From 713802d07c7a724953fe8f7d4365a7c5857947b5 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 12:33:44 +0000
Subject: [PATCH] simulations complete for AA1000, AA2024, AA5000, AA6000,
AA7075, Al
---
can_baybe-inhibitor.ipynb | 763 +++++++++---------
data/averaged_filtered_AA5000.xlsx | Bin 5683 -> 7720 bytes
data/averaged_filtered_AA6000.xlsx | Bin 0 -> 9202 bytes
data/averaged_filtered_AA7075.xlsx | Bin 11072 -> 15237 bytes
img/AA1000_simulation_10MC_50exp_1batch.png | Bin 0 -> 69240 bytes
...0_simulation_10MC_50exp_1batch_first10.png | Bin 0 -> 59011 bytes
...0_simulation_10MC_50exp_1batch_first25.png | Bin 0 -> 68151 bytes
img/AA2024_simulation_10MC_50exp_1batch.png | Bin 36064 -> 36032 bytes
...4_simulation_10MC_50exp_1batch_first10.png | Bin 40972 -> 40829 bytes
...4_simulation_10MC_50exp_1batch_first25.png | Bin 36077 -> 38533 bytes
img/AA5000_simulation_10MC_50exp_1batch.png | Bin 0 -> 42058 bytes
...0_simulation_10MC_50exp_1batch_first10.png | Bin 0 -> 58431 bytes
...0_simulation_10MC_50exp_1batch_first25.png | Bin 0 -> 48040 bytes
img/AA6000_simulation_10MC_50exp_1batch.png | Bin 0 -> 41141 bytes
...0_simulation_10MC_50exp_1batch_first10.png | Bin 0 -> 55741 bytes
...0_simulation_10MC_50exp_1batch_first25.png | Bin 0 -> 49688 bytes
img/AA7075_simulation_10MC_50exp_1batch.png | Bin 0 -> 72920 bytes
...5_simulation_10MC_50exp_1batch_first10.png | Bin 0 -> 57812 bytes
...5_simulation_10MC_50exp_1batch_first25.png | Bin 0 -> 68135 bytes
img/Al_simulation_10MC_50exp_1batch.png | Bin 0 -> 53401 bytes
...l_simulation_10MC_50exp_1batch_first10.png | Bin 0 -> 57750 bytes
...l_simulation_10MC_50exp_1batch_first25.png | Bin 0 -> 57345 bytes
.../AA1000_simulation_10MC_50exp_1batch.xlsx | Bin 0 -> 95099 bytes
.../AA2024_simulation_10MC_50exp_1batch.xlsx | Bin 94158 -> 94161 bytes
.../AA5000_simulation_10MC_50exp_1batch.xlsx | Bin 0 -> 93215 bytes
.../AA6000_simulation_10MC_50exp_1batch.xlsx | Bin 0 -> 44831 bytes
.../AA7075_simulation_10MC_50exp_1batch.xlsx | Bin 0 -> 89896 bytes
results/Al_simulation_10MC_50exp_1batch.xlsx | Bin 0 -> 96128 bytes
28 files changed, 364 insertions(+), 399 deletions(-)
create mode 100644 data/averaged_filtered_AA6000.xlsx
create mode 100644 img/AA1000_simulation_10MC_50exp_1batch.png
create mode 100644 img/AA1000_simulation_10MC_50exp_1batch_first10.png
create mode 100644 img/AA1000_simulation_10MC_50exp_1batch_first25.png
create mode 100644 img/AA5000_simulation_10MC_50exp_1batch.png
create mode 100644 img/AA5000_simulation_10MC_50exp_1batch_first10.png
create mode 100644 img/AA5000_simulation_10MC_50exp_1batch_first25.png
create mode 100644 img/AA6000_simulation_10MC_50exp_1batch.png
create mode 100644 img/AA6000_simulation_10MC_50exp_1batch_first10.png
create mode 100644 img/AA6000_simulation_10MC_50exp_1batch_first25.png
create mode 100644 img/AA7075_simulation_10MC_50exp_1batch.png
create mode 100644 img/AA7075_simulation_10MC_50exp_1batch_first10.png
create mode 100644 img/AA7075_simulation_10MC_50exp_1batch_first25.png
create mode 100644 img/Al_simulation_10MC_50exp_1batch.png
create mode 100644 img/Al_simulation_10MC_50exp_1batch_first10.png
create mode 100644 img/Al_simulation_10MC_50exp_1batch_first25.png
create mode 100644 results/AA1000_simulation_10MC_50exp_1batch.xlsx
create mode 100644 results/AA5000_simulation_10MC_50exp_1batch.xlsx
create mode 100644 results/AA6000_simulation_10MC_50exp_1batch.xlsx
create mode 100644 results/AA7075_simulation_10MC_50exp_1batch.xlsx
create mode 100644 results/Al_simulation_10MC_50exp_1batch.xlsx
diff --git a/can_baybe-inhibitor.ipynb b/can_baybe-inhibitor.ipynb
index a1d5dbd..4e98bce 100644
--- a/can_baybe-inhibitor.ipynb
+++ b/can_baybe-inhibitor.ipynb
@@ -30,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 164,
"metadata": {},
"outputs": [
{
@@ -68,16 +68,16 @@
"
C(=O)(C(=O)[O-])[O-] | \n",
" 24.0 | \n",
" 4.0 | \n",
- " 0.0010 | \n",
+ " 1.000000e-03 | \n",
" 0.10 | \n",
- " 20.00 | \n",
+ " 15.00 | \n",
" \n",
" \n",
" 1 | \n",
" C(=O)(C(=O)[O-])[O-] | \n",
" 24.0 | \n",
" 7.0 | \n",
- " 0.0005 | \n",
+ " 5.000000e-04 | \n",
" 0.05 | \n",
" 12.35 | \n",
"
\n",
@@ -86,27 +86,27 @@
" C(=O)(C(=O)[O-])[O-] | \n",
