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@canozkan42
canozkan42 committed Mar 27, 2024
2 parents bfc02c2 + 1a53665 commit 2363950
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193 changes: 193 additions & 0 deletions BayBE_basic.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import filtered data set"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"df_AA2024 = pd.read_excel('data/filtered_AA2024.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Time_h pH Inhib_Concentrat_M Efficiency\n",
"count 611.000000 611.000000 611.000000 611.000000\n",
"mean 135.801964 6.342062 0.006808 26.736841\n",
"std 201.683867 2.529080 0.014059 288.788317\n",
"min 0.500000 0.000000 0.000010 -4834.000000\n",
"25% 24.000000 4.000000 0.000500 30.000000\n",
"50% 24.000000 7.000000 0.001000 58.000000\n",
"75% 144.000000 7.000000 0.003000 87.950000\n",
"max 672.000000 10.000000 0.100000 100.000000\n"
]
}
],
"source": [
"print(df_AA2024.describe())\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" SMILES Time_h pH Inhib_Concentrat_M \\\n",
"0 COCCOC(=O)OCSc1nc2c(s1)cccc2 24.0 4.0 0.001 \n",
"1 COCCOC(=O)OCSc1nc2c(s1)cccc2 24.0 10.0 0.001 \n",
"2 Cc1ccc(c(c1)n1nc2c(n1)cccc2)O 24.0 4.0 0.001 \n",
"3 Cc1ccc(c(c1)n1nc2c(n1)cccc2)O 24.0 10.0 0.001 \n",
"4 Clc1ccc(cc1)CC[C@](C(C)(C)C)(Cn1cncn1)O 24.0 4.0 0.001 \n",
"\n",
" Efficiency \n",
"0 0.0 \n",
"1 0.0 \n",
"2 30.0 \n",
"3 30.0 \n",
"4 30.0 \n"
]
}
],
"source": [
"print(df_AA2024.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set objectives and parameters in BayBE to define the search space"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from baybe.targets import NumericalTarget\n",
"from baybe.objective import Objective\n",
"\n",
"target = NumericalTarget(\n",
" name=\"Efficiency\",\n",
" mode=\"MAX\",\n",
")\n",
"objective = Objective(mode=\"SINGLE\", targets=[target])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"from baybe.parameters import (\n",
" NumericalContinuousParameter,\n",
")\n",
"\n",
"parameters = [\n",
" NumericalContinuousParameter(\n",
" name=\"Time[h]\",\n",
" bounds=(0.500000, 672),\n",
" ),\n",
" NumericalContinuousParameter(\n",
" name=\"pH\",\n",
" bounds=(1, 14),\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"from baybe.searchspace import SearchSpace\n",
"\n",
"searchspace = SearchSpace.from_product(parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the campaign"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"from baybe import Campaign\n",
"\n",
"campaign = Campaign(searchspace, objective)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Time[h] pH\n",
"0 638.310058 2.638211\n",
"1 475.753030 2.599470\n",
"2 452.512074 4.018597\n"
]
}
],
"source": [
"df = campaign.recommend(batch_size=3)\n",
"print(df)"
]
}
],
"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
}
37 changes: 26 additions & 11 deletions baybe-inhibitor.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -11,29 +11,44 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"df_AA2024 = pd.read_excel('data/filtered_AA2024.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"name": "stdout",
"output_type": "stream",
"text": [
"/home/vscode/.local/lib/python3.10/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\"<urllib3.connection.HTTPConnection object at 0x7f151b4947f0>: Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n",
" warnings.warn(\n",
"/home/vscode/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
" Time_h pH Inhib_Concentrat_M Efficiency\n",
"count 611.000000 611.000000 611.000000 611.000000\n",
"mean 135.801964 6.342062 0.006808 26.736841\n",
"std 201.683867 2.529080 0.014059 288.788317\n",
"min 0.500000 0.000000 0.000010 -4834.000000\n",
"25% 24.000000 4.000000 0.000500 30.000000\n",
"50% 24.000000 7.000000 0.001000 58.000000\n",
"75% 144.000000 7.000000 0.003000 87.950000\n",
"max 672.000000 10.000000 0.100000 100.000000\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from baybe import Campaign"
"print(df_AA2024.describe())"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
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