From 09a40141ba28d299813e62d2056806541f910eb3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Can=20=C3=96zkan?= <128815525+canozkan42@users.noreply.github.com> Date: Thu, 28 Mar 2024 19:57:50 +0000 Subject: [PATCH] making my jupyter notebook main, moving others into the legacy_code folder --- baybe-inhibitor.ipynb | 4198 +++++++++++++++- can_baybe-inhibitor.ipynb | 4325 ----------------- {Alex6022 => legacy_code/Alex6022}/Scenarios | Bin .../Alex6022}/Scenarios.png | Bin .../Alex6022}/baybe-inhibitor Legacy.ipynb | 0 .../Alex6022}/baybe-inhibitor.ipynb | 0 legacy_code/baybe-inhibitor.ipynb | 187 + .../michalis-baybe-inhibitor.ipynb | 0 run_impute_mode.png | Bin 58164 -> 0 bytes 9 files changed, 4355 insertions(+), 4355 deletions(-) delete mode 100644 can_baybe-inhibitor.ipynb rename {Alex6022 => legacy_code/Alex6022}/Scenarios (100%) rename {Alex6022 => legacy_code/Alex6022}/Scenarios.png (100%) rename {Alex6022 => legacy_code/Alex6022}/baybe-inhibitor Legacy.ipynb (100%) rename {Alex6022 => legacy_code/Alex6022}/baybe-inhibitor.ipynb (100%) create mode 100644 legacy_code/baybe-inhibitor.ipynb rename michalis-baybe-inhibitor.ipynb => legacy_code/michalis-baybe-inhibitor.ipynb (100%) delete mode 100644 run_impute_mode.png diff --git a/baybe-inhibitor.ipynb b/baybe-inhibitor.ipynb index c584dc7..8ca1038 100644 --- a/baybe-inhibitor.ipynb +++ b/baybe-inhibitor.ipynb @@ -18,7 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Initizalization" + "# Initialization" ] }, { @@ -30,45 +30,327 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 297, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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SMILESTime_hpHInhib_Concentrat_MSalt_Concentrat_MEfficiency
0C(=O)(C(=O)[O-])[O-]24.04.00.00100.1020.00
1C(=O)(C(=O)[O-])[O-]24.07.00.00050.0512.35
2C(=O)(C(=O)[O-])[O-]24.010.00.00100.1020.00
3C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O24.04.00.00100.1030.00
4C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O24.07.00.00050.05-23.95
.....................
510c1ccc2c(c1)[nH]nn224.07.00.00050.0597.95
511c1ccc2c(c1)[nH]nn224.010.00.00100.1090.00
512c1ccc2c(c1)[nH]nn2672.07.00.00100.1098.00
513c1ncn[nH]124.04.00.00100.1030.00
514c1ncn[nH]124.010.00.00100.1090.00
\n", + "

515 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " SMILES Time_h pH Inhib_Concentrat_M \\\n", + "0 C(=O)(C(=O)[O-])[O-] 24.0 4.0 0.0010 \n", + "1 C(=O)(C(=O)[O-])[O-] 24.0 7.0 0.0005 \n", + "2 C(=O)(C(=O)[O-])[O-] 24.0 10.0 0.0010 \n", + "3 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O 24.0 4.0 0.0010 \n", + "4 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O 24.0 7.0 0.0005 \n", + ".. ... ... ... ... \n", + "510 c1ccc2c(c1)[nH]nn2 24.0 7.0 0.0005 \n", + "511 c1ccc2c(c1)[nH]nn2 24.0 10.0 0.0010 \n", + "512 c1ccc2c(c1)[nH]nn2 672.0 7.0 0.0010 \n", + "513 c1ncn[nH]1 24.0 4.0 0.0010 \n", + "514 c1ncn[nH]1 24.0 10.0 0.0010 \n", + "\n", + " Salt_Concentrat_M Efficiency \n", + "0 0.10 20.00 \n", + "1 0.05 12.35 \n", + "2 0.10 20.00 \n", + "3 0.10 30.00 \n", + "4 0.05 -23.95 \n", + ".. ... ... \n", + "510 0.05 97.95 \n", + "511 0.10 90.00 \n", + "512 0.10 98.00 \n", + "513 0.10 30.00 \n", + "514 0.10 90.00 \n", + "\n", + "[515 rows x 6 columns]" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "\n", "from baybe import Campaign\n", + "from baybe.objective import Objective\n", + "from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter, CategoricalParameter\n", + "from baybe.recommenders import RandomRecommender, TwoPhaseMetaRecommender\n", + "from baybe.searchspace import SearchSpace\n", + "from baybe.simulation import simulate_scenarios\n", + "from baybe.targets import NumericalTarget\n", + "\n", + "# these are datasets already preprocessed, filtered, and grouped by \n", + "so we have only one row for each unique combination of parameters\n", + "df_AA2024 = pd.read_excel('data/averaged_filtered_AA2024.xlsx')\n", + "df_AA5000 = pd.read_excel('data/averaged_filtered_AA5000.xlsx')\n", + "df_AA6000 = pd.read_excel('data/averaged_filtered_AA6000.xlsx')\n", + "df_AA7075 = pd.read_excel('data/averaged_filtered_AA7075.xlsx')\n", + "df_AA1000 = pd.read_excel('data/averaged_filtered_AA1000.xlsx')\n", + "df_Al = pd.read_excel('data/averaged_filtered_Al.xlsx')\n", + "\n", + "# change this for campaigns on different datasets\n", + "df_active = df_AA2024\n", "\n", - "df_AA2024 = pd.read_excel('data/filtered_AA2024.xlsx')\n", - "df_AA1000 = pd.read_excel('data/filtered_AA1000.xlsx')\n", - "df_Al = pd.read_excel('data/filtered_Al.xlsx')" + "\n", + "if df_active is df_AA2024:\n", + " exp_dataset_name = 'AA2024'\n", + "elif df_active is df_AA7075:\n", + " exp_dataset_name = 'AA7075'\n", + "elif df_active is df_AA5000:\n", + " exp_dataset_name = 'AA5000'\n", + "elif df_active is df_AA6000:\n", + " exp_dataset_name = 'AA6000'\n", + "elif df_active is df_AA1000:\n", + " exp_dataset_name = 'AA1000'\n", + "elif df_active is df_Al:\n", + " exp_dataset_name = 'Al'\n", + "\n", + "df_active" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 298, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# def required from baybe package\n", + "lookup = df_active" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [], + "source": [ + "# chemical space dictionary\n", + "unique_SMILES = df_active.SMILES.unique()\n", + "\n", + "def list_to_dict(input_list):\n", + " return {item: item for item in input_list}\n", + "\n", + "smiles_dict =list_to_dict(unique_SMILES)" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Processing" + "# Defining parameters for the search space" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 300, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# parameters\n", + "\n", + "basic_parameters=[\n", + "NumericalDiscreteParameter(\n", + " name=\"Time_h\",\n", + " values=df_active[\"Time_h\"].unique(),\n", + " # tolerance = 0.004, assume certain experimental noise for each parameter measurement?\n", + "),\n", + "NumericalDiscreteParameter(\n", + " name=\"pH\",\n", + " values=df_active[\"pH\"].unique(),\n", + " ), \n", + "NumericalDiscreteParameter(\n", + " name=\"Inhib_Concentrat_M\",\n", + " values=df_active[\"Inhib_Concentrat_M\"].unique(),\n", + " ),\n", + "NumericalDiscreteParameter(\n", + " name=\"Salt_Concentrat_M\",\n", + " values=df_active[\"Salt_Concentrat_M\"].unique(),\n", + " ),\n", + "]\n", + "\n", + "# mordred\n", + "parameters_mordred = basic_parameters + [\n", + " SubstanceParameter(\n", + " name=\"SMILES\",\n", + " data=smiles_dict,\n", + " encoding=\"MORDRED\", # optional\n", + " decorrelate=0.7, # optional\n", + " ) \n", + " ]\n", + "\n", + "# morgan fingerprints\n", + "parameters_morgan_fp = basic_parameters + [\n", + " SubstanceParameter(\n", + " name=\"SMILES\",\n", + " data=smiles_dict,\n", + " encoding=\"MORGAN_FP\", # optional\n", + " decorrelate=0.7, # optional\n", + " ) \n", + " ]\n", + "\n", + "# rdkit\n", + "parameters_rdkit = basic_parameters + [\n", + " SubstanceParameter(\n", + " name=\"SMILES\",\n", + " data=smiles_dict,\n", + " encoding=\"RDKIT\", # optional\n", + " decorrelate=0.7, # optional\n", + " ) \n", + " ]\n", + "\n", + "# one-hot encoding\n", + "parameters_ohe = basic_parameters + [\n", + " CategoricalParameter(\n", + " name=\"SMILES\",\n", + " values=unique_SMILES,\n", + " encoding=\"OHE\",\n", + " )\n", + " ]" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Anaylsis" + "# Setting the target" ] }, { @@ -76,41 +358,250 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "df_no_target = lookup.drop('Efficiency', axis=1)" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Bayesian Optimization" + "# Creating the searchspace\n", + "Multiple searchspaces and parameter groups are initialized to investigate the influence of built-in featurization methods on the Bayesian optimization process." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 301, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "\n", + "# searchspace = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters)\n", + "# print('Print test 1')\n", + "# objective = Objective(\n", + "# mode=\"SINGLE\", targets=[NumericalTarget(name=\"Efficiency\", mode=\"MAX\")]\n", + "# )\n", + "\n", + "\n", + "searchspace_mordred = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_mordred)\n", + "\n", + "searchspace_morgan = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_morgan_fp)\n", + "\n", + "searchspace_rdkit = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_rdkit)\n", + "\n", + "searchspace_ohe = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_ohe)\n", + "\n", + "\n", + "objective = Objective(\n", + " mode=\"SINGLE\", targets=[NumericalTarget(name=\"Efficiency\", mode=\"MAX\")]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SearchSpace(discrete=SubspaceDiscrete(parameters=[NumericalDiscreteParameter(name='Time_h', encoding=None, _values=[0.5, 1.0, 2.0, 3.0, 6.0, 24.0, 48.0, 72.0, 96.0, 120.0, 144.0, 168.0, 192.0, 240.0, 288.0, 336.0, 360.0, 384.0, 432.0, 480.0, 528.0, 576.0, 600.0, 624.0, 672.0], tolerance=0.0), NumericalDiscreteParameter(name='pH', encoding=None, _values=[0.0, 3.3, 4.0, 4.4, 5.4, 5.5, 5.6, 7.0, 10.0], tolerance=0.0), NumericalDiscreteParameter(name='Inhib_Concentrat_M', encoding=None, _values=[1e-05, 5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0008, 0.001, 0.0012, 0.0018, 0.0024, 0.003, 0.005, 0.01, 0.011, 0.021, 0.022, 0.031, 0.033, 0.042, 0.044, 0.05, 0.1], tolerance=0.0), NumericalDiscreteParameter(name='Salt_Concentrat_M', encoding=None, _values=[0.0, 0.01, 0.05, 0.1, 0.5, 0.6], tolerance=0.0), SubstanceParameter(name='SMILES', data={'C(=O)(C(=O)[O-])[O-]': 'C(=O)(C(=O)[O-])[O-]', 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'CC1(C(N2C(S1)C(C2=O)NC(=O)C(C3=CC=C(C=C3)O)N)C(=O)O)C', 'CCCCCCCC/C=C\\\\CCCCCCCC(=O)O': 'CCCCCCCC/C=C\\\\CCCCCCCC(=O)O', 'CCCCCCCCCCCCCCCCCC(=O)O': 'CCCCCCCCCCCCCCCCCC(=O)O', 'CCCCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]': 'CCCCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]', 'CCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]': 'CCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]', 'CCCCCCCCCCCCOS(=O)(=O)O': 'CCCCCCCCCCCCOS(=O)(=O)O', 'CCCCCCCCCCCCc1ccccc1S([O])([O])O': 'CCCCCCCCCCCCc1ccccc1S([O])([O])O', 'CCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]': 'CCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]', 'CCCCN(CCCC)C1=NC(=NC(=N1)NC(CCSC)C(=O)O)NC(CCSC)C(=O)O': 'CCCCN(CCCC)C1=NC(=NC(=N1)NC(CCSC)C(=O)O)NC(CCSC)C(=O)O', 'CCCCOP(=O)(OCCCC)O': 'CCCCOP(=O)(OCCCC)O', 'CCN(C(=S)S)CC': 'CCN(C(=S)S)CC', 'CCOc1ccc2c(c1)nc([nH]2)S': 'CCOc1ccc2c(c1)nc([nH]2)S', 'CCSc1nnc(s1)N': 'CCSc1nnc(s1)N', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C': 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C', 'CNCC(C1=CC(=CC=C1)O)O': 'CNCC(C1=CC(=CC=C1)O)O', 'COC(=O)CCCC1=CNC2=CC=CC=C21': 'COC(=O)CCCC1=CNC2=CC=CC=C21', 'COC(=O)n1nnc2ccccc12': 'COC(=O)n1nnc2ccccc12', 'COCCOC(=O)OCSc1nc2c(s1)cccc2': 'COCCOC(=O)OCSc1nc2c(s1)cccc2', 'COc1ccc2c(c1)[nH]c(=S)[nH]2': 'COc1ccc2c(c1)[nH]c(=S)[nH]2', 'COc1cccc(c1)c1n[nH]c(=S)[nH]1': 'COc1cccc(c1)c1n[nH]c(=S)[nH]1', 'CS[C]1N[N]C(=N1)N': 'CS[C]1N[N]C(=N1)N', 'CSc1[nH]c2c(n1)cc(c(c2)C)C': 'CSc1[nH]c2c(n1)cc(c(c2)C)C', 'CSc1nnc(s1)N': 'CSc1nnc(s1)N', 'Cc1cc(C)nc(n1)S': 'Cc1cc(C)nc(n1)S', 'Cc1ccc(c(c1)n1nc2c(n1)cccc2)O': 'Cc1ccc(c(c1)n1nc2c(n1)cccc2)O', 'Cc1ccc2c(c1)nc([nH]2)S': 'Cc1ccc2c(c1)nc([nH]2)S', 'Cc1n[nH]c(=S)s1': 'Cc1n[nH]c(=S)s1', 'Cc1nsc(c1)N': 'Cc1nsc(c1)N', 'ClC([C]1N[N]C=N1)(Cl)Cl': 'ClC([C]1N[N]C=N1)(Cl)Cl', 'Clc1cc2[nH]c(=S)[nH]c2cc1Cl': 'Clc1cc2[nH]c(=S)[nH]c2cc1Cl', 'Clc1ccc(cc1)CC[C@](C(C)(C)C)(Cn1cncn1)O': 'Clc1ccc(cc1)CC[C@](C(C)(C)C)(Cn1cncn1)O', 'Clc1ccc(cc1Cl)c1n[nH]c(=S)[nH]1': 'Clc1ccc(cc1Cl)c1n[nH]c(=S)[nH]1', 'Clc1ccc2c(c1)[nH]c(n2)S': 'Clc1ccc2c(c1)[nH]c(n2)S', 'Clc1cccc(c1)c1n[nH]c(=S)[nH]1': 'Clc1cccc(c1)c1n[nH]c(=S)[nH]1', 'Cn1cnnc1S': 'Cn1cnnc1S', 'Cn1nnnc1S': 'Cn1nnnc1S', 'N.N.[N+](=O)(O)[O-].[N+](=O)(O)[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].O.O.O.O.[Ce+3]': 'N.N.[N+](=O)(O)[O-].[N+](=O)(O)[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].O.O.O.O.[Ce+3]', 'NC(=S)NN=CC1=C(C(=C(C=C1)O)O)O': 'NC(=S)NN=CC1=C(C(=C(C=C1)O)O)O', 'NCC(=O)O': 'NCC(=O)O', 'NO': 'NO', 'Nc1cc(N)nc(n1)S': 'Nc1cc(N)nc(n1)S', 'Nc1cc(S)nc(n1)N': 'Nc1cc(S)nc(n1)N', 'Nc1ccc2c(c1)sc(=S)[nH]2': 'Nc1ccc2c(c1)sc(=S)[nH]2', 'Nc1ccnc(n1)S': 'Nc1ccnc(n1)S', 'Nc1n[nH]c(=S)s1': 'Nc1n[nH]c(=S)s1', 'Nc1n[nH]c(n1)S': 'Nc1n[nH]c(n1)S', 'Nc1n[nH]cn1': 'Nc1n[nH]cn1', 'Nc1nc([nH]n1)C(=O)O': 'Nc1nc([nH]n1)C(=O)O', 'Nc1ncncc1N': 'Nc1ncncc1N', 'Nn1c(NN)nnc1S': 'Nn1c(NN)nnc1S', 'Nn1c(S)nnc1c1ccccc1': 'Nn1c(S)nnc1c1ccccc1', 'Nn1cnnc1': 'Nn1cnnc1', 'O/N=C(/C(=N/O)/C)\\\\C': 'O/N=C(/C(=N/O)/C)\\\\C', 'O/N=C(\\\\C(=N/O)\\\\c1ccco1)/c1ccco1': 'O/N=C(\\\\C(=N/O)\\\\c1ccco1)/c1ccco1', 'O=C([O-])C(O)C(O)C(O)C(O)CO.[Na+]': 'O=C([O-])C(O)C(O)C(O)C(O)CO.[Na+]', 'OC(=O)/C=C/c1ccccc1': 'OC(=O)/C=C/c1ccccc1', 'OC(=O)CCCCC(=O)O': 'OC(=O)CCCCC(=O)O', 'OC(=O)CCCCCCCCCCCCCCC(=O)O': 'OC(=O)CCCCCCCCCCCCCCC(=O)O', 'OC(=O)CCS': 'OC(=O)CCS', 'OC(=O)CN(CC(=O)O)CCN(CC(=O)O)CC(=O)O': 'OC(=O)CN(CC(=O)O)CCN(CC(=O)O)CC(=O)O', 'OC(=O)CS': 'OC(=O)CS', 'OC(=O)Cn1nnnc1S': 'OC(=O)Cn1nnnc1S', 'OC(=O)c1ccc(=S)[nH]c1': 'OC(=O)c1ccc(=S)[nH]c1', 'OC(=O)c1ccc(cc1)N': 'OC(=O)c1ccc(cc1)N', 'OC(=O)c1ccc(cc1)S': 'OC(=O)c1ccc(cc1)S', 'OC(=O)c1ccc(cc1)c1ccccc1': 'OC(=O)c1ccc(cc1)c1ccccc1', 'OC(=O)c1ccccc1': 'OC(=O)c1ccccc1', 'OC(=O)c1ccccc1O': 'OC(=O)c1ccccc1O', 'OC(=O)c1ccccc1S': 'OC(=O)c1ccccc1S', 'OC(=O)c1ccccn1': 'OC(=O)c1ccccn1', 'OC(=O)c1cccnc1': 'OC(=O)c1cccnc1', 'OC(=O)c1cccnc1S': 'OC(=O)c1cccnc1S', 'OC(=O)c1ccncc1': 'OC(=O)c1ccncc1', 'OC(=O)c1n[nH]c(n1)N': 'OC(=O)c1n[nH]c(n1)N', 'OCC(CO)O': 'OCC(CO)O', 'OC[C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O': 'OC[C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O', 'OC[C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O': 'OC[C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O', 'OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O': 'OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O', 'O[C@H]1C(=O)OCC1(C)C': 'O[C@H]1C(=O)OCC1(C)C', 'Oc1ccc(cc1)C(=O)O': 'Oc1ccc(cc1)C(=O)O', 'Oc1ccc(cc1)S([O])([O])O': 'Oc1ccc(cc1)S([O])([O])O', 'Oc1cccc2c1nccc2': 'Oc1cccc2c1nccc2', 'Oc1ccccc1c1nnc([nH]1)S': 'Oc1ccccc1c1nnc([nH]1)S', 'On1nnc2c1cccc2': 'On1nnc2c1cccc2', 'S=c1[nH]c2c([nH]1)c(=O)n(cn2)C': 'S=c1[nH]c2c([nH]1)c(=O)n(cn2)C', 'S=c1[nH]c2c([nH]1)cncn2': 'S=c1[nH]c2c([nH]1)cncn2', 'S=c1[nH]c2c([nH]1)nccn2': 'S=c1[nH]c2c([nH]1)nccn2', 'S=c1[nH]nc([nH]1)c1cccnc1': 'S=c1[nH]nc([nH]1)c1cccnc1', 'S=c1[nH]nc([nH]1)c1ccco1': 'S=c1[nH]nc([nH]1)c1ccco1', 'S=c1[nH]nc([nH]1)c1ccncc1': 'S=c1[nH]nc([nH]1)c1ccncc1', 'S=c1sc2c([nH]1)cccc2': 'S=c1sc2c([nH]1)cccc2', 'SC#N': 'SC#N', 'S[C]1NC2=C[CH]C=NC2=N1': 'S[C]1NC2=C[CH]C=NC2=N1', 'Sc1n[nH]cn1': 'Sc1n[nH]cn1', 'Sc1nc(N)c(c(n1)S)N': 'Sc1nc(N)c(c(n1)S)N', 'Sc1nc(N)c2c(n1)[nH]nc2': 'Sc1nc(N)c2c(n1)[nH]nc2', 'Sc1nc2c([nH]1)cccc2': 'Sc1nc2c([nH]1)cccc2', 'Sc1ncc[nH]1': 'Sc1ncc[nH]1', 'Sc1ncccn1': 'Sc1ncccn1', 'Sc1nnc(s1)S': 'Sc1nnc(s1)S', '[Cl-].[Cl-].[Cl-].[Ce+3]': '[Cl-].[Cl-].[Cl-].[Ce+3]', '[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+3]': '[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+3]', '[NH4+].[NH4+].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+4]': '[NH4+].[NH4+].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+4]', '[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Ce+3].[Ce+3]': '[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Ce+3].[Ce+3]', '[O-]S(=O)[O-].[Na+].[Na+]': '[O-]S(=O)[O-].[Na+].[Na+]', 'c1cc(ccc1c2[nH]c(nn2)S)[N+](=O)[O-]': 'c1cc(ccc1c2[nH]c(nn2)S)[N+](=O)[O-]', 'c1ccc(nc1)c1ccccn1': 'c1ccc(nc1)c1ccccn1', 'c1ccc2c(c1)[nH]nn2': 'c1ccc2c(c1)[nH]nn2', 'c1ncn[nH]1': 'c1ncn[nH]1'}, decorrelate=0.7, encoding=)], exp_rep= Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", + "0 24.0 4.0 0.0010 0.10 \n", + "1 24.0 7.0 0.0005 0.05 \n", + "2 24.0 10.0 0.0010 0.10 \n", + "3 24.0 4.0 0.0010 0.10 \n", + "4 24.0 7.0 0.0005 0.05 \n", + ".. ... ... ... ... \n", + "510 24.0 7.0 0.0005 0.05 \n", + "511 24.0 10.0 0.0010 0.10 \n", + "512 672.0 7.0 0.0010 0.10 \n", + "513 24.0 4.0 0.0010 0.10 \n", + "514 24.0 10.0 0.0010 0.10 \n", + "\n", + " SMILES \n", + "0 C(=O)(C(=O)[O-])[O-] \n", + "1 C(=O)(C(=O)[O-])[O-] \n", + "2 C(=O)(C(=O)[O-])[O-] \n", + "3 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O \n", + "4 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O \n", + ".. ... \n", + "510 c1ccc2c(c1)[nH]nn2 \n", + "511 c1ccc2c(c1)[nH]nn2 \n", + "512 c1ccc2c(c1)[nH]nn2 \n", + "513 c1ncn[nH]1 \n", + "514 c1ncn[nH]1 \n", + "\n", + "[515 rows x 5 columns], metadata= was_recommended was_measured dont_recommend\n", + "0 False False False\n", + "1 False False False\n", + "2 False False False\n", + "3 False False False\n", + "4 False False False\n", + ".. ... ... ...