tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIBO tutorial\n",
    "\n",
    "\n",
    "In this tuturial we will show how to prepare your own datasets containing a list of procured reactions and inventory prices to perform a cost-informed Bayesian optimization.\n",
    "\n",
    "First, we will inspect the processed direct arylation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [14:49:17] Enabling RDKit 2019.09.3 jupyter extensions\n",
      "[14:49:17] Enabling RDKit 2019.09.3 jupyter extensions\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import copy as cp\n",
    "import pandas as pd\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "\n",
    "from cibo.data.datasets import directaryl, Evaluation_data\n",
    "from cibo.BO import update_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the dataset contains quite a lot of information. In particular the ligand price in [$/gram] in the `Ligand_Cost_fixed` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ligand_inchi</th>\n",
       "      <th>Base_inchi</th>\n",
       "      <th>Solvent_inchi</th>\n",
       "      <th>Product_inchi</th>\n",
       "      <th>Electrophile_inchi</th>\n",
       "      <th>Nucleophile_inchi</th>\n",
       "      <th>Precatalyst_inchi</th>\n",
       "      <th>Base</th>\n",
       "      <th>Electrophile</th>\n",
       "      <th>Electrophile_PCI_Name</th>\n",
       "      <th>...</th>\n",
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       "      <th>Precatalyst_mg</th>\n",
       "      <th>Ligand_mg</th>\n",
       "      <th>Base_mg</th>\n",
       "      <th>Total_mg</th>\n",
       "      <th>PMI</th>\n",
       "      <th>Ligand_Cost_fixed</th>\n",
       "      <th>Base_SMILES</th>\n",
       "      <th>Ligand_SMILES</th>\n",
       "      <th>Solvent_SMILES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>InChI=1S/C35H53O2P/c1-23(2)26-21-29(24(3)4)33(...</td>\n",
       "      <td>InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...</td>\n",
       "      <td>InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>KOAc</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.402577</td>\n",
       "      <td>4.41630</td>\n",
       "      <td>151.495920</td>\n",
       "      <td>917.668323</td>\n",
       "      <td>297.0</td>\n",
       "      <td>CC(=O)[O-].[K+]</td>\n",
       "      <td>COc1ccc(OC)c(P(C2CCCCC2)C2CCCCC2)c1-c1c(C(C)C)...</td>\n",
       "      <td>CC(=O)N(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>InChI=1S/C14H23P/c1-13(2,3)15(14(4,5)6)12-10-8...</td>\n",
       "      <td>InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...</td>\n",
       "      <td>InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>KOAc</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.166733</td>\n",
       "      <td>4.41630</td>\n",
       "      <td>151.094987</td>\n",
       "      <td>inf</td>\n",
       "      <td>102.0</td>\n",
       "      <td>CC(=O)[O-].[K+]</td>\n",
       "      <td>CC(C)(C)P(c1ccccc1)C(C)(C)C</td>\n",
       "      <td>CC(=O)N(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>InChI=1S/C26H33N2P/c1-26(2,3)29(20-14-9-8-10-1...</td>\n",
       "      <td>InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...</td>\n",
       "      <td>InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>KOAc</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.303397</td>\n",
       "      <td>4.41630</td>\n",
       "      <td>153.614410</td>\n",
       "      <td>64.469151</td>\n",
       "      <td>800.0</td>\n",
       "      <td>CC(=O)[O-].[K+]</td>\n",
       "      <td>CN(C)c1cccc(N(C)C)c1-c1ccccc1P(c1ccccc1)C(C)(C)C</td>\n",
       "      <td>CC(=O)N(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>InChI=1S/C18H33P.BF4/c1-4-10-16(11-5-1)19(17-1...</td>\n",
       "      <td>InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...</td>\n",
       "      <td>InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>KOAc</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.276180</td>\n",
       "      <td>4.41630</td>\n",
       "      <td>151.423546</td>\n",
       "      <td>691.080949</td>\n",
       "      <td>22.0</td>\n",
       "      <td>CC(=O)[O-].[K+]</td>\n",
       "      <td>C1CCC([PH+](C2CCCCC2)C2CCCCC2)CC1.F[B-](F)(F)F</td>\n",
       "      <td>CC(=O)N(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>InChI=1S/C18H15P/c1-4-10-16(11-5-1)19(17-12-6-...</td>\n",
       "      <td>InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...</td>\n",
       "      <td>InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>KOAc</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.196717</td>\n",
       "      <td>4.41630</td>\n",
       "      <td>151.974556</td>\n",
       "      <td>178.881165</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CC(=O)[O-].[K+]</td>\n",
       "      <td>c1ccc(P(c2ccccc2)c2ccccc2)cc1</td>\n",
       "      <td>CC(=O)N(C)C</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>1723</th>\n",
       "      <td>InChI=1S/C13H13P/c1-14(12-8-4-2-5-9-12)13-10-6...</td>\n",
       "      <td>InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...</td>\n",
       "      <td>InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>CsOPiv</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.150165</td>\n",
       "      <td>10.53135</td>\n",
       "      <td>101.005484</td>\n",
       "      <td>2091.688946</td>\n",
       "      <td>8.0</td>\n",
       "      <td>CC(C)(C)C(=O)[O-].[Cs+]</td>\n",
       "      <td>CP(c1ccccc1)c1ccccc1</td>\n",
       "      <td>Cc1ccc(C)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>InChI=1S/C24H39O2P/c1-18(2)25-22-16-11-17-23(2...</td>\n",
       "      <td>InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...</td>\n",
       "      <td>InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>CsOPiv</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.339202</td>\n",
       "      <td>10.53135</td>\n",
       "      <td>101.399448</td>\n",
       "      <td>400.447659</td>\n",
       "      <td>36.0</td>\n",
       "      <td>CC(C)(C)C(=O)[O-].[Cs+]</td>\n",
       "      <td>CC(C)Oc1cccc(OC(C)C)c1P(C1CCCCC1)C1CCCCC1</td>\n",
       "      <td>Cc1ccc(C)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>InChI=1S/C39H37F12O2P/c1-19(2)22-11-29(20(3)4)...</td>\n",
       "      <td>InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...</td>\n",
       "      <td>InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>CsOPiv</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.597495</td>\n",
       "      <td>10.53135</td>\n",
       "      <td>101.807134</td>\n",
       "      <td>252.868372</td>\n",
       "      <td>552.0</td>\n",
       "      <td>CC(C)(C)C(=O)[O-].[Cs+]</td>\n",
       "      <td>COc1ccc(OC)c(P(c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c2...</td>\n",
