{ "cells": [ { "cell_type": "markdown", "id": "cfb4ab4a", "metadata": {}, "source": [ "# Example 7: Model Calibration β€” Bayesian Optimization \n", "\n", "[(GitHub link)](https://github.com/heberlr/UQ_PhysiCell/tree/main/examples/virus-mac-new/ex7_Calib_BO.ipynb)\n", "\n", "This notebook calibrates the [virus-mac-new](https://github.com/heberlr/UQ_PhysiCell/tree/main/examples/virus-mac-new) model against observed data using **Bayesian Optimization (BO)**. BO fits a Gaussian Process surrogate to the parameter-to-QoI mapping and uses an acquisition function to decide which parameter set to simulate next β€” far more efficient than grid search for expensive models.\n", "\n", "**What you will learn:**\n", "- How to configure `bo.CalibrationContext` with observed data, QoI functions, and a search space\n", "- How multi-objective BO works and what the Pareto front represents\n", "- How to read the Pareto front: each point is a parameter set that minimizes QoI discrepancy with no dominated alternative\n", "- How to visualize parameter–fitness relationships to assess identifiability\n", "\n", "**Ground truth (in-silico data from [ex7_ObsData.csv](ex7_ObsData.csv)):**\n", "- `mac_phag_rate_infected` = 1.0\n", "- `epi2infected_hfm` = 0.4" ] }, { "cell_type": "code", "execution_count": null, "id": "6e32d59b", "metadata": {}, "outputs": [], "source": [ "import warnings\n", "import matplotlib.pyplot as plt\n", "warnings.filterwarnings('ignore', message='.*not p.d., added jitter.*')\n", "\n", "from uq_physicell.bo import CalibrationContext, run_bayesian_optimization, analyze_pareto_results, plot_parameter_space, plot_qoi_param, plot_parameter_vs_fitness, get_observed_qoi\n", "from uq_physicell.database.bo_db import load_structure\n", "\n", "db_path = \"ex7_Calib_BO.db\"\n", "obs_data_path = \"ex7_ObsData.csv\"\n", "dic_real_value = {\"mac_phag_rate_infected\": 1.0, \"epi2infected_hfm\": 0.4}\n", "\n", "model_config = {\"ini_path\": \"uq_pc_struc.ini\", \"struc_name\": \"Model_struc_Calib\"}\n", "\n", "# df_cell β†’ cell DataFrame (see ex1 for the full dispatch table)\n", "qoi_functions = {\n", " \"epi_\": lambda df_cell: len(df_cell[df_cell['cell_type'] == 'epithelial']),\n", " \"epi_infected\": lambda df_cell: len(df_cell[df_cell['cell_type'] == 'epithelial_infected']),\n", "}\n", "\n", "obs_data_columns = {\n", " \"time\": \"Time\",\n", " \"epi_\": \"Healthy Epithelial Cells\",\n", " \"epi_infected\": \"Infected Epithelial Cells\",\n", "}\n", "\n", "search_space = {\n", " \"mac_phag_rate_infected\": {\"type\": \"real\", \"lower_bound\": 0.7, \"upper_bound\": 1.5},\n", " \"epi2infected_hfm\": {\"type\": \"real\", \"lower_bound\": 0.1, \"upper_bound\": 0.5},\n", "}\n", "\n", "bo_options = {\n", " \"num_initial_samples\": 10, # initial random evaluations before GP fitting starts\n", " \"num_iterations\": 30, # BO iterations after initial samples\n", " \"max_workers\": 10,\n", "}" ] }, { "cell_type": "markdown", "id": "922f8b87", "metadata": {}, "source": [ "## Create context and run Bayesian Optimization" ] }, { "cell_type": "code", "execution_count": 2, "id": "a16b42ae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2026-02-18 12:21:23,447 - INFO - πŸ“Š Estimating weights from observational data ranges:\n", " πŸ“ˆ epi_ - weight: 4.44e-04 (range=2.50e+02)\n", " πŸ“ˆ epi_infected - weight: 1.19e-04 (range=9.36e+02)\n", "\n", "2026-02-18 12:21:23,447 - INFO - πŸ”§ CalibrationContext initialized with 10 max workers, 5 inner workers, and 2 outer workers.\n", "2026-02-18 12:21:23,447 - INFO - πŸ†• Starting fresh optimization with database: ex7_Calib_BO.db\n", "2026-02-18 12:21:23,452 - INFO - 🎲 Generating 10 initial samples...\n", "2026-02-18 12:28:19,477 - INFO - πŸ”¬ Detected multiple QoIs - using multi-objective Bayesian optimization loop.\n", "2026-02-18 12:28:19,478 - INFO - ============================================================\n", "2026-02-18 12:28:19,478 - INFO - πŸ”„ Multi-Objective BO Iteration 1/30\n", "2026-02-18 12:28:19,479 - INFO - ============================================================\n", "2026-02-18 12:28:19,479 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:28:19,921 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:28:20,221 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:28:20,222 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:28:52,727 - INFO - \t Results for Sample ID 10: Objectives = {'epi_': np.float64(6.904451696603849e-22), 'epi_infected': np.float64(9.51323044221627e-41)}, Noise Std = {'epi_': np.float64(1.3808845488099218e-21), 'epi_infected': np.float64(1.9026460751201044e-40)}\n", "2026-02-18 12:28:52,732 - INFO - πŸ“Š Iteration 1 Sample(s) [10] : Hypervolume = 3.3318883059853214e-07\n", "2026-02-18 12:28:52,732 - INFO - βœ… Completed iteration 1/30 - Total samples: 11\n", "2026-02-18 12:28:52,732 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:28:52,733 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:28:52,733 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:28:52,733 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:28:52,734 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:28:52,734 - INFO - ============================================================\n", "2026-02-18 12:28:52,734 - INFO - πŸ”„ Multi-Objective BO Iteration 2/30\n", "2026-02-18 12:28:52,734 - INFO - ============================================================\n", "2026-02-18 12:28:52,734 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:28:52,766 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:28:53,067 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:28:53,068 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:29:26,961 - INFO - \t Results for Sample ID 11: Objectives = {'epi_': np.float64(2.801304338306533e-19), 'epi_infected': np.float64(1.888821579468422e-30)}, Noise Std = {'epi_': np.float64(3.393452973197422e-19), 'epi_infected': np.float64(2.1140782962970733e-30)}\n", "2026-02-18 12:29:26,966 - INFO - πŸ“Š Iteration 2 Sample(s) [11] : Hypervolume = 2.827772313037443e-07\n", "2026-02-18 12:29:26,967 - INFO - βœ… Completed iteration 2/30 - Total samples: 12\n", "2026-02-18 12:29:26,967 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:29:26,967 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:29:26,967 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:29:26,968 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:29:26,968 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:29:26,968 - INFO - ============================================================\n", "2026-02-18 12:29:26,968 - INFO - πŸ”„ Multi-Objective BO Iteration 3/30\n", "2026-02-18 12:29:26,968 - INFO - ============================================================\n", "2026-02-18 12:29:26,969 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:29:26,998 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:29:27,295 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:29:27,295 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:30:43,621 - INFO - \t Results for Sample ID 12: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(6.004458643581347e-74)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(1.2008916813613292e-73)}\n", "2026-02-18 12:30:43,627 - INFO - πŸ“Š Iteration 3 Sample(s) [12] : Hypervolume = 2.7539351069161347e-07\n", "2026-02-18 12:30:43,627 - INFO - βœ… Completed iteration 3/30 - Total samples: 13\n", "2026-02-18 12:30:43,627 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:30:43,628 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:30:43,628 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:30:43,628 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:30:43,628 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:30:43,629 - INFO - ============================================================\n", "2026-02-18 12:30:43,629 - INFO - πŸ”„ Multi-Objective BO Iteration 4/30\n", "2026-02-18 12:30:43,629 - INFO - ============================================================\n", "2026-02-18 12:30:43,629 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:30:43,661 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:30:44,010 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:30:44,010 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:32:07,409 - INFO - \t Results for Sample ID 13: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(2.146640432509137e-97)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(4.2932808644486864e-97)}\n", "2026-02-18 12:32:07,414 - INFO - πŸ“Š Iteration 4 Sample(s) [13] : Hypervolume = 2.543430155109e-07\n", "2026-02-18 12:32:07,414 - INFO - βœ… Completed iteration 4/30 - Total samples: 14\n", "2026-02-18 12:32:07,415 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:32:07,415 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:32:07,415 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:32:07,416 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:32:07,416 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:32:07,416 - INFO - ============================================================\n", "2026-02-18 12:32:07,416 - INFO - πŸ”„ Multi-Objective BO Iteration 5/30\n", "2026-02-18 12:32:07,417 - INFO - ============================================================\n", "2026-02-18 12:32:07,417 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:32:07,449 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:32:07,998 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:32:07,998 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:32:41,466 - INFO - \t Results for Sample ID 14: Objectives = {'epi_': np.float64(6.159261103291327e-16), 'epi_infected': np.float64(5.177666197383322e-25)}, Noise Std = {'epi_': np.float64(5.258524721271365e-16), 'epi_infected': np.float64(6.295747418606901e-25)}\n", "2026-02-18 12:32:41,471 - INFO - πŸ“Š Iteration 5 Sample(s) [14] : Hypervolume = 2.2023433760537272e-07\n", "2026-02-18 12:32:41,472 - INFO - βœ… Completed iteration 5/30 - Total samples: 15\n", "2026-02-18 12:32:41,472 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:32:41,472 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:32:41,472 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:32:41,473 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:32:41,473 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:32:41,473 - INFO - ============================================================\n", "2026-02-18 12:32:41,473 - INFO - πŸ”„ Multi-Objective BO Iteration 6/30\n", "2026-02-18 12:32:41,473 - INFO - ============================================================\n", "2026-02-18 12:32:41,474 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:32:41,505 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:32:41,807 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:32:41,808 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:33:16,013 - INFO - \t Results for Sample ID 15: Objectives = {'epi_': np.float64(1.7628941529757444e-15), 'epi_infected': np.float64(1.9888973973706661e-22)}, Noise Std = {'epi_': np.float64(3.525785646889657e-15), 'epi_infected': np.float64(3.9777947947209247e-22)}\n", "2026-02-18 12:33:16,017 - INFO - πŸ“Š Iteration 6 Sample(s) [15] : Hypervolume = 2.6121420082173e-07\n", "2026-02-18 12:33:16,018 - INFO - βœ… Completed iteration 6/30 - Total samples: 16\n", "2026-02-18 12:33:16,018 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:33:16,018 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:33:16,019 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:33:16,019 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:33:16,019 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:33:16,019 - INFO - ============================================================\n", "2026-02-18 12:33:16,019 - INFO - πŸ”„ Multi-Objective BO Iteration 7/30\n", "2026-02-18 12:33:16,020 - INFO - ============================================================\n", "2026-02-18 12:33:16,020 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:33:16,051 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:33:16,370 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:33:16,370 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:34:35,612 - INFO - \t Results for Sample ID 16: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(3.010188869094858e-95)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(6.020377738189715e-95)}\n", "2026-02-18 12:34:35,617 - INFO - πŸ“Š Iteration 7 Sample(s) [16] : Hypervolume = 2.445780075847768e-07\n", "2026-02-18 12:34:35,617 - INFO - βœ… Completed iteration 7/30 - Total samples: 17\n", "2026-02-18 12:34:35,617 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:34:35,618 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:34:35,618 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:34:35,618 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:34:35,618 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:34:35,618 - INFO - ============================================================\n", "2026-02-18 12:34:35,619 - INFO - πŸ”„ Multi-Objective BO Iteration 8/30\n", "2026-02-18 12:34:35,619 - INFO - ============================================================\n", "2026-02-18 12:34:35,619 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:34:35,704 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:34:36,051 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:34:36,051 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:35:55,820 - INFO - \t Results for Sample ID 17: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(1.6342927341065665e-82)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(3.268585466183488e-82)}\n", "2026-02-18 12:35:55,825 - INFO - πŸ“Š Iteration 8 Sample(s) [17] : Hypervolume = 2.259433614404301e-07\n", "2026-02-18 12:35:55,825 - INFO - βœ… Completed iteration 8/30 - Total samples: 18\n", "2026-02-18 12:35:55,825 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:35:55,826 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:35:55,826 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:35:55,826 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:35:55,826 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:35:55,826 - INFO - ============================================================\n", "2026-02-18 12:35:55,827 - INFO - πŸ”„ Multi-Objective BO Iteration 9/30\n", "2026-02-18 12:35:55,827 - INFO - ============================================================\n", "2026-02-18 12:35:55,827 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:35:55,859 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:35:56,201 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:35:56,201 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:37:10,461 - INFO - \t Results for Sample ID 