\n", + "510 False False False\n", + "511 False False False\n", + "512 False False False\n", + "513 False False False\n", + "514 False False False\n", + "\n", + "[515 rows x 3 columns], empty_encoding=False, constraints=[], comp_rep= Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", + "0 24.0 4.0 0.0010 0.10 \n", + "1 24.0 7.0 0.0005 0.05 \n", + "2 24.0 10.0 0.0010 0.10 \n", + "3 24.0 4.0 0.0010 0.10 \n", + "4 24.0 7.0 0.0005 0.05 \n", + ".. ... ... ... ... \n", + "510 24.0 7.0 0.0005 0.05 \n", + "511 24.0 10.0 0.0010 0.10 \n", + "512 672.0 7.0 0.0010 0.10 \n", + "513 24.0 4.0 0.0010 0.10 \n", + "514 24.0 10.0 0.0010 0.10 \n", + "\n", + " SMILES_RDKIT_MaxAbsEStateIndex SMILES_RDKIT_MinAbsEStateIndex \\\n", + "0 8.925926 2.185185 \n", + "1 8.925926 2.185185 \n", + "2 8.925926 2.185185 \n", + "3 10.148889 1.357824 \n", + "4 10.148889 1.357824 \n", + ".. ... ... \n", + "510 3.813148 0.914352 \n", + "511 3.813148 0.914352 \n", + "512 3.813148 0.914352 \n", + "513 3.555556 1.444444 \n", + "514 3.555556 1.444444 \n", + "\n", + " SMILES_RDKIT_MinEStateIndex SMILES_RDKIT_qed SMILES_RDKIT_SPS \\\n", + "0 -2.185185 0.287408 7.333333 \n", + "1 -2.185185 0.287408 7.333333 \n", + "2 -2.185185 0.287408 7.333333 \n", + "3 -2.974537 0.454904 10.846154 \n", + "4 -2.974537 0.454904 10.846154 \n", + ".. ... ... ... \n", + "510 0.914352 0.560736 10.222222 \n", + "511 0.914352 0.560736 10.222222 \n", + "512 0.914352 0.560736 10.222222 \n", + "513 1.444444 0.458207 8.000000 \n", + "514 1.444444 0.458207 8.000000 \n", + "\n", + " SMILES_RDKIT_MolWt ... SMILES_RDKIT_fr_nitro \\\n", + "0 88.018 ... 0 \n", + "1 88.018 ... 0 \n", + "2 88.018 ... 0 \n", + "3 189.099 ... 0 \n", + "4 189.099 ... 0 \n", + ".. ... ... ... \n", + "510 119.127 ... 0 \n", + "511 119.127 ... 0 \n", + "512 119.127 ... 0 \n", + "513 69.067 ... 0 \n", + "514 69.067 ... 0 \n", + "\n", + " SMILES_RDKIT_fr_nitro_arom_nonortho SMILES_RDKIT_fr_oxime \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + ".. ... ... \n", + "510 0 0 \n", + "511 0 0 \n", + "512 0 0 \n", + "513 0 0 \n", + "514 0 0 \n", + "\n", + " SMILES_RDKIT_fr_para_hydroxylation SMILES_RDKIT_fr_phos_acid \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + ".. ... ... \n", + "510 1 0 \n", + "511 1 0 \n", + "512 1 0 \n", + "513 0 0 \n", + "514 0 0 \n", + "\n", + " SMILES_RDKIT_fr_pyridine SMILES_RDKIT_fr_quatN SMILES_RDKIT_fr_sulfide \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + ".. ... ... ... \n", + "510 0 0 0 \n", + "511 0 0 0 \n", + "512 0 0 0 \n", + "513 0 0 0 \n", + "514 0 0 0 \n", + "\n", + " SMILES_RDKIT_fr_tetrazole SMILES_RDKIT_fr_thiazole \n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + ".. ... ... \n", + "510 0 0 \n", + "511 0 0 \n", + "512 0 0 \n", + "513 0 0 \n", + "514 0 0 \n", + "\n", + "[515 rows x 94 columns]), continuous=SubspaceContinuous(parameters=[], constraints_lin_eq=[], constraints_lin_ineq=[]))" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "searchspace_rdkit" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Search Space" + "# Defining the campaign = searchspace + objective" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 303, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "campaign_mordred = Campaign(searchspace=searchspace_mordred, objective=objective)\n", + "campaign_morgan = Campaign(searchspace=searchspace_morgan, objective=objective)\n", + "campaign_rdkit = Campaign(searchspace=searchspace_rdkit, objective=objective)\n", + "campaign_ohe = Campaign(searchspace=searchspace_ohe, objective=objective)\n", + "\n", + "# not all randoms are used but checked for differences in behaviour\n", + "campaign_rand_mordred = Campaign(\n", + " searchspace=searchspace_mordred,\n", + " recommender=TwoPhaseMetaRecommender(recommender=RandomRecommender()),\n", + " objective=objective,\n", + ")\n", + "campaign_rand_morgan = Campaign(\n", + " searchspace=searchspace_morgan,\n", + " recommender=TwoPhaseMetaRecommender(recommender=RandomRecommender()),\n", + " objective=objective,\n", + ")\n", + "campaign_rand_rdkit = Campaign(\n", + " searchspace=searchspace_rdkit,\n", + " recommender=TwoPhaseMetaRecommender(recommender=RandomRecommender()),\n", + " objective=objective,\n", + ")" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Objective" + "# Puttting the campaigns that we are interested in a scenario" ] }, { @@ -118,41 +609,3654 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "scenarios = {\"Mordred\": campaign_mordred, #\"Random\": campaign_rand_mordred,\n", + " \"Morgan\": campaign_morgan, #\"Morgan Random\": campaign_rand_morgan,\n", + " \"RDKIT\": campaign_rdkit,\n", + " \"OHE\": campaign_ohe, \n", + " \"Random\": campaign_rand_rdkit\n", + " }" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Recommender" + "# Start our simulations" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 305, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/50 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "max_yield = lookup[\"Efficiency\"].max()\n", + "\n", + "# until 10\n", + "limit = 10\n", + "\n", + "# Create a figure and axis object\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "# Plot the lineplot\n", + "sns.lineplot(\n", + " data=results, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\", ax=ax1, style = 'Scenario'\n", + ")\n", + "\n", + "# Set legend\n", + "ax1.legend(loc=\"lower right\")\n", + "\n", + "# Add a horizontal line\n", + "ax1.plot([0.5, limit+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", + "\n", + "# Set x-axis limit\n", + "ax1.set_xlim(0, limit+1)\n", + "ax1.set_ylim(50, 101)\n", + "\n", + "# Create a new axis for the histogram on the right side\n", + "ax2 = fig.add_axes([0.905, 0.1, 0.05, 0.8])\n", + "ax2.hist(df_active['Efficiency'], bins=2000, color='orange', alpha=0.5, orientation='horizontal') \n", + "ax2.set_ylim(ax1.get_ylim()) \n", + "ax2.set_axis_off() # Hide axis ticks and labels\n", + "\n", + "# Set x and y titles\n", + "ax1.set_xlabel('Number of Experiments')\n", + "ax1.set_ylabel('Cumulative Best Efficiency')\n", + "\n", + "# Save the plot\n", + "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first{limit}.png\", bbox_inches='tight')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# until 25\n", + "limit = 25\n", + "\n", + "# Create a figure and axis object\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "# Plot the lineplot\n", + "sns.lineplot(\n", + " data=results, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\", ax=ax1, style = 'Scenario'\n", + ")\n", + "\n", + "# Set legend\n", + "ax1.legend(loc=\"lower right\")\n", + "\n", + "# Add a horizontal line\n", + "ax1.plot([0.5, limit+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", + "\n", + "# Set x-axis limit\n", + "ax1.set_xlim(0, limit+1)\n", + "ax1.set_ylim(50, 101)\n", + "\n", + "# Create a new axis for the histogram on the right side\n", + "ax2 = fig.add_axes([0.905, 0.1, 0.05, 0.8])\n", + "ax2.hist(df_active['Efficiency'], bins=2000, color='orange', alpha=0.5, orientation='horizontal') \n", + "ax2.set_ylim(ax1.get_ylim()) \n", + "ax2.set_axis_off() # Hide axis ticks and labels\n", + "\n", + "# Set x and y titles\n", + "ax1.set_xlabel('Number of Experiments')\n", + "ax1.set_ylabel('Cumulative Best Efficiency')\n", + "\n", + "# Save the plot\n", + "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first{limit}.png\", bbox_inches='tight')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# all experiments\n", + "\n", + "# until 50\n", + "limit = 50\n", + "\n", + "# Create a figure and axis object\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "# Plot the lineplot\n", + "sns.lineplot(\n", + " data=results, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\", ax=ax1, style = 'Scenario'\n", + ")\n", + "\n", + "# Set legend\n", + "ax1.legend(loc=\"lower right\")\n", + "\n", + "# Add a horizontal line\n", + "ax1.plot([0.5, limit+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", + "\n", + "# Set x-axis limit\n", + "ax1.set_xlim(0, limit+1)\n", + "ax1.set_ylim(50, 101)\n", + "\n", + "# Create a new axis for the histogram on the right side\n", + "ax2 = fig.add_axes([0.905, 0.1, 0.05, 0.8])\n", + "ax2.hist(df_active['Efficiency'], bins=2000, color='orange', alpha=0.5, orientation='horizontal') \n", + "ax2.set_ylim(ax1.get_ylim()) \n", + "ax2.set_axis_off() # Hide axis ticks and labels\n", + "\n", + "# Set x and y titles\n", + "ax1.set_xlabel('Number of Experiments')\n", + "ax1.set_ylabel('Cumulative Best Efficiency')\n", + "\n", + "# Save the plot\n", + "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first{limit}.png\", bbox_inches='tight')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ScenarioRandom_SeedIterationNum_ExperimentsEfficiency_MeasurementsEfficiency_IterBestEfficiency_CumBest
0Mordred133701[10.0]10.00000010.000000
1Mordred133712[96.43666666666667]96.43666796.436667
2Mordred133723[25.25]25.25000096.436667
3Mordred133734[99.21666666666665]99.21666799.216667
4Mordred133745[93.8]93.80000099.216667
........................