       "      <td>Cc1ccc(C)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>InChI=1S/C16H21O3P/c1-13-10-15(3)19-14(2,17-13...</td>\n",
       "      <td>InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...</td>\n",
       "      <td>InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>CsOPiv</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.219232</td>\n",
       "      <td>10.53135</td>\n",
       "      <td>101.603617</td>\n",
       "      <td>175.981222</td>\n",
       "      <td>144.0</td>\n",
       "      <td>CC(C)(C)C(=O)[O-].[Cs+]</td>\n",
       "      <td>CC12CC3(C)OC(C)(CC(C)(O1)P3c1ccccc1)O2</td>\n",
       "      <td>Cc1ccc(C)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>InChI=1S/C8H11P/c1-9(2)8-6-4-3-5-7-8/h3-7H,1-2H3</td>\n",
       "      <td>InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...</td>\n",
       "      <td>InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3</td>\n",
       "      <td>InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...</td>\n",
       "      <td>InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H</td>\n",
       "      <td>InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3</td>\n",
       "      <td>InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...</td>\n",
       "      <td>CsOPiv</td>\n",
       "      <td>1-Bromo-2-fluorobenzene</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.123474</td>\n",
       "      <td>0.103612</td>\n",
       "      <td>10.53135</td>\n",
       "      <td>100.910642</td>\n",
       "      <td>inf</td>\n",
       "      <td>24.0</td>\n",
       "      <td>CC(C)(C)C(=O)[O-].[Cs+]</td>\n",
       "      <td>CP(C)c1ccccc1</td>\n",
       "      <td>Cc1ccc(C)cc1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1728 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Ligand_inchi  \\\n",
       "0     InChI=1S/C35H53O2P/c1-23(2)26-21-29(24(3)4)33(...   \n",
       "1     InChI=1S/C14H23P/c1-13(2,3)15(14(4,5)6)12-10-8...   \n",
       "2     InChI=1S/C26H33N2P/c1-26(2,3)29(20-14-9-8-10-1...   \n",
       "3     InChI=1S/C18H33P.BF4/c1-4-10-16(11-5-1)19(17-1...   \n",
       "4     InChI=1S/C18H15P/c1-4-10-16(11-5-1)19(17-12-6-...   \n",
       "...                                                 ...   \n",
       "1723  InChI=1S/C13H13P/c1-14(12-8-4-2-5-9-12)13-10-6...   \n",
       "1724  InChI=1S/C24H39O2P/c1-18(2)25-22-16-11-17-23(2...   \n",
       "1725  InChI=1S/C39H37F12O2P/c1-19(2)22-11-29(20(3)4)...   \n",
       "1726  InChI=1S/C16H21O3P/c1-13-10-15(3)19-14(2,17-13...   \n",
       "1727   InChI=1S/C8H11P/c1-9(2)8-6-4-3-5-7-8/h3-7H,1-2H3   \n",
       "\n",
       "                                             Base_inchi  \\\n",
       "0     InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...   \n",
       "1     InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...   \n",
       "2     InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...   \n",
       "3     InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...   \n",
       "4     InChI=1S/C2H4O2.K/c1-2(3)4;/h1H3,(H,3,4);/q;+1...   \n",
       "...                                                 ...   \n",
       "1723  InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...   \n",
       "1724  InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...   \n",
       "1725  InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...   \n",
       "1726  InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...   \n",
       "1727  InChI=1S/C5H10O2.Cs/c1-5(2,3)4(6)7;/h1-3H3,(H,...   \n",
       "\n",
       "                                      Solvent_inchi  \\\n",
       "0               InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3   \n",
       "1               InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3   \n",
       "2               InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3   \n",
       "3               InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3   \n",
       "4               InChI=1S/C4H9NO/c1-4(6)5(2)3/h1-3H3   \n",
       "...                                             ...   \n",
       "1723  InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3   \n",
       "1724  InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3   \n",
       "1725  InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3   \n",
       "1726  InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3   \n",
       "1727  InChI=1S/C8H10/c1-7-3-5-8(2)6-4-7/h3-6H,1-2H3   \n",
       "\n",
       "                                          Product_inchi  \\\n",
       "0     InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "1     InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "2     InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "3     InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "4     InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "...                                                 ...   \n",
       "1723  InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "1724  InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "1725  InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "1726  InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "1727  InChI=1S/C11H8FN3/c1-15-7-14-10(6-13)11(15)8-4...   \n",
       "\n",
       "                             Electrophile_inchi  \\\n",
       "0     InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "1     InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "2     InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "3     InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "4     InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "...                                         ...   \n",
       "1723  InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "1724  InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "1725  InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "1726  InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "1727  InChI=1S/C6H4BrF/c7-5-3-1-2-4-6(5)8/h1-4H   \n",
       "\n",
       "                                 Nucleophile_inchi  \\\n",
       "0     InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "1     InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "2     InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "3     InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "4     InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "...                                            ...   \n",
       "1723  InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "1724  InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "1725  InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "1726  InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "1727  InChI=1S/C5H5N3/c1-8-3-5(2-6)7-4-8/h3-4H,1H3   \n",
       "\n",
       "                                      Precatalyst_inchi    Base  \\\n",
