd3Jwj837mPRZ3ZwLCjbdgRBQEpKMlxt9Utc59vtl9HdzQA9bR8gPiUDgrRAVk+1IAuWBTfhWJD7+o04BpapL6Uq4z4TlbfdZ0RERP+v+KhkWSgV7F6911gxqVSKI0eOICQkBFevXgXwcubO2vrfKy9fd6+xK1euYPPmzYiPj8eGDRvQrl07mJubo2/fvhg+fDgyMzNLDGqampolPvtTVVUVqqqqygxLSUVlqmWkrQIjW/mZQ11NFZjpq6OJrR6AIvxy+j7M9TVgZ6aNxNvPMemni+jZ0gpd3Mxk2/ELjUctYy0s7N8QADBnxzW0djSCo5UuMnMKsSo6BQm3MrBmqAtU/9O3i3ef45dT95CwoB1UUQQXGx2oSICIwymwMtTEldQstKpnoLCegrfen2XbZ6JSqZ9BIiKqSZTJNUoFu4q+11jxlYjfffcd9PT0IJVKUVDw8sa5xf8tKhJvKEhNz0PAz5fwICMP1kZa8PuwNr791FGuzu0nuVB5ZbbzWU4BRv2YiLSMPBjqqKFZHUMc+dYTHvWN5dYTBAGjfryA7we7yO6Jp62hiojRTTEu4h/kFRYhZIgraploV/5AiYiI6J0o11Wxr/Ly8pJdFQsAixcvxsKFC7Fx40Y4OjpiwYIFOHz4sOx2J68KCwtDdHQ0fv31VwDAmTNn0KlTJ+zfvx979+7Fr7/+qnAPvNJUx6tiReNtr4rlPiMiIiq3Sr8q9nXKcq8xAHjw4AEWLFiAEydOyMo8PDwwdepU+Pr6wsLCApGRkRXdPSIiIiLReusZu+qCM3aViDN2yuOMHRERVRBlMs5bPVKMiIiIiKoPBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikVAq2IWGhsLNzQ0GBgYwMDBAmzZtsHfvXtlyQRAQFBQEGxsbaGtrw8vLCxcvXpRrIyAgACYmJrCzs8PWrVvllm3fvh3du3d/i+EQERER1VxKBbvatWtj0aJFOHv2LM6ePYuPPvoIn3zyiSy8LVmyBMuXL0dISAji4uJgZWWFTp064fnz5wCA3bt3IyoqCtHR0Vi8eDGGDRuGJ0+eAACePXuGWbNmYc2aNRU8RCIiIqKaQalg1717d3Tt2hVOTk5wcnLC/Pnzoaenh1OnTkEQBKxYsQKzZs1Cr1694OrqisjISOTk5CAqKgoAcPnyZXh5eaFly5YYMGAADAwMcPPmTQDA9OnT4e/vDzs7u4ofJREREVENoFbeFaVSKX755RdkZ2ejTZs2SE5ORlpaGry9vWV1NDU10b59e5w4cQKjR49GkyZNEBYWhvT0dNy8eRO5ublwcHDAsWPHcP78eYSGhpZ5+3l5ecjLy5O9z8zMlPVLKpWWd1hlUANPS3zr/cl9RkREVF7K5Bqlg11iYiLatGmDFy9eQE9PD7t27UKjRo1w4sQJAIClpaVcfUtLS9y6dQsA4OPjg8GDB8Pd3R3a2tqIjIyErq4uxo4di4iICISGhmL16tUwMzNDWFgYXFxcSu3HwoULMWfOHIXyGzduQE9PT9lhlZ1608pru7pKSnq79bnPiIiIyi0rK6vMdSWCIAjKNJ6fn4/bt2/j2bNn2LFjB3788UfExsbi2bNnaNu2Le7fvw9ra2tZ/ZEjR+LOnTvYt29fie0FBQUhIyMDw4YNg7e3NxITE7Fnzx6EhITg3LlzpfajpBk7W1tbPH36FAYGBsoMSTnbelVe29VVv51vtz73GRERUbllZmbCxMQEGRkZb8w4Ss/YaWhowMHBAQDQsmVLxMXFYeXKlfjqq68AAGlpaXLB7uHDhwqzeMWuXLmCzZs3Iz4+Hhs2bEC7du1gbm6Ovn37Yvjw4cjMzCx1AJqamtDU1FQoV1VVhaqqqrLDUkJRJbZdTb31/nzzPgvacRVzdsrPclkaaiJtbSfZ+8v3nuOrrVcQe/kJigQBLrX0sX1iC9iZaZfY5sW7zxH461WcS87Arce5+H5wI0zuUk+uzubjdzFj6xVk50kxwssWSwc2ki1LeZQD70WncXbeBzDQUVdmwBWwz4iIiF5SJteU+xy7YoIgIC8vD/b29rCyskJMTAyaNWsG4OXsXmxsLBYvXlzieqNGjcJ3330HPT09SKVSFBQUAIDsv0VFNTBE1WAutfXxv5mtZO9VVSSyf994kI0P5p7AiPa2mPOZEwx11HD5Xha01Es/fy8nT4p6Fjro08oaU36+pLD88fN8fLH+AiJGN0U9Cx34LjsDr4am8G328ovI2A2JWNS/gfKhjoiIqIooFey+/vprdOnSBba2tnj+/Dm2bt2Kw4cPY9++fZBIJJg8eTIWLFgAR0dHODo6YsGCBdDR0cHAgQMV2lq/fj0sLCzQo0cPAEDbtm0RFBSEU6dOYe/evWjUqBGMjIwqZJD0flBTkcDKSKvEZbO2X0XXJhZY8sqMWj0L3de2517fCO71jQAAM7ZeUVh+82E2DHXU0a+NDQCgQ0NTXLqXBd9mlog6fg8aairo5W6tsB4REVF1pVSwe/DgAT7//HOkpqbC0NAQbm5u2LdvHzp1enm4bPr06cjNzYW/vz/S09PRqlUrREdHQ19fX6GdBQsWyC64AAAPDw9MnToVvr6+sLCwQGRkZAUMj94nSQ+yYTMuBprqKmhV3xgL+jmjnoUuiooE/JnwANO71YfPotOIv5UBe3MdzOzhgJ4trcq9PUcrXeTkSRGfkoE6ZtqIu5mB4V62eJqVj8AdV3FoVpsKHB0REVHlU/riieoqMzMThoaGZTqx8K1E1cAnYwzc/Xbrl2Gf7U14iJx8KZysdPEgMw/BvyXhyv1sXFzcHgXSIliP+x90NFUR3McZHRqaYt+FR/h6+xUcmtUG7RuavrH9upMOYHJne4Vz7HbFpSLw12vILZBicNtaCPrMGcPD/kYTO300q2OIST9dRIFUQFAvR/RuZVP2Mb/tPiMiIvp/ymSctz7HjqgidGlqIft3YwBtHIxRP+AQIo/eRf//P1T6SXNLTPn/YNa0riFOJKVj3YFbZQp2pfnU3RqfvnK49fClx0i8k4mQIa5wCDiILeObw8pQEx6Bx9CugSksDBUv2CEiIqouauCdY+l9oKulhsa2+khKy4aZvgbUVCVoVEv+kH5DGz3cfpxbYdvMK5DCf+M/+GG4G64/yEZhkYD2DU3hbKMHJ2tdnL7xrMK2RUREVBkY7KhayiuQ4vK9LFgbaUJDTQXu9YxwNVX+Bo3X0rJQp5RbnZTHvF1J6NLEAs3tDSEtElAo/fcshYJCAdIiUZy1QEREIsZDsVQtfLn5Ero3t4SdqTYe/v85dpm5hRjyYW0AwDTfeui3+jzaNTBBh0Zm2HfhIXaff4jD3/x7gYNfaDxqGWthYf+GAID8wiJcuvtc9u976S+QkJIBPS01OFjJX1F78e5zbDt1HwkL2gEAGtjoQUUChB++DStDTVxJzYJ7PcN3sSuIiIjKjcGOqoW7T19gQMh5PH6eD3MDDbR2MMapOW1Rx1wHwMtz4dYNb4yFf1zHxE0X4Wythx2TWuADZxNZG7ef5EJF8u+97+6nv0CzWUdl75f9eRPL/ryJ9g1NcPgbT1m5IAgY9eMFfD/YBbpaL38ktDVUETG6KcZF/IO8wiKEDHFFLZOKmx0kIiKqDLwqVlm8KlZ53GdERETlpkzG4Tl2RERERCLBYEdEREQkEgx2RERERCLBYEdEREQkEgx2RERERCLBYEdEREQkEryPnZKCTf+o6i68c99UdQeIiIioTDhjR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSSgW7hQsXwt3dHfr6+rCwsEDPnj1x9epVuTqCICAoKAg2NjbQ1taGl5cXLl68KFcnICAAJiYmsLOzw9atW+WWbd++Hd27dy/ncIiIiIhqLqWCXWxsLMaNG4dTp04hJiYGhYWF8Pb2RnZ2tqzOkiVLsHz5coSEhCAuLg5WVlbo1KkTnj9/DgDYvXs3oqKiEB0djcWLF2PYsGF48uQJAODZs2eYNWsW1qxZU4FDJCIiIqoZ1JSpvG/fPrn3GzduhIWFBc6dO4d27dpBEASsWLECs2bNQq9evQAAkZGRsLS0RFRUFEaPHo3Lly/Dy8sLLVu2RMuWLTF58mTcvHkTpqammD59Ovz9/WFnZ/fGvuTl5SEvL0/2PjMzEwAglUohlUqVGZZSJIJQaW1XV1Kp5C1bqIFH/CvxM0hERDWLMrlGqWD3XxkZGQAAExMTAEBycjLS0tLg7e0tq6OpqYn27dvjxIkTGD16NJo0aYKwsDCkp6fj5s2byM3NhYODA44dO4bz588jNDS0TNteuHAh5syZo1B+48YN6Onpvc2wXsu8sOYFu6Sktwx26k0rpB/vlaSkqu4BERGJRFZWVpnrljvYCYKAgIAAfPDBB3B1dQUApKWlAQAsLS3l6lpaWuLWrVsAAB8fHwwePBju7u7Q1tZGZGQkdHV1MXbsWERERCA0NBSrV6+GmZkZwsLC4OLiUuL2Z86ciYCAANn7zMxM2Nraon79+jAwMCjvsN5oR0rNC3aOjm8Z7M4nVEg/3iuOgVXdAyIiEonio5JlUe5gN378eFy4cAHHjh1TWCaRyAcBQRDkyoKCghAUFCT3vmPHjlBXV0dwcDASExOxZ88e+Pn54dy5cyVuX1NTE5qamgrlqqqqUFVVLeeo3kyQ1Lxgp6r6todiiyqkH++VSvwMEhFRzaJMrinXyU8TJkzAH3/8gUOHDqF27dqycisrKwD/ztwVe/jwocIsXrErV65g8+bNmDdvHg4fPox27drB3Nwcffv2xfnz55VKqUREREQ1mVLBThAEjB8/Hjt37sTBgwdhb28vt9ze3h5WVlaIiYmRleXn5yM2Nhaenp4ltjdq1Ch899130NPTg1QqRUFBAQDI/ltUVANne4iIiIjKQalDsePGjUNUVBR+//136Ovry2bmDA0Noa2tDYlEgsmTJ2PBggVwdHSEo6MjFixYAB0dHQwcOFChvfXr18PCwgI9evQAALRt2xZBQUE4deoU9u7di