2495Random13464546[99.9]99.900000100.000000
2496Random13464647[40.0]40.000000100.000000
2497Random13464748[10.0]10.000000100.000000
2498Random13464849[91.7]91.700000100.000000
2499Random13464950[0.0]0.000000100.000000
\n", + "

2500 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Scenario Random_Seed Iteration Num_Experiments \\\n", + "0 Mordred 1337 0 1 \n", + "1 Mordred 1337 1 2 \n", + "2 Mordred 1337 2 3 \n", + "3 Mordred 1337 3 4 \n", + "4 Mordred 1337 4 5 \n", + "... ... ... ... ... \n", + "2495 Random 1346 45 46 \n", + "2496 Random 1346 46 47 \n", + "2497 Random 1346 47 48 \n", + "2498 Random 1346 48 49 \n", + "2499 Random 1346 49 50 \n", + "\n", + " Efficiency_Measurements Efficiency_IterBest Efficiency_CumBest \n", + "0 [10.0] 10.000000 10.000000 \n", + "1 [96.43666666666667] 96.436667 96.436667 \n", + "2 [25.25] 25.250000 96.436667 \n", + "3 [99.21666666666665] 99.216667 99.216667 \n", + "4 [93.8] 93.800000 99.216667 \n", + "... ... ... ... \n", + "2495 [99.9] 99.900000 100.000000 \n", + "2496 [40.0] 40.000000 100.000000 \n", + "2497 [10.0] 10.000000 100.000000 \n", + "2498 [91.7] 91.700000 100.000000 \n", + "2499 [0.0] 0.000000 100.000000 \n", + "\n", + "[2500 rows x 7 columns]" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Transfer Learning" + "# Transfer Learning\n", + "### Use transfer learning to gain information from prior experimental campaigns." + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [], + "source": [ + "df_active = df_AA2024\n", + "df_transfer = df_AA1000" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.parameters import TaskParameter\n", + "\n", + "taskparam = TaskParameter(\n", + " name=\"Al_alloys\",\n", + " values=[\"AA1000\", \"AA2024\"],\n", + " active_values=[\"AA2024\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Time_hpHInhib_Concentrat_MSalt_Concentrat_MEfficiency
count848.000000848.0000008.480000e+02848.000000848.000000
mean126.8431604.1895806.352976e-020.08896235.066659
std192.0556763.6961833.690920e-010.227758245.617010
min0.000000-0.6000001.000000e-070.000000-4834.000000
25%6.0000000.0000005.000000e-040.00000035.000000
50%24.0000004.0000001.000000e-030.01000060.000000
75%144.0000007.0000004.200000e-030.10000080.507500
max720.00000013.0000003.280000e+002.000000100.000000
\n", + "
" + ], + "text/plain": [ + " Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", + "count 848.000000 848.000000 8.480000e+02 848.000000 \n", + "mean 126.843160 4.189580 6.352976e-02 0.088962 \n", + "std 192.055676 3.696183 3.690920e-01 0.227758 \n", + "min 0.000000 -0.600000 1.000000e-07 0.000000 \n", + "25% 6.000000 0.000000 5.000000e-04 0.000000 \n", + "50% 24.000000 4.000000 1.000000e-03 0.010000 \n", + "75% 144.000000 7.000000 4.200000e-03 0.100000 \n", + "max 720.000000 13.000000 3.280000e+00 2.000000 \n", + "\n", + " Efficiency \n", + "count 848.000000 \n", + "mean 35.066659 \n", + "std 245.617010 \n", + "min -4834.000000 \n", + "25% 35.000000 \n", + "50% 60.000000 \n", + "75% 80.507500 \n", + "max 100.000000 " + ] + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df_combined = pd.concat([df_active, df_transfer], axis=0)\n", + "df_combined.describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [], + "source": [ + "unique_SMILES_transfer = df_transfer[\"SMILES\"].unique()\n", + "unique_SMILES = df_combined[\"SMILES\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [], + "source": [ + "from baybe.parameters import NumericalContinuousParameter, CategoricalParameter, NumericalDiscreteParameter\n", + "from baybe.searchspace import SearchSpace\n", + "\n", + "transfer_parameters=[\n", + "NumericalDiscreteParameter(\n", + " name=\"Time_h\",\n", + " values=df_combined[\"Time_h\"].unique(),\n", + " tolerance=5/60,\n", + "),\n", + "NumericalDiscreteParameter(\n", + " name=\"pH\",\n", + " values=df_combined[\"pH\"].unique(),\n", + " ), \n", + "NumericalDiscreteParameter(\n", + " name=\"Inhib_Concentrat_M\",\n", + " values=df_combined[\"Inhib_Concentrat_M\"].unique(),\n", + " ),\n", + "NumericalDiscreteParameter(\n", + " name=\"Salt_Concentrat_M\",\n", + " values=df_combined[\"Salt_Concentrat_M\"].unique(),\n", + " ),\n", + "CategoricalParameter(\n", + " name=\"SMILES\",\n", + " values=unique_SMILES,\n", + " encoding=\"OHE\",\n", + " )\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [], + "source": [ + "searchspace_transfer = SearchSpace.from_dataframe(df_transfer.drop(\"Efficiency\", axis = 1), transfer_parameters)\n", + "\n", + "campaign_transfer = Campaign(searchspace_transfer, objective)" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "df_features = df_active.drop(\"Efficiency\", axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/10 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Time_hpHInhib_Concentrat_MSalt_Concentrat_MEfficiency
count258.000000258.000000258.000000258.000000258.000000
mean167.6027136.6360470.0073860.11790728.268191
std220.4887882.1496130.0132020.166813265.800655
min0.5000000.0000000.0000100.000000-3813.000000
25%24.0000005.4000000.0010000.05000030.000000
50%24.0000007.0000000.0010000.10000055.000000
75%240.0000007.0000000.0045000.10000089.000000
max672.00000010.0000000.0440000.600000100.000000
\n", + "" + ], + "text/plain": [ + " Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", + "count 258.000000 258.000000 258.000000 258.000000 \n", + "mean 167.602713 6.636047 0.007386 0.117907 \n", + "std 220.488788 2.149613 0.013202 0.166813 \n", + "min 0.500000 0.000000 0.000010 0.000000 \n", + "25% 24.000000 5.400000 0.001000 0.050000 \n", + "50% 24.000000 7.000000 0.001000 0.100000 \n", + "75% 240.000000 7.000000 0.004500 0.100000 \n", + "max 672.000000 10.000000 0.044000 0.600000 \n", + "\n", + " Efficiency \n", + "count 258.000000 \n", + "mean 28.268191 \n", + "std 265.800655 \n", + "min -3813.000000 \n", + "25% 30.000000 \n", + "50% 55.000000 \n", + "75% 89.000000 \n", + "max 100.000000 " + ] + }, + "execution_count": 338, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fraction_df.describe()" ] }, { @@ -160,7 +4264,41 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "concatenated_df = pd.concat([result_fresh_start, result_transfer_learning], axis=0, ignore_index=True)\n", + "concatenated_df" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 339, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# until 50\n", + "limit = 50\n", + "exp_dataset_name = 'transferAA1000_to_AA2024'\n", + "sns.lineplot(\n", + " data=concatenated_df, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\"\n", + ")\n", + "plt.plot([0.5, N_DOE_ITERATIONS+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", + "plt.legend(loc=\"lower right\")\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.xlim(0, limit+1)\n", + "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first25.png\")" + ] } ], "metadata": { diff --git a/can_baybe-inhibitor.ipynb b/can_baybe-inhibitor.ipynb deleted file mode 100644 index 8ca1038..0000000 --- a/can_baybe-inhibitor.ipynb +++ /dev/null @@ -1,4325 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This project will focus on exploring the capabilities of Bayesian optimization, specifically employing BayBE, in the discovery of novel corrosion inhibitors for materials design. Initially, we will work with a randomly chosen subset from a comprehensive database of electrochemical responses of small organic molecules. Our goal is to assess how Bayesian optimization can speed up the screening process across the design space to identify promising compounds. We will compare different strategies for incorporating alloy information, while optimizing the experimental parameters with respect to the inhibitive performance of the screened compounds." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Initialization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading libraries and data files:" - ] - }, - { - "cell_type": "code", - "execution_count": 297, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SMILESTime_hpHInhib_Concentrat_MSalt_Concentrat_MEfficiency
0C(=O)(C(=O)[O-])[O-]24.04.00.00100.1020.00
1C(=O)(C(=O)[O-])[O-]24.07.00.00050.0512.35
2C(=O)(C(=O)[O-])[O-]24.010.00.00100.1020.00
3C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O24.04.00.00100.1030.00
4C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O24.07.00.00050.05-23.95
.....................