       "0     InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...    KOAc   \n",
       "1     InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...    KOAc   \n",
       "2     InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...    KOAc   \n",
       "3     InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...    KOAc   \n",
       "4     InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...    KOAc   \n",
       "...                                                 ...     ...   \n",
       "1723  InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...  CsOPiv   \n",
       "1724  InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...  CsOPiv   \n",
       "1725  InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...  CsOPiv   \n",
       "1726  InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...  CsOPiv   \n",
       "1727  InChI=1S/2C3H5.2ClH.2Pd/c2*1-3-2;;;;/h2*3H,1-2...  CsOPiv   \n",
       "\n",
       "                 Electrophile  Electrophile_PCI_Name  ... Electrophile_mg  \\\n",
       "0     1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "1     1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "2     1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "3     1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "4     1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "...                       ...                    ...  ...             ...   \n",
       "1723  1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "1724  1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "1725  1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "1726  1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "1727  1-Bromo-2-fluorobenzene                      7  ...           2.625   \n",
       "\n",
       "     Precatalyst_mg Ligand_mg   Base_mg    Total_mg          PMI  \\\n",
       "0          0.123474  0.402577   4.41630  151.495920   917.668323   \n",
       "1          0.123474  0.166733   4.41630  151.094987          inf   \n",
       "2          0.123474  0.303397   4.41630  153.614410    64.469151   \n",
       "3          0.123474  0.276180   4.41630  151.423546   691.080949   \n",
       "4          0.123474  0.196717   4.41630  151.974556   178.881165   \n",
       "...             ...       ...       ...         ...          ...   \n",
       "1723       0.123474  0.150165  10.53135  101.005484  2091.688946   \n",
       "1724       0.123474  0.339202  10.53135  101.399448   400.447659   \n",
       "1725       0.123474  0.597495  10.53135  101.807134   252.868372   \n",
       "1726       0.123474  0.219232  10.53135  101.603617   175.981222   \n",
       "1727       0.123474  0.103612  10.53135  100.910642          inf   \n",
       "\n",
       "     Ligand_Cost_fixed              Base_SMILES  \\\n",
       "0                297.0          CC(=O)[O-].[K+]   \n",
       "1                102.0          CC(=O)[O-].[K+]   \n",
       "2                800.0          CC(=O)[O-].[K+]   \n",
       "3                 22.0          CC(=O)[O-].[K+]   \n",
       "4                  1.0          CC(=O)[O-].[K+]   \n",
       "...                ...                      ...   \n",
       "1723               8.0  CC(C)(C)C(=O)[O-].[Cs+]   \n",
       "1724              36.0  CC(C)(C)C(=O)[O-].[Cs+]   \n",
       "1725             552.0  CC(C)(C)C(=O)[O-].[Cs+]   \n",
       "1726             144.0  CC(C)(C)C(=O)[O-].[Cs+]   \n",
       "1727              24.0  CC(C)(C)C(=O)[O-].[Cs+]   \n",
       "\n",
       "                                          Ligand_SMILES  Solvent_SMILES  \n",
       "0     COc1ccc(OC)c(P(C2CCCCC2)C2CCCCC2)c1-c1c(C(C)C)...     CC(=O)N(C)C  \n",
       "1                           CC(C)(C)P(c1ccccc1)C(C)(C)C     CC(=O)N(C)C  \n",
       "2      CN(C)c1cccc(N(C)C)c1-c1ccccc1P(c1ccccc1)C(C)(C)C     CC(=O)N(C)C  \n",
       "3        C1CCC([PH+](C2CCCCC2)C2CCCCC2)CC1.F[B-](F)(F)F     CC(=O)N(C)C  \n",
       "4                         c1ccc(P(c2ccccc2)c2ccccc2)cc1     CC(=O)N(C)C  \n",
       "...                                                 ...             ...  \n",
       "1723                               CP(c1ccccc1)c1ccccc1    Cc1ccc(C)cc1  \n",
       "1724          CC(C)Oc1cccc(OC(C)C)c1P(C1CCCCC1)C1CCCCC1    Cc1ccc(C)cc1  \n",
       "1725  COc1ccc(OC)c(P(c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c2...    Cc1ccc(C)cc1  \n",
       "1726             CC12CC3(C)OC(C)(CC(C)(O1)P3c1ccccc1)O2    Cc1ccc(C)cc1  \n",
       "1727                                      CP(C)c1ccccc1    Cc1ccc(C)cc1  \n",
       "\n",
       "[1728 rows x 83 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reaction_data = directaryl()\n",
    "reaction_data.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will inspect the ligands in this dataset as well as the prices that were extracted from supplier websites;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 11 unique ligands in the dataset with costs ranging from 1.0 to 800.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_ligands = np.unique(reaction_data.data[\"Ligand_Cost_fixed\"].values)\n",
    "unique_smiles = [\n",
    "    reaction_data.data[reaction_data.data[\"Ligand_Cost_fixed\"] == ligand][\n",
    "        \"Ligand_SMILES\"\n",
    "    ].values[0]\n",
    "    for ligand in unique_ligands\n",
    "]\n",
    "\n",
    "print(\"There are\", len(unique_ligands), \"unique ligands in the dataset with costs ranging from\", min(unique_ligands), \"to\", max(unique_ligands))\n",
    "\n",
    "plt.hist(unique_ligands)\n",
    "plt.xlabel(\"Ligand Cost [$/gram]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we visualize the ligands and their corresponding prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x800>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem import rdDepictor\n",
    "\n",
    "\n",
    "# Generate molecules from SMILES and compute 2D coordinates\n",
    "molecules = [Chem.MolFromSmiles(smiles) for smiles in unique_smiles]\n",
    "for mol in molecules:\n",
    "    rdDepictor.Compute2DCoords(mol)\n",
    "\n",
    "# Create a dictionary of ligand prices and their corresponding molecules\n",
    "ligand_dict = {f\"${price}\": mol for price, mol in zip(unique_ligands, molecules)}\n",
    "\n",
    "# Draw the molecules with prices per gram as legends\n",
    "img = Draw.MolsToGridImage(\n",
    "    list(ligand_dict.values()), legends=list(ligand_dict.keys()), molsPerRow=3\n",
    ")\n",
    "\n",
    "\n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we initialize the data with a random split between all measurements of the Direct Arylation dataset and featurize the datapoints via ECFP fingerprints and reaction conditions such as temperature and concentration. Finally, the feature vectors will be normalized to have a range of 0 to 1 in each dimension. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET = Evaluation_data(\n",