0aNGsHIyOjtR0lERERUAygV7IqvWPXy8pIr37hxI4YOHQoAmD59OnJzc+Hv74/09HS0atUK0dHR0NfXl1vnwYMHWLBgAU6cOCEr8/DwwNSpU+Hr6wsLCwtERkaWY0hERERENZNEEMRxY7bMzEwYGhoiIyOjUq+KDd4vit2llG983vLiiaga+CSRgburugdERCQSymScGnjnWCIiIiJxYrAjIiIiEgkGOyIiIiKRYLAjIiIiEgkGOyIiIiKRYLAjIiIiEgkGOyIiIiKRYLAjIiIiEgmlnjxBVB7Bpn9UdRfeuW+qugNERFQjccaOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEgsGOiIiISCQY7IiIiIhEQq2qOyBWB38KwqHNc+XK9Iwt8dWWVACAIAg49PMcnN27HrlZ6ajt3ArdxoXAsq5LqW2ej47AruXDFcoD/8iBuoYWAODvg5sRvWEmCl5ko7nPcHQeuVRWLz0tBZGzfDBmVRy0dA0qYphERERUjTDYVSKLOi4YujBG9l5FRVX276O/LMGJXd/j04CNMKvthMNb5iPya29M+vEKNHX0S21TU8cAk368IldWHOqyMx7jtxUj0WvqRhhb1cPPgd1g7+YF51a+AIDdIf7oNGwhQx0REZFI8VBsJVJRVYO+iZXspWtkDuDlbN3JXSvRrv/XcPmgFyzruuKzqREoyMvBhUNRr21TIpHItalvYiVblp52E1q6hmjcvh9qO7vDvkkHPLp9CQDw96EoqKppwOWDXpU3YCIiIqpSDHaV6Mm9JCwZWAvfDamHbQsH4GnqTQBAeloystLT4NDcW1ZXTUMTdRu3x+3LJ1/bZn5uFpb51cXSwbb4KbA77l+Ply0ztXFEQV4O7l+PR87zp7h3LQ6W9m7Ief4UBzfNRrdxqytnoERERFQt8FBsJandoBU+mxYJ01pOyE5/gMNb5mN9QFtM+OEfZKWnAXh5zt2r9Iwt8OzB7VLbNLdtgE+nboSlfWPk5WTi5G+r8OPUDzBubQJMazlCW98YvaZGYMeyISjMy0XTjz+HY0sf7Fo+HK0/GY/0tGT8HPQJigoL0GHwbLh+2LtS9wERERG9Wwx2lcTJvcu/b+wbw7ZRG3w/zAHxMZGwbdgaACCBRG4dQRAAiXzZq2wbtpatCwB2jdoidHwLnPp9NXz9VwEAGrX9FI3afiqrk/z3YTxI/ge+/iFYMdwRfWZEQc/YCj9MaoW6jdtBz8iiIoZLRERE1QAPxb4jGlq6sKzbGE/uJ0HP+OV5cc//f+auWPazRwqzeK+joqKCWk4t8eT+9RKXF+bnYfeacegxcR2e3r+OImkh7N3aw9zWGWa1nHD3yunyD4iIiIiqHQa7d6QwPw+P7lyGvok1jK3soWdshRvx/14xW1iQj5TEWNg1bFPmNgVBQNqNv+UuoHjV4ah5cGzZGTaOzVFUJEWRtFC2TCotQFGRtPwDIiIiomqHh2Iryb71X8K5VXcYWdgh69lDxG6Zj7ycTDTrOAQSiQRtPp2EI1sXwtTGEaa1HBG7dSHUNXXg1mGgrI1flw6BgakNvIcvBAAc/HkObBu0hmktR+TlZOLU76uQejMB3caHKGz/QcpFJB7ZjnFrX15cYW7bABIVFZzbFw49Yys8vnMFtZ3c383OICIioneCwa6SZDy+h18WDURO5mPoGJrDtkFrjPr+JIws6wAAPuwzHYV5udgdMg4vstJRu0ErDFmwX+4edhkPb0NF8u+k6ovsZ/h91WhkpadBS8cQ1vWbYcTSWNR29pDbtiAI+GPVaHQdvRwaWroAAHVNbfSauhG714yHtCAPvv6rYWBW6x3sCSIiInpXJIIgCFXdiYqQmZkJQ0NDZGRkwMCg8m7AG7xfFLtLKd/4lH5BR1lwnxEREZWfMhmH59gRERERiQSDHREREZFIMNgRERERiQSDHREREZFIMNgRERERiQSDHREREZFIMNgRERERiQSDHREREZFIMNgRERERiQSDHREREZFIMNgRERERiQSDHREREZFIMNgRERERiYTSwe7IkSPo3r07bGxsIJFI8Ntvv8ktFwQBQUFBsLGxgba2Nry8vHDx4kW5OgEBATAxMYGdnR22bt0qt2z79u3o3r278iMhIiIiquGUDnbZ2dlo0qQJQkJCSly+ZMkSLF++HCEhIYiLi4OVlRU6deqE58+fAwB2796NqKgoREdHY/HixRg2bBiePHkCAHj27BlmzZqFNWvWvMWQiIiIiGompYNdly5dEBwcjF69eiksEwQBK1aswKxZs9CrVy+4uroiMjISOTk5iIqKAgBcvnwZXl5eaNmyJQYMGAADAwPcvHkTADB9+nT4+/vDzs7uLYdFREREVPOoVWRjycnJSEtLg7e3t6xMU1MT7du3x4kTJzB69Gg0adIEYWFhSE9Px82bN5GbmwsHBwccO3YM58+fR2hoaJm2lZeXh7y8PNn7zMxMAIBUKoVUKq3IYcmRCEKltV1dSaWSt1qf+4yIiKj8lMk1FRrs0tLSAACWlpZy5ZaWlrh16xYAwMfHB4MHD4a7uzu0tbURGRkJXV1djB07FhEREQgNDcXq1athZmaGsLAwuLi4lLithQsXYs6cOQrlN27cgJ6eXkUOS455Yc0LKUlJbxdSuM+IiIjKLysrq8x1KzTYFZNI5P+oCYIgVxYUFISgoCC59x07doS6ujqCg4ORmJiIPXv2wM/PD+fOnStxGzNnzkRAQIDsfWZmJmxtbVG/fn0YGBhU7IBesSOl5oUUR8e3CyncZ0REROVXfFSyLCo02FlZWQF4OXNnbW0tK3/48KHCLF6xK1euYPPmzYiPj8eGDRvQrl07mJubo2/fvhg+fDgyMzNLDGqamprQ1NRUKFdVVYWqqmoFjUiRIKl5IUVV9e1CCvcZERFR+SmTayr0Pnb29vawsrJCTEyMrCw/Px+xsbHw9PRUqC8IAkaNGoXvvvsOenp6kEqlKCgoAADZf4uKiiqyi/SeiN26EN92VsFf6ybLyg7+FISVXzTE3E/0ML+3CTbO6IQ7V06/th1pYQEObZ6L5cMcMKe7NkLGNkXS2X1ydf4+uBlLB9thQW9T7Fs/TW5ZeloKVoxwxovssn9bIiIiqipKz9hlZWXh+vXrsvfJyclISEiQ3Zdu8uTJWLBgARwdHeHo6IgFCxZAR0cHAwcOVGhr/fr1sLCwQI8ePQAAbdu2RVBQEE6dOoW9e/eiUaNGMDIyKv/o6L1092oczu5dD0t7N7ly09pO6Oa/GsbW9VCQl4uTu75H5Nc+mLIhCbpG5iW29b/Ib/D3wc3oOSkMZrYNcP3cfkTN7YWRy4/DxqEZsjMe47cVI9Fr6kYYW9XDz4HdYO/mBedWvgCA3SH+6DRsIbR0K+/wPhERUUVROtidPXsWHTp0kL0vPs9tyJAhiIiIwPTp05Gbmwt/f3+kp6ejVatWiI6Ohr6+vlw7Dx48wIIFC3DixAlZmYeHB6ZOnQpfX19YWFggMjKyvOOi91RebhZ+XTIYPSeF4fCW+XLLmnSQ/3LQedRynNu/AWnJF1C/2ccltvf3gZ/Rvv/XcPLoCgDw6DYWSeeicXzHcvT56iekp92Elq4hGrfvBwCwb9IBj25fgnMrX/x9KAqqahpw+UDx1j5ERETVkdKHYr28vCAIgsIrIiICwMsLJ4KCgpCamooXL14gNjYWrq6uCu1YWloiJSUFNjY2cuWBgYF48uQJLl++DA8Pj/KNit5be9aMh5NHV9Rv3vG19QoL8nF2bxi0dA1hVa/Ja+rlQU1DS65MXUMbty8eAwCY2jiiIC8H96/HI+f5U9y7FgdLezfkPH+Kg5tmo9u41W8/KCIionekUq6KJSqPC4e34v718xiz6kypda6e3oPtCwegIC8HeibWGLIgGrqGZqXWd2jhg+M7v0fdxu1gbF0fNxMO4Mqp31FU9PKeQNr6xug1NQI7lg1BYV4umn78ORxb+mDX8uFo/cl4pKcl4+egT1BUWIAOg2fD9cPeFT5uIiKiisJgR9VCxqM7+GvdZAxZsB/q/5lhe5V9kw7wXxuPnIzHOLt3PbYt6IfRK09Bz8iixPq+Y1bgt5WjsHJkQ0gggbF1fTTrNBTxMRGyOo3afopGbT+VvU/++zAeJP8DX/8QrBjuiD4zoqBnbIUfJrVC3cbtSt0WERFRVWOwo2rhXtI5ZD97iHXjW8rKioqkuPXPEZz+Yw1m734BFVVVaGjpwtTGAaY2DrBt2BrfD3fCuX3haN9/Zont6hqZY9DsXSjIf4HczCfQN7VB9IYZMLK0L7F+YX4edq8Zh97TfsLT+9dRJC2EvVt7AIBZLSfcvXIaDVp3r/gdQEREVAEY7KhaqN/0Y4xfd0GubNd3w2Fm2wAf9p0OldLu4SMIkBbklbzsFeoaWlA3qwVpYQEuHdsJ13Z9Sqx3OGoeHFt2ho1jc9y/Ho8iaaFsmVRaIDuES0REVB0x2FG1oKmjD8u68hfZqGvpQsfABJZ1XZH/IhuxW+ajQese0DexRk7mE5zZsxaZj+/C5cN/Q9qvS4fAwNQG3sMXAgDuXDmNzMf3YF2/KTKf3MOhn+dAEIrwQZ/pCn14kHIRiUe2Y9zaeACAuW0DSFRUcG5fOPSMrfD4zhXUdnKvxL1ARET0dhjs6L0gUVHFoztXEf+/3sjJfAwdfVPUcnLHiGVHYFn33+cJZzy8DRXJvxd7F+a/wIFN3yI99SY0tPXg6N4Vn03bBG09I7n2BUHAH6tGo+vo5dDQ0gUAqGtqo9fUjdi9ZjykBXnw9V8NA7Na72S8RERE5SERBEEUz3vKzMyEoaEhMjIyKvVZscH7RbG7lPKNz9s9Hov7jIiIqPyUyTgV+kgxIiIiIqo6DHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEREREIsFgR0RERCQSDHZEIrFw4UK4u7tDX18fFhYW6NmzJ65evfrG9TZv3owmTZpAR0cH1tbWGDZsGJ48eSJbHhMTAycnJxgaGmLIkCHIz8+XLcvIyICTkxNu375dKWMiIiLlMNgRiURsbCzGjRuHU6dOISYmBoWFhfD29kZ2dnap6xw7dgx+fn4YMWIELl68iF9++QVxcXH44osvAABFRUUYNGgQxowZgxMnTuDMmTNYv369bP2vvvoKY8aMgZ2dXaWPj4iI3kytqjtARBVj3759cu83btwICwsLnDt3Du3atStxnVOnTqFu3bqYOHEiAMDe3h6jR4/GkiVLAACPHz/Go0eP4O/vDy0tLfTo0QOXLl0CABw/fhxnz57FmjVrKnFURESkDM7YEYlURkYGAMDExKTUOp6enrh79y7++usvCIKABw8e4Ndff4Wvry8AwNzcHNbW1oiOjkZubi6OHj0KNzc35OfnY+zYsVi3bh1UVVXfyXiIiOjNGOyIREgQBAQEBOCDDz6Aq6trqfU8PT2xefNm9OvXDxoaGrCysoKRkRFWr14NAJBIJNi+fTvmzZuHRo0aoVmzZhg+fDgWLVqEjz/+GNra2mjbti2cnZ0REhLyroZHRESl4KFYIhEaP348Lly4gGPHjr223qVLlzBx4kQEBgbCx8cHqampmDZtGsaMGYPw8HAAwAcffIC4uDjZOteuXcNPP/2E+Ph4tGvXDpMnT0bnzp3h6uqKdu3awc3NrVLHRkREpWOwIxKZCRMm4I8//sCRI0dQu3bt19ZduHAh2rZti2nTpgEA3NzcoKuriw8//BDBwcGwtraWqy8IAkaNGoXvvvsORUVFiI+PR+/evaGjo4P27dsjNjaWwY6IqArxUCyRSAiCgPHjx2Pnzp04ePAg7O3t37hOTk4OVFTkfw0UnzMnCIJC/fDwcJiamqJHjx6QSqUAgIKCAtl/i8uIiKhqMNgRicS4cePw888/IyoqCvr6+khLS0NaWhpyc3NldWbOnAk/Pz/Z++7du2Pnzp0IDQ3FzZs3cfz4cUycOBEeHh6wsbGRa//hw4cIDg7GqlWrAADGxsZo2LAhVqxYgZMnT+LAgQPw9PR8N4MlIqISMdgRiURoaCgyMjLg5eUFa2tr2Wvbtm2yOqmpqXI3Ex46dCiWL1+OkJAQuLq6ok+fPnB2dsbOnTsV2p80aRK+/PJL1KpVS1YWERGBrVu3olu3bpg2bRo8PDwqd5BERPRaEqGk4y3voczMTBgaGiIjIwMGBgaVtp3g/aLYXUr5xkfyVutzn1F1dOTIESxduhTnzp1Damoqdu3ahZ49e5Za/9ixY/jqq69w5coV5OTkoE6dOhg9ejSmTJkiqxMTE4Nx48bhwYMH6NmzJ9avXw8NDQ0AL28/4+7ujv/973+8oTMRKUWZjMOLJ4iqm6juVd2Dd2/g7ne+yezsbDRp0gTDhg3DZ5999sb6urq6GD9+vOwCk2PHjmH06NHQ1dXFqFGjZE/pmDFjBnx8fNC7d2+sX78e48aNA8CndBDRu8FDsURUI3Xp0gXBwcHo1atXmeo3a9YMAwYMgIuLC+rWrYvBgwfDx8cHR48eBSD/lA4XF5cSn9IxadKkShtPZTty5Ai6d+8OGxsbSCQS/Pbbb29cJzY2Fi1atICWlhbq1auHdevWyS3nc4iJKh5n7IiqmWDTP6q6C+/cN1XdgXKIj4/HiRMnEBwcDED+KR2dOnXC0aNHZWFl7Nix2LBhw3v9lA5lZziTk5PRtWtXjBw5Ej///DOOHz8Of39/mJub47PPPuMMJ1ElYbAjIlJC7dq18ejRIxQWFiIoKAhffPEFgH+f0jFlyhRMmjQJXbt2xfDhw7Fw4UK5p3Q8fvwYEyZMwPjx46t4JMrp0qULunTpUub669atg52dHVasWAEAaNiwIc6ePYtly5bhs88+43OIiSoJgx0RkRKOHj2KrKwsnDp1CjNmzICDgwMGDBgAgE/peNXJkyfh7e0tV+bj44Pw8HAUFBSIfoaTqKrwHDsiIiXY29ujcePGGDlyJKZMmYKgoKAS65X2lA4LCwvZUzrELC0tDZaWlnJllpaWKCwsxOPHj2vMc4jXrl0Le3t7aGlpoUWLFrJzMt/k+PHjUFNTQ9OmTeXKeV4ivQmDHRFROQmCgLy8vBKX8SkdLw9Pv6r47lrF5cUznMnJyVizZg2Sk5Px008/Yd68efj8888xevRoHD16FHPnzsWFCxfeef/f1rZt2zB58mTMmjUL8fHx+PDDD9GlS5c3Bq+MjAz4+fnh448/lisvPi9xzJgxOHHiBM6cOYP169fLlovhvERlgnBqaioGDhwIZ2dnqKioYPLkyQp1amIQZrAjohopKysLCQkJSEhIAPDyZP+EhATZL/n/PqVjzZo12L17N5KSkpCUlISNGzdi2bJlGDx4sELbfEoHYGVlhbS0NLmyhw8fQk1NDaampgr1xTjDuXz5cowYMQJffPGF7P+/ra0tQkNDX7ve6NGjMXDgQLRp00auXOxXXisbhPPy8mBubo5Zs2ahSZMmCstrQhAuCYMdEdVIZ8+eRbNmzdCsWTMAQEBAAJo1a4bAwEAAik/pKCoqwsyZM9G0aVO0bNkSq1evxqJFizB37lyFtvmUDqBNmzaIiYmRK4uOjkbLli2hrq6uUF9sM5z5+fk4d+6cwnmG3t7eOHHiRKnrbdy4ETdu3MDs2bMVlr16XmJubi6OHj0KNzc32XmJ69ate6/PS1Q2CNetWxcrV66En58fDA0NFZaLPQiXhhdPEFGN5OXlhdc9eCciIkLu/YQJEzBhwoQytb1lyxaFMg8PD1y+fFmpPlYnWVlZuH79uux98QyniYkJ7OzsMHPmTNy7dw+bNm0CAIwZMwYhISEICAjAyJEjcfLkSYSHh5e4b4pnOI8fPw5AfobT29sbBw4cwNdff/1uBlpBHj9+DKlUWuJ5hv+dySyWlJSEGTNm4OjRo1BTU/zzLOYrr4uD8IwZM+TK3xSEX6emXqDDYEdE7zc+qeOdOHv2LDp06CB7HxAQAAAYMmQIIiIiFGY47e3t8ddff2HKlClYs2YNbGxssGrVqhLvgVfaDOeQIUOwatWq93qGs6TzDP9bBgBSqRQDBw7EnDlz4OTkVGp7Yr3yujxB+E3EHIRfh8GOiIjeSNkZTgBo3749zp8//8a2xTjDaWZmBlVV1RLPM/xveAGA58+f4+zZs4iPj5cFjKKiIgiCADU1NURHR+Ojjz6SW6e08xJ1dHRk5yW+L8GuWFmDcFmJNQi/DoMdEb3X+KQOJXGG853Q0NBAixYtEBMTg08//VRWHhMTg08++UShvoGBARITE+XK1q5di4MHD+LXX3+Fvb29wjqvnpeYnp4O4P09L1HZIFweYgzCJWGwIyKqQRiE352AgAB8/vnnaNmyJdq0aYOwsDDcvn0bY8aMAQC58xJVVFTg6uoqt76FhQW0tLQUygHxnZeobBAuDzEF4ddhsCMiIqoE/fr1w5MnTzB37lykpqbC1dUVf/31F+rUqQNA8cprZYjxvERlgnCx4tsVZWVl4dGjR0hISICGhgYaNWok17bYgvDrSITXnTTxFtauXYulS5ciNTUVLi4uWLFiBT788EMAwLJly7B06VIAwIwZMzBlyhTZeqdPn4a/vz/OnDmj1NUqmZmZMDQ0REZGBgwMDCp2MK8I3l8pu6ta+8an/Oc3ANxnyuL+Ug73l3K4v6g6W7t2LZYsWSILwt9//z3atWsHABg6dChSUlJw+PBhWf2Szr+rU6cOUlJS5MoGDBiAtm3byl0gcebMGQwZMgQPHz7EpEmTZLc6qo6UyTiVMmNXfJPBtWvXom3btvjhhx/QpUsXXLp0CRkZGQgMDMSePXsgCAK6deuGTp06wdXVFQUFBRgzZgzCwsJEeQkyERG9Z3hO4jvl7+8Pf3//EpeVdIFOWeemxHiBTmkqJdi9epNBAFixYgX279+P0NBQNGvWDG5ubrKre9zc3HD58mW4urpi6dKlaNeuHdzd3SujW0RERErhOYn0vqnwYPemmwz6+fnh2rVruH37NgRBwLVr1+Dq6orr168jIiIC586dK9N28vLy5J7RmJGRAQBIT0+v1BMg87Jq3mGM9PS3O4zBfaYc7i/lcH8ph/tLOdxfyll+sObtr4CPKv9Qf2ZmJoAyzlAKFezevXsCAOH48eNy5fPnzxecnJwEQRCE0NBQwcnJSXBychJCQ0MFQRCEjz/+WNi1a5fwyy+/CC4uLkLTpk2F2NjYUrcze/ZsAQBffPHFF1988cVXjXjduXPnjTms0q6Kfd1NBseMGSO7ygV4edxcX18fbdq0gbOzM+Li4nD37l30798fycnJ0NTUVGh/5syZsjufAy9v5Pj06VOYmpq+1c0Mq6vMzEzY2trizp07lXpxiFhwfymH+0s53F/K4f5SHveZcsS+vwRBwPPnz2FjY/PGuhUe7JS9yeDjx48xd+5cHDlyBKdPn4aTkxMcHR3h6OiIgoICXLt2DY0bN1ZYT1NTUyHwGRkZVehYqiMDAwNRfmgrC/eXcri/lMP9pRzuL+VxnylHzPvL0NCwTPVUKnrDr95k8FUxMTHw9PRUqD958mRMmTIFtWvXhlQqld0sEAAKCwtFc8NAIiIiospWKYdi33STwWIxMTFISkqS3WzQw8MDV65cwd69e3Hnzh2oqqrC2dm5MrpIREREJDqVEuzedLdtAMjNzcX48eOxbds2qKi8nDisVasWVq9ejWHDhkFTUxORkZHQ1taujC6+dzQ1NTF79uwSzzckRdxfyuH+Ug73l3K4v5THfaYc7q9/VdqTJ4iIiIjo3arwc+yIiIiIqGow2BERERGJBIMdERERkUgw2BEREdF7ISgoCE2bNq3qblRrDHbvSFpaGiZMmIB69epBU1MTtra26N69Ow4cOAAAqFu3LiQSCSQSCbS1tdGgQQMsXbpU7rlwKSkpsjoSiQQaGhpwcHBAcHCwXL2goCBIJBKF28skJCRAIpEgJSXlnYy5onTv3h0dO3YscdnJkychkUhw/vx5SCQSJCQkKNTx8vLC5MmTS31fEwwdOhQ9e/aUKztx4gRUVVXRuXNnhfr//awZGxujXbt2iI2NfUc9rnhDhw4t8ecCAPz9/SGRSDB06FC5uhKJBOrq6rC0tESnTp2wYcMGFBUVya1b/LO7detWhXZdXFwgkUgQEREBAHj69CkmTJgAZ2dn6OjowM7ODhMnTpQ96/p9JJFI8NtvvymUjxo1CqqqqiXul+LfURKJBKqqqrC1tcUXX3yBR48eyerMnz8fnp6e0NHRqdY3n3/1s/Lq6/r166Uue/VnTpnPDwCEhYXBy8sLBgYGkEgkePbsmcJ6r25LTU0NdnZ2CAgIkHu+enVU2u/m3377rUKeKLVz5074+PjAzMys1L8XYsBg9w6kpKSgRYsWOHjwIJYsWYLExETs27cPHTp0wLhx42T1im8Pc/nyZXz55Zf4+uuvERYWptDe//73P6SmpiIpKQlz5szB/PnzsWHDBrk6WlpaCA8Px7Vr1yp9fJVtxIgROHjwIG7duqWwbMOGDWjatClMTEyqoGfvtw0bNmDChAk4duwYbt++XWKd4s9abGwsDAwM0LVrVyQnJ7/jnlYcW1tbbN26Fbm5ubKyFy9eYMuWLbCzs5Or27lzZ6SmpiIlJQV79+5Fhw4dMGnSJHTr1g2FhYUK7W7cuFGu7NSpU0hLS4Ourq6s7P79+7h//z6WLVuGxMREREREYN++fRgxYkQljLbq5OTkYNu2bZg2bRrCw8NLrOPi4oLU1FTcvn0boaGh2L17N/z8/GTL8/Pz0adPH4wdO/Zddbvcij8rr77s7e1LXbZlyxa59cv6+QFe7tvOnTvj66+/fm2fNm7ciNTUVCQnJ2Pt2rX46aefEBwcXAGjff/k5+cDALKzs9G2bVssWrSointUuRjs3oHi2YAzZ86gd+/ecHJygouLCwICAnDq1ClZPX19fVhZWaFu3br44osv4ObmhujoaIX2TE1NYWVlhTp16mDQoEHw9PTE+fPn5eo4OzujQ4cO+Oabbyp9fJWtW7dusLCwkPvWCvz7x0NsfxTfhezsbGzfvh1jx45Ft27dFPZtseLPmpubG3744Qfk5OSU+Jl8XzRv3hx2dnbYuXOnrGznzp2wtbVFs2bN5OpqamrCysoKtWrVQvPmzfH111/j999/x969exX216BBgxAbG4s7d+7IyjZs2IBBgwZBTe3f24W6urpix44d6N69O+rXr4+PPvoI8+fPx+7duxXCYnVSVFSExYsXw8HBAZqamrCzs8P8+fNLrf/LL7+gUaNGmDlzJo4fP17iUQI1NTXZ/u3WrRsmTpyI6OhoWeieM2cOpkyZUuIjJaub4s/Kqy9VVdVSlxkbG8utX9bPD/DyaU0zZsxA69atX9snIyMjWFlZwdbWFt26dUOPHj0U/k68z3744QfY2tpCR0cHffr0kZu5LD5CsXDhQtjY2MDJyQkA8PnnnyMwMLDUI0BiwWBXyZ4+fYp9+/Zh3LhxCt+8gJKfbysIAg4fPozLly9DXV39te2fPXsW58+fR6tWrRSWLVq0CDt27EBcXFy5+18dqKmpwc/PDxEREXKHnH/55Rfk5+dj0KBBVdi799O2bdvg7OwMZ2dnDB48GBs3bsSbbmmpo6MDAHKP/XsfDRs2TG52ZMOGDRg+fHiZ1v3oo4/QpEkTuWAIAJaWlvDx8UFkZCSAf790lKXdjIwMGBgYKPwBr05mzpyJxYsX49tvv8WlS5cQFRVV4rO/i4WHh2Pw4MEwNDRE165dFWajSqKtrY2ioqJqHXAry9t8fsri2rVrOHToUIl/J95H169fx/bt27F7927s27cPCQkJcke/AODAgQO4fPkyYmJisGfPnirqadVgsKtk169fhyAIaNCgwRvrfvXVV9DT04OmpiY6dOgAQRAwceJEhXqenp7Q09ODhoYG3N3d0bdvX7lDGMWaN2+Ovn37YsaMGRUylqo0fPhwpKSk4PDhw7KyDRs2oFevXnLffov3zauvo0ePVkGPq7fiP7zAy0NFWVlZsvM9S5KdnY2ZM2dCVVUV7du3f1fdrBSff/45jh07hpSUFNy6dQvHjx+X7YuyaNCgQYkzUMOHD5d9+fj1119Rv379N57k/eTJE8ybNw+jR49WchTvzvPnz7Fy5UosWbIEQ4YMQf369fHBBx/giy++KLF+UlISTp06hX79+gGA7IvDf89NfNWVK1cQGhoKDw8P6OvrV8o4KtOePXvkfuf06dOn1GV6enqYN2+eQhvl+fy8zoABA6CnpwctLS04OzvDxcUFM2fOLHd71cmLFy8QGRmJpk2bol27dli9ejW2bt2KtLQ0WR1dXV38+OOPcHFxgauraxX29t2rvl8RRaJ4FqQsJ35OmzYNQ4cOxaNHjzBr1ix89NFH8PT0VKi3bds2NGzYEAUFBUhMTMTEiRNhbGxc4nkDwcHBaNiwIaKjo2FhYfH2A6oiDRo0gKenJzZs2IAOHTrgxo0bOHr0qMJhweJ98yrO6Mm7evUqzpw5I5t1UlNTQ79+/bBhwwaFQxSenp5QUVFBTk4OrK2tERER8V4cGnsdMzMz+Pr6IjIyEoIgwNfXF2ZmZmVeXxCEEn+efX19MXr0aBw5cqRMs4CZmZnw9fVFo0aNMHv2bKXH8a5cvnwZeXl5+Pjjj8tUPzw8XHaCOgB07doVI0aMwP/+9z94e3vL6iUmJkJPTw9SqRR5eXnw8vIq8Zzi90GHDh0QGhoqe//q0Zn/LgNQ4jnByn5+3uT7779Hx44dIZVKcf36ddkz3Eu6SON9Y2dnh9q1a8vet2nTBkVFRbh69SqsrKwAAI0bN4aGhkZVdbFKMdhVMkdHR0gkEly+fFnhqsT/MjMzg4ODAxwcHLBjxw44ODigdevWCn9sbW1t4eDgAABo2LAhbt68iW+//RZBQUHQ0tKSq1u/fn2MHDkSM2bMKPUk5vfFiBEjMH78eKxZswYbN25EnTp1FP7YvLpvivF5w/LCw8NRWFiIWrVqycoEQYC6ujrS09PlZkC3bduGRo0awcjICKamplXR3UoxfPhwjB8/HgCwZs0apda9fPmy7MT4V6mpqeHzzz/H7Nmzcfr0aezatavUNp4/f47OnTtDT08Pu3bteuMpF1VJmZ8fqVSKTZs2IS0tTe7QslQqRXh4uFywc3Z2xh9//AFVVVXY2Ni818/41NXVVfi9U5Zlr1Lm81MWVlZWsu06Ozvj+fPnGDBgAIKDg8vUn6pgYGBQ4hXiz549g4GBQanrFX/RevULV0mnPtUUPBRbyUxMTODj44M1a9YgOztbYXlJl6oDgLGxMSZMmIAvv/zyjec+qaqqorCwUHblz38FBgbi2rVr7/03tb59+0JVVRVRUVGIjIzEsGHDKuQS+JqksLAQmzZtwnfffYeEhATZ6++//0adOnWwefNmufq2traoX7++qEId8PLwc35+PvLz8+Hj41Pm9Q4ePIjExER89tlnJS4fPnw4YmNj8cknnyicIF8sMzMT3t7e0NDQwB9//KHwZay6cXR0hLa29msP1Rf766+/8Pz5c8THx8t9vn755Rf89ttvePLkiaxu8e2a7O3t3+tQV5HK8vkpr+KLOV69Iry6adCgAc6ePatQHhcXB2dnZ9n727dv4/79+7L3J0+ehIqKiuwiiZqOM3bvwNq1a+Hp6QkPDw/MnTsXbm5uKCwsRExMDEJDQ3H58uUS1xs3bhwWL16MHTt2oHfv3rLyJ0+eIC0tDYWFhUhMTMTKlSvRoUOHUr/RWFpaIiAgAEuXLq2U8b0renp66NevH77++mtkZGTI7jlWHo8ePVK4h1HxFWtitmfPHqSnp2PEiBEwNDSUW9a7d2+Eh4fLZrLETFVVVfZzV/wH77/y8vKQlpYGqVSKBw8eYN++fVi4cCG6detW4jmtwMsZ9MePH8suNPmv58+fw9vbGzk5Ofj555+RmZmJzMxMAIC5uXmpfalKWlpa+OqrrzB9+nRoaGigbdu2ePToES5evKhwRXp4eDh8fX3RpEkTuXIXFxdMnjwZP//8MyZNmlSm7d6+fRtPnz7F7du3IZVKZT+vDg4O0NPTq5CxvQvFn6NXqamplXj4/02fH+DlPVHT0tJw/fp1AC8Paevr68POzk7uEO+zZ8+QlpaGoqIiJCUlYe7cuXByclI4VaU68ff3R0hICMaNG4dRo0ZBW1sbMTExCA8Px08//SSrp6WlhSFDhmDZsmXIzMzExIkT0bdv3zf+/i7+PBWHwqtXrwIQ3+9+zti9A/b29jh//jw6dOiAqVOnwtXVFZ06dcKBAwcUzr14lbm5OT7//HMEBQXJnXjcsWNHWFtbo27duhg1ahS6du2Kbdu2vbYP06ZNe69+GZZmxIgRSE9PR8eOHRXuO6aMqKgoNGvWTO61bt26Cuxp9RQeHo6OHTsqhDoA+Oyzz5CQkCCqWyK8joGBwWsP7+zbt0/2c9a5c2ccOnQIq1atwu+///7aAGZqalrq4ctz587h9OnTSExMhIODA6ytrWWvV291Ud18++23mDp1KgIDA9GwYUP069cPDx8+lKvz4MED/PnnnyXOZkokEvTq1Uup00ECAwPRrFkzzJ49G1lZWbKf05JmdKqz4s/Rq68PPvig1Pqv+/wAwLp169CsWTOMHDkSANCuXTs0a9YMf/zxh1y9YcOGwdraGrVr18aAAQPg4uKCvXv3Vuurr+vWrYujR4/ixo0b8Pb2hru7OyIiIhARESF3QYqDgwN69eqFrl27wtvbG66urli7du0b2//jjz/QrFkz+Pr6AgD69+8vyt/9EuFNx/mIiIiI6L3AGTsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiIikWCwIyIiIhIJBjsiIiVIJBL89ttvVd0NIqISMdgR0Xtp6NChkEgkCq/OnTvL6owePRr169eHtrY2zM3N8cknn+DKlStvbLdnz56lLk9NTUWXLl0qahhERBWq+j40jojoDTp37oyNGzfKlWlqasr+3aJFCwwaNAh2dnZ4+vQpgoKC4O3tjeTk5Nc+7/V1xPSwcCISH87YEdF7S1NTE1ZWVnIvY2Nj2fJRo0ahXbt2qFu3Lpo3b47g4GDcuXMHKSkp5d7mfw/FnjhxAk2bNoWWlhZatmyJ3377DRKJBAkJCQAAqVSKESNGwN7eHtra2nB2dsbKlSvl2jx8+DA8PDygq6sLIyMjtG3bFrdu3ZIt3717N1q0aAEtLS3Uq1cPc+bMQWFhYbnHQETixRk7IqoRsrOzsXHjRtjb28PW1rZC2nz+/Dm6d++Orl27IioqCrdu3cLkyZPl6hQVFaF27drYvn07zMzMcOLECYwaNQrW1tbo27cvCgsL0bNnT4wcORJbtmxBfn4+zpw5A4lEAgDYv38/Bg8ejFWrVuHDDz/EjRs3MGrUKADA7NmzK2QcRCQeDHZE9N7as2cP9PT05Mq++uorfPvtt7L3a9euxfTp05GdnY0GDRogJiYGGhoaFbL9zZs3QyKRYP369dDS0kKjRo1w7949jBw5UlZHXV0dc+bMkb23t7fHiRMnsH37dvTt2xeZmZnIyMhAt27dUL9+fQBAw4YNZfXnz5+PGTNmYMiQIQCAevXqYd68eZg+fTqDHREpYLAjovdWhw4dEBoaKldmYmIi937QoEHo1KkTUlNTsWzZMvTt2xfHjx+HlpbWW2//6tWrcHNzk2vLw8NDod66devw448/4tatW8jNzUV+fj6aNm0q6+/QoUPh4+ODTp06oWPHjujbty+sra0BAOfOnUNcXBzmz58va08qleLFixfIycmBjo7OW4+DiMSDwY6I3lu6urpwcHB4bR1DQ0MYGhrC0dERrVu3hrGxMXbt2oUBAwa89fYFQZAdMn217FXbt2/HlClT8N1336FNmzbQ19fH0qVLcfr0aVmdjRs3YuLEidi3bx+2bduGb775BjExMWjdujWKioowZ84c9OrVS2H7FRFOiUhcGOyIqEYRBAF5eXkV0laDBg2wefNm5OXlya7GPXv2rFydo0ePwtPTE/7+/rKyGzduKLTVrFkzNGvWDDNnzkSbNm0QFRWF1q1bo3nz5rh69eobAywREcBgR0Tvsby8PKSlpcmVqampwczMDDdv3sS2bdvg7e0Nc3Nz3Lt3D4sXL4a2tja6du362nYzMjJkV7UWMzExgZ2dnVzZwIEDMWvWLIwaNQozZszA7du3sWzZMgCQzeQ5ODhg06ZN2L9/P+zt7fHTTz8hLi4O9vb2AIDk5GSEhYWhR48esLGxwdWrV3Ht2jX4+fkBAAIDA9GtWzfY2tqiT58+UFFRwYULF5CYmIjg4OBy7zsiEimBiOg9NGTIEAGAwsvZ2VkQBEG4d++e0KVLF8HCwkJQV1cXateuLQwcOFC4cuVKudodMmSIIAiCAEDYtWuXrP7x48cFNzc3QUNDQ2jRooUQFRUlAJBt58WLF8LQoUMFQ0NDwcjISBg7dqwwY8YMoUmTJoIgCEJaWprQs2dPwdraWtDQ0BDq1KkjBAYGClKpVLaNffv2CZ6enoK2trZgYGAgeHh4CGFhYRW3M4lINCSC8J8TQoiIqNw2b96MYcOGISMjA9ra2lXdHSKqYXgolojoLWzatAn16tVDrVq18Pfff+Orr75C3759GeqIqEow2BERvYW0tDQEBgYiLS0N1tbW6NOnj9ytSYiI3iUeiiUiIiISCT4rloiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgk1Kq6A0TVXVFREfLz86u6G0T0HlBXV4eqqmpVd4NqMAY7otfIz89HcnIyioqKqrorRPSeMDIygpWVFSQSSVV3hWogBjuiUgiCgNTUVKiqqsLW1hYqKjxzgYhKJwgCcnJy8PDhQwCAtbV1FfeIaiIGO6JSFBYWIicnBzY2NtDR0anq7hDRe0BbWxsA8PDhQ1hYWPCwLL1znIIgKoVUKgUAaGhoVHFPiOh9UvxFsKCgoIp7QjURgx3RG/A8GSJSBn9nUFVisCMiIiISCQY7IiIiIpHgxRNESgreL7zT7X3j834e1jl8+DA6dOiA9PR0GBkZVeq2vv32Wzx48ABhYWHlbuPLL79Efn4+Vq1aVYE9U0JU93e7vYG73+32ShEREYHJkyfj2bNnlb6tzz//HA0bNsTXX39d7jZ69+4NT09PBAQEVGDPiCoOZ+yIRGbo0KHo2bPnO92ml5cXJk+eLFfm6emJ1NRUGBoaVuq2Hzx4gJUrV8r9sd68eTNsbW1hYmKCadOmydVPSUmBk5MTMjMz5cqnT5+OjRs3Ijk5uVL7+747ceIEVFVV0blzZ6XXrVu3LlasWCFX1q9fP1y7dq2Cele6Cxcu4M8//8SECRNkZcuWLYOlpSUsLS3x/fffy9U/ffo0WrRoIbuIqlhgYCDmz5+v8Pkhqi4Y7IioUmhoaLyTm7SGh4ejTZs2qFu3LgDg8ePH+OKLL7Bs2TLs378fkZGR+PPPP2X1x44di0WLFsHAwECuHQsLC3h7e2PdunWV2t/33YYNGzBhwgQcO3YMt2/ffuv2tLW1YWFhUQE9e72QkBD06dMH+vr6AIDExEQEBgZiy5YtiIqKwtdff41//vkHwMurWceMGYN169Yp3K7Ezc0NdevWxebNmyu9z0TlwWBHJHJeXl6YOHEipk+fDhMTE1hZWSEoKEiuzvLly9G4cWPo6urC1tYW/v7+yMrKkqtz/PhxtG/fHjo6OjA2NoaPjw/S09MxdOhQxMbGYuXKlZBIJJBIJEhJScHhw4chkUjw7NkzZGRkQFtbG/v27ZNrc+fOndDV1ZVt6969e+jXrx+MjY1hamqKTz75BCkpKa8d39atW9GjRw/Z+5s3b8LQ0BD9+vWDu7s7OnTogEuXLgEAoqKioKGhgV69epXYVo8ePbBly5ay7NYaKTs7G9u3b8fYsWPRrVs3REREKNT5448/0LJlS2hpacHMzEy2r728vHDr1i1MmTJF9jkBXh6KLT5Uf/XqVUgkEly5ckWuzeXLl6Nu3boQhJenQVy6dAldu3aFnp4eLC0t8fnnn+Px48el9ruoqAi//PKL3Ofk8uXLcHNzw0cffYSPP/4Ybm5uuHz5MgBg6dKlaNeuHdzd3Utsj58Tqs4Y7IhqgMjISOjq6uL06dNYsmQJ5s6di5iYGNlyFRUVrFq1Cv/88w8iIyNx8OBBTJ8+XbY8ISEBH3/8MVxcXHDy5EkcO3YM3bt3h1QqxcqVK9GmTRuMHDkSqampSE1Nha2trdz2DQ0N4evrqzDLERUVhU8++QR6enrIyclBhw4doKenhyNHjuDYsWPQ09ND586dS31Wb3p6Ov755x+0bNlSVubo6IicnBzEx8fj6dOniIuLg5ubG54+fYrAwECEhISUup88PDxw584d3Lp1S6n9W1Ns27YNzs7OcHZ2xuDBg7Fx40ZZ2AKAP//8E7169YKvry/i4+Nx4MAB2f+bnTt3onbt2pg7d67sc/Jfzs7OaNGiRYmfk4EDB0IikSA1NRXt27dH06ZNcfbsWezbtw8PHjxA3759S+33hQsX8OzZM7nPSePGjXHt2jXcvn0bt27dwrVr1+Dq6orr168jIiICwcHBpbbn4eGBM2fOIC8vr8z7juhd4cUTRDWAm5sbZs+eDeBl8AkJCcGBAwfQqVMnAJA7P87e3h7z5s3D2LFjsXbtWgDAkiVL0LJlS9l7AHBxcZH9W0NDAzo6OrCysiq1D4MGDYKfnx9ycnKgo6ODzMxM/Pnnn9ixYweAlzNvKioq+PHHH2WzORs3boSRkREOHz4Mb29vhTZv3boFQRBgY2MjKzM2NkZkZCT8/PyQm5sLPz8/+Pj4YPjw4ZgwYQKSk5PRo0cPFBQUICgoCL1795atW6tWLQAvz8OrU6dO2XZuDRIeHo7BgwcDADp37oysrCwcOHAAHTt2BADMnz8f/fv3x5w5c2TrNGnSBABgYmICVVVV6Ovrv/FzEhISgnnz5gEArl27hnPnzmHTpk0AgNDQUDRv3hwLFiyQrbNhwwbY2tri2rVrcHJyUmgzJSUFqqqqcod8GzZsiAULFsh+BhYuXIiGDRuiY8eOWLJkCfbv34+goCCoq6tj5cqVaNeunWzdWrVqIS8vD2lpafycULXDYEdUA7i5ucm9t7a2lj3PEgAOHTqEBQsW4NKlS8jMzERhYSFevHiB7Oxs6OrqIiEhAX369HmrPvj6+kJNTQ1//PEH+vfvjx07dkBfX18W2M6dO4fr16/LzoEq9uLFC9y4caPENnNzcwEAWlpacuWffvopPv30U9n7w4cPIzExESEhIXBwcMCWLVtgZWUFDw8PtGvXTvYHv/hxUDk5OW81VjG6evUqzpw5g507dwIA1NTU0K9fP2zYsEEW7BISEjBy5Mi32k7//v0xbdo0nDp1Cq1bt8bmzZvRtGlTNGrUCMDLz8mhQ4egp6ensO6NGzdKDHa5ubnQ1NRUON9zzJgxGDNmjOx9REQE9PX10aZNGzg7OyMuLg53795F//79kZycDE1NTQD8nFD1xmBHVAOoq6vLvZdIJCgqKgLwctara9euGDNmDObNmwcTExMcO3YMI0aMkD0SqfgP2dvQ0NBA7969ERUVhf79+yMqKgr9+vWDmtrLX0NFRUUlHoYDAHNz8xLbNDMzA/DykGxpdfLy8uDv74+ff/4Z169fR2FhIdq3bw8AcHJywunTp9G9+8tbjTx9+vS126vJwsPDUVhYKJvVBF4+9F5dXR3p6ekwNjaukM+JtbU1OnTogKioKLRu3RpbtmzB6NGjZcuLiorQvXt3LF68uMR1S2JmZoacnBzk5+eX+ojAx48fY+7cuThy5AhOnz4NJycnODo6wtHREQUFBbh27RoaN24MgJ8Tqt54jh1RDXf27FkUFhbiu+++Q+vWreHk5IT79+/L1XFzc8OBAwdKbUNDQ0PhthAlGTRoEPbt24eLFy/i0KFDGDRokGxZ8+bNkZSUBAsLCzg4OMi9SrtlSv369WFgYCC7OKIk8+bNQ5cuXdC8eXNIpVIUFhbKlhUUFMj1+59//oG6urrcYWYCCgsLsWnTJnz33XdISEiQvf7++2/UqVNHFsYr8nOybds2nDx5Ejdu3ED//v1ly5o3b46LFy+ibt26Cp8TXV3dEttr2rQpALz2czJ58mRMmTIFtWvXhlQqlXvOa2FhocLnpHbt2rIvFkTVikBEJcrNzRUuXbok5ObmVnVXlDJkyBDhk08+kb1v3769MGnSJLk6n3zyiTBkyBBBEAQhPj5eACCsWLFCuHHjhrBp0yahVq1aAgAhPT1dEARBuHr1qqChoSGMHTtW+Pvvv4XLly8La9euFR49eiQIgiCMHDlScHd3F5KTk4VHjx4JUqlUOHTokFwbgiAIRUVFQu3atYUmTZoI9evXl+tTdna24OjoKHh5eQlHjhwRbt68KRw+fFiYOHGicOfOnVLH26tXL2Hq1KklLvvnn38EBwcHISsrSxAEQcjJyRFMTU2FH3/8UdizZ4+gqakp3L17V1Z/9uzZwkcfffS63Vsj7dq1S9DQ0BCePXumsOzrr78WmjZtKgiCIBw6dEhQUVERAgMDhUuXLgkXLlwQFi9eLKvbqVMnoUePHsLdu3dln52NGzcKhoaGcm1mZGQIWlpaQpMmTYSPP/5Ybtm9e/cEc3NzoXfv3sLp06eFGzduCPv37xeGDRsmFBYWljqG5s2bC6tXry5xWXR0tODh4SFIpVJBEATh7t27gpaWlvDXX38JP/zwg2Bqairk5OTI6g8ZMkQYPnx4qdt6X393kDgw2BGV4n395axssBMEQVi+fLlgbW0taGtrCz4+PsKmTZsUQtnhw4cFT09PQVNTUzAyMhJ8fHzkgl/r1q0FbW1tAYCQnJxcYrATBEGYNm2aAEAIDAxU6Htqaqrg5+cnmJmZCZqamkK9evWEkSNHChkZGaWOd9++fUKtWrVkf5SLFRUVCZ6ensLu3bvlynfv3i3Y2dkJlpaWwvr16+WWOTk5CVu2bCl1WzVVt27dhK5du5a47Ny5cwIA4dy5c4IgCMKOHTuEpk2bChoaGoKZmZnQq1cvWd2TJ08Kbm5ugqamplA8r1BSsBMEQejTp48AQNiwYYPCsmvXrgmffvqpYGRkJGhrawsNGjQQJk+eLBQVFZU6hnXr1gmtW7dWKM/JyRGcnJyE+Ph4ufL169cLlpaWgp2dnbBnzx5ZeW5urmBgYCCcPHmy1G29r787SBwkgiC82+cjEb0nXrx4geTkZNjb2yucnE/VhyAIaN26NSZPnowBAwaUu50///wT06ZNw4ULF2Tn/ZF4vHjxAs7Ozti6dSvatGlT7nbWrFmD33//HdHR0a/dFn93UFXhOXZE9F6TSCQICwuTO3euPLKzs7Fx40aGOpHS0tLCpk2bXnsj47JQV1fH6tWrK6hXRBWPM3ZEpeC3biIqD/7uoKrEGTsiIiIikWCwI3oDTmoTkTL4O4OqEoMdUSlUVVUBoNTnlBIRlaT4iRT/vTE40bvAs4SJSqGmpgYdHR08evQI6urqUFHh9yAiKp0gCMjJycHDhw9hZGQk+3JI9C7x4gmi18jPz0dycrLs8VtERG9iZGQEKysrhWfTEr0LDHZEb1BUVMTDsURUJurq6pypoyrFYEdEREQkEjxpiIiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgkGOyIiIiIRILBjoiIiEgk/g+vqY0Ikuy71QAAAABJRU5ErkJggg==",
+      "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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",
+      "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": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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": 16,
+   "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>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>e3_ligase</th>\n",
+       "      <td>0.531</td>\n",
+       "      <td>0.806</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>0.478</td>\n",
+       "      <td>0.565</td>\n",
+       "    </tr>\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>0.542</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>0.843</td>\n",
+       "      <td>0.587</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>0.914</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",
+       "e3_ligase           0.531    0.806        0.894           0.824     0.478   \n",
+       "random              0.515    0.846        0.905           0.535     0.714   \n",
+       "tanimoto            0.542    0.768        0.843           0.587     0.797   \n",
+       "uniprot             0.580    0.628        0.654           0.541     0.584   \n",
+       "\n",
+       "            test_roc_auc  \n",
+       "group_type                \n",
+       "e3_ligase          0.565  \n",
+       "random             0.779  \n",
+       "tanimoto           0.914  \n",
+       "uniprot            0.592  "
+      ]
+     },
+     "execution_count": 16,
+     "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",
+    "# active_and = pd.read_csv('../reports/cv_report_hparam_search_5-splits_active-and.csv')\n",
+    "# active_or = pd.read_csv('../reports/cv_report_hparam_search_5-splits_active-or.csv')\n",
+    "active_and = pd.read_csv('../reports/cv_report_hparam_search_5-splits_active-and_fp224.csv')\n",
+    "active_or = pd.read_csv('../reports/cv_report_hparam_search_5-splits_active-or_fp224.csv')\n",
+    "# active_and = pd.read_csv('../reports/cv_report_hparam_search_5-splits_active-and_fp512.csv')\n",
+    "# active_or = pd.read_csv('../reports/cv_report_hparam_search_5-splits_active-or_fp512.csv')\n",
+    "active_and['active'] = 'and'\n",
+    "active_or['active'] = 'or'\n",
+    "report = pd.concat([active_and, active_or])\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 = 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",
+    "\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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "857"
+      ]
+     },
+     "execution_count": 17,
+     "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": 18,
+   "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": 18,
+     "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": 19,
+   "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": 20,
+   "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": 21,
+   "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": 21,
+     "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": 22,
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "    # num_data = len(protac_df[protac_df['Active'].notna()])\n",
+    "    # plt.title(f'Activity prediction on {n_splits}-fold cross-validation and separate test set')\n",
+    "    if title is not None:\n",
+    "        plt.title(title)\n",
+    "    # else:\n",