510c1ccc2c(c1)[nH]nn224.07.00.00050.0597.95
511c1ccc2c(c1)[nH]nn224.010.00.00100.1090.00
512c1ccc2c(c1)[nH]nn2672.07.00.00100.1098.00
513c1ncn[nH]124.04.00.00100.1030.00
514c1ncn[nH]124.010.00.00100.1090.00
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515 rows × 6 columns

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" - ], - "text/plain": [ - " SMILES Time_h pH Inhib_Concentrat_M \\\n", - "0 C(=O)(C(=O)[O-])[O-] 24.0 4.0 0.0010 \n", - "1 C(=O)(C(=O)[O-])[O-] 24.0 7.0 0.0005 \n", - "2 C(=O)(C(=O)[O-])[O-] 24.0 10.0 0.0010 \n", - "3 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O 24.0 4.0 0.0010 \n", - "4 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O 24.0 7.0 0.0005 \n", - ".. ... ... ... ... \n", - "510 c1ccc2c(c1)[nH]nn2 24.0 7.0 0.0005 \n", - "511 c1ccc2c(c1)[nH]nn2 24.0 10.0 0.0010 \n", - "512 c1ccc2c(c1)[nH]nn2 672.0 7.0 0.0010 \n", - "513 c1ncn[nH]1 24.0 4.0 0.0010 \n", - "514 c1ncn[nH]1 24.0 10.0 0.0010 \n", - "\n", - " Salt_Concentrat_M Efficiency \n", - "0 0.10 20.00 \n", - "1 0.05 12.35 \n", - "2 0.10 20.00 \n", - "3 0.10 30.00 \n", - "4 0.05 -23.95 \n", - ".. ... ... \n", - "510 0.05 97.95 \n", - "511 0.10 90.00 \n", - "512 0.10 98.00 \n", - "513 0.10 30.00 \n", - "514 0.10 90.00 \n", - "\n", - "[515 rows x 6 columns]" - ] - }, - "execution_count": 297, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import os\n", - "\n", - "from baybe import Campaign\n", - "from baybe.objective import Objective\n", - "from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter, CategoricalParameter\n", - "from baybe.recommenders import RandomRecommender, TwoPhaseMetaRecommender\n", - "from baybe.searchspace import SearchSpace\n", - "from baybe.simulation import simulate_scenarios\n", - "from baybe.targets import NumericalTarget\n", - "\n", - "# these are datasets already preprocessed, filtered, and grouped by \n", - "so we have only one row for each unique combination of parameters\n", - "df_AA2024 = pd.read_excel('data/averaged_filtered_AA2024.xlsx')\n", - "df_AA5000 = pd.read_excel('data/averaged_filtered_AA5000.xlsx')\n", - "df_AA6000 = pd.read_excel('data/averaged_filtered_AA6000.xlsx')\n", - "df_AA7075 = pd.read_excel('data/averaged_filtered_AA7075.xlsx')\n", - "df_AA1000 = pd.read_excel('data/averaged_filtered_AA1000.xlsx')\n", - "df_Al = pd.read_excel('data/averaged_filtered_Al.xlsx')\n", - "\n", - "# change this for campaigns on different datasets\n", - "df_active = df_AA2024\n", - "\n", - "\n", - "if df_active is df_AA2024:\n", - " exp_dataset_name = 'AA2024'\n", - "elif df_active is df_AA7075:\n", - " exp_dataset_name = 'AA7075'\n", - "elif df_active is df_AA5000:\n", - " exp_dataset_name = 'AA5000'\n", - "elif df_active is df_AA6000:\n", - " exp_dataset_name = 'AA6000'\n", - "elif df_active is df_AA1000:\n", - " exp_dataset_name = 'AA1000'\n", - "elif df_active is df_Al:\n", - " exp_dataset_name = 'Al'\n", - "\n", - "df_active" - ] - }, - { - "cell_type": "code", - "execution_count": 298, - "metadata": {}, - "outputs": [], - "source": [ - "# def required from baybe package\n", - "lookup = df_active" - ] - }, - { - "cell_type": "code", - "execution_count": 299, - "metadata": {}, - "outputs": [], - "source": [ - "# chemical space dictionary\n", - "unique_SMILES = df_active.SMILES.unique()\n", - "\n", - "def list_to_dict(input_list):\n", - " return {item: item for item in input_list}\n", - "\n", - "smiles_dict =list_to_dict(unique_SMILES)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Defining parameters for the search space" - ] - }, - { - "cell_type": "code", - "execution_count": 300, - "metadata": {}, - "outputs": [], - "source": [ - "# parameters\n", - "\n", - "basic_parameters=[\n", - "NumericalDiscreteParameter(\n", - " name=\"Time_h\",\n", - " values=df_active[\"Time_h\"].unique(),\n", - " # tolerance = 0.004, assume certain experimental noise for each parameter measurement?\n", - "),\n", - "NumericalDiscreteParameter(\n", - " name=\"pH\",\n", - " values=df_active[\"pH\"].unique(),\n", - " ), \n", - "NumericalDiscreteParameter(\n", - " name=\"Inhib_Concentrat_M\",\n", - " values=df_active[\"Inhib_Concentrat_M\"].unique(),\n", - " ),\n", - "NumericalDiscreteParameter(\n", - " name=\"Salt_Concentrat_M\",\n", - " values=df_active[\"Salt_Concentrat_M\"].unique(),\n", - " ),\n", - "]\n", - "\n", - "# mordred\n", - "parameters_mordred = basic_parameters + [\n", - " SubstanceParameter(\n", - " name=\"SMILES\",\n", - " data=smiles_dict,\n", - " encoding=\"MORDRED\", # optional\n", - " decorrelate=0.7, # optional\n", - " ) \n", - " ]\n", - "\n", - "# morgan fingerprints\n", - "parameters_morgan_fp = basic_parameters + [\n", - " SubstanceParameter(\n", - " name=\"SMILES\",\n", - " data=smiles_dict,\n", - " encoding=\"MORGAN_FP\", # optional\n", - " decorrelate=0.7, # optional\n", - " ) \n", - " ]\n", - "\n", - "# rdkit\n", - "parameters_rdkit = basic_parameters + [\n", - " SubstanceParameter(\n", - " name=\"SMILES\",\n", - " data=smiles_dict,\n", - " encoding=\"RDKIT\", # optional\n", - " decorrelate=0.7, # optional\n", - " ) \n", - " ]\n", - "\n", - "# one-hot encoding\n", - "parameters_ohe = basic_parameters + [\n", - " CategoricalParameter(\n", - " name=\"SMILES\",\n", - " values=unique_SMILES,\n", - " encoding=\"OHE\",\n", - " )\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setting the target" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_no_target = lookup.drop('Efficiency', axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating the searchspace\n", - "Multiple searchspaces and parameter groups are initialized to investigate the influence of built-in featurization methods on the Bayesian optimization process." - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# searchspace = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters)\n", - "# print('Print test 1')\n", - "# objective = Objective(\n", - "# mode=\"SINGLE\", targets=[NumericalTarget(name=\"Efficiency\", mode=\"MAX\")]\n", - "# )\n", - "\n", - "\n", - "searchspace_mordred = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_mordred)\n", - "\n", - "searchspace_morgan = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_morgan_fp)\n", - "\n", - "searchspace_rdkit = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_rdkit)\n", - "\n", - "searchspace_ohe = SearchSpace.from_dataframe(df = df_no_target, parameters=parameters_ohe)\n", - "\n", - "\n", - "objective = Objective(\n", - " mode=\"SINGLE\", targets=[NumericalTarget(name=\"Efficiency\", mode=\"MAX\")]\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 302, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SearchSpace(discrete=SubspaceDiscrete(parameters=[NumericalDiscreteParameter(name='Time_h', encoding=None, _values=[0.5, 1.0, 2.0, 3.0, 6.0, 24.0, 48.0, 72.0, 96.0, 120.0, 144.0, 168.0, 192.0, 240.0, 288.0, 336.0, 360.0, 384.0, 432.0, 480.0, 528.0, 576.0, 600.0, 624.0, 672.0], tolerance=0.0), NumericalDiscreteParameter(name='pH', encoding=None, _values=[0.0, 3.3, 4.0, 4.4, 5.4, 5.5, 5.6, 7.0, 10.0], tolerance=0.0), NumericalDiscreteParameter(name='Inhib_Concentrat_M', encoding=None, _values=[1e-05, 5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0008, 0.001, 0.0012, 0.0018, 0.0024, 0.003, 0.005, 0.01, 0.011, 0.021, 0.022, 0.031, 0.033, 0.042, 0.044, 0.05, 0.1], tolerance=0.0), NumericalDiscreteParameter(name='Salt_Concentrat_M', encoding=None, _values=[0.0, 0.01, 0.05, 0.1, 0.5, 0.6], tolerance=0.0), SubstanceParameter(name='SMILES', data={'C(=O)(C(=O)[O-])[O-]': 'C(=O)(C(=O)[O-])[O-]', 'C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O': 'C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O', 'C(C(C(C(C(C(=O)[O-])O)O)O)O)O.C(C(C(C(C(C(=O)[O-])O)O)O)O)O.[Fe+2]': 'C(C(C(C(C(C(=O)[O-])O)O)O)O)O.C(C(C(C(C(C(=O)[O-])O)O)O)O)O.[Fe+2]', 'C(C(C(C(C(C(=O)[O-])O)O)O)O)O.C(C(C(C(C(C(=O)[O-])O)O)O)O)O.[Zn+2]': 'C(C(C(C(C(C(=O)[O-])O)O)O)O)O.C(C(C(C(C(C(=O)[O-])O)O)O)O)O.