    "    \"BMS\",\n",
    "    300,\n",
    "    \"random\",\n",
    "    init_strategy=\"random\",\n",
    ")\n",
    "\n",
    "DATASET.get_init_holdout_data(111)\n",
    "\n",
    "(\n",
    "    X_init,\n",
    "    y_init,\n",
    "    costs_init,\n",
    "    X_candidate,\n",
    "    y_candidate,\n",
    "    costs_candidate,\n",
    ") = DATASET.get_init_holdout_data(111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results from the GPR model\n",
      "train size =  300\n",
      "test size =  1428\n",
      "r2 =  0.7425401589484195\n",
      "N =  300 MAE =  8.210028587115572\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, scaler_y = update_model(X_init, y_init,fit_y=False, bounds_norm=DATASET.bounds_norm, FIT_METHOD=True)\n",
    "\n",
    "\n",
    "y_pred = model.posterior(X_candidate).mean.detach().flatten().numpy()\n",
    "y_std = np.sqrt(model.posterior(X_candidate).variance.detach().flatten().numpy())\n",
    "\n",
    "y_pred = y_pred.flatten()\n",
    "y_candidate = y_candidate.flatten()\n",
    "\n",
    "\n",
    "r2 = r2_score(y_candidate,y_pred)\n",
    "mae = mean_absolute_error(y_candidate, y_pred)\n",
    "\n",
    "print(\"Results from the GPR model\")\n",
    "print(\"train size = \", len(X_init))\n",
    "print(\"test size = \", len(X_candidate))\n",
    "print(\"r2 = \", r2)\n",
    "print(\"N = \", len(X_init), \"MAE = \", mae)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
    "\n",
    "dataset = \"C-H Acrylation\"\n",
    "\n",
    "\n",
    "ax.set_title(\"{} GPR\".format(dataset))\n",
    "ax.errorbar(y_candidate, y_pred, yerr=y_std, marker=None, fmt=\",\", alpha=0.1)\n",
    "ax.plot(y_candidate, y_candidate, color=\"black\", alpha=0.1)\n",
    "ax.scatter(y_candidate, y_pred, alpha=0.6)\n",
    "# Setting the axis limits\n",
    "ax.set_xlim(0, 102)\n",
    "ax.set_ylim(0, 102)\n",
    "ax.text(\n",
    "    5, 95, f\"R² score: {r2:.2f}\", fontsize=12\n",
    ")  # Adjust position and fontsize as needed\n",
    "ax.text(5, 90, f\"MAE: {mae:.2f}\", fontsize=12)  # Adjust position and fontsize as needed\n",
    "ax.grid(True, which=\"both\", linestyle=\"--\", linewidth=0.5)\n",
    "ax.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
    "ax.set_xlabel(\"Experiment\", fontsize=12, fontweight=\"bold\")\n",
    "ax.set_ylabel(\"Prediction\", fontsize=12, fontweight=\"bold\")\n",
    "plt.savefig(f\"correlation_{dataset}_GPR.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, in real experimental settings often only a few data points for a handful of ligands are availible. In the extremecase only a single ligand was investigated. Therefore we use a different initialization scheme in which only a single ligand was tested, here corresponding to the one with the worst yield across all reaction conditions corresponding to the `worst_ligand` flag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET = Evaluation_data(\n",
    "    \"BMS\",\n",
    "    200,\n",
    "    \"update_ligand_when_used\",\n",
    "    init_strategy=\"worst_ligand\",\n",
    ")\n",
    "\n",
    "\n",
    "(\n",
    "    X_init,\n",
    "    y_init,\n",
    "    costs_init,\n",
    "    X_candidate,\n",
    "    y_candidate,\n",
    "    costs_candidate,\n",
    "    LIGANDS_init,\n",
    "    LIGANDS_candidate,\n",
    "    price_dict,\n",
    "    exp_init,\n",
    "    exp_candidate,\n",
    ") = DATASET.get_init_holdout_data(111)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`X_init` are the representation vectors of the initial experiments, and `y_init` are the corresponding yield measurements.\n",
    "\n",
    "`costs_init` contains an array of all the costs for the ligands from the initially collected data.\n",
    "\n",
    "`X_candidate` and `y_candidate` are the representation vectors and measurements of the holdout set, respectively.\n",
    "\n",
    "`price_dict` is a dictionary of the current prices. Note that the initial ligand was already set to zero price as it was acquired beforehand.\n",
    "\n",
    "`exp_init` and `exp_candidate` are the experimental conditions corresponding to their vectors `X_init` and `X_candidate`, respectively.\n",
    "\n",
    "Next, we will show a simple example of how to perform Cost-informed Bayesian optimization with CIBO. First, we create a dictionary that covers all the parameters for the Bayesian optimization. We select the Direct Arylation dataset with the `BMS` keyword. As mentioned before, we start with the `worst_ligand` and set the `cost_aware` flag to `True`. Use `False` for normal BO.\n",
    "\n",
    "To use the modified acquisition function as described in the paper, use the `minus` flag as well as the `NEI` acquisition function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "confs =[{\n",
    "    \"dataset\": \"BMS\",\n",
    "    \"init_strategy\": \"worst_ligand\",\n",
    "    \"cost_aware\": True,\n",
    "    \"n_runs\": 5,\n",
    "    \"n_iter\": 12,\n",
    "    \"batch_size\": 5,\n",
    "    \"ntrain\": 200,\n",
    "    \"prices\": \"update_ligand_when_used\",\n",
    "    \"surrogate\": \"GP\",\n",
    "    \"acq_func\": \"NEI\",\n",
    "    \"label\": \"BMS_COST_GP_NEI\",\n",
    "    \"cost_mod\": \"minus\",\n",
    "},\n",
    "{\n",
    "    \"dataset\": \"BMS\",\n",
    "    \"init_strategy\": \"worst_ligand\",\n",
    "    \"cost_aware\": False,\n",
    "    \"n_runs\": 5,\n",
    "    \"n_iter\": 12,\n",
    "    \"batch_size\": 5,\n",
    "    \"ntrain\": 200,\n",
    "    \"prices\": \"update_ligand_when_used\",\n",
    "    \"surrogate\": \"GP\",\n",
    "    \"acq_func\": \"NEI\",\n",
    "    \"label\": \"BMS_NORMAL_GP_NEI\",\n",
    "    \"cost_mod\": \"minus\",\n",
    "}]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next cell we run the CIBO and BO after each other and same the results into the `RESULTS` dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experiment:  {'dataset': 'BMS', 'init_strategy': 'worst_ligand', 'cost_aware': True, 'n_runs': 5, 'n_iter': 12, 'batch_size': 5, 'ntrain': 200, 'prices': 'update_ligand_when_used', 'surrogate': 'GP', 'acq_func': 'NEI', 'label': 'BMS_COST_GP_NEI', 'cost_mod': 'minus'}\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 53.02 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $529.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 98.38 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $698.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |3/12|\tBO 89.71\tRS 98.38 \tSUM(COSTS BO): $103.0\tSUM(COSTS RS): $2050.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 89.71\tRS 98.38 \tSUM(COSTS BO): $655.0\tSUM(COSTS RS): $2050.