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{
"metadata": {
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" #\"PL\": \"/pnfs/psi.ch/cms/trivcat/store/user/gkrzmanc/jetclustering/results/train/Eval_eval_19March2025_pt1e-2_500particles_2025_04_01_11_57_07_994/clustering_tuning_11_sp_comp_only.log\",\n",
" #\"PL_R20\": \"/pnfs/psi.ch/cms/trivcat/store/user/gkrzmanc/jetclustering/results/train/Eval_eval_19March2025_pt1e-2_500particles_2025_04_01_14_04_05_945/clustering_tuning_11_sp_comp_only.log\",\n",
" #\"PL_FTPL\": \"/pnfs/psi.ch/cms/trivcat/store/user/gkrzmanc/jetclustering/results/train/Eval_eval_19March2025_pt1e-2_500particles_FT_PL_2025_04_01_18_23_46_933/clustering_tuning_11_sp_comp_only.log\",\n",
" #\"R20\": \"/work/gkrzmanc/jetclustering/results/train/Eval_eval_19March2025_pt1e-2_500particles_NoQMinReprod_2025_04_04_10_31_53_462/clustering_tuning_11_sp_comp_only.log\",\n",
" \"PLgluons\": \"/work/gkrzmanc/jetclustering/results/train/Eval_IRCLossDebug_Reproduce_GluonFix_2025_04_14_10_24_23_167/clustering_tuning_11_sp_comp_only.log\"\n",
"}\n",
"#studies = {\n",
"# \"PL_IRC\": \"/work/gkrzmanc/jetclustering/results/train/Eval_IRCLossDebugW100PlusGhosts_2025_04_09_09_39_23_20/clustering_tuning_11_sp_comp_only.log\",\n",
"# #\"GL_IRC\": \"/work/gkrzmanc/jetclustering/results/train/Eval_IRCLossDebugW100PlusGhosts_2025_04_09_09_39_27_409/clustering_tuning_11_sp_comp_only.log\",\n",
"# \"scouting_IRC\": \"/work/gkrzmanc/jetclustering/results/train/Eval_IRCLossDebugW100PlusGhosts_2025_04_09_09_39_21_844/clustering_tuning_11_sp_comp_only.log\",\n",
"#}\n",
"studies = {key: optuna.load_study(study_name=\"clustering\", storage=optuna.storages.JournalStorage(\n",
" optuna.storages.journal.JournalFileBackend(studies[key])\n",
")) for key in studies}"
],
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},
{
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"# Visualize the study\n",
"study = studies[\"PLgluons\"]\n",
"print(study.best_params)\n",
"# best value:\n",
"print(study.best_value)\n",
"suffix = \"{}-{}-{}\".format(study.best_params[\"min_cluster_size\"], study.best_params[\"min_samples\"], study.best_params[\"epsilon\"])\n",
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],
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{
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"\n",
"# Visualize the study\n",
"study = studies[\"PL\"]\n",
"print(study.best_params)\n",
"# best value:\n",
"print(study.best_value)\n",
"suffix = \"{}-{}-{}\".format(study.best_params[\"min_cluster_size\"], study.best_params[\"min_samples\"], study.best_params[\"epsilon\"])\n",
"print(suffix)\n",
"optuna.visualization.plot_optimization_history(study)\n"
],
"id": "c01a0cab54a63489",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'min_cluster_size': 2, 'min_samples': 1, 'epsilon': 0.3875578273817563}\n",
"0.3050233039875712\n",
"2-1-0.3875578273817563\n"
]
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},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-20T14:00:43.132879Z",
"start_time": "2025-01-20T14:00:43.091696Z"
}
},
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"source": [
"# visualize the study\n",
"study = studies[\"LGATr_L_cos_sim\"]\n",
"print(study.best_params)\n",
"# Best value:\n",
"print(study.best_value)\n",
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],
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{
"ename": "KeyError",
"evalue": "'LGATr_L_cos_sim'",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)",
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"\u001B[0;31mKeyError\u001B[0m: 'LGATr_L_cos_sim'"
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