[Zn+2]', 'C1=CC(=C(C=C1O)O)C=NNC(=S)N': 'C1=CC(=C(C=C1O)O)C=NNC(=S)N', 'C1=CC(=C(C=C1SSC2=CC(=C(C=C2)[N+](=O)[O-])C(=O)O)C(=O)O)[N+](=O)[O-]': 'C1=CC(=C(C=C1SSC2=CC(=C(C=C2)[N+](=O)[O-])C(=O)O)C(=O)O)[N+](=O)[O-]', 'C1=CC(=CC(=C1)S)C(=O)O': 'C1=CC(=CC(=C1)S)C(=O)O', 'C1=CC2=NNN=C2C=C1Cl': 'C1=CC2=NNN=C2C=C1Cl', 'C1=CC=C(C(=C1)C=NNC(=S)N)O': 'C1=CC=C(C(=C1)C=NNC(=S)N)O', 'C1COCCN1CCCS(=O)(=O)O': 'C1COCCN1CCCS(=O)(=O)O', 'C1N2CN3CN1CN(C2)C3': 'C1N2CN3CN1CN(C2)C3', 'C=CC(=O)OCCOC(=O)OCCSc1ncccn1': 'C=CC(=O)OCCOC(=O)OCCSc1ncccn1', 'CC(=O)O': 'CC(=O)O', 'CC(=O)SSC(=O)C': 'CC(=O)SSC(=O)C', 'CC1(C(N2C(S1)C(C2=O)NC(=O)C(C3=CC=C(C=C3)O)N)C(=O)O)C': 'CC1(C(N2C(S1)C(C2=O)NC(=O)C(C3=CC=C(C=C3)O)N)C(=O)O)C', 'CCCCCCCC/C=C\\\\CCCCCCCC(=O)O': 'CCCCCCCC/C=C\\\\CCCCCCCC(=O)O', 'CCCCCCCCCCCCCCCCCC(=O)O': 'CCCCCCCCCCCCCCCCCC(=O)O', 'CCCCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]': 'CCCCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]', 'CCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]': 'CCCCCCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]', 'CCCCCCCCCCCCOS(=O)(=O)O': 'CCCCCCCCCCCCOS(=O)(=O)O', 'CCCCCCCCCCCCc1ccccc1S([O])([O])O': 'CCCCCCCCCCCCc1ccccc1S([O])([O])O', 'CCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]': 'CCCCCCCCN(CC(=O)O[Na])CC(=O)O[Na]', 'CCCCN(CCCC)C1=NC(=NC(=N1)NC(CCSC)C(=O)O)NC(CCSC)C(=O)O': 'CCCCN(CCCC)C1=NC(=NC(=N1)NC(CCSC)C(=O)O)NC(CCSC)C(=O)O', 'CCCCOP(=O)(OCCCC)O': 'CCCCOP(=O)(OCCCC)O', 'CCN(C(=S)S)CC': 'CCN(C(=S)S)CC', 'CCOc1ccc2c(c1)nc([nH]2)S': 'CCOc1ccc2c(c1)nc([nH]2)S', 'CCSc1nnc(s1)N': 'CCSc1nnc(s1)N', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C': 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C', 'CNCC(C1=CC(=CC=C1)O)O': 'CNCC(C1=CC(=CC=C1)O)O', 'COC(=O)CCCC1=CNC2=CC=CC=C21': 'COC(=O)CCCC1=CNC2=CC=CC=C21', 'COC(=O)n1nnc2ccccc12': 'COC(=O)n1nnc2ccccc12', 'COCCOC(=O)OCSc1nc2c(s1)cccc2': 'COCCOC(=O)OCSc1nc2c(s1)cccc2', 'COc1ccc2c(c1)[nH]c(=S)[nH]2': 'COc1ccc2c(c1)[nH]c(=S)[nH]2', 'COc1cccc(c1)c1n[nH]c(=S)[nH]1': 'COc1cccc(c1)c1n[nH]c(=S)[nH]1', 'CS[C]1N[N]C(=N1)N': 'CS[C]1N[N]C(=N1)N', 'CSc1[nH]c2c(n1)cc(c(c2)C)C': 'CSc1[nH]c2c(n1)cc(c(c2)C)C', 'CSc1nnc(s1)N': 'CSc1nnc(s1)N', 'Cc1cc(C)nc(n1)S': 'Cc1cc(C)nc(n1)S', 'Cc1ccc(c(c1)n1nc2c(n1)cccc2)O': 'Cc1ccc(c(c1)n1nc2c(n1)cccc2)O', 'Cc1ccc2c(c1)nc([nH]2)S': 'Cc1ccc2c(c1)nc([nH]2)S', 'Cc1n[nH]c(=S)s1': 'Cc1n[nH]c(=S)s1', 'Cc1nsc(c1)N': 'Cc1nsc(c1)N', 'ClC([C]1N[N]C=N1)(Cl)Cl': 'ClC([C]1N[N]C=N1)(Cl)Cl', 'Clc1cc2[nH]c(=S)[nH]c2cc1Cl': 'Clc1cc2[nH]c(=S)[nH]c2cc1Cl', 'Clc1ccc(cc1)CC[C@](C(C)(C)C)(Cn1cncn1)O': 'Clc1ccc(cc1)CC[C@](C(C)(C)C)(Cn1cncn1)O', 'Clc1ccc(cc1Cl)c1n[nH]c(=S)[nH]1': 'Clc1ccc(cc1Cl)c1n[nH]c(=S)[nH]1', 'Clc1ccc2c(c1)[nH]c(n2)S': 'Clc1ccc2c(c1)[nH]c(n2)S', 'Clc1cccc(c1)c1n[nH]c(=S)[nH]1': 'Clc1cccc(c1)c1n[nH]c(=S)[nH]1', 'Cn1cnnc1S': 'Cn1cnnc1S', 'Cn1nnnc1S': 'Cn1nnnc1S', 'N.N.[N+](=O)(O)[O-].[N+](=O)(O)[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].O.O.O.O.[Ce+3]': 'N.N.[N+](=O)(O)[O-].[N+](=O)(O)[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].O.O.O.O.[Ce+3]', 'NC(=S)NN=CC1=C(C(=C(C=C1)O)O)O': 'NC(=S)NN=CC1=C(C(=C(C=C1)O)O)O', 'NCC(=O)O': 'NCC(=O)O', 'NO': 'NO', 'Nc1cc(N)nc(n1)S': 'Nc1cc(N)nc(n1)S', 'Nc1cc(S)nc(n1)N': 'Nc1cc(S)nc(n1)N', 'Nc1ccc2c(c1)sc(=S)[nH]2': 'Nc1ccc2c(c1)sc(=S)[nH]2', 'Nc1ccnc(n1)S': 'Nc1ccnc(n1)S', 'Nc1n[nH]c(=S)s1': 'Nc1n[nH]c(=S)s1', 'Nc1n[nH]c(n1)S': 'Nc1n[nH]c(n1)S', 'Nc1n[nH]cn1': 'Nc1n[nH]cn1', 'Nc1nc([nH]n1)C(=O)O': 'Nc1nc([nH]n1)C(=O)O', 'Nc1ncncc1N': 'Nc1ncncc1N', 'Nn1c(NN)nnc1S': 'Nn1c(NN)nnc1S', 'Nn1c(S)nnc1c1ccccc1': 'Nn1c(S)nnc1c1ccccc1', 'Nn1cnnc1': 'Nn1cnnc1', 'O/N=C(/C(=N/O)/C)\\\\C': 'O/N=C(/C(=N/O)/C)\\\\C', 'O/N=C(\\\\C(=N/O)\\\\c1ccco1)/c1ccco1': 'O/N=C(\\\\C(=N/O)\\\\c1ccco1)/c1ccco1', 'O=C([O-])C(O)C(O)C(O)C(O)CO.[Na+]': 'O=C([O-])C(O)C(O)C(O)C(O)CO.[Na+]', 'OC(=O)/C=C/c1ccccc1': 'OC(=O)/C=C/c1ccccc1', 'OC(=O)CCCCC(=O)O': 'OC(=O)CCCCC(=O)O', 'OC(=O)CCCCCCCCCCCCCCC(=O)O': 'OC(=O)CCCCCCCCCCCCCCC(=O)O', 'OC(=O)CCS': 'OC(=O)CCS', 'OC(=O)CN(CC(=O)O)CCN(CC(=O)O)CC(=O)O': 'OC(=O)CN(CC(=O)O)CCN(CC(=O)O)CC(=O)O', 'OC(=O)CS': 'OC(=O)CS', 'OC(=O)Cn1nnnc1S': 'OC(=O)Cn1nnnc1S', 'OC(=O)c1ccc(=S)[nH]c1': 'OC(=O)c1ccc(=S)[nH]c1', 'OC(=O)c1ccc(cc1)N': 'OC(=O)c1ccc(cc1)N', 'OC(=O)c1ccc(cc1)S': 'OC(=O)c1ccc(cc1)S', 'OC(=O)c1ccc(cc1)c1ccccc1': 'OC(=O)c1ccc(cc1)c1ccccc1', 'OC(=O)c1ccccc1': 'OC(=O)c1ccccc1', 'OC(=O)c1ccccc1O': 'OC(=O)c1ccccc1O', 'OC(=O)c1ccccc1S': 'OC(=O)c1ccccc1S', 'OC(=O)c1ccccn1': 'OC(=O)c1ccccn1', 'OC(=O)c1cccnc1': 'OC(=O)c1cccnc1', 'OC(=O)c1cccnc1S': 'OC(=O)c1cccnc1S', 'OC(=O)c1ccncc1': 'OC(=O)c1ccncc1', 'OC(=O)c1n[nH]c(n1)N': 'OC(=O)c1n[nH]c(n1)N', 'OCC(CO)O': 'OCC(CO)O', 'OC[C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O': 'OC[C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O', 'OC[C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O': 'OC[C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O', 'OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O': 'OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O', 'O[C@H]1C(=O)OCC1(C)C': 'O[C@H]1C(=O)OCC1(C)C', 'Oc1ccc(cc1)C(=O)O': 'Oc1ccc(cc1)C(=O)O', 'Oc1ccc(cc1)S([O])([O])O': 'Oc1ccc(cc1)S([O])([O])O', 'Oc1cccc2c1nccc2': 'Oc1cccc2c1nccc2', 'Oc1ccccc1c1nnc([nH]1)S': 'Oc1ccccc1c1nnc([nH]1)S', 'On1nnc2c1cccc2': 'On1nnc2c1cccc2', 'S=c1[nH]c2c([nH]1)c(=O)n(cn2)C': 'S=c1[nH]c2c([nH]1)c(=O)n(cn2)C', 'S=c1[nH]c2c([nH]1)cncn2': 'S=c1[nH]c2c([nH]1)cncn2', 'S=c1[nH]c2c([nH]1)nccn2': 'S=c1[nH]c2c([nH]1)nccn2', 'S=c1[nH]nc([nH]1)c1cccnc1': 'S=c1[nH]nc([nH]1)c1cccnc1', 'S=c1[nH]nc([nH]1)c1ccco1': 'S=c1[nH]nc([nH]1)c1ccco1', 'S=c1[nH]nc([nH]1)c1ccncc1': 'S=c1[nH]nc([nH]1)c1ccncc1', 'S=c1sc2c([nH]1)cccc2': 'S=c1sc2c([nH]1)cccc2', 'SC#N': 'SC#N', 'S[C]1NC2=C[CH]C=NC2=N1': 'S[C]1NC2=C[CH]C=NC2=N1', 'Sc1n[nH]cn1': 'Sc1n[nH]cn1', 'Sc1nc(N)c(c(n1)S)N': 'Sc1nc(N)c(c(n1)S)N', 'Sc1nc(N)c2c(n1)[nH]nc2': 'Sc1nc(N)c2c(n1)[nH]nc2', 'Sc1nc2c([nH]1)cccc2': 'Sc1nc2c([nH]1)cccc2', 'Sc1ncc[nH]1': 'Sc1ncc[nH]1', 'Sc1ncccn1': 'Sc1ncccn1', 'Sc1nnc(s1)S': 'Sc1nnc(s1)S', '[Cl-].[Cl-].[Cl-].[Ce+3]': '[Cl-].[Cl-].[Cl-].[Ce+3]', '[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+3]': '[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+3]', '[NH4+].[NH4+].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+4]': '[NH4+].[NH4+].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[N+](=O)([O-])[O-].[Ce+4]', '[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Ce+3].[Ce+3]': '[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Ce+3].[Ce+3]', '[O-]S(=O)[O-].[Na+].[Na+]': '[O-]S(=O)[O-].[Na+].[Na+]', 'c1cc(ccc1c2[nH]c(nn2)S)[N+](=O)[O-]': 'c1cc(ccc1c2[nH]c(nn2)S)[N+](=O)[O-]', 'c1ccc(nc1)c1ccccn1': 'c1ccc(nc1)c1ccccn1', 'c1ccc2c(c1)[nH]nn2': 'c1ccc2c(c1)[nH]nn2', 'c1ncn[nH]1': 'c1ncn[nH]1'}, decorrelate=0.7, encoding=)], exp_rep= Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", - "0 24.0 4.0 0.0010 0.10 \n", - "1 24.0 7.0 0.0005 0.05 \n", - "2 24.0 10.0 0.0010 0.10 \n", - "3 24.0 4.0 0.0010 0.10 \n", - "4 24.0 7.0 0.0005 0.05 \n", - ".. ... ... ... ... \n", - "510 24.0 7.0 0.0005 0.05 \n", - "511 24.0 10.0 0.0010 0.10 \n", - "512 672.0 7.0 0.0010 0.10 \n", - "513 24.0 4.0 0.0010 0.10 \n", - "514 24.0 10.0 0.0010 0.10 \n", - "\n", - " SMILES \n", - "0 C(=O)(C(=O)[O-])[O-] \n", - "1 C(=O)(C(=O)[O-])[O-] \n", - "2 C(=O)(C(=O)[O-])[O-] \n", - "3 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O \n", - "4 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O \n", - ".. ... \n", - "510 c1ccc2c(c1)[nH]nn2 \n", - "511 c1ccc2c(c1)[nH]nn2 \n", - "512 c1ccc2c(c1)[nH]nn2 \n", - "513 c1ncn[nH]1 \n", - "514 c1ncn[nH]1 \n", - "\n", - "[515 rows x 5 columns], metadata= was_recommended was_measured dont_recommend\n", - "0 False False False\n", - "1 False False False\n", - "2 False False False\n", - "3 False False False\n", - "4 False False False\n", - ".. ... ... ...\n", - "510 False False False\n", - "511 False False False\n", - "512 False False False\n", - "513 False False False\n", - "514 False False False\n", - "\n", - "[515 rows x 3 columns], empty_encoding=False, constraints=[], comp_rep= Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", - "0 24.0 4.0 0.0010 0.10 \n", - "1 24.0 7.0 0.0005 0.05 \n", - "2 24.0 10.0 0.0010 0.10 \n", - "3 24.0 4.0 0.0010 0.10 \n", - "4 24.0 7.0 0.0005 0.05 \n", - ".. ... ... ... ... \n", - "510 24.0 7.0 0.0005 0.05 \n", - "511 24.0 10.0 0.0010 0.10 \n", - "512 672.0 7.0 0.0010 0.10 \n", - "513 24.0 4.0 0.0010 0.10 \n", - "514 24.0 10.0 0.0010 0.10 \n", - "\n", - " SMILES_RDKIT_MaxAbsEStateIndex SMILES_RDKIT_MinAbsEStateIndex \\\n", - "0 8.925926 2.185185 \n", - "1 8.925926 2.185185 \n", - "2 8.925926 2.185185 \n", - "3 10.148889 1.357824 \n", - "4 10.148889 1.357824 \n", - ".. ... ... \n", - "510 3.813148 0.914352 \n", - "511 3.813148 0.914352 \n", - "512 3.813148 0.914352 \n", - "513 3.555556 1.444444 \n", - "514 3.555556 1.444444 \n", - "\n", - " SMILES_RDKIT_MinEStateIndex SMILES_RDKIT_qed SMILES_RDKIT_SPS \\\n", - "0 -2.185185 0.287408 7.333333 \n", - "1 -2.185185 0.287408 7.333333 \n", - "2 -2.185185 0.287408 7.333333 \n", - "3 -2.974537 0.454904 10.846154 \n", - "4 -2.974537 0.454904 10.846154 \n", - ".. ... ... ... \n", - "510 0.914352 0.560736 10.222222 \n", - "511 0.914352 0.560736 10.222222 \n", - "512 0.914352 0.560736 10.222222 \n", - "513 1.444444 0.458207 8.000000 \n", - "514 1.444444 0.458207 8.000000 \n", - "\n", - " SMILES_RDKIT_MolWt ... SMILES_RDKIT_fr_nitro \\\n", - "0 88.018 ... 