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 89.71\tRS 98.38 \tSUM(COSTS BO): $691.0\tSUM(COSTS RS): $2050.0\tN_train 169\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |6/12|\tBO 89.71\tRS 98.38 \tSUM(COSTS BO): $988.0\tSUM(COSTS RS): $2058.0\tN_train 174\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |7/12|\tBO 96.64\tRS 98.38 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $2058.0\tN_train 179\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |8/12|\tBO 99.22\tRS 98.38 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $2058.0\tN_train 184\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |9/12|\tBO 99.22\tRS 98.38 \tSUM(COSTS BO): $1234.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |10/12|\tBO 99.22\tRS 98.38 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |11/12|\tBO 99.22\tRS 98.38 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |12/12|\tBO 99.22\tRS 98.38 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 87.00 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1090.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 87.00 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1484.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |3/12|\tBO 89.71\tRS 87.83 \tSUM(COSTS BO): $103.0\tSUM(COSTS RS): $1484.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 89.71\tRS 87.83 \tSUM(COSTS BO): $655.0\tSUM(COSTS RS): $2058.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 89.71\tRS 87.83 \tSUM(COSTS BO): $691.0\tSUM(COSTS RS): $2058.0\tN_train 169\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |6/12|\tBO 89.71\tRS 87.83 \tSUM(COSTS BO): $988.0\tSUM(COSTS RS): $2058.0\tN_train 174\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |7/12|\tBO 96.64\tRS 87.83 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $2058.0\tN_train 179\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |8/12|\tBO 99.22\tRS 87.83 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $2058.0\tN_train 184\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |9/12|\tBO 99.22\tRS 87.83 \tSUM(COSTS BO): $1234.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |10/12|\tBO 99.22\tRS 91.19 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |11/12|\tBO 99.22\tRS 91.19 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |12/12|\tBO 99.22\tRS 91.19 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 19.02 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1353.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 36.65 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1385.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |3/12|\tBO 89.71\tRS 66.90 \tSUM(COSTS BO): $103.0\tSUM(COSTS RS): $1740.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 89.71\tRS 66.90 \tSUM(COSTS BO): $655.0\tSUM(COSTS RS): $1740.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 89.71\tRS 66.90 \tSUM(COSTS BO): $691.0\tSUM(COSTS RS): $1740.0\tN_train 169\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |6/12|\tBO 89.71\tRS 71.43 \tSUM(COSTS BO): $988.0\tSUM(COSTS RS): $1812.0\tN_train 174\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |7/12|\tBO 96.64\tRS 71.43 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $1914.0\tN_train 179\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |8/12|\tBO 99.22\tRS 71.43 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $1914.0\tN_train 184\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |9/12|\tBO 99.22\tRS 99.22 \tSUM(COSTS BO): $1234.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |10/12|\tBO 99.22\tRS 99.22 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |11/12|\tBO 99.22\tRS 99.22 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |12/12|\tBO 99.22\tRS 99.22 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 19.02 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1285.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 19.02 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1860.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |3/12|\tBO 89.71\tRS 68.35 \tSUM(COSTS BO): $103.0\tSUM(COSTS RS): $1860.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 89.71\tRS 68.35 \tSUM(COSTS BO): $655.0\tSUM(COSTS RS): $1884.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 89.71\tRS 84.48 \tSUM(COSTS BO): $691.0\tSUM(COSTS RS): $1884.0\tN_train 169\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |6/12|\tBO 89.71\tRS 84.48 \tSUM(COSTS BO): $988.0\tSUM(COSTS RS): $1884.0\tN_train 174\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |7/12|\tBO 96.64\tRS 84.48 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $1956.0\tN_train 179\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |8/12|\tBO 99.22\tRS 84.48 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $1956.0\tN_train 184\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |9/12|\tBO 99.22\tRS 84.48 \tSUM(COSTS BO): $1234.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |10/12|\tBO 99.22\tRS 84.48 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |11/12|\tBO 99.22\tRS 84.48 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |12/12|\tBO 99.22\tRS 84.48 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 61.94 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1157.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 91.27 \tSUM(COSTS BO): $9.0\tSUM(COSTS RS): $1732.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 72.0     \n",
      "--------------------\n",
      "# |3/12|\tBO 89.71\tRS 91.27 \tSUM(COSTS BO): $103.0\tSUM(COSTS RS): $1956.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 89.71\tRS 92.19 \tSUM(COSTS BO): $655.0\tSUM(COSTS RS): $1956.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 89.71\tRS 92.19 \tSUM(COSTS BO): $691.0\tSUM(COSTS RS): $1956.0\tN_train 169\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |6/12|\tBO 89.71\tRS 92.19 \tSUM(COSTS BO): $988.0\tSUM(COSTS RS): $2058.0\tN_train 174\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 144.0     | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |7/12|\tBO 96.64\tRS 92.19 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $2058.0\tN_train 179\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |8/12|\tBO 99.22\tRS 92.19 \tSUM(COSTS BO): $1132.0\tSUM(COSTS RS): $2058.0\tN_train 184\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 102.0     | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |9/12|\tBO 99.22\tRS 92.19 \tSUM(COSTS BO): $1234.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 24.0      | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |10/12|\tBO 99.22\tRS 92.19 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |11/12|\tBO 99.22\tRS 92.19 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |12/12|\tBO 99.22\tRS 92.19 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "Starting experiment:  {'dataset': 'BMS', 'init_strategy': 'worst_ligand', 'cost_aware': False, 'n_runs': 5, 'n_iter': 12, 'batch_size': 5, 'ntrain': 200, 'prices': 'update_ligand_when_used', 'surrogate': 'GP', 'acq_func': 'NEI', 'label': 'BMS_NORMAL_GP_NEI', 'cost_mod': 'minus'}\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 53.02 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $529.