+    "    #     plt.title(f'Activity prediction performance on {n_splits}-fold cross-validation')\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 external on the left side of the plot\n",
+    "    # plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\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",
+    "        if i % 2 == 0:\n",
+    "            value = '{:.1f}%'.format(100 * p.get_height())\n",
+    "        else:\n",
+    "            value = '{:.2f}'.format(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",
+    "    # Rotate x-axis labels to 90 degrees\n",
+    "    # plt.xticks(rotation=90)\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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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 &  0.554 \\\\\n",
+      "     Dummy model &  Test ROC AUC &  0.500 \\\\\n",
+      "     Dummy model &  Val Accuracy &  0.519 \\\\\n",
+      "     Dummy model &   Val ROC AUC &  0.500 \\\\\n",
+      "Similarity split & Test Accuracy &  0.797 \\\\\n",
+      "Similarity split &  Test ROC AUC &  0.914 \\\\\n",
+      "Similarity split &  Val Accuracy &  0.768 \\\\\n",
+      "Similarity split &   Val ROC AUC &  0.843 \\\\\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>0.554</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>0.519</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>0.797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Similarity split</td>\n",
+       "      <td>Test ROC AUC</td>\n",
+       "      <td>0.914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Similarity split</td>\n",
+       "      <td>Val Accuracy</td>\n",
+       "      <td>0.768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Similarity split</td>\n",
+       "      <td>Val ROC AUC</td>\n",
+       "      <td>0.843</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  0.554\n",
+       "1        Dummy model   Test ROC AUC  0.500\n",
+       "2        Dummy model   Val Accuracy  0.519\n",
+       "3        Dummy model    Val ROC AUC  0.500\n",
+       "4   Similarity split  Test Accuracy  0.797\n",
+       "5   Similarity split   Test ROC AUC  0.914\n",
+       "6   Similarity split   Val Accuracy  0.768\n",
+       "7   Similarity split    Val ROC AUC  0.843\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"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# tmp = report[report['active'] == 'and']\n",
+    "# tmp = tmp[tmp['disabled_embeddings'].isna()]\n",
+    "# plot_report(tmp, title='Active and')\n",
+    "\n",
+    "# tmp = report[report['active'] == 'or']\n",
+    "# tmp = tmp[tmp['disabled_embeddings'].isna()]\n",
+    "# plot_report(tmp)\n",
+    "\n",
+    "tmp = report[report['disabled_embeddings'].isna()]\n",
+    "plot_report(tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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": 24,
+     "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": 46,
+   "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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 &  0.797 \\\\\n",
+      "1  &                             All embeddings enabled &  Validation Accuracy &  0.768 \\\\\n",
+      "2  &                            Disabled E3 information &        Test Accuracy &  0.775 \\\\\n",
+      "3  &                            Disabled E3 information &  Validation Accuracy &  0.718 \\\\\n",
+      "4  &                          Disabled cell information &        Test Accuracy &  0.790 \\\\\n",
+      "5  &                          Disabled cell information &  Validation Accuracy &  0.677 \\\\\n",
+      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.638 \\\\\n",
+      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.592 \\\\\n",
+      "8  &                      Disabled compound information &        Test Accuracy &  0.819 \\\\\n",
+      "9  &                      Disabled compound information &  Validation Accuracy &  0.745 \\\\\n",
+      "10 &                        Disabled target information &        Test Accuracy &  0.756 \\\\\n",
+      "11 &                        Disabled target information &  Validation Accuracy &  0.672 \\\\\n",
+      "12 &                                        Dummy model &        Test Accuracy &  0.587 \\\\\n",
+      "13 &                                        Dummy model &  Validation Accuracy &  0.542 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\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"
+     ]
+    },
+    {