18: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(3.090115602220368e-79)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(3.784603235826527e-79)}\n", "2026-02-18 12:37:10,466 - INFO - πŸ“Š Iteration 9 Sample(s) [18] : Hypervolume = 1.7718519253522947e-07\n", "2026-02-18 12:37:10,467 - INFO - βœ… Completed iteration 9/30 - Total samples: 19\n", "2026-02-18 12:37:10,467 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:37:10,467 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:37:10,467 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:37:10,468 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:37:10,468 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:37:10,468 - INFO - ============================================================\n", "2026-02-18 12:37:10,468 - INFO - πŸ”„ Multi-Objective BO Iteration 10/30\n", "2026-02-18 12:37:10,468 - INFO - ============================================================\n", "2026-02-18 12:37:10,469 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:37:10,503 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:37:10,869 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:37:10,869 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.3960050520245887}\n", "2026-02-18 12:37:51,555 - INFO - \t Results for Sample ID 19: Objectives = {'epi_': np.float64(0.13415081706695936), 'epi_infected': np.float64(0.13026814600514763)}, Noise Std = {'epi_': np.float64(0.12548530482404663), 'epi_infected': np.float64(0.23629883209275723)}\n", "2026-02-18 12:37:51,560 - INFO - πŸ“Š Iteration 10 Sample(s) [19] : Hypervolume = 1.834816265861776e-07\n", "2026-02-18 12:37:51,560 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:37:51,561 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:37:51,562 - INFO - \tπŸ“‹ Convergence Status: in_progress\n", "2026-02-18 12:37:51,562 - INFO - \tπŸ’‘ Reason: Slow but steady progress\n", "2026-02-18 12:37:51,563 - INFO - \t🎯 Confidence: 33.35%\n", "2026-02-18 12:37:51,563 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:37:51,563 - INFO - βœ… Completed iteration 10/30 - Total samples: 20\n", "2026-02-18 12:37:51,563 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:37:51,564 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:37:51,564 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:37:51,564 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:37:51,564 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:37:51,564 - INFO - ============================================================\n", "2026-02-18 12:37:51,564 - INFO - πŸ”„ Multi-Objective BO Iteration 11/30\n", "2026-02-18 12:37:51,565 - INFO - ============================================================\n", "2026-02-18 12:37:51,565 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:37:51,598 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:37:51,934 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:37:51,934 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:38:25,776 - INFO - \t Results for Sample ID 20: Objectives = {'epi_': np.float64(2.85271932026508e-15), 'epi_infected': np.float64(4.295999183593931e-21)}, Noise Std = {'epi_': np.float64(5.704925310411305e-15), 'epi_infected': np.float64(8.591998361684287e-21)}\n", "2026-02-18 12:38:25,781 - INFO - πŸ“Š Iteration 11 Sample(s) [20] : Hypervolume = 1.8742064737827542e-07\n", "2026-02-18 12:38:25,781 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:38:25,781 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:38:25,782 - INFO - \tπŸ“‹ Convergence Status: in_progress\n", "2026-02-18 12:38:25,782 - INFO - \tπŸ’‘ Reason: Normal optimization progress\n", "2026-02-18 12:38:25,782 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:38:25,782 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:38:25,782 - INFO - βœ… Completed iteration 11/30 - Total samples: 21\n", "2026-02-18 12:38:25,783 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:38:25,783 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:38:25,783 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:38:25,783 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:38:25,783 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:38:25,784 - INFO - ============================================================\n", "2026-02-18 12:38:25,784 - INFO - πŸ”„ Multi-Objective BO Iteration 12/30\n", "2026-02-18 12:38:25,784 - INFO - ============================================================\n", "2026-02-18 12:38:25,784 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:38:25,817 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:38:26,188 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:38:26,189 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:39:49,050 - INFO - \t Results for Sample ID 21: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(4.180928406683552e-81)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(8.3618568133671035e-81)}\n", "2026-02-18 12:39:49,055 - INFO - πŸ“Š Iteration 12 Sample(s) [21] : Hypervolume = 1.9123079032428408e-07\n", "2026-02-18 12:39:49,055 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:39:49,056 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:39:49,056 - INFO - \tπŸ“‹ Convergence Status: in_progress\n", "2026-02-18 12:39:49,057 - INFO - \tπŸ’‘ Reason: Slow but steady progress\n", "2026-02-18 12:39:49,057 - INFO - \t🎯 Confidence: 33.36%\n", "2026-02-18 12:39:49,057 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:39:49,057 - INFO - βœ… Completed iteration 12/30 - Total samples: 22\n", "2026-02-18 12:39:49,057 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:39:49,058 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:39:49,058 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:39:49,058 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:39:49,058 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:39:49,058 - INFO - ============================================================\n", "2026-02-18 12:39:49,059 - INFO - πŸ”„ Multi-Objective BO Iteration 13/30\n", "2026-02-18 12:39:49,059 - INFO - ============================================================\n", "2026-02-18 12:39:49,059 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:39:49,091 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:39:49,432 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:39:49,433 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:40:22,983 - INFO - \t Results for Sample ID 22: Objectives = {'epi_': np.float64(9.982310256274536e-15), 'epi_infected': np.float64(2.664742391135821e-25)}, Noise Std = {'epi_': np.float64(1.2225783286694666e-14), 'epi_infected': np.float64(3.263629577123357e-25)}\n", "2026-02-18 12:40:22,988 - INFO - πŸ“Š Iteration 13 Sample(s) [22] : Hypervolume = 1.7626235733113791e-07\n", "2026-02-18 12:40:22,988 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:40:22,988 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:40:22,989 - INFO - \tπŸ“‹ Convergence Status: in_progress\n", "2026-02-18 12:40:22,989 - INFO - \tπŸ’‘ Reason: Slow but steady progress\n", "2026-02-18 12:40:22,989 - INFO - \t🎯 Confidence: 33.35%\n", "2026-02-18 12:40:22,990 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:40:22,990 - INFO - βœ… Completed iteration 13/30 - Total samples: 23\n", "2026-02-18 12:40:22,990 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:40:22,990 