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 98.38 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $698.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |3/12|\tBO 95.48\tRS 98.38 \tSUM(COSTS BO): $961.0\tSUM(COSTS RS): $2050.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 95.48\tRS 98.38 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2050.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 99.22\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2050.0\tN_train 169\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |6/12|\tBO 99.81\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 174\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |7/12|\tBO 99.81\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 179\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |8/12|\tBO 99.81\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 184\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |9/12|\tBO 99.81\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |10/12|\tBO 99.81\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |11/12|\tBO 99.81\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |12/12|\tBO 99.81\tRS 98.38 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Bought all ligands\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 87.00 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1090.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 87.00 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1484.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |3/12|\tBO 95.48\tRS 87.83 \tSUM(COSTS BO): $961.0\tSUM(COSTS RS): $1484.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 95.48\tRS 87.83 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $2058.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 99.22\tRS 87.83 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 169\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |6/12|\tBO 99.81\tRS 87.83 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 174\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |7/12|\tBO 99.81\tRS 87.83 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 179\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |8/12|\tBO 99.81\tRS 87.83 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 184\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |9/12|\tBO 99.81\tRS 87.83 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |10/12|\tBO 99.81\tRS 91.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |11/12|\tBO 99.81\tRS 91.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |12/12|\tBO 99.81\tRS 91.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Bought all ligands\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 19.02 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1353.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 36.65 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1385.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |3/12|\tBO 95.48\tRS 66.90 \tSUM(COSTS BO): $961.0\tSUM(COSTS RS): $1740.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 95.48\tRS 66.90 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $1740.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 99.22\tRS 66.90 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1740.0\tN_train 169\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |6/12|\tBO 99.81\tRS 71.43 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1812.0\tN_train 174\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |7/12|\tBO 99.81\tRS 71.43 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1914.0\tN_train 179\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |8/12|\tBO 99.81\tRS 71.43 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1914.0\tN_train 184\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |9/12|\tBO 99.81\tRS 99.22 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |10/12|\tBO 99.81\tRS 99.22 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |11/12|\tBO 99.81\tRS 99.22 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |12/12|\tBO 99.81\tRS 99.22 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Bought all ligands\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 19.02 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1285.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 19.02 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1860.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |3/12|\tBO 95.48\tRS 68.35 \tSUM(COSTS BO): $961.0\tSUM(COSTS RS): $1860.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 95.48\tRS 68.35 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $1884.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 99.22\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1884.0\tN_train 169\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |6/12|\tBO 99.81\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1884.0\tN_train 174\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |7/12|\tBO 99.81\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1956.0\tN_train 179\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |8/12|\tBO 99.81\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1956.0\tN_train 184\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |9/12|\tBO 99.81\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |10/12|\tBO 99.81\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |11/12|\tBO 99.81\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |12/12|\tBO 99.81\tRS 84.48 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Bought all ligands\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "22.0      | 102.0     | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 8.0       | 1.0       | 72.0     \n",
      "--------------------\n",
      "# |1/12|\tBO 89.71\tRS 61.94 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1157.0\tN_train 149\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |2/12|\tBO 89.71\tRS 91.27 \tSUM(COSTS BO): $205.0\tSUM(COSTS RS): $1732.0\tN_train 154\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 36.0      | 24.0      | 144.0     | 800.0     | 297.0     | 552.0     | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |3/12|\tBO 95.48\tRS 91.27 \tSUM(COSTS BO): $961.0\tSUM(COSTS RS): $1956.0\tN_train 159\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 297.0     | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |4/12|\tBO 95.48\tRS 92.19 \tSUM(COSTS BO): $1258.0\tSUM(COSTS RS): $1956.0\tN_train 164\n",
      "Ligand 1  | Ligand 2  | Ligand 3  | Ligand 4  | Ligand 5  | Ligand 6  | Ligand 7  | Ligand 8  | Ligand 9  | Ligand 10 | Ligand 11 | Ligand 12\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "0         | 0         | 0         | 0         | 0         | 800.0     | 0         | 0         | 0         | 0         | 0         | 0        \n",