+     "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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NG3PvP3bsWHz605+O48ePn8k5AaAiewmAogpF0L59+6K3tzdWrVoVjY2NMWPGjOju7o5NmzblPufWW2+Nq6666owPCgCnspcAGI36Ijfv3r07pk6dGtOmTTt5raWlJQ4cOBBHjhyJKVOmlN1/7733xr59+2Lt2rWxfv36UR2wVCpFqVQa1XPPV8PzMJdy5lKZueQzm8rG0zxqsZci7KZTeS/lM5vKzCWf2VQ21vMoFEFHjx6NxsbGsmvDjwcGBsqWzZ49e+KOO+6Iu+66K+rq6kZ9wJ07d476uee7vr6+Wh/hnGQulZlLPrMZv2qxlyLspjzeS/nMpjJzyWc2Z1ehCGpqaopjx46VXRt+PGnSpJPXfvvb38ZNN90UX/jCF+JNb3rTGR1w7ty50dDQcEb/jvNNqVSKvr6+aG1tPeNFfj4xl8rMJZ/ZVHbixIlx84v8WuylCLvpVN5L+cymMnPJZzaVjfVuKhRBs2bNisOHD8fBgwejubk5In73X9amT58ekydPPnlfX19f7N27N1avXh2rV68+ef0Tn/hELFu2LG655ZYRv2ZdXZ0vgBxmU5m5VGYu+cym3HiaRS32UoSvmTzmks9sKjOXfGZTbqxnUSiCZs6cGfPnz49169bFmjVr4n//939j/fr1sXz58rL7Ojo64plnnim7dskll8Q///M/R2dn55mfGgDCXgJgdAp/i+yenp4YHByMrq6uuP7662PhwoXR3d0dERHt7e1x3333jfkhASCPvQRAUYV+Jygiorm5OXp6eir+2I4dO3Kft2vXrqIvBQBvyF4CoKjCvxMEAAAwnokgAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEhK4Qg6dOhQdHd3R0dHR3R2dsbatWtjcHCw4r133XVXXH311dHe3h5XX311bNq06YwPDACvZS8BUFThCFq5cmU0NTXF1q1bY/PmzbFt27bYuHHjaff94Ac/iK997Wvxla98JZ566qm4/fbb4+tf/3o8/PDDY3FuAIgIewmA4gpF0L59+6K3tzdWrVoVjY2NMWPGjOju7q74X9JefPHF+OhHPxptbW0xYcKEaG9vj87OznjiiSfG7PAApM1eAmA06ovcvHv37pg6dWpMmzbt5LWWlpY4cOBAHDlyJKZMmXLy+oc//OGy5x46dCieeOKJ+PznP1/ogKVSKUqlUqHnnO+G52Eu5cylMnPJZzaVjad51GIvRdhNp/Jeymc2lZlLPrOpbKznUSiCjh49Go2NjWXXhh8PDAyULZvXeumll+LjH/94zJs3L6677rpCB9y5c2eh+1PS19dX6yOck8ylMnPJZzbjVy32UoTdlMd7KZ/ZVGYu+czm7CoUQU1NTXHs2LGya8OPJ02aVPE5Tz/9dNx4443R0dERf/M3fxP19YVeMubOnRsNDQ2FnnO+K5VK0dfXF62trVFXV1fr45wzzKUyc8lnNpWdOHFi3PwivxZ7KcJuOpX3Uj6zqcxc8plNZWO9mwp98s+aNSsOHz4cBw8ejObm5oiI2LNnT0yfPj0mT5582v2bN2+OL3/5y/GpT30q/uqv/mpUB6yrq/MFkMNsKjOXyswln9mUG0+zqMVeivA1k8dc8plNZeaSz2zKjfUsCn1jhJkzZ8b8+fNj3bp18corr8T+/ftj/fr1sXz58tPuffjhh+OWW26Jb3zjG2e0aAAgj70EwGgU/hbZPT09MTg4GF1dXXH99dfHwoULo7u7OyIi2tvb47777ouIiH/8x3+MUqkUn/rUp6K9vf3kP1/60pfG9mcAQNLsJQCKKvwHoZubm6Onp6fij+3YsePk/77//vtHfyoAGCF7CYCiCv9OEAAAwHgmggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKYUj6NChQ9Hd3R0dHR3R2dkZa9eujcHBwYr3Pv7447FkyZJoa2uLa665Jh599NEzPjAAvJa9BEBRhSNo5cqV0dTUFFu3bo3NmzfHtm3bYuPGjafdt3fv3lixYkXceOON8eSTT8aKFSti5cqV8eKLL47FuQEgIuwlAIorFEH79u2L3t7eWLVqVTQ2NsaMGTOiu7s7Nm3adNq999xzT3R0dMRVV10V9fX1sXjx4liwYEF873vfG7PDA5A2ewmA0agvcvPu3btj6tSpMW3atJPXWlpa4sCBA3HkyJGYMmXKyev9/f0xe/bssudffPHF8dxzz43otbIsi4iIEydOFDliEkqlUkT8bjZ1dXU1Ps25w1wqM5d8ZlPZ8Ofu8OfwuayaeynCbsrjvZTPbCozl3xmU9lY76ZCEXT06NFobGwsuzb8eGBgoGzZVLr3wgsvjIGBgRG91tDQUERE7Nq1q8gRk7Jz585aH+GcZC6VmUs+s6ls+HP4XFbNvRRhN70R76V8ZlOZueQzm8rGajcViqCmpqY4duxY2bXhx5MmTSq73tjYGMePHy+7dvz48dPuyz1YfX20trbGxIkTY8KECUWOCcAZyLIshoaGor6+0IqoiWrupQi7CaBWxno3Ffq3zJo1Kw4fPhwHDx6M5ubmiIjYs2dPTJ8+PSZPnlx27+zZs+PZZ58tu9bf3x/z5s0b0WtNnDgxGhoaihwPgMRUcy9F2E0A54tC3xhh5syZMX/+/Fi3bl288sorsX///li/fn0sX778tHuXLl0avb298eCDD8bg4GA8+OCD0dvbG8uWLRuzwwOQNnsJgNGYkBX820UHDx6MNWvWxPbt22PixInxvve9Lz7zmc9EXV1dtLe3x6233hpLly6NiIitW7fG3//938cLL7wQb37zm2PVqlWxaNGis/ITASBN9hIARRWOIAAAgPGs8P9ZKgAAwHgmggAAgKSIIAAAICkiCAAASEpNI+jQoUPR3d0dHR0d0dnZGWvXro3BwcGK9z7++OOxZMmSaGtri2uuuSYeffTRKp+2eorM5a677oqrr7462tvb4+qrr45NmzZV+bTVVWQ2w372s5/FZZddFtu3b6/SKauvyFx6e3vjgx/8YLS3t8eiRYtiw4YNVT5tdRWZzXe+85248sor4/LLL48lS5bEww8/XOXTVt+vf/3reO973/u674+UPn8j7KY8dlM+u6kyuymf3fT6qrKbshr68z//8+zTn/50NjAwkL3wwgvZtddem/3Lv/zLaff9/Oc/z1pbW7NHHnkke/XVV7MHHnggu/TSS7Nf/epXNTj12TfSuTzyyCNZR0dHtmPHjmxoaCh76qmnso6OjmzLli01OHV1jHQ2wwYGBrLrrrsumz17dvbf//3fVTxpdY10Lv39/dlll12W3X333dnQ0FD205/+NHvHO96RPfTQQzU4dXWMdDaPPfZY9q53vSvbs2dPlmVZtmXLlmzOnDnZ/v37q33kqnnyySezq6666nXfH6l9/maZ3ZTHbspnN1VmN+Wzm/JVazfVLIL27t2bzZ49u+ywDzzwQHbFFVecdu/Xvva17C//8i/Lrv31X/919g//8A9n/ZzVVmQu3/3ud7MNGzaUXfvkJz+Z3XbbbWf9nLVQZDbDPvvZz2Zf//rXz+tFU2Qua9asyW6++eaya88//3z2P//zP2f9nLVQZDbf/va3s3e+851Zf39/NjQ0lD3yyCNZa2tr9stf/rKaR66au+++O7viiiuyBx544HXfHyl9/maZ3ZTHbspnN1VmN+Wzm/JVczfV7I/D7d69O6ZOnRrTpk07ea2lpSUOHDgQR44cKbu3v78/Zs+eXXbt4osvjueee64qZ62mInP58Ic/HB/72MdOPj506FA88cQTMW/evKqdt5qKzCYi4t577419+/bFDTfcUM1jVl2RuTzzzDPxlre8JW6++ebo7OyMa665Jnp7e+Oiiy6q9rGroshsrr322mhubo7FixfH29/+9rjxxhvj9ttvj+nTp1f72FXx7ne/Ox555JFYvHjx696X0udvhN2Ux27KZzdVZjfls5vyVXM31SyCjh49Go2NjWXXhh8PDAy84b0XXnjhafedD4rM5bVeeuml+OhHPxrz5s2L66677qyesVaKzGbPnj1xxx13xFe/+tWoq6ur2hlrochcXn755bjzzjtj6dKl8aMf/SjWrFkTX/nKV2LLli1VO281FZnNq6++GnPmzIl///d/j6effjrWrFkTq1evjl27dlXtvNV00UUXRX19/Rvel9Lnb4TdlMduymc3VWY35bOb8lVzN9UsgpqamuLYsWNl14YfT5o0qex6Y2NjHD9+vOza8ePHT7vvfFBkLsOefvrpWL58ebz1rW+Nf/qnfxrRF894NNLZ/Pa3v42bbropvvCFL8Sb3vSmqp6xFop8zTQ0NERXV1dcccUVUV9fHwsWLIhly5bFQw89VLXzVlOR2dx2220xa9asuPTSS6OhoSE+8IEPRFtbW9xzzz1VO++5KKXP3wi7KY/dlM9uqsxuymc3nbmx+PytWQTNmjUrDh8+HAcPHjx5bc+ePTF9+vSYPHly2b2zZ8+O3bt3l13r7++PWbNmVeWs1VRkLhERmzdvjo985CPxF3/xF/HVr341GhoaqnncqhrpbPr6+mLv3r2xevXq6OjoiI6OjoiI+MQnPhG33HJLtY991hX5mmlpaYkTJ06UXSuVSpFlWVXOWm1FZnPgwIHTZlNfXx8XXHBBVc56rkrp8zfCbspjN+Wzmyqzm/LZTWduTD5/z+DvLp2xP/3TP81uuumm7De/+c3J74zR09Nz2n39/f1Za2tr9sADD5z8DhCtra3Z888/X4NTn30jncuWLVuyt7/97dkPf/jDGpyyNkY6m1Odz3/5NMtGPpf/+q//yubOnZvde++92dDQUNbb25u1tbVlP/jBD2pw6uoY6WzuuOOOrLOzM/vJT36SlUql7KGHHspaW1uznTt31uDU1fV674/UPn+zzG7KYzfls5sqs5vy2U1v7GzvpppG0EsvvZStWLEie8c73pG9853vzG6//fZscHAwy7Isa2try77//e+fvPeHP/xhtnTp0qytrS279tprs8cee6xWxz7rRjqX6667LpszZ07W1tZW9s8Xv/jFWh7/rCryNfNa5/uiKTKXxx57LPvjP/7jrL29Pevq6sruuuuuWh27KkY6m1dffTXr6enJ3vOe92SXX3559v73vz+ZX8Sd+v5I+fM3y+ymPHZTPrupMrspn930xs72bpqQZefp7zUCAABUULO/EwQAAFALIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICk/B+B08IzA7GIfAAAAABJRU5ErkJggg==",
+      "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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",
+      "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",
+   "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}