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:40:22,991 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:40:22,991 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:40:22,991 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:40:22,991 - INFO - ============================================================\n", "2026-02-18 12:40:22,991 - INFO - πŸ”„ Multi-Objective BO Iteration 14/30\n", "2026-02-18 12:40:22,991 - INFO - ============================================================\n", "2026-02-18 12:40:22,992 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:40:23,025 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:40:23,383 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:40:23,383 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:40:57,164 - INFO - \t Results for Sample ID 23: Objectives = {'epi_': np.float64(1.0611929182501927e-17), 'epi_infected': np.float64(3.239481183006405e-29)}, Noise Std = {'epi_': np.float64(2.122207503667933e-17), 'epi_infected': np.float64(6.47896236196141e-29)}\n", "2026-02-18 12:40:57,168 - INFO - πŸ“Š Iteration 14 Sample(s) [23] : Hypervolume = 1.7829566238168823e-07\n", "2026-02-18 12:40:57,169 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:40:57,169 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:40:57,170 - INFO - \tπŸ“‹ Convergence Status: in_progress\n", "2026-02-18 12:40:57,170 - INFO - \tπŸ’‘ Reason: Slow but steady progress\n", "2026-02-18 12:40:57,170 - INFO - \t🎯 Confidence: 33.35%\n", "2026-02-18 12:40:57,170 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:40:57,171 - INFO - βœ… Completed iteration 14/30 - Total samples: 24\n", "2026-02-18 12:40:57,171 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:40:57,171 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:40:57,171 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:40:57,172 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:40:57,172 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:40:57,172 - INFO - ============================================================\n", "2026-02-18 12:40:57,172 - INFO - πŸ”„ Multi-Objective BO Iteration 15/30\n", "2026-02-18 12:40:57,172 - INFO - ============================================================\n", "2026-02-18 12:40:57,172 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:40:57,212 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:40:57,650 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:40:57,651 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:42:21,367 - INFO - \t Results for Sample ID 24: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(1.9954762218278099e-97)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(3.990952443655251e-97)}\n", "2026-02-18 12:42:21,372 - INFO - πŸ“Š Iteration 15 Sample(s) [24] : Hypervolume = 1.7667033684454167e-07\n", "2026-02-18 12:42:21,373 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:42:21,373 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:42:21,374 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:42:21,374 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:42:21,374 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:42:21,374 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:42:21,374 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:42:21,374 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:42:21,375 - INFO - βœ… Completed iteration 15/30 - Total samples: 25\n", "2026-02-18 12:42:21,375 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:42:21,375 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:42:21,375 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:42:21,376 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:42:21,376 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:42:21,376 - INFO - ============================================================\n", "2026-02-18 12:42:21,376 - INFO - πŸ”„ Multi-Objective BO Iteration 16/30\n", "2026-02-18 12:42:21,376 - INFO - ============================================================\n", "2026-02-18 12:42:21,376 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:42:21,411 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:42:21,762 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:42:21,762 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:42:57,766 - INFO - \t Results for Sample ID 25: Objectives = {'epi_': np.float64(2.203844942588266e-16), 'epi_infected': np.float64(9.442126965464527e-25)}, Noise Std = {'epi_': np.float64(4.3655547939270984e-16), 'epi_infected': np.float64(1.1500584303556516e-24)}\n", "2026-02-18 12:42:57,771 - INFO - πŸ“Š Iteration 16 Sample(s) [25] : Hypervolume = 1.6137238607367503e-07\n", "2026-02-18 12:42:57,771 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:42:57,772 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:42:57,772 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:42:57,772 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:42:57,772 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:42:57,772 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:42:57,773 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:42:57,773 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:42:57,773 - INFO - βœ… Completed iteration 16/30 - Total samples: 26\n", "2026-02-18 12:42:57,773 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:42:57,774 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:42:57,774 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:42:57,774 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:42:57,774 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:42:57,774 - INFO - ============================================================\n", "2026-02-18 12:42:57,775 - INFO - πŸ”„ Multi-Objective BO Iteration 17/30\n", "2026-02-18 12:42:57,775 - INFO - ============================================================\n", "2026-02-18 12:42:57,775 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:42:57,808 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:42:58,234 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:42:58,234 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.29780231823620695}\n", "2026-02-18 12:43:58,658 - INFO - \t Results for Sample ID 26: Objectives = {'epi_': np.float64(2.7267232912784007e-11), 'epi_infected': np.float64(7.417162658443315e-19)}, Noise Std = {'epi_': np.float64(2.225091540136075e-11), 'epi_infected': np.float64(6.05608795080377e-19)}\n", "2026-02-18 12:43:58,665 - INFO - πŸ“Š Iteration 17 Sample(s) [26] : Hypervolume = 1.6612659263879304e-07\n", "2026-02-18 12:43:58,666 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:43:58,666 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:43:58,667 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:43:58,667 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:43:58,667 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:43:58,667 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:43:58,667 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:43:58,667 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:43:58,668 - INFO - βœ… Completed iteration 17/30 - Total samples: 27\n", "2026-02-18 12:43:58,668 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:43:58,668 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:43:58,668 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:43:58,669 