      "--------------------\n",
      "# |5/12|\tBO 99.22\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $1956.0\tN_train 169\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |6/12|\tBO 99.81\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 174\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |7/12|\tBO 99.81\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 179\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |8/12|\tBO 99.81\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 184\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |9/12|\tBO 99.81\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 189\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |10/12|\tBO 99.81\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 194\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |11/12|\tBO 99.81\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 199\n",
      "Bought all ligands\n",
      "--------------------\n",
      "# |12/12|\tBO 99.81\tRS 92.19 \tSUM(COSTS BO): $2058.0\tSUM(COSTS RS): $2058.0\tN_train 204\n",
      "Bought all ligands\n"
     ]
    }
   ],
   "source": [
    "from cibo.utils import (\n",
    "    create_aligned_transposed_price_table,\n",
    "    data_dict_BO_LIGAND,\n",
    "    data_dict_RS_LIGAND,\n",
    ")\n",
    "from cibo.experiments import (\n",
    "    BO_LIGAND,\n",
    "    BO_COI_LIGAND,\n",
    "    RS_LIGAND,\n",
    ")\n",
    "\n",
    "\n",
    "RESULTS = {}\n",
    "\n",
    "for conf in confs:\n",
    "\n",
    "    print(\"Starting experiment: \", conf)\n",
    "    y_better_BO_ALL, y_better_RANDOM_ALL = [], []\n",
    "    running_costs_BO_ALL, running_costs_RANDOM_ALL = [], []\n",
    "\n",
    "    DATASET = Evaluation_data(\n",
    "        conf[\"dataset\"],\n",
    "        conf[\"ntrain\"],\n",
    "        conf[\"prices\"],\n",
    "        init_strategy=conf[\"init_strategy\"],\n",
    "    )\n",
    "    bounds_norm = DATASET.bounds_norm\n",
    "    N_RUNS = conf[\"n_runs\"]\n",
    "    NITER = conf[\"n_iter\"]\n",
    "    BATCH_SIZE = conf[\"batch_size\"]\n",
    "    SURROGATE = conf[\"surrogate\"]\n",
    "    COST_AWARE_BO = conf[\"cost_aware\"]\n",
    "\n",
    "    for run in range(N_RUNS):\n",
    "        SEED = 111 + run\n",
    "        random.seed(SEED)\n",
    "        np.random.seed(SEED)\n",
    "        torch.manual_seed(SEED)\n",
    "\n",
    "        (\n",
    "            X_init,\n",
    "            y_init,\n",
    "            costs_init,\n",
    "            X_candidate,\n",
    "            y_candidate,\n",
    "            costs_candidate,\n",
    "            LIGANDS_init,\n",
    "            LIGANDS_candidate,\n",
    "            price_dict,\n",
    "            exp_init,\n",
    "            exp_candidate,\n",
    "        ) = DATASET.get_init_holdout_data(SEED)\n",
    "\n",
    "        print(create_aligned_transposed_price_table(price_dict))\n",
    "        X, y = cp.deepcopy(X_init), cp.deepcopy(y_init)\n",
    "        y_best = float(torch.max(y))\n",
    "        model, scaler_y = update_model(X, y, bounds_norm, surrogate=SURROGATE)\n",
    "        X_candidate_FULL, y_candidate_FULL = cp.deepcopy(X_candidate), cp.deepcopy(\n",
    "            y_candidate\n",
    "        )\n",
    "        X_candidate_BO = cp.deepcopy(X_candidate)\n",
    "        y_candidate_BO = cp.deepcopy(y_candidate)\n",
    "        exp_candidate_BO = cp.deepcopy(exp_candidate)\n",
    "\n",
    "        y_candidate_RANDOM = cp.deepcopy(y_candidate).detach().numpy()\n",
    "        exp_candidate_RANDOM = cp.deepcopy(exp_candidate)\n",
    "\n",
    "        running_costs_BO = [0]\n",
    "        running_costs_RANDOM = [0]\n",
    "\n",
    "        price_dict_BO = cp.deepcopy(price_dict)\n",
    "        price_dict_RANDOM = cp.deepcopy(price_dict)\n",
    "\n",
    "        LIGANDS_candidate_BO = cp.deepcopy(LIGANDS_candidate)\n",
    "        LIGANDS_candidate_RANDOM = cp.deepcopy(LIGANDS_candidate)\n",
    "\n",
    "        y_better_BO = []\n",
    "        y_better_RANDOM = []\n",
    "\n",
    "        y_better_BO.append(y_best)\n",
    "        y_better_RANDOM.append(y_best)\n",
    "        y_best_BO, y_best_RANDOM = y_best, y_best\n",
    "\n",
    "        BO_data = data_dict_BO_LIGAND(\n",
    "            model,\n",
    "            y_best_BO,\n",
    "            scaler_y,\n",
    "            X,\n",
    "            y,\n",
    "            X_candidate_BO,\n",
    "            y_candidate_BO,\n",
    "            LIGANDS_candidate_BO,\n",
    "            y_better_BO,\n",
    "            price_dict_BO,\n",
    "            running_costs_BO,\n",
    "            bounds_norm,\n",
    "            BATCH_SIZE,\n",
    "            None,\n",
    "            SURROGATE,\n",
    "            conf[\"acq_func\"],\n",
    "            exp_init,\n",
    "            exp_candidate_BO,\n",
    "        )\n",
    "\n",
    "        BO_data[\"cost_mod\"] = conf[\"cost_mod\"]\n",
    "        BO_data[\"cost_weight\"] = 1.0\n",
    "\n",
    "        RANDOM_data = data_dict_RS_LIGAND(\n",
    "            y_candidate_RANDOM,\n",
    "            y_best_RANDOM,\n",
    "            LIGANDS_candidate_RANDOM,\n",
    "            price_dict_RANDOM,\n",
    "            BATCH_SIZE,\n",
    "            None,\n",
    "            y_better_RANDOM,\n",
    "            running_costs_RANDOM,\n",
    "            exp_init,\n",
    "            exp_candidate_RANDOM,\n",
    "        )\n",
    "\n",
    "        for i in range(NITER):\n",
    "            if COST_AWARE_BO == False:\n",
    "                BO_data = BO_LIGAND(BO_data)\n",
    "            else:\n",
    "                BO_data = BO_COI_LIGAND(BO_data)\n",
    "\n",
    "            RANDOM_data = RS_LIGAND(RANDOM_data)\n",
    "\n",
    "            print(\"--------------------\")\n",
    "            print(\n",
    "                \"# |{}/{}|\\tBO {:.2f}\\tRS {:.2f} \\tSUM(COSTS BO): ${}\\tSUM(COSTS RS): ${}\\tN_train {}\".format(\n",
    "                    i + 1,\n",
    "                    NITER,\n",
    "                    BO_data[\"y_best_BO\"],\n",
    "                    RANDOM_data[\"y_best_RANDOM\"],\n",
    "                    BO_data[\"running_costs_BO\"][-1],\n",
    "                    RANDOM_data[\"running_costs_RANDOM\"][-1],\n",
    "                    BO_data[\"N_train\"],\n",
    "                )\n",
    "            )\n",
    "            print(create_aligned_transposed_price_table(price_dict_BO))\n",
    "\n",
    "        y_better_BO_ALL.append(BO_data[\"y_better_BO\"])\n",
    "        y_better_RANDOM_ALL.append(RANDOM_data[\"y_better_RANDOM\"])\n",
    "        running_costs_BO_ALL.append(BO_data[\"running_costs_BO\"])\n",
    "        running_costs_RANDOM_ALL.append(RANDOM_data[\"running_costs_RANDOM\"])\n",
    "\n",
    "    y_better_BO_ALL = np.array(y_better_BO_ALL)\n",