" 24.0 | \n",
" 10.0 | \n",
- " 0.0010 | \n",
+ " 1.000000e-03 | \n",
" 0.10 | \n",
- " 20.00 | \n",
+ " 30.00 | \n",
" \n",
" \n",
" 3 | \n",
" C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O | \n",
- " 24.0 | \n",
- " 4.0 | \n",
- " 0.0010 | \n",
- " 0.10 | \n",
- " 30.00 | \n",
+ " 0.0 | \n",
+ " 2.0 | \n",
+ " 5.000000e-07 | \n",
+ " 2.00 | \n",
+ " 53.85 | \n",
"
\n",
" \n",
" 4 | \n",
" C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O | \n",
- " 24.0 | \n",
- " 7.0 | \n",
- " 0.0005 | \n",
- " 0.05 | \n",
- " -23.95 | \n",
+ " 0.0 | \n",
+ " 2.0 | \n",
+ " 1.000000e-06 | \n",
+ " 2.00 | \n",
+ " 58.55 | \n",
"
\n",
" \n",
" ... | \n",
@@ -118,86 +118,86 @@
" ... | \n",
"
\n",
" \n",
- " 510 | \n",
+ " 986 | \n",
" c1ccc2c(c1)[nH]nn2 | \n",
" 24.0 | \n",
" 7.0 | \n",
- " 0.0005 | \n",
+ " 5.000000e-04 | \n",
" 0.05 | \n",
" 97.95 | \n",
"
\n",
" \n",
- " 511 | \n",
+ " 987 | \n",
" c1ccc2c(c1)[nH]nn2 | \n",
" 24.0 | \n",
" 10.0 | \n",
- " 0.0010 | \n",
+ " 1.000000e-03 | \n",
" 0.10 | \n",
- " 90.00 | \n",
+ " 60.00 | \n",
"
\n",
" \n",
- " 512 | \n",
+ " 988 | \n",
" c1ccc2c(c1)[nH]nn2 | \n",
" 672.0 | \n",
" 7.0 | \n",
- " 0.0010 | \n",
+ " 1.000000e-03 | \n",
" 0.10 | \n",
- " 98.00 | \n",
+ " 95.00 | \n",
"
\n",
" \n",
- " 513 | \n",
+ " 989 | \n",
" c1ncn[nH]1 | \n",
" 24.0 | \n",
" 4.0 | \n",
- " 0.0010 | \n",
+ " 1.000000e-03 | \n",
" 0.10 | \n",
- " 30.00 | \n",
+ " 35.00 | \n",
"
\n",
" \n",
- " 514 | \n",
+ " 990 | \n",
" c1ncn[nH]1 | \n",
" 24.0 | \n",
" 10.0 | \n",
- " 0.0010 | \n",
+ " 1.000000e-03 | \n",
" 0.10 | \n",
- " 90.00 | \n",
+ " 50.00 | \n",
"
\n",
" \n",
"\n",
- "515 rows × 6 columns
\n",
+ "991 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",
+ "0 C(=O)(C(=O)[O-])[O-] 24.0 4.0 1.000000e-03 \n",
+ "1 C(=O)(C(=O)[O-])[O-] 24.0 7.0 5.000000e-04 \n",
+ "2 C(=O)(C(=O)[O-])[O-] 24.0 10.0 1.000000e-03 \n",
+ "3 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O 0.0 2.0 5.000000e-07 \n",
+ "4 C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O 0.0 2.0 1.000000e-06 \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",
+ "986 c1ccc2c(c1)[nH]nn2 24.0 7.0 5.000000e-04 \n",
+ "987 c1ccc2c(c1)[nH]nn2 24.0 10.0 1.000000e-03 \n",
+ "988 c1ccc2c(c1)[nH]nn2 672.0 7.0 1.000000e-03 \n",
+ "989 c1ncn[nH]1 24.0 4.0 1.000000e-03 \n",
+ "990 c1ncn[nH]1 24.0 10.0 1.000000e-03 \n",
"\n",
" Salt_Concentrat_M Efficiency \n",
- "0 0.10 20.00 \n",
+ "0 0.10 15.00 \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",
+ "2 0.10 30.00 \n",
+ "3 2.00 53.85 \n",
+ "4 2.00 58.55 \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",
+ "986 0.05 97.95 \n",
+ "987 0.10 60.00 \n",
+ "988 0.10 95.00 \n",
+ "989 0.10 35.00 \n",
+ "990 0.10 50.00 \n",
"\n",
- "[515 rows x 6 columns]"
+ "[991 rows x 6 columns]"
]
},
- "execution_count": 27,
+ "execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
@@ -219,19 +219,23 @@
"\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",
+ "df_active = df_Al\n",
+ "\n",
"\n",
"if df_active is df_AA2024:\n",
" exp_dataset_name = 'AA2024'\n",
- "elif df_active is df_AA5000:\n",
- " exp_dataset_name = 'AA5000'\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",
@@ -242,7 +246,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 165,
"metadata": {},
"outputs": [],
"source": [
@@ -251,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 166,
"metadata": {},
"outputs": [],
"source": [
@@ -266,136 +270,108 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 167,
"metadata": {},
"outputs": [],
"source": [
"# parameters\n",
"\n",
- "# mordred\n",
- "parameters_mordred = [\n",
+ "basic_parameters=[\n",
"NumericalDiscreteParameter(\n",
" name=\"Time_h\",\n",
- " values=df_active['Time_h'].unique(),\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",
- " # tolerance = 0.004\n",
+ " values=df_active[\"pH\"].unique(),\n",
" ), \n",
- "NumericalDiscreteParameter( # Set this as continuous, the values seem quite small?\n",
+ "NumericalDiscreteParameter(\n",
" name=\"Inhib_Concentrat_M\",\n",
- " values= df_active['Inhib_Concentrat_M'].unique(),\n",
- " # tolerance = 0.004\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",