0 \n", - "1 88.018 ... 0 \n", - "2 88.018 ... 0 \n", - "3 189.099 ... 0 \n", - "4 189.099 ... 0 \n", - ".. ... ... ... \n", - "510 119.127 ... 0 \n", - "511 119.127 ... 0 \n", - "512 119.127 ... 0 \n", - "513 69.067 ... 0 \n", - "514 69.067 ... 0 \n", - "\n", - " SMILES_RDKIT_fr_nitro_arom_nonortho SMILES_RDKIT_fr_oxime \\\n", - "0 0 0 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 \n", - ".. ... ... \n", - "510 0 0 \n", - "511 0 0 \n", - "512 0 0 \n", - "513 0 0 \n", - "514 0 0 \n", - "\n", - " SMILES_RDKIT_fr_para_hydroxylation SMILES_RDKIT_fr_phos_acid \\\n", - "0 0 0 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 \n", - ".. ... ... \n", - "510 1 0 \n", - "511 1 0 \n", - "512 1 0 \n", - "513 0 0 \n", - "514 0 0 \n", - "\n", - " SMILES_RDKIT_fr_pyridine SMILES_RDKIT_fr_quatN SMILES_RDKIT_fr_sulfide \\\n", - "0 0 0 0 \n", - "1 0 0 0 \n", - "2 0 0 0 \n", - "3 0 0 0 \n", - "4 0 0 0 \n", - ".. ... ... ... \n", - "510 0 0 0 \n", - "511 0 0 0 \n", - "512 0 0 0 \n", - "513 0 0 0 \n", - "514 0 0 0 \n", - "\n", - " SMILES_RDKIT_fr_tetrazole SMILES_RDKIT_fr_thiazole \n", - "0 0 0 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 \n", - ".. ... ... \n", - "510 0 0 \n", - "511 0 0 \n", - "512 0 0 \n", - "513 0 0 \n", - "514 0 0 \n", - "\n", - "[515 rows x 94 columns]), continuous=SubspaceContinuous(parameters=[], constraints_lin_eq=[], constraints_lin_ineq=[]))" - ] - }, - "execution_count": 302, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "searchspace_rdkit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Defining the campaign = searchspace + objective" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": {}, - "outputs": [], - "source": [ - "campaign_mordred = Campaign(searchspace=searchspace_mordred, objective=objective)\n", - "campaign_morgan = Campaign(searchspace=searchspace_morgan, objective=objective)\n", - "campaign_rdkit = Campaign(searchspace=searchspace_rdkit, objective=objective)\n", - "campaign_ohe = Campaign(searchspace=searchspace_ohe, objective=objective)\n", - "\n", - "# not all randoms are used but checked for differences in behaviour\n", - "campaign_rand_mordred = Campaign(\n", - " searchspace=searchspace_mordred,\n", - " recommender=TwoPhaseMetaRecommender(recommender=RandomRecommender()),\n", - " objective=objective,\n", - ")\n", - "campaign_rand_morgan = Campaign(\n", - " searchspace=searchspace_morgan,\n", - " recommender=TwoPhaseMetaRecommender(recommender=RandomRecommender()),\n", - " objective=objective,\n", - ")\n", - "campaign_rand_rdkit = Campaign(\n", - " searchspace=searchspace_rdkit,\n", - " recommender=TwoPhaseMetaRecommender(recommender=RandomRecommender()),\n", - " objective=objective,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Puttting the campaigns that we are interested in a scenario" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scenarios = {\"Mordred\": campaign_mordred, #\"Random\": campaign_rand_mordred,\n", - " \"Morgan\": campaign_morgan, #\"Morgan Random\": campaign_rand_morgan,\n", - " \"RDKIT\": campaign_rdkit,\n", - " \"OHE\": campaign_ohe, \n", - " \"Random\": campaign_rand_rdkit\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Start our simulations" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/50 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "max_yield = lookup[\"Efficiency\"].max()\n", - "\n", - "# until 10\n", - "limit = 10\n", - "\n", - "# Create a figure and axis object\n", - "fig, ax1 = plt.subplots()\n", - "\n", - "# Plot the lineplot\n", - "sns.lineplot(\n", - " data=results, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\", ax=ax1, style = 'Scenario'\n", - ")\n", - "\n", - "# Set legend\n", - "ax1.legend(loc=\"lower right\")\n", - "\n", - "# Add a horizontal line\n", - "ax1.plot([0.5, limit+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", - "\n", - "# Set x-axis limit\n", - "ax1.set_xlim(0, limit+1)\n", - "ax1.set_ylim(50, 101)\n", - "\n", - "# Create a new axis for the histogram on the right side\n", - "ax2 = fig.add_axes([0.905, 0.1, 0.05, 0.8])\n", - "ax2.hist(df_active['Efficiency'], bins=2000, color='orange', alpha=0.5, orientation='horizontal') \n", - "ax2.set_ylim(ax1.get_ylim()) \n", - "ax2.set_axis_off() # Hide axis ticks and labels\n", - "\n", - "# Set x and y titles\n", - "ax1.set_xlabel('Number of Experiments')\n", - "ax1.set_ylabel('Cumulative Best Efficiency')\n", - "\n", - "# Save the plot\n", - "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first{limit}.png\", bbox_inches='tight')\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 314, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# until 25\n", - "limit = 25\n", - "\n", - "# Create a figure and axis object\n", - "fig, ax1 = plt.subplots()\n", - "\n", - "# Plot the lineplot\n", - "sns.lineplot(\n", - " data=results, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\", ax=ax1, style = 'Scenario'\n", - ")\n", - "\n", - "# Set legend\n", - "ax1.legend(loc=\"lower right\")\n", - "\n", - "# Add a horizontal line\n", - "ax1.plot([0.5, limit+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", - "\n", - "# Set x-axis limit\n", - "ax1.set_xlim(0, limit+1)\n", - "ax1.set_ylim(50, 101)\n", - "\n", - "# Create a new axis for the histogram on the right side\n", - "ax2 = fig.add_axes([0.905, 0.1, 0.05, 0.8])\n", - "ax2.hist(df_active['Efficiency'], bins=2000, color='orange', alpha=0.5, orientation='horizontal') \n", - "ax2.set_ylim(ax1.get_ylim()) \n", - "ax2.set_axis_off() # Hide axis ticks and labels\n", - "\n", - "# Set x and y titles\n", - "ax1.set_xlabel('Number of Experiments')\n", - "ax1.set_ylabel('Cumulative Best Efficiency')\n", - "\n", - "# Save the plot\n", - "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first{limit}.png\", bbox_inches='tight')\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 315, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# all experiments\n", - "\n", - "# until 50\n", - "limit = 50\n", - "\n", - "# Create a figure and axis object\n", - "fig, ax1 = plt.subplots()\n", - "\n", - "# Plot the lineplot\n", - "sns.lineplot(\n", - " data=results, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\", ax=ax1, style = 'Scenario'\n", - ")\n", - "\n", - "# Set legend\n", - "ax1.legend(loc=\"lower right\")\n", - "\n", - "# Add a horizontal line\n", - "ax1.plot([0.5, limit+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", - "\n", - "# Set x-axis limit\n", - "ax1.set_xlim(0, limit+1)\n", - "ax1.set_ylim(50, 101)\n", - "\n", - "# Create a new axis for the histogram on the right side\n", - "ax2 = fig.add_axes([0.905, 0.1, 0.05, 0.8])\n", - "ax2.hist(df_active['Efficiency'], bins=2000, color='orange', alpha=0.5, orientation='horizontal') \n", - "ax2.set_ylim(ax1.get_ylim()) \n", - "ax2.set_axis_off() # Hide axis ticks and labels\n", - "\n", - "# Set x and y titles\n", - "ax1.set_xlabel('Number of Experiments')\n", - "ax1.set_ylabel('Cumulative Best Efficiency')\n", - "\n", - "# Save the plot\n", - "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first{limit}.png\", bbox_inches='tight')\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 310, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ScenarioRandom_SeedIterationNum_ExperimentsEfficiency_MeasurementsEfficiency_IterBestEfficiency_CumBest
0Mordred133701[10.0]10.00000010.000000
1Mordred133712[96.43666666666667]96.43666796.436667
2Mordred133723[25.25]25.25000096.436667
3Mordred133734[99.21666666666665]99.21666799.216667
4Mordred133745[93.8]93.80000099.216667
........................
2495Random13464546[99.9]99.900000100.000000
2496Random13464647[40.0]40.000000100.000000
2497Random13464748[10.0]10.000000100.000000
2498Random13464849[91.7]91.700000100.000000
2499Random13464950[0.0]0.000000100.000000
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" - ], - "text/plain": [ - " Scenario Random_Seed Iteration Num_Experiments \\\n", - "0 Mordred 1337 0 1 \n", - "1 Mordred 1337 1 2 \n", - "2 Mordred 1337 2 3 \n", - "3 Mordred 1337 3 4 \n", - "4 Mordred 1337 4 5 \n", - "... ... ... ... ... \n", - "2495 Random 1346 45 46 \n", - "2496 Random 1346 46 47 \n", - "2497 Random 1346 47 48 \n", - "2498 Random 1346 48 49 \n", - "2499 Random 1346 49 50 \n", - "\n", - " Efficiency_Measurements Efficiency_IterBest Efficiency_CumBest \n", - "0 [10.0] 10.000000 10.000000 \n", - "1 [96.43666666666667] 96.436667 96.436667 \n", - "2 [25.25] 25.250000 96.436667 \n", - "3 [99.21666666666665] 99.216667 99.216667 \n", - "4 [93.8] 93.800000 99.216667 \n", - "... ... ... ... \n", - "2495 [99.9] 99.900000 100.000000 \n", - "2496 [40.0] 40.000000 100.000000 \n", - "2497 [10.0] 10.000000 100.000000 \n", - "2498 [91.7] 91.700000 100.000000 \n", - "2499 [0.0] 0.000000 100.000000 \n", - "\n", - "[2500 rows x 7 columns]" - ] - }, - "execution_count": 310, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Transfer Learning\n", - "### Use transfer learning to gain information from prior experimental campaigns." - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": {}, - "outputs": [], - "source": [ - "df_active = df_AA2024\n", - "df_transfer = df_AA1000" - ] - }, - { - "cell_type": "code", - "execution_count": 318, - "metadata": {}, - "outputs": [], - "source": [ - "from baybe.parameters import TaskParameter\n", - "\n", - "taskparam = TaskParameter(\n", - " name=\"Al_alloys\",\n", - " values=[\"AA1000\", \"AA2024\"],\n", - " active_values=[\"AA2024\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 321, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Time_hpHInhib_Concentrat_MSalt_Concentrat_MEfficiency
count848.000000848.0000008.480000e+02848.000000848.000000
mean126.8431604.1895806.352976e-020.08896235.066659
std192.0556763.6961833.690920e-010.227758245.617010
min0.000000-0.6000001.000000e-070.000000-4834.000000
25%6.0000000.0000005.000000e-040.00000035.000000
50%24.0000004.0000001.000000e-030.01000060.000000
75%144.0000007.0000004.200000e-030.10000080.507500
max720.00000013.0000003.280000e+002.000000100.000000
\n", - "
" - ], - "text/plain": [ - " Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", - "count 848.000000 848.000000 8.480000e+02 848.000000 \n", - "mean 126.843160 4.189580 6.352976e-02 0.088962 \n", - "std 192.055676 3.696183 3.690920e-01 0.227758 \n", - "min 0.000000 -0.600000 1.000000e-07 0.000000 \n", - "25% 6.000000 0.000000 5.000000e-04 0.000000 \n", - "50% 24.000000 4.000000 1.000000e-03 0.010000 \n", - "75% 144.000000 7.000000 4.200000e-03 0.100000 \n", - "max 720.000000 13.000000 3.280000e+00 2.000000 \n", - "\n", - " Efficiency \n", - "count 848.000000 \n", - "mean 35.066659 \n", - "std 245.617010 \n", - "min -4834.000000 \n", - "25% 35.000000 \n", - "50% 60.000000 \n", - "75% 80.507500 \n", - "max 100.000000 " - ] - }, - "execution_count": 321, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "df_combined = pd.concat([df_active, df_transfer], axis=0)\n", - "df_combined.describe()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "metadata": {}, - "outputs": [], - "source": [ - "unique_SMILES_transfer = df_transfer[\"SMILES\"].unique()\n", - "unique_SMILES = df_combined[\"SMILES\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 333, - "metadata": {}, - "outputs": [], - "source": [ - "from baybe.parameters import NumericalContinuousParameter, CategoricalParameter, NumericalDiscreteParameter\n", - "from baybe.searchspace import SearchSpace\n", - "\n", - "transfer_parameters=[\n", - "NumericalDiscreteParameter(\n", - " name=\"Time_h\",\n", - " values=df_combined[\"Time_h\"].unique(),\n", - " tolerance=5/60,\n", - "),\n", - "NumericalDiscreteParameter(\n", - " name=\"pH\",\n", - " values=df_combined[\"pH\"].unique(),\n", - " ), \n", - "NumericalDiscreteParameter(\n", - " name=\"Inhib_Concentrat_M\",\n", - " values=df_combined[\"Inhib_Concentrat_M\"].unique(),\n", - " ),\n", - "NumericalDiscreteParameter(\n", - " name=\"Salt_Concentrat_M\",\n", - " values=df_combined[\"Salt_Concentrat_M\"].unique(),\n", - " ),\n", - "CategoricalParameter(\n", - " name=\"SMILES\",\n", - " values=unique_SMILES,\n", - " encoding=\"OHE\",\n", - " )\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 334, - "metadata": {}, - "outputs": [], - "source": [ - "searchspace_transfer = SearchSpace.from_dataframe(df_transfer.drop(\"Efficiency\", axis = 1), transfer_parameters)\n", - "\n", - "campaign_transfer = Campaign(searchspace_transfer, objective)" - ] - }, - { - "cell_type": "code", - "execution_count": 328, - "metadata": {}, - "outputs": [], - "source": [ - "df_features = df_active.drop(\"Efficiency\", axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 335, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/10 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Time_hpHInhib_Concentrat_MSalt_Concentrat_MEfficiency
count258.000000258.000000258.000000258.000000258.000000
mean167.6027136.6360470.0073860.11790728.268191
std220.4887882.1496130.0132020.166813265.800655
min0.5000000.0000000.0000100.000000-3813.000000
25%24.0000005.4000000.0010000.05000030.000000
50%24.0000007.0000000.0010000.10000055.000000
75%240.0000007.0000000.0045000.10000089.000000
max672.00000010.0000000.0440000.600000100.000000
\n", - "" - ], - "text/plain": [ - " Time_h pH Inhib_Concentrat_M Salt_Concentrat_M \\\n", - "count 258.000000 258.000000 258.000000 258.000000 \n", - "mean 167.602713 6.636047 0.007386 0.117907 \n", - "std 220.488788 2.149613 0.013202 0.166813 \n", - "min 0.500000 0.000000 0.000010 0.000000 \n", - "25% 24.000000 5.400000 0.001000 0.050000 \n", - "50% 24.000000 7.000000 0.001000 0.100000 \n", - "75% 240.000000 7.000000 0.004500 0.100000 \n", - "max 672.000000 10.000000 0.044000 0.600000 \n", - "\n", - " Efficiency \n", - "count 258.000000 \n", - "mean 28.268191 \n", - "std 265.800655 \n", - "min -3813.000000 \n", - "25% 30.000000 \n", - "50% 55.000000 \n", - "75% 89.000000 \n", - "max 100.000000 " - ] - }, - "execution_count": 338, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fraction_df.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "concatenated_df = pd.concat([result_fresh_start, result_transfer_learning], axis=0, ignore_index=True)\n", - "concatenated_df" - ] - }, - { - "cell_type": "code", - "execution_count": 339, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 339, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# until 50\n", - "limit = 50\n", - "exp_dataset_name = 'transferAA1000_to_AA2024'\n", - "sns.lineplot(\n", - " data=concatenated_df, x=\"Num_Experiments\", y=\"Efficiency_CumBest\", hue=\"Scenario\", marker=\"x\"\n", - ")\n", - "plt.plot([0.5, N_DOE_ITERATIONS+0.5], [max_yield, max_yield], \"--r\", alpha=0.4)\n", - "plt.legend(loc=\"lower right\")\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.xlim(0, limit+1)\n", - "plt.savefig(f\"./img/{exp_dataset_name}_simulation_{N_MC_ITERATIONS}MC_{N_DOE_ITERATIONS}exp_{BATCH_SIZE}batch_first25.png\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Alex6022/Scenarios b/legacy_code/Alex6022/Scenarios similarity index 100% rename from Alex6022/Scenarios rename to legacy_code/Alex6022/Scenarios diff --git a/Alex6022/Scenarios.png b/legacy_code/Alex6022/Scenarios.png similarity index 100% rename from Alex6022/Scenarios.png rename to legacy_code/Alex6022/Scenarios.png diff --git a/Alex6022/baybe-inhibitor Legacy.ipynb b/legacy_code/Alex6022/baybe-inhibitor Legacy.ipynb similarity index 100% rename from Alex6022/baybe-inhibitor Legacy.ipynb rename to legacy_code/Alex6022/baybe-inhibitor Legacy.ipynb diff --git a/Alex6022/baybe-inhibitor.ipynb b/legacy_code/Alex6022/baybe-inhibitor.ipynb similarity index 100% rename from Alex6022/baybe-inhibitor.ipynb rename to legacy_code/Alex6022/baybe-inhibitor.ipynb diff --git a/legacy_code/baybe-inhibitor.ipynb b/legacy_code/baybe-inhibitor.ipynb new file mode 100644 index 0000000..c584dc7 --- /dev/null +++ b/legacy_code/baybe-inhibitor.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This project will focus on exploring the capabilities of Bayesian optimization, specifically employing BayBE, in the discovery of novel corrosion inhibitors for materials design. Initially, we will work with a randomly chosen subset from a comprehensive database of electrochemical responses of small organic molecules. Our goal is to assess how Bayesian optimization can speed up the screening process across the design space to identify promising compounds. We will compare different strategies for incorporating alloy information, while optimizing the experimental parameters with respect to the inhibitive performance of the screened compounds." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initizalization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading libraries and data files:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from baybe import Campaign\n", + "\n", + "df_AA2024 = pd.read_excel('data/filtered_AA2024.xlsx')\n", + "df_AA1000 = pd.read_excel('data/filtered_AA1000.xlsx')\n", + "df_Al = pd.read_excel('data/filtered_Al.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Processing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Anaylsis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Search Space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Objective" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recommender" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Benchmarking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transfer Learning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/michalis-baybe-inhibitor.ipynb 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