Multi Objective Loading BOA from JSON and Plotting Results#
This notebook demonstrates how to:
Loading a Scheduler from JSON from a multi objective optimization previous run (If you want to see the Experiment that this is from, see Running a Multi Objective Optimization Directly in Python. We will look at the output and plot some exploratory data analysis.
1import pathlib
2import os
3
4import numpy as np
5from ax.utils.notebook.plotting import init_notebook_plotting
6from ax.plot.trace import optimization_trace_single_method_plotly
7from ax.service.utils.report_utils import get_standard_plots, exp_to_df
8import boa
9from botorch.test_functions.synthetic import Cosine8
10
11init_notebook_plotting()
[WARNING 08-11 16:41:52] ax.service.utils.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
[INFO 08-11 16:41:53] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.
Loading the Scheduler from our JSON file#
1# setup stuff just because this gets reused from the latest run for the case of the docs
2try:
3 run = list(pathlib.Path().resolve().glob("moo_run*"))[-1]
4except IndexError:
5 print("No run to load. Make sure you run optimization_run.ipynb first")
1# Filepath to the scheduler.json
2
3scheduler_fp = run / "scheduler.json"
1scheduler = boa.scheduler_from_json_file(scheduler_fp)
1scheduler
Scheduler(experiment=Experiment(moo_run), generation_strategy=GenerationStrategy(name='Sobol+MOO', steps=[Sobol for 5 trials, MOO for subsequent trials]), options=SchedulerOptions(max_pending_trials=10, trial_type=<TrialType.TRIAL: 0>, batch_size=None, total_trials=None, tolerated_trial_failure_rate=0.5, min_failed_trials_for_failure_rate_check=5, log_filepath=None, logging_level=20, ttl_seconds_for_trials=None, init_seconds_between_polls=1, min_seconds_before_poll=1.0, seconds_between_polls_backoff_factor=1.5, timeout_hours=None, run_trials_in_batches=False, debug_log_run_metadata=False, early_stopping_strategy=None, global_stopping_strategy=None, suppress_storage_errors_after_retries=False))
Show the Best Fitted Trial#
best_fitted_trials uses the data to do a fitting from all trials and with the noise levels you provided (or if no noise levels was provided, it assumed an unknown level of noise and inferred the noise level from the trial runs)
1trial = scheduler.best_fitted_trials()
2trial
[INFO 08-11 16:41:54] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
{13: {'params': {'x0': 0.0, 'x1': 1.0},
'means': {'branin': -17.506128716556617, 'currin': -1.1801004499882204},
'cov_matrix': {'branin': {'branin': 0.00012833812209078621, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 4.598433926897388e-06}}},
17: {'params': {'x0': 0.05203450605953412, 'x1': 1.0},
'means': {'branin': -5.456585654828148, 'currin': -3.1790542798249275},
'cov_matrix': {'branin': {'branin': 5.360154401336147e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.6474532110189548e-06}}},
18: {'params': {'x0': 0.023237135349614718, 'x1': 1.0},
'means': {'branin': -10.850276443943525, 'currin': -2.118888573684143},
'cov_matrix': {'branin': {'branin': 4.657063374588379e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.3791262956472956e-06}}},
20: {'params': {'x0': 0.0658158366594332, 'x1': 1.0},
'means': {'branin': -4.102841723240731, 'currin': -3.6192209444344683},
'cov_matrix': {'branin': {'branin': 0.00014964740089944793, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 4.119054368012358e-06}}},
21: {'params': {'x0': 0.011215603413767292, 'x1': 1.0},
'means': {'branin': -14.053895050727434, 'currin': -1.638076602887332},
'cov_matrix': {'branin': {'branin': 4.596235551038663e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.5192865341577259e-06}}},
22: {'params': {'x0': 0.03636469865528415, 'x1': 1.0},
'means': {'branin': -7.977789373939016, 'currin': -2.622258888873898},
'cov_matrix': {'branin': {'branin': 5.137751760695312e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.5199265418818584e-06}}},
23: {'params': {'x0': 0.09102111616476667, 'x1': 0.934116582546777},