- " # tolerance = 0.004\n",
+ " values=df_active[\"Salt_Concentrat_M\"].unique(),\n",
" ),\n",
- "SubstanceParameter(\n",
- " name=\"SMILES\",\n",
- " data=smiles_dict,\n",
- " encoding=\"MORDRED\", # optional\n",
- " decorrelate=0.7, # optional\n",
- " ) \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 = [\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",
- " # tolerance = 0.004\n",
- " ), \n",
- "NumericalDiscreteParameter( # Set this as continuous, the values seem quite small?\n",
- " name=\"Inhib_Concentrat_M\",\n",
- " values= df_active['Inhib_Concentrat_M'].unique(),\n",
- " # tolerance = 0.004\n",
- " ),\n",
- "NumericalDiscreteParameter(\n",
- " name=\"Salt_Concentrat_M\",\n",
- " values=df_active['Salt_Concentrat_M'].unique(),\n",
- " # tolerance = 0.004\n",
- " ),\n",
- "SubstanceParameter(\n",
- " name=\"SMILES\",\n",
- " data=smiles_dict,\n",
- " encoding=\"MORGAN_FP\", # optional\n",
- " decorrelate=0.7, # optional\n",
- " ) \n",
- " ]\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 = [\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",
- " # tolerance = 0.004\n",
- " ), \n",
- "NumericalDiscreteParameter( # Set this as continuous, the values seem quite small?\n",
- " name=\"Inhib_Concentrat_M\",\n",
- " values= df_active['Inhib_Concentrat_M'].unique(),\n",
- " # tolerance = 0.004\n",
- " ),\n",
- "NumericalDiscreteParameter(\n",
- " name=\"Salt_Concentrat_M\",\n",
- " values=df_active['Salt_Concentrat_M'].unique(),\n",
- " # tolerance = 0.004\n",
- " ),\n",
- "SubstanceParameter(\n",
- " name=\"SMILES\",\n",
- " data=smiles_dict,\n",
- " encoding=\"RDKIT\", # optional\n",
- " decorrelate=0.7, # optional\n",
- " ) \n",
- " ]\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 = [\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",
- " # tolerance = 0.004\n",
- " ), \n",
- "NumericalDiscreteParameter( # Set this as continuous, the values seem quite small?\n",
- " name=\"Inhib_Concentrat_M\",\n",
- " values= df_active['Inhib_Concentrat_M'].unique(),\n",
- " # tolerance = 0.004\n",
- " ),\n",
- "NumericalDiscreteParameter(\n",
- " name=\"Salt_Concentrat_M\",\n",
- " values=df_active['Salt_Concentrat_M'].unique(),\n",
- " # tolerance = 0.004\n",
- " ),\n",
- "CategoricalParameter(\n",
- " name=\"SMILES\",\n",
- " values=unique_SMILES,\n",
- " encoding=\"OHE\",\n",
- " )\n",
- "]\n"
+ "parameters_ohe = basic_parameters + [\n",
+ " CategoricalParameter(\n",
+ " name=\"SMILES\",\n",
+ " values=unique_SMILES,\n",
+ " encoding=\"OHE\",\n",
+ " )\n",
+ " ]"
]
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 168,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "________________________________________________________________________________\n",
+ "[Memory] Calling baybe.utils.chemistry._smiles_to_mordred_features...\n",
+ "_smiles_to_mordred_features('C1=CC(=C(C=C1C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)O)O)O')\n",
+ "_______________________________________smiles_to_mordred_features - 0.3s, 0.0min\n",
+ "________________________________________________________________________________\n",
+ "[Memory] Calling baybe.utils.chemistry._smiles_to_mordred_features...\n",
+ "_smiles_to_mordred_features('C1=CC=C(C=C1)C(=O)SC(=N)N')\n",
+ "_______________________________________smiles_to_mordred_features - 0.1s, 0.0min\n",
+ "________________________________________________________________________________\n",
+ "[Memory] Calling baybe.utils.chemistry._smiles_to_mordred_features...\n",
+ "_smiles_to_mordred_features('C1=CC=C(C=C1)C(=O)SC(=N)NC2=CC=C(C=C2)C')\n",
+ "_______________________________________smiles_to_mordred_features - 0.1s, 0.0min\n",
+ "________________________________________________________________________________\n",
+ "[Memory] Calling baybe.utils.chemistry._smiles_to_mordred_features...\n",
+ "_smiles_to_mordred_features('C1=CC=C(C=C1)C(=O)SC(=N)NC2=CC=C(C=C2)OC')\n",
+ "_______________________________________smiles_to_mordred_features - 0.1s, 0.0min\n",
+ "________________________________________________________________________________\n",
+ "[Memory] Calling baybe.utils.chemistry._smiles_to_mordred_features...\n",