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:43:58,669 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:43:58,669 - INFO - ============================================================\n", "2026-02-18 12:43:58,669 - INFO - πŸ”„ Multi-Objective BO Iteration 18/30\n", "2026-02-18 12:43:58,669 - INFO - ============================================================\n", "2026-02-18 12:43:58,670 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:43:58,704 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:43:59,055 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:43:59,055 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:45:19,157 - INFO - \t Results for Sample ID 27: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(7.661785434275104e-88)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(1.5323570868547972e-87)}\n", "2026-02-18 12:45:19,162 - INFO - πŸ“Š Iteration 18 Sample(s) [27] : Hypervolume = 1.633420237316383e-07\n", "2026-02-18 12:45:19,163 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:45:19,163 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:45:19,164 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:45:19,164 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:45:19,164 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:45:19,164 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:45:19,165 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:45:19,165 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:45:19,165 - INFO - βœ… Completed iteration 18/30 - Total samples: 28\n", "2026-02-18 12:45:19,166 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:45:19,166 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:45:19,166 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:45:19,166 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:45:19,166 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:45:19,166 - INFO - ============================================================\n", "2026-02-18 12:45:19,167 - INFO - πŸ”„ Multi-Objective BO Iteration 19/30\n", "2026-02-18 12:45:19,167 - INFO - ============================================================\n", "2026-02-18 12:45:19,167 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:45:19,201 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:45:19,842 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:45:19,842 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.25442937810270316}\n", "2026-02-18 12:46:31,252 - INFO - \t Results for Sample ID 28: Objectives = {'epi_': np.float64(1.869739722454674e-25), 'epi_infected': np.float64(1.2867614210333958e-76)}, Noise Std = {'epi_': np.float64(2.466400341047496e-25), 'epi_infected': np.float64(2.5735228418658415e-76)}\n", "2026-02-18 12:46:31,257 - INFO - πŸ“Š Iteration 19 Sample(s) [28] : Hypervolume = 1.499825628849099e-07\n", "2026-02-18 12:46:31,257 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:46:31,258 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:46:31,258 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:46:31,259 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:46:31,259 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:46:31,259 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:46:31,259 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:46:31,260 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:46:31,260 - INFO - βœ… Completed iteration 19/30 - Total samples: 29\n", "2026-02-18 12:46:31,260 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:46:31,260 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:46:31,261 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:46:31,261 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:46:31,261 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:46:31,261 - INFO - ============================================================\n", "2026-02-18 12:46:31,261 - INFO - πŸ”„ Multi-Objective BO Iteration 20/30\n", "2026-02-18 12:46:31,262 - INFO - ============================================================\n", "2026-02-18 12:46:31,262 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:46:31,294 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:46:31,685 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:46:31,685 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.2086123022997175}\n", "2026-02-18 12:47:49,116 - INFO - \t Results for Sample ID 29: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(1.619269507368022e-64)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(3.238539014736044e-64)}\n", "2026-02-18 12:47:49,121 - INFO - πŸ“Š Iteration 20 Sample(s) [29] : Hypervolume = 1.4051352949658585e-07\n", "2026-02-18 12:47:49,122 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:47:49,122 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:47:49,123 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:47:49,123 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:47:49,123 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:47:49,123 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:47:49,124 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:47:49,124 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:47:49,124 - INFO - βœ… Completed iteration 20/30 - Total samples: 30\n", "2026-02-18 12:47:49,125 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:47:49,125 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:47:49,125 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:47:49,125 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:47:49,125 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:47:49,126 - INFO - ============================================================\n", "2026-02-18 12:47:49,126 - INFO - πŸ”„ Multi-Objective BO Iteration 21/30\n", "2026-02-18 12:47:49,126 - INFO - ============================================================\n", "2026-02-18 12:47:49,126 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:47:49,159 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:47:49,618 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:47:49,619 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.1010270306065713, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:48:26,767 - INFO - \t Results for Sample ID 30: Objectives = {'epi_': np.float64(7.912455302334922e-11), 'epi_infected': np.float64(8.047483828106391e-15)}, Noise Std = {'epi_': np.float64(1.5824910051609545e-10), 'epi_infected': np.float64(1.6094967655869596e-14)}\n", "2026-02-18 12:48:26,772 - INFO - πŸ“Š Iteration 21 Sample(s) [30] : Hypervolume = 1.4147796308417654e-07\n", "2026-02-18 12:48:26,772 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:48:26,772 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:48:26,773 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:48:26,773 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:48:26,773 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:48:26,773 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:48:26,773 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:48:26,774 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:48:26,774 - INFO - βœ… Completed iteration 21/30 - Total samples: 31\n", "2026-02-18 12:48:26,774 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:48:26,774 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:48:26,775 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:48:26,775 