'means': {'branin': -1.8130137235679005, 'currin': -4.517543043120717},
'cov_matrix': {'branin': {'branin': 0.0001568060404903958, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 3.828899352585122e-06}}},
24: {'params': {'x0': 0.01708388128533853, 'x1': 1.0},
'means': {'branin': -12.423269439970882, 'currin': -1.8745527183016235},
'cov_matrix': {'branin': {'branin': 4.5403822224681704e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.4083579812606884e-06}}},
25: {'params': {'x0': 0.04374628938171563, 'x1': 1.0},
'means': {'branin': -6.663644311567827, 'currin': -2.8913564761860133},
'cov_matrix': {'branin': {'branin': 5.593159291920814e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.6186170209265697e-06}}},
26: {'params': {'x0': 0.07557399963886184, 'x1': 0.9604531907396057},
'means': {'branin': -2.915758943614179, 'currin': -4.022216859042424},
'cov_matrix': {'branin': {'branin': 9.944290711848409e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.1162329523716017e-06}}},
27: {'params': {'x0': 0.10597263172039384, 'x1': 0.8925403944662526},
'means': {'branin': -0.9531718459221654, 'currin': -5.013621011672689},
'cov_matrix': {'branin': {'branin': 0.00014479512348054618, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 3.7551545485284803e-06}}},
28: {'params': {'x0': 0.02957204520974565, 'x1': 1.0},
'means': {'branin': -9.380388586189419, 'currin': -2.3652984701113637},
'cov_matrix': {'branin': {'branin': 4.834749078837536e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.4364897985645896e-06}}},
29: {'params': {'x0': 0.0055211189688683104, 'x1': 1.0},
'means': {'branin': -15.754477057126202, 'currin': -1.4063013791781613},
'cov_matrix': {'branin': {'branin': 5.0914871694141925e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.5416137141685568e-06}}},
31: {'params': {'x0': 0.05834153023782312, 'x1': 0.9991747886097033},
'means': {'branin': -4.727380037716463, 'currin': -3.3888533347002983},
'cov_matrix': {'branin': {'branin': 6.695635267806029e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.8649989421909652e-06}}},
32: {'params': {'x0': 0.08224276040284126, 'x1': 0.942422952768897},
'means': {'branin': -2.307060420097084, 'currin': -4.2647403984389705},
'cov_matrix': {'branin': {'branin': 7.400972044697507e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.8675551323316678e-06}}},
33: {'params': {'x0': 0.09650359917465445, 'x1': 0.9093742663445639},
'means': {'branin': -1.323305440234929, 'currin': -4.739833447856926},
'cov_matrix': {'branin': {'branin': 0.00010435169147326781, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.6316398583176597e-06}}},
34: {'params': {'x0': 0.06943995566372359, 'x1': 0.9736395703099116},
'means': {'branin': -3.505604075725792, 'currin': -3.8037934590830975},
'cov_matrix': {'branin': {'branin': 8.459370632346134e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.459344213637315e-06}}},
35: {'params': {'x0': 0.020121745427913274, 'x1': 1.0},
'means': {'branin': -11.629009510437932, 'currin': -1.9957174931768487},
'cov_matrix': {'branin': {'branin': 4.6094513241603523e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.375508269548321e-06}}},
36: {'params': {'x0': 0.11234941277601834, 'x1': 0.8699017556766556},
'means': {'branin': -0.6660112255410304, 'currin': -5.2367713355077665},
'cov_matrix': {'branin': {'branin': 0.0001580652209711211, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 3.7877192734296893e-06}}},
37: {'params': {'x0': 0.04774672444463946, 'x1': 1.0},
'means': {'branin': -6.0451056407086865, 'currin': -3.0322364587240918},
'cov_matrix': {'branin': {'branin': 5.662514816834588e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.6375471103947677e-06}}},
38: {'params': {'x0': 0.03996098452781553, 'x1': 1.0},
'means': {'branin': -7.309809884623295, 'currin': -2.7547793962005747},
'cov_matrix': {'branin': {'branin': 5.3408936288984705e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.5723373389953523e-06}}},