+ "_smiles_to_mordred_features('N=C(NC1=CC=C(C=C1)Br)SC(O)C2=CC=CC=C2')\n",
+ "_______________________________________smiles_to_mordred_features - 0.1s, 0.0min\n",
+ "________________________________________________________________________________\n",
+ "[Memory] Calling baybe.utils.chemistry._smiles_to_mordred_features...\n",
+ "_smiles_to_mordred_features('N=C(NC1=CC=C(C=C1)Cl)SC(O)C2=CC=CC=C2')\n",
+ "_______________________________________smiles_to_mordred_features - 0.1s, 0.0min\n"
+ ]
+ }
+ ],
"source": [
"df_no_target = lookup.drop('Efficiency', axis=1)\n",
"\n",
@@ -422,24 +398,24 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 169,
"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",
+ "SearchSpace(discrete=SubspaceDiscrete(parameters=[NumericalDiscreteParameter(name='Time_h', encoding=None, _values=[0.0, 0.25, 0.33, 0.5, 0.58, 0.67, 0.75, 1.0, 1.5, 1.67, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 8.0, 10.0, 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, 720.0], tolerance=0.0), NumericalDiscreteParameter(name='pH', encoding=None, _values=[-0.6, -0.4771212547196624, -0.3979400086720376, -0.3010299956639812, -0.3, -0.1760912590556812, -0.1367205671564068, 0.0, 0.3, 0.45, 0.7, 1.0, 1.7, 2.0, 3.3, 4.0, 4.4, 4.6, 5.4, 5.5, 5.6, 7.0, 7.6, 10.0, 11.0, 13.0, 13.7, 14.30102999566398], tolerance=0.0), NumericalDiscreteParameter(name='Inhib_Concentrat_M', encoding=None, _values=[1e-07, 5e-07, 1e-06, 2e-06, 4e-06, 5e-06, 6e-06, 8e-06, 8.271845945141117e-06, 1e-05, 1.2e-05, 1.5e-05, 1.654369189028223e-05, 2e-05, 2.481553783542335e-05, 3e-05, 3.308738378056447e-05, 4e-05, 4.135922972570559e-05, 5e-05, 6e-05, 7e-05, 8e-05, 8.271845945141118e-05, 0.0001, 0.00015, 0.0001958863858961802, 0.0002, 0.00021, 0.0003, 0.0003566333808844508, 0.0003917727717923605, 0.0004, 0.00042, 0.0005, 0.0005876591576885406, 0.0006, 0.0007, 0.0007132667617689017, 0.0007835455435847209, 0.0008, 0.00084, 0.0009, 0.0009794319294809011, 0.001, 0.0011, 0.0012, 0.0013, 0.0014, 0.0015, 0.0016, 0.001783166904422254, 0.0018, 0.0019, 0.002, 0.002139800285306705, 0.0024, 0.0025, 0.0026, 0.003, 0.0032, 0.003566333808844508, 0.0039, 0.004, 0.0042, 0.00427960057061341, 0.0045, 0.005, 0.0053, 0.005706134094151214, 0.0065, 0.007, 0.0075, 0.0085, 0.009, 0.01, 0.011, 0.015, 0.02, 0.021, 0.022, 0.025, 0.031, 0.033, 0.04, 0.042, 0.044, 0.05, 0.06, 0.08, 0.1, 0.66, 1.32, 1.97, 2.63, 3.28], tolerance=0.0), NumericalDiscreteParameter(name='Salt_Concentrat_M', encoding=None, _values=[0.0, 0.01, 0.05, 0.1, 0.5, 0.6, 1.0, 2.0], tolerance=0.0), SubstanceParameter(name='SMILES', 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'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 1.000000e-03 0.10 \n",
+ "1 24.0 7.0 5.000000e-04 0.05 \n",
+ "2 24.0 10.0 1.000000e-03 0.10 \n",
+ "3 0.0 2.0 5.000000e-07 2.00 \n",
+ "4 0.0 2.0 1.000000e-06 2.00 \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",
+ "986 24.0 7.0 5.000000e-04 0.05 \n",
+ "987 24.0 10.0 1.000000e-03 0.10 \n",
+ "988 672.0 7.0 1.000000e-03 0.10 \n",
+ "989 24.0 4.0 1.000000e-03 0.10 \n",
+ "990 24.0 10.0 1.000000e-03 0.10 \n",
"\n",
" SMILES \n",
"0 C(=O)(C(=O)[O-])[O-] \n",
@@ -448,37 +424,37 @@
"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",
+ "986 c1ccc2c(c1)[nH]nn2 \n",
+ "987 c1ccc2c(c1)[nH]nn2 \n",
+ "988 c1ccc2c(c1)[nH]nn2 \n",
+ "989 c1ncn[nH]1 \n",
+ "990 c1ncn[nH]1 \n",
"\n",
- "[515 rows x 5 columns], metadata= was_recommended was_measured dont_recommend\n",
+ "[991 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",
+ "986 False False False\n",
+ "987 False False False\n",
+ "988 False False False\n",
+ "989 False False False\n",
+ "990 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",
+ "[991 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 1.000000e-03 0.10 \n",
+ "1 24.0 7.0 5.000000e-04 0.05 \n",
+ "2 24.0 10.0 1.000000e-03 0.10 \n",
+ "3 0.0 2.0 5.000000e-07 2.00 \n",
+ "4 0.0 2.0 1.000000e-06 2.00 \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",
+ "986 24.0 7.0 5.000000e-04 0.05 \n",
+ "987 24.0 10.0 1.000000e-03 0.10 \n",
+ "988 672.0 7.0 1.000000e-03 0.10 \n",
+ "989 24.0 4.0 1.000000e-03 0.10 \n",
+ "990 24.0 10.0 1.000000e-03 0.10 \n",
"\n",
" SMILES_RDKIT_MaxAbsEStateIndex SMILES_RDKIT_MinAbsEStateIndex \\\n",
"0 8.925926 2.185185 \n",
@@ -487,11 +463,11 @@
"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",
+ "986 3.813148 0.914352 \n",
+ "987 3.813148 0.914352 \n",
+ "988 3.813148 0.914352 \n",
+ "989 3.555556 1.444444 \n",
+ "990 3.555556 1.444444 \n",
"\n",
" SMILES_RDKIT_MinEStateIndex SMILES_RDKIT_qed SMILES_RDKIT_SPS \\\n",
"0 -2.185185 0.287408 7.333333 \n",
@@ -500,50 +476,50 @@
"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",
+ "986 0.914352 0.560736 10.222222 \n",
+ "987 0.914352 0.560736 10.222222 \n",
+ "988 0.914352 0.560736 10.222222 \n",
+ "989 1.444444 0.458207 8.000000 \n",
+ "990 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",
+ " SMILES_RDKIT_MolWt ... SMILES_RDKIT_fr_nitro_arom_nonortho \\\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",
+ "986 119.127 ... 0 \n",
+ "987 119.127 ... 0 \n",
+ "988 119.127 ... 0 \n",
+ "989 69.067 ... 0 \n",
+ "990 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",
+ " SMILES_RDKIT_fr_oxime SMILES_RDKIT_fr_para_hydroxylation \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ ".. ... ... \n",
+ "986 0 1 \n",
+ "987 0 1 \n",
+ "988 0 1 \n",
+ "989 0 0 \n",
+ "990 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",
+ " SMILES_RDKIT_fr_phos_acid SMILES_RDKIT_fr_priamide \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ ".. ... ... \n",
+ "986 0 0 \n",
+ "987 0 0 \n",
+ "988 0 0 \n",
+ "989 0 0 \n",
+ "990 0 0 \n",
"\n",
" SMILES_RDKIT_fr_pyridine SMILES_RDKIT_fr_quatN SMILES_RDKIT_fr_sulfide \\\n",
"0 0 0 0 \n",
@@ -552,11 +528,11 @@
"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",
+ "986 0 0 0 \n",
+ "987 0 0 0 \n",
+ "988 0 0 0 \n",
+ "989 0 0 0 \n",
+ "990 0 0 0 \n",
"\n",
" SMILES_RDKIT_fr_tetrazole SMILES_RDKIT_fr_thiazole \n",
"0 0 0 \n",
@@ -565,16 +541,16 @@
"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",
+ "986 0 0 \n",
+ "987 0 0 \n",
+ "988 0 0 \n",
+ "989 0 0 \n",
+ "990 0 0 \n",
"\n",
- "[515 rows x 94 columns]), continuous=SubspaceContinuous(parameters=[], constraints_lin_eq=[], constraints_lin_ineq=[]))"
+ "[991 rows x 99 columns]), continuous=SubspaceContinuous(parameters=[], constraints_lin_eq=[], constraints_lin_ineq=[]))"
]
},
- "execution_count": 32,
+ "execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
@@ -585,7 +561,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 170,
"metadata": {},
"outputs": [],
"source": [
@@ -613,7 +589,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 171,
"metadata": {},
"outputs": [],
"source": [
@@ -627,50 +603,53 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 172,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- " 0%| | 0/50 [00:00, ?it/s]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
- " stdvs = Y.std(dim=-2, keepdim=True)\n",
- "/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
- " Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 2%|2 | 1/50 [00:19<16:09, 19.79s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 0%| | 0/50 [00:00, ?it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
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+ " 2%|2 | 1/50 [00:15<13:02, 15.97s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
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" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
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" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 10%|# | 5/50 [01:21<12:17, 16.39s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 8%|8 | 4/50 [01:05<12:35, 16.42s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 12%|#2 | 6/50 [01:37<11:56, 16.29s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 10%|# | 5/50 [01:23<12:28, 16.64s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 14%|#4 | 7/50 [01:49<11:14, 15.69s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 12%|#2 | 6/50 [01:39<12:12, 16.64s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 16%|#6 | 8/50 [02:07<11:08, 15.91s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 14%|#4 | 7/50 [01:54<11:40, 16.30s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 18%|#8 | 9/50 [02:19<10:36, 15.52s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 16%|#6 | 8/50 [02:08<11:15, 16.09s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
@@ -684,145 +663,131 @@
" warnings.warn(\n",
"/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-04 to the diagonal\n",
" warnings.warn(\n",
- "/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-03 to the diagonal\n",
- " warnings.warn(\n",
- "/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal\n",
- " warnings.warn(\n",
- "/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-07 to the diagonal\n",
- " warnings.warn(\n",
- "/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
- " warnings.warn(\n",
- "/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
- " warnings.warn(\n",
- "/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-04 to the diagonal\n",
- " warnings.warn(\n",
- "/home/vscode/.local/lib/python3.10/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-03 to the diagonal\n",
- " warnings.warn(\n",
- " 20%|## | 10/50 [02:34<10:16, 15.40s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 18%|#8 | 9/50 [02:22<10:48, 15.83s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 22%|##2 | 11/50 [02:51<10:08, 15.60s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 20%|## | 10/50 [02:43<10:55, 16.38s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 24%|##4 | 12/50 [03:07<09:53, 15.63s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 22%|##2 | 11/50 [03:17<11:40, 17.97s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 26%|##6 | 13/50 [03:23<09:38, 15.64s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 24%|##4 | 12/50 [03:45<11:53, 18.77s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 28%|##8 | 14/50 [03:40<09:27, 15.77s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 26%|##6 | 13/50 [04:13<12:00, 19.46s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 30%|### | 15/50 [03:57<09:15, 15.87s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 28%|##8 | 14/50 [04:40<12:01, 20.03s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 32%|###2 | 16/50 [04:13<08:57, 15.82s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 30%|### | 15/50 [05:08<11:58, 20.54s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 34%|###4 | 17/50 [04:28<08:41, 15.82s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 32%|###2 | 16/50 [05:35<11:52, 20.95s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 36%|###6 | 18/50 [04:45<08:27, 15.85s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 34%|###4 | 17/50 [06:02<11:43, 21.33s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 38%|###8 | 19/50 [05:02<08:13, 15.92s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 36%|###6 | 18/50 [06:29<11:33, 21.66s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 40%|#### | 20/50 [05:16<07:55, 15.85s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 38%|###8 | 19/50 [06:57<11:20, 21.96s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 42%|####2 | 21/50 [05:27<07:32, 15.61s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 40%|#### | 20/50 [07:24<11:06, 22.21s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 44%|####4 | 22/50 [05:38<07:10, 15.37s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 42%|####2 | 21/50 [07:34<10:27, 21.64s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 46%|####6 | 23/50 [05:49<06:50, 15.19s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 44%|####4 | 22/50 [07:44<09:50, 21.11s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 48%|####8 | 24/50 [05:57<06:27, 14.92s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 46%|####6 | 23/50 [07:56<09:18, 20.70s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 50%|##### | 25/50 [06:08<06:08, 14.74s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 48%|####8 | 24/50 [08:07<08:48, 20.32s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 52%|#####2 | 26/50 [06:18<05:49, 14.57s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 50%|##### | 25/50 [08:18<08:18, 19.95s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 54%|#####4 | 27/50 [06:28<05:31, 14.40s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 52%|#####2 | 26/50 [08:31<07:52, 19.67s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 56%|#####6 | 28/50 [06:38<05:12, 14.23s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 54%|#####4 | 27/50 [08:41<07:23, 19.30s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- "/home/vscode/.local/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n",
- " warn(\n",
- " 58%|#####8 | 29/50 [06:48<04:55, 14.09s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 56%|#####6 | 28/50 [08:53<06:59, 19.06s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 60%|###### | 30/50 [06:58<04:39, 13.96s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 58%|#####8 | 29/50 [09:08<06:37, 18.91s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 62%|######2 | 31/50 [07:09<04:23, 13.85s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 60%|###### | 30/50 [09:18<06:12, 18.62s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 64%|######4 | 32/50 [07:20<04:07, 13.76s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 62%|######2 | 31/50 [09:29<05:48, 18.36s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 66%|######6 | 33/50 [07:31<03:52, 13.68s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 64%|######4 | 32/50 [09:39<05:25, 18.10s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 68%|######8 | 34/50 [07:39<03:36, 13.53s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 66%|######6 | 33/50 [09:50<05:04, 17.91s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 70%|####### | 35/50 [07:50<03:21, 13.44s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 68%|######8 | 34/50 [10:02<04:43, 17.72s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 72%|#######2 | 36/50 [08:01<03:07, 13.36s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 70%|####### | 35/50 [10:13<04:22, 17.52s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 74%|#######4 | 37/50 [08:11<02:52, 13.28s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 72%|#######2 | 36/50 [10:26<04:03, 17.39s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- " 76%|#######6 | 38/50 [08:21<02:38, 13.18s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 74%|#######4 | 37/50 [10:36<03:43, 17.19s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- "/home/vscode/.local/lib/python3.10/site-packages/botorch/optim/fit.py:102: OptimizationWarning: `scipy_minimize` terminated with status 3, displaying original message from `scipy.optimize.minimize`: ABNORMAL_TERMINATION_IN_LNSRCH\n",
- " warn(\n",
- " 78%|#######8 | 39/50 [08:31<02:24, 13.11s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " 76%|#######6 | 38/50 [10:48<03:24, 17.07s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" stdvs = Y.std(dim=-2, keepdim=True)\n",
"/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
" Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
- "100%|##########| 50/50 [08:47<00:00, 10.55s/it]\n"
+ " 78%|#######8 | 39/50 [11:03<03:07, 17.01s/it]/home/vscode/.local/lib/python3.10/site-packages/botorch/models/transforms/outcome.py:304: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " stdvs = Y.std(dim=-2, keepdim=True)\n",
+ "/home/vscode/.local/lib/python3.10/site-packages/botorch/models/utils/assorted.py:194: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1760.)\n",
+ " Ymean, Ystd = torch.mean(Y, dim=-2), torch.std(Y, dim=-2)\n",
+ "100%|##########| 50/50 [11:19<00:00, 13.58s/it]\n"
]
}
],
@@ -843,7 +808,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 173,
"metadata": {},
"outputs": [],
"source": [
@@ -852,12 +817,12 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -885,12 +850,12 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 175,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -910,18 +875,18 @@
"plt.legend(loc=\"lower right\")\n",
"import matplotlib.pyplot as plt\n",
"\n",
- "plt.xlim(0, N_DOE_ITERATIONS+1)\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\")"
]
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
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",
"text/plain": [
""
]
@@ -945,7 +910,7 @@
},
{
"cell_type": "code",
- "execution_count": 125,
+ "execution_count": 177,
"metadata": {},
"outputs": [
{
@@ -985,9 +950,9 @@
" 1337 | \n",
" 0 | \n",
" 1 | \n",
- " [71.38] | \n",
- " 71.380000 | \n",
- " 71.380 | \n",
+ " [91.5] | \n",
+ " 91.500000 | \n",
+ " 91.500000 | \n",
" \n",
" \n",
" 1 | \n",
@@ -995,9 +960,9 @@
" 1337 | \n",
" 1 | \n",
" 2 | \n",
- " [83.742] | \n",
- " 83.742000 | \n",
- " 83.742 | \n",
+ " [66.66499999999999] | \n",
+ " 66.665000 | \n",
+ " 91.500000 | \n",
"
\n",
" \n",
" 2 | \n",
@@ -1005,9 +970,9 @@
" 1337 | \n",
" 2 | \n",
" 3 | \n",
- " [67.0] | \n",
- " 67.000000 | \n",
- " 83.742 | \n",
+ " [65.0] | \n",
+ " 65.000000 | \n",
+ " 91.500000 | \n",
"
\n",
" \n",
" 3 | \n",
@@ -1015,9 +980,9 @@
" 1337 | \n",
" 3 | \n",
" 4 | \n",
- " [67.04] | \n",
- " 67.040000 | \n",
- " 83.742 | \n",
+ " [96.43666666666667] | \n",
+ " 96.436667 | \n",
+ " 96.436667 | \n",
"
\n",
" \n",
" 4 | \n",
@@ -1025,9 +990,9 @@
" 1337 | \n",
" 4 | \n",
" 5 | \n",
- " [47.0] | \n",
- " 47.000000 | \n",
- " 83.742 | \n",
+ " [98.13333333333333] | \n",
+ " 98.133333 | \n",
+ " 98.133333 | \n",
"
\n",
" \n",
" ... | \n",
@@ -1040,91 +1005,91 @@
" ... | \n",
"
\n",
" \n",
- " 395 | \n",
+ " 2495 | \n",
" Random | \n",
" 1346 | \n",
- " 5 | \n",
- " 6 | \n",
- " [71.72] | \n",
- " 71.720000 | \n",
- " 92.500 | \n",
+ " 45 | \n",
+ " 46 | \n",
+ " [10.0] | \n",
+ " 10.000000 | \n",
+ " 99.900000 | \n",
"
\n",
" \n",
- " 396 | \n",
+ " 2496 | \n",
" Random | \n",
" 1346 | \n",
- " 6 | \n",
- " 7 | \n",
- " [68.00666666666666] | \n",
- " 68.006667 | \n",
- " 92.500 | \n",
+ " 46 | \n",
+ " 47 | \n",
+ " [65.0] | \n",
+ " 65.000000 | \n",
+ " 99.900000 | \n",
"
\n",
" \n",
- " 397 | \n",
+ " 2497 | \n",
" Random | \n",
" 1346 | \n",
- " 7 | \n",
- " 8 | \n",
- " [6.08] | \n",
- " 6.080000 | \n",
- " 92.500 | \n",
+ " 47 | \n",
+ " 48 | \n",
+ " [53.85] | \n",
+ " 53.850000 | \n",
+ " 99.900000 | \n",
"
\n",
" \n",
- " 398 | \n",
+ " 2498 | \n",
" Random | \n",
" 1346 | \n",
- " 8 | \n",
- " 9 | \n",
- " [90.0] | \n",
- " 90.000000 | \n",
- " 92.500 | \n",
+ " 48 | \n",
+ " 49 | \n",
+ " [64.0] | \n",
+ " 64.000000 | \n",
+ " 99.900000 | \n",
"
\n",
" \n",
- " 399 | \n",
+ " 2499 | \n",
" Random | \n",
" 1346 | \n",
- " 9 | \n",
- " 10 | \n",
- " [45.37] | \n",
- " 45.370000 | \n",
- " 92.500 | \n",
+ " 49 | \n",
+ " 50 | \n",
+ " [72.378] | \n",
+ " 72.378000 | \n",
+ " 99.900000 | \n",
"
\n",
" \n",
"\n",
- "400 rows × 7 columns
\n",
+ "2500 rows × 7 columns
\n",
""
],
"text/plain": [
- " Scenario Random_Seed Iteration Num_Experiments Efficiency_Measurements \\\n",
- "0 Mordred 1337 0 1 [71.38] \n",
- "1 Mordred 1337 1 2 [83.742] \n",
- "2 Mordred 1337 2 3 [67.0] \n",
- "3 Mordred 1337 3 4 [67.04] \n",
- "4 Mordred 1337 4 5 [47.0] \n",
- ".. ... ... ... ... ... \n",
- "395 Random 1346 5 6 [71.72] \n",
- "396 Random 1346 6 7 [68.00666666666666] \n",
- "397 Random 1346 7 8 [6.08] \n",
- "398 Random 1346 8 9 [90.0] \n",
- "399 Random 1346 9 10 [45.37] \n",
+ " 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_IterBest Efficiency_CumBest \n",
- "0 71.380000 71.380 \n",
- "1 83.742000 83.742 \n",
- "2 67.000000 83.742 \n",
- "3 67.040000 83.742 \n",
- "4 47.000000 83.742 \n",
- ".. ... ... \n",
- "395 71.720000 92.500 \n",
- "396 68.006667 92.500 \n",
- "397 6.080000 92.500 \n",
- "398 90.000000 92.500 \n",
- "399 45.370000 92.500 \n",
+ " Efficiency_Measurements Efficiency_IterBest Efficiency_CumBest \n",
+ "0 [91.5] 91.500000 91.500000 \n",
+ "1 [66.66499999999999] 66.665000 91.500000 \n",
+ "2 [65.0] 65.000000 91.500000 \n",
+ "3 [96.43666666666667] 96.436667 96.436667 \n",
+ "4 [98.13333333333333] 98.133333 98.133333 \n",
+ "... ... ... ... \n",
+ "2495 [10.0] 10.000000 99.900000 \n",
+ "2496 [65.0] 65.000000 99.900000 \n",
+ "2497 [53.85] 53.850000 99.900000 \n",
+ "2498 [64.0] 64.000000 99.900000 \n",
+ "2499 [72.378] 72.378000 99.900000 \n",
"\n",
- "[400 rows x 7 columns]"
+ "[2500 rows x 7 columns]"
]
},
- "execution_count": 125,
+ "execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
@@ -1135,7 +1100,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 178,
"metadata": {},
"outputs": [],
"source": [
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