    "    y_better_RANDOM_ALL = np.array(y_better_RANDOM_ALL)\n",
    "\n",
    "    if conf[\"cost_aware\"] == True:\n",
    "        RESULTS[\"CIBO\"] = { \"y_better\": y_better_BO_ALL, \"running_costs\": running_costs_BO_ALL}\n",
    "    else:\n",
    "        RESULTS[\"BO\"] = { \"y_better\": y_better_BO_ALL, \"running_costs\": running_costs_BO_ALL}\n",
    "        RESULTS[\"RANDOM\"] = { \"y_better\": y_better_RANDOM_ALL, \"running_costs\": running_costs_RANDOM_ALL}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIBO = np.mean(RESULTS[\"CIBO\"][\"y_better\"], axis=0)\n",
    "BO = np.mean(RESULTS[\"BO\"][\"y_better\"], axis=0)\n",
    "\n",
    "CIBO_COST = np.mean(RESULTS[\"CIBO\"][\"running_costs\"], axis=0)\n",
    "BO_COST = np.mean(RESULTS[\"BO\"][\"running_costs\"], axis=0)\n",
    "\n",
    "\n",
    "RANDOM = np.mean(y_better_RANDOM_ALL, axis=0)\n",
    "RANDOM_COST = np.mean(running_costs_RANDOM_ALL, axis=0)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
    "ax[0].plot(CIBO, label=\"CIBO\")\n",
    "ax[0].plot(BO, label=\"BO\")\n",
    "ax[0].plot(RANDOM, label=\"RANDOM\")\n",
    "\n",
    "\n",
    "ax[1].plot(CIBO_COST, label=\"CIBO\")\n",
    "ax[1].plot(BO_COST, label=\"BO\")\n",
    "ax[1].plot(RANDOM_COST, label=\"RANDOM\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax[0].set_xlabel(\"Iteration\")\n",
    "ax[0].set_ylabel(\"Best Yield\")\n",
    "\n",
    "ax[1].set_xlabel(\"Iteration\")\n",
    "ax[1].set_ylabel(\"Costs [$]\")\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we discuss how to use the code for your own dataset! All that is needed is a CSV file with the following naming conventions for the columns\n",
    "\n",
    "- As before `Ligand_Cost` is the cost per gram of each ligand\n",
    "\n",
    "- `Base_SMILES`,`Ligand_SMILES` and `Solvent_SMILES` are the SMILES strings of basis, ligand and solvent respectively\n",
    "\n",
    "- Finally, the `Concentration`, reaction temperature in degree celcius `Temp_C` and the yield `Yield`\n",
    "\n",
    "\n",
    "In the next we show a chunk of such dataframe with the corresponding convention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Ligand_Cost_fixed              Base_SMILES  \\\n",
      "0                1.0   CC(C)(C)C(=O)[O-].[K+]   \n",
      "1              552.0  CC(C)(C)C(=O)[O-].[Cs+]   \n",
      "2              102.0   CC(C)(C)C(=O)[O-].[K+]   \n",
      "\n",
      "                                       Ligand_SMILES Solvent_SMILES  \\\n",
      "0                      c1ccc(P(c2ccccc2)c2ccccc2)cc1   Cc1ccc(C)cc1   \n",
      "1  COc1ccc(OC)c(P(c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c2...    CC(=O)N(C)C   \n",
      "2                        CC(C)(C)P(c1ccccc1)C(C)(C)C   Cc1ccc(C)cc1   \n",
      "\n",
      "   Concentration  Temp_C  Yield  \n",
      "0          0.100     105   1.41  \n",
      "1          0.153     120   5.65  \n",
      "2          0.100      90   0.00  \n"
     ]
    }
   ],
   "source": [
    "data_dict = {\n",
    "    \"Ligand_Cost_fixed\": [1.0, 552.0, 102.0],\n",
    "    \"Base_SMILES\": [\n",
    "        \"CC(C)(C)C(=O)[O-].[K+]\",\n",
    "        \"CC(C)(C)C(=O)[O-].[Cs+]\",\n",
    "        \"CC(C)(C)C(=O)[O-].[K+]\",\n",
    "    ],\n",
    "    \"Ligand_SMILES\": [\n",
    "        \"c1ccc(P(c2ccccc2)c2ccccc2)cc1\",\n",
    "        \"COc1ccc(OC)c(P(c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c2cc(C(F)(F)F)cc(C(F)(F)F)c2)c1-c1c(C(C)C)cc(C(C)C)cc1C(C)C\",\n",
    "        \"CC(C)(C)P(c1ccccc1)C(C)(C)C\",\n",
    "    ],\n",
    "    \"Solvent_SMILES\": [\"Cc1ccc(C)cc1\", \"CC(=O)N(C)C\", \"Cc1ccc(C)cc1\"],\n",
    "    \"Concentration\": [0.1, 0.153, 0.1],\n",
    "    \"Temp_C\": [105, 120, 90],\n",
    "    \"Yield\": [1.41, 5.65, 0.0],\n",
    "}\n",
    "\n",
    "# Load the dictionary into a DataFrame\n",
    "df = pd.DataFrame(data_dict)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we load the full dataframe. To bring your own data just change the `my_data_path` accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cibo.data.user_data.user_data at 0x7da4631cdbe0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cibo.data.datasets import user_data\n",
    "\n",
    "#change the path to whereever you keep your data\n",
    "my_data_path = \"./cibo/data/user_data.csv\"\n",
    "\n",
    "user_data(csv_file=my_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the dataloader is actually much more flexible. To add an arbitrary number of compounds that are part of the reaction as well as numerical parameters, just follow the dictionary structure as shown here. In this example, we used the same dataset as before. The names of the columns can be adapted to the CSV file at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cibo.data.datasets import user_data\n",
    "\n",
    "description = {\n",
    "    \"compounds\": {\n",
    "        \"1\": {\"name\": \"Ligand_SMILES\", \"inp_type\": \"smiles\"},\n",
    "        \"2\": {\"name\": \"Base_SMILES\", \"inp_type\": \"smiles\"},\n",
    "        \"3\": {\"name\": \"Solvent_SMILES\", \"inp_type\": \"smiles\"},\n",
    "    },\n",
    "    \"parameters\": {\n",
    "        \"1\": {\"name\": \"Concentration\", \"inp_type\": \"float\"},\n",
    "        \"2\": {\"name\": \"Temp_C\", \"inp_type\": \"float\"},\n",
    "    },\n",
    "    \"cost\": {\"name\": \"Ligand_Cost_fixed\", \"inp_type\": \"float\"},\n",
    "    \"target\": {\"name\": \"Yield\", \"inp_type\": \"float\"},\n",
    "}\n",
    "\n",
    "data = user_data(csv_file=my_data_path, description=description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize the space of reactions covered in this dataset with a `UMAP` projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-01 14:56:50.811266: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-07-01 14:56:50.854330: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-07-01 14:56:51.036181: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-07-01 14:56:51.536045: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import umap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming data.X and data.y are already defined\n",
    "dimred = umap.UMAP(n_components=2, random_state=42)\n",
    "\n",
    "X, y = data.X, data.y\n",
    "\n",
    "X_umap = dimred.fit_transform(X)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7, 5))\n",
    "scatter = ax.scatter(X_umap[:, 0], X_umap[:, 1], c=y, cmap=\"viridis\", edgecolor=\"k\")\n",
    "ax.set_title(\"UMAP\")\n",
    "ax.set_xlabel(\"UMAP Dimension 1\")\n",
    "ax.set_ylabel(\"UMAP Dimension 2\")\n",
    "\n",
    "# Add colorbar\n",
    "cbar = plt.colorbar(scatter, ax=ax)\n",
    "cbar.set_label(\"Reaction Yield\")\n",
    "cbar.set_ticks([0, 20, 40, 60, 80, 100])  # Assuming yield is in the range 0-100\n",
    "\n",
    "plt.show()"
   ]
  }
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