39: {'params': {'x0': 0.11783575021336015, 'x1': 0.8440443364894583},
'means': {'branin': -0.46561282817389404, 'currin': -5.456868521784372},
'cov_matrix': {'branin': {'branin': 0.00016883328816931766, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 7.076124264568503e-06}}},
40: {'params': {'x0': 0.0027455685218516056, 'x1': 1.0},
'means': {'branin': -16.623200268955532, 'currin': -1.292733310071306},
'cov_matrix': {'branin': {'branin': 5.3566863200475374e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.1363142657618445e-06}}},
41: {'params': {'x0': 0.05511253317860454, 'x1': 1.0},
'means': {'branin': -5.082255577785249, 'currin': -3.281656588371045},
'cov_matrix': {'branin': {'branin': 5.649301746816007e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.7252078119128399e-06}}},
42: {'params': {'x0': 0.0263546645956135, 'x1': 1.0},
'means': {'branin': -10.107709651527564, 'currin': -2.2408844886617567},
'cov_matrix': {'branin': {'branin': 4.724018030502946e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.4037155102916997e-06}}},
43: {'params': {'x0': 0.032901205897832915, 'x1': 1.0},
'means': {'branin': -8.67017698924029, 'currin': -2.492265470544703},
'cov_matrix': {'branin': {'branin': 4.976611146529823e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.4742813964737551e-06}}},
44: {'params': {'x0': 0.09168115288618003, 'x1': 0.9140534069660875},
'means': {'branin': -1.5570525262871175, 'currin': -4.6073722500402985},
'cov_matrix': {'branin': {'branin': 0.00011191467210004916, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.97261578896042e-06}}},
45: {'params': {'x0': 0.014118986676679964, 'x1': 1.0},
'means': {'branin': -13.231369684329694, 'currin': -1.7554312544795656},
'cov_matrix': {'branin': {'branin': 4.480726141262154e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.4708610226280347e-06}}},
46: {'params': {'x0': 0.0854193006653092, 'x1': 0.9307528158024131},
'means': {'branin': -2.018709144826797, 'currin': -4.38878420569708},
'cov_matrix': {'branin': {'branin': 0.00010201877395587617, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.1939507079329417e-06}}},
47: {'params': {'x0': 0.06662741330447378, 'x1': 0.980596996858974},
'means': {'branin': -3.802258875527942, 'currin': -3.698932701915769},
'cov_matrix': {'branin': {'branin': 0.00010626647155817673, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.462415733609613e-06}}},
48: {'params': {'x0': 0.008342318515166378, 'x1': 1.0},
'means': {'branin': -14.89778550166701, 'currin': -1.5213626904202897},
'cov_matrix': {'branin': {'branin': 4.9508425703014344e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 1.5094486120659318e-06}}},
49: {'params': {'x0': 0.07858564508878803, 'x1': 0.9495088262544781},
'means': {'branin': -2.6070300466202596, 'currin': -4.14168731682001},
'cov_matrix': {'branin': {'branin': 8.110052015349306e-05, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 2.0457017353211875e-06}}}}
Show the Best Raw Trial#
if you need the exact points of the best trial, maybe because you need the trial number of the best trial to plot results, or for any other reason, best_raw_trails does not do any fitting
1trial = scheduler.best_raw_trials()
2trial
[INFO 08-11 16:41:54] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
{13: {'params': {'x0': 0.0, 'x1': 1.0},
'means': {'branin': -17.5082969666, 'currin': -1.1804080009},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
17: {'params': {'x0': 0.05203450605953412, 'x1': 1.0},
'means': {'branin': -5.4566316605, 'currin': -3.1790568829},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
18: {'params': {'x0': 0.023237135349614718, 'x1': 1.0},
'means': {'branin': -10.8504209518, 'currin': -2.1189432144},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
20: {'params': {'x0': 0.0658158366594332, 'x1': 1.0},
'means': {'branin': -4.1013307571, 'currin': -3.6192605495},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
21: {