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:48:26,775 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:48:26,775 - INFO - ============================================================\n", "2026-02-18 12:48:26,776 - INFO - πŸ”„ Multi-Objective BO Iteration 22/30\n", "2026-02-18 12:48:26,776 - INFO - ============================================================\n", "2026-02-18 12:48:26,776 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:48:26,808 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:48:27,254 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:48:27,254 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.039128635282658, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:49:52,049 - INFO - \t Results for Sample ID 31: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(3.1955237977668985e-114)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(6.3910475955337955e-114)}\n", "2026-02-18 12:49:52,055 - INFO - πŸ“Š Iteration 22 Sample(s) [31] : Hypervolume = 1.3706512326801445e-07\n", "2026-02-18 12:49:52,055 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:49:52,056 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:49:52,056 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:49:52,056 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:49:52,056 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:49:52,057 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:49:52,057 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:49:52,057 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:49:52,057 - INFO - βœ… Completed iteration 22/30 - Total samples: 32\n", "2026-02-18 12:49:52,058 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:49:52,058 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:49:52,058 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:49:52,058 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:49:52,059 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:49:52,059 - INFO - ============================================================\n", "2026-02-18 12:49:52,059 - INFO - πŸ”„ Multi-Objective BO Iteration 23/30\n", "2026-02-18 12:49:52,059 - INFO - ============================================================\n", "2026-02-18 12:49:52,059 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:49:52,094 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:49:52,602 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:49:52,603 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:50:29,165 - INFO - \t Results for Sample ID 32: Objectives = {'epi_': np.float64(1.371183105099572e-11), 'epi_infected': np.float64(2.174102551812975e-15)}, Noise Std = {'epi_': np.float64(1.4128152098980658e-11), 'epi_infected': np.float64(4.111223627589928e-15)}\n", "2026-02-18 12:50:29,172 - INFO - πŸ“Š Iteration 23 Sample(s) [32] : Hypervolume = 1.4630331093911723e-07\n", "2026-02-18 12:50:29,172 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:50:29,173 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:50:29,173 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:50:29,174 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:50:29,174 - INFO - \t🎯 Confidence: 33.35%\n", "2026-02-18 12:50:29,174 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:50:29,174 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:50:29,174 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:50:29,175 - INFO - βœ… Completed iteration 23/30 - Total samples: 33\n", "2026-02-18 12:50:29,175 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:50:29,175 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:50:29,175 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:50:29,176 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:50:29,176 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:50:29,176 - INFO - ============================================================\n", "2026-02-18 12:50:29,176 - INFO - πŸ”„ Multi-Objective BO Iteration 24/30\n", "2026-02-18 12:50:29,176 - INFO - ============================================================\n", "2026-02-18 12:50:29,176 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:50:29,214 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:50:29,587 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:50:29,587 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:51:06,445 - INFO - \t Results for Sample ID 33: Objectives = {'epi_': np.float64(2.3272009839907876e-13), 'epi_infected': np.float64(1.0301420173836829e-17)}, Noise Std = {'epi_': np.float64(1.9001458443694612e-13), 'epi_infected': np.float64(8.411074350637656e-18)}\n", "2026-02-18 12:51:06,451 - INFO - πŸ“Š Iteration 24 Sample(s) [33] : Hypervolume = 1.3881996584273743e-07\n", "2026-02-18 12:51:06,451 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:51:06,452 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:51:06,452 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:51:06,452 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:51:06,453 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:51:06,453 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:51:06,453 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:51:06,453 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:51:06,453 - INFO - βœ… Completed iteration 24/30 - Total samples: 34\n", "2026-02-18 12:51:06,454 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:51:06,454 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:51:06,454 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:51:06,454 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:51:06,454 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:51:06,455 - INFO - ============================================================\n", "2026-02-18 12:51:06,455 - INFO - πŸ”„ Multi-Objective BO Iteration 25/30\n", "2026-02-18 12:51:06,455 - INFO - ============================================================\n", "2026-02-18 12:51:06,455 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:51:06,485 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:51:06,886 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:51:06,887 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 0.7, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:52:27,382 - INFO - \t Results for Sample ID 34: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(2.924557505580308e-62)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(5.849115011160616e-62)}\n", "2026-02-18 12:52:27,389 - INFO - πŸ“Š Iteration 25 Sample(s) [34] : Hypervolume = 1.3180275135170288e-07\n", "2026-02-18 12:52:27,389 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:52:27,390 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:52:27,391 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:52:27,391 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:52:27,391 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:52:27,391 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:52:27,392 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:52:27,392 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:52:27,392 - INFO - βœ… Completed iteration 25/30 - Total samples: 35\n", "2026-02-18 12:52:27,392 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:52:27,393 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:52:27,393 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:52:27,393 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:52:27,393 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:52:27,393 - INFO - ============================================================\n", "2026-02-18 12:52:27,396 - INFO - πŸ”„ Multi-Objective BO Iteration 26/30\n", "2026-02-18 12:52:27,398 - INFO - ============================================================\n", "2026-02-18 12:52:27,399 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:52:27,487 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:52:27,947 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:52:27,948 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:53:04,431 - INFO - \t Results for Sample ID 35: Objectives = {'epi_': np.float64(1.2823088991691388e-11), 'epi_infected': np.float64(2.9184257097353474e-15)}, Noise Std = {'epi_': np.float64(2.5533903404224433e-11), 'epi_infected': np.float64(5.836821106120516e-15)}\n", "2026-02-18 12:53:04,436 - INFO - πŸ“Š Iteration 26 Sample(s) [35] : Hypervolume = 1.3466395642030761e-07\n", "2026-02-18 12:53:04,436 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:53:04,436 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:53:04,437 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:53:04,437 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:53:04,438 - INFO - \t🎯 Confidence: 33.35%\n", "2026-02-18 12:53:04,438 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:53:04,438 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:53:04,438 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:53:04,438 - INFO - βœ… Completed iteration 26/30 - Total samples: 36\n", "2026-02-18 12:53:04,438 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:53:04,439 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:53:04,439 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:53:04,439 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:53:04,439 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:53:04,440 - INFO - ============================================================\n", "2026-02-18 12:53:04,440 - INFO - πŸ”„ Multi-Objective BO Iteration 27/30\n", "2026-02-18 12:53:04,440 - INFO - ============================================================\n", "2026-02-18 12:53:04,440 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:53:04,470 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:53:04,984 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:53:04,984 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.3213625630354267}\n", "2026-02-18 12:54:00,824 - INFO - \t Results for Sample ID 36: Objectives = {'epi_': np.float64(3.7247919672227634e-10), 'epi_infected': np.float64(9.060625189458948e-17)}, Noise Std = {'epi_': np.float64(3.073402915965217e-10), 'epi_infected': np.float64(1.1096944199011393e-16)}\n", "2026-02-18 12:54:00,831 - INFO - πŸ“Š Iteration 27 Sample(s) [36] : Hypervolume = 1.394256400865417e-07\n", "2026-02-18 12:54:00,831 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:54:00,832 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:54:00,833 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:54:00,833 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:54:00,833 - INFO - \t🎯 Confidence: 33.33%\n", "2026-02-18 12:54:00,833 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:54:00,833 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:54:00,834 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:54:00,834 - INFO - βœ… Completed iteration 27/30 - Total samples: 37\n", "2026-02-18 12:54:00,834 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:54:00,834 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:54:00,835 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:54:00,835 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:54:00,835 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:54:00,835 - INFO - ============================================================\n", "2026-02-18 12:54:00,835 - INFO - πŸ”„ Multi-Objective BO Iteration 28/30\n", "2026-02-18 12:54:00,835 - INFO - ============================================================\n", "2026-02-18 12:54:00,836 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:54:00,873 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:54:01,321 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:54:01,322 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.2774053689060576, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:55:22,749 - INFO - \t Results for Sample ID 37: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(6.914038480543755e-104)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(1.3828076961085382e-103)}\n", "2026-02-18 12:55:22,754 - INFO - πŸ“Š Iteration 28 Sample(s) [37] : Hypervolume = 1.2077457981889272e-07\n", "2026-02-18 12:55:22,754 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:55:22,755 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:55:22,755 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:55:22,755 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:55:22,756 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:55:22,756 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:55:22,756 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:55:22,756 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:55:22,756 - INFO - βœ… Completed iteration 28/30 - Total samples: 38\n", "2026-02-18 12:55:22,757 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:55:22,757 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:55:22,757 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:55:22,757 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:55:22,758 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:55:22,758 - INFO - ============================================================\n", "2026-02-18 12:55:22,758 - INFO - πŸ”„ Multi-Objective BO Iteration 29/30\n", "2026-02-18 12:55:22,758 - INFO - ============================================================\n", "2026-02-18 12:55:22,758 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:55:22,794 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:55:23,199 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:55:23,199 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.5}\n", "2026-02-18 12:55:56,690 - INFO - \t Results for Sample ID 38: Objectives = {'epi_': np.float64(9.434521699421294e-17), 'epi_infected': np.float64(5.3604852184266915e-28)}, Noise Std = {'epi_': np.float64(1.8867362761481427e-16), 'epi_infected': np.float64(1.0720970431862703e-27)}\n", "2026-02-18 12:55:56,695 - INFO - πŸ“Š Iteration 29 Sample(s) [38] : Hypervolume = 1.2737830332079182e-07\n", "2026-02-18 12:55:56,695 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:55:56,696 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:55:56,696 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:55:56,697 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:55:56,697 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:55:56,697 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:55:56,697 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:55:56,697 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:55:56,698 - INFO - βœ… Completed iteration 29/30 - Total samples: 39\n", "2026-02-18 12:55:56,698 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:55:56,698 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:55:56,699 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:55:56,699 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:55:56,699 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:55:56,699 - INFO - ============================================================\n", "2026-02-18 12:55:56,699 - INFO - πŸ”„ Multi-Objective BO Iteration 30/30\n", "2026-02-18 12:55:56,699 - INFO - ============================================================\n", "2026-02-18 12:55:56,700 - INFO - πŸ”§ Fitting Gaussian Process models...\n", "2026-02-18 12:55:56,812 - INFO - 🎯 Optimizing acquisition function...\n", "2026-02-18 12:55:57,309 - INFO - πŸ“Š Evaluating 1 new candidate(s)...\n", "2026-02-18 12:55:57,310 - INFO - \t Candidate 1: {'mac_phag_rate_infected': 1.5, 'epi2infected_hfm': 0.1}\n", "2026-02-18 12:57:19,246 - INFO - \t Results for Sample ID 39: Objectives = {'epi_': np.float64(3.522708646794959e-26), 'epi_infected': np.float64(1.8678654579021542e-91)}, Noise Std = {'epi_': np.float64(0.0), 'epi_infected': np.float64(3.7357309158043084e-91)}\n", "2026-02-18 12:57:19,253 - INFO - πŸ“Š Iteration 30 Sample(s) [39] : Hypervolume = 1.2915195113349911e-07\n", "2026-02-18 12:57:19,253 - INFO - πŸ” Analyzing convergence...\n", "2026-02-18 12:57:19,254 - WARNING - ⚠️ High noise detected (max 112.1%) - using relaxed convergence criteria\n", "2026-02-18 12:57:19,254 - INFO - \tπŸ“‹ Convergence Status: stuck_suboptimal\n", "2026-02-18 12:57:19,255 - INFO - \tπŸ’‘ Reason: Stuck in suboptimal region: poor exploration or quality despite stagnation\n", "2026-02-18 12:57:19,255 - INFO - \t🎯 Confidence: 33.34%\n", "2026-02-18 12:57:19,255 - INFO - \tπŸ“Š Noise level: max=112.1%, avg=96.7%, SNR=0.9\n", "2026-02-18 12:57:19,255 - INFO - \tπŸ’‘ Suggestion: Poor fitness values: check distance function weights or try different acquisition strategy\n", "2026-02-18 12:57:19,255 - WARNING - \t⚠️ Suboptimal stagnation detected - consider restarting with different settings\n", "2026-02-18 12:57:19,255 - INFO - βœ… Completed iteration 30/30 - Total samples: 40\n", "2026-02-18 12:57:19,256 - INFO - 🎯 Best fitness values:\n", "2026-02-18 12:57:19,256 - INFO - \t Best parameters for best fitness of epi_ (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:57:19,256 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:57:19,256 - INFO - \t Best parameters for best fitness of epi_infected (sample 9): {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n", "2026-02-18 12:57:19,257 - INFO - \t Fitness: {'epi_': 0.19017990271039606, 'epi_infected': 0.2508230722131106}.\n", "2026-02-18 12:57:19,257 - INFO - βœ… Multi-objective Bayesian optimization completed successfully!\n" ] } ], "source": [ "# Create the calibration context\n", "calib_context = CalibrationContext(\n", " db_path=db_path,\n", " obsData=obs_data_path,\n", " obsData_columns=obs_data_columns,\n", " model_config=model_config,\n", " qoi_functions=qoi_functions,\n", " search_space=search_space,\n", " bo_options=bo_options,\n", ")\n", "# Run the Bayesian Optimization calibration\n", "run_bayesian_optimization(calib_context)" ] }, { "cell_type": "code", "execution_count": 3, "id": "adbe71da", "metadata": {}, "outputs": [], "source": [ "# Load the database and perform analysis\n", "df_metadata, df_param_space, df_qois, df_gp_models, df_samples, df_output = load_structure(db_path)\n", " " ] }, { "cell_type": "markdown", "id": "a94e2fd7", "metadata": {}, "source": [ "## Pareto front analysis\n", "\n", "Each Pareto point is a parameter set that minimizes QoI discrepancy for at least one objective without being dominated across all objectives." ] }, { "cell_type": "code", "execution_count": 4, "id": "6dab284d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================\n", "πŸ” COMPREHENSIVE PARETO ANALYSIS\n", "============================================================\n", "\n", "πŸ“Š Extracting all Pareto points ...\n", "🎯 Current Pareto front size: 1\n", "\n", "🎯 CURRENT PARETO FRONT:\n", " Number of Pareto optimal points: 1\n", " Sample IDs: [9]\n", "\n", " πŸ“‹ Pareto Front Details:\n", " Point 1 (Sample 9):\n", " Fitness: {'epi_': np.float64(0.19017990271039606), 'epi_infected': np.float64(0.2508230722131106)}\n", " Parameters: {'mac_phag_rate_infected': 0.9920958995819091, 'epi2infected_hfm': 0.39502637386322026}\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Comprehensive Pareto analysis\n", "pareto_data = analyze_pareto_results(df_qois, df_samples, df_output)\n", "# Plot with pareto front points\n", "pareto_points = {f\"Pareto Point {i}\": param for i, param in enumerate(pareto_data['pareto_front']['parameters'])}\n", "plot_parameter_space(df_samples, df_param_space, params=pareto_points, real_value=dic_real_value)\n", "df_obs_qoi = get_observed_qoi(df_metadata['ObsData_Path'].values[0], df_qois)\n" ] }, { "cell_type": "markdown", "id": "2817aba1", "metadata": {}, "source": [ "## Visualize QoIs from Pareto front points" ] }, { "cell_type": "code", "execution_count": 5, "id": "3e95d5c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plotting epi_\n", "Sample ID: [9]\n", "Objective Function Values:\n", "{'epi_': np.float64(0.19017990271039606), 'epi_infected': np.float64(0.2508230722131106)}\n", "Noise of Objective Function:\n", "{'epi_': np.float64(0.16660431304183063), 'epi_infected': np.float64(0.1889275558955298)}\n", "Plotting epi_infected\n", "Sample ID: [9]\n", "Objective Function Values:\n", "{'epi_': np.float64(0.19017990271039606), 'epi_infected': np.float64(0.2508230722131106)}\n", "Noise of Objective Function:\n", "{'epi_': np.float64(0.16660431304183063), 'epi_infected': np.float64(0.1889275558955298)}\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot QoIs of best parameters\n", "for qoi in df_qois['QoI_Name']:\n", " print(f\"Plotting {qoi}\")\n", " plot_qoi_param(df_obs_qoi, df_output, pareto_data['pareto_front']['sample_ids'], x_var='time', y_var=qoi)" ] }, { "cell_type": "markdown", "id": "674fca1f", "metadata": {}, "source": [ "## Visualize QoI fitness vs parameter values" ] }, { "cell_type": "code", "execution_count": 6, "id": "beec518f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plotting mac_phag_rate_infected vs epi_\n", "Plotting mac_phag_rate_infected vs epi_infected\n", "Plotting epi2infected_hfm vs epi_\n", "Plotting epi2infected_hfm vs epi_infected\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot each parameter vs each QoI\n", "fig, axes = plt.subplots(len(df_param_space), len(df_qois['QoI_Name']), figsize=(12, 10))\n", "fig.subplots_adjust(hspace=0.5, wspace=0.5) # Increased spacing\n", "for i, param in enumerate(df_param_space['ParamName']):\n", " for j, qoi in enumerate(df_qois['QoI_Name']):\n", " print(f\"Plotting {param} vs {qoi}\")\n", " plot_parameter_vs_fitness(df_samples, df_output, param, qoi, samples_id=pareto_data['pareto_front']['sample_ids'], axis= axes[i, j])\n", "\n", "# Ensure proper layout and save the figure\n", "plt.tight_layout() # This will automatically adjust spacing to prevent overlaps" ] }, { "cell_type": "markdown", "id": "aa8129b7", "metadata": {}, "source": [ "---\n", "**Next:** [ex8](ex8_ABC_Calib.ipynb) calibrates the same model using ABC-SMC β€” returns full posterior distributions over parameters rather than point estimates." ] } ], "metadata": { "kernelspec": { "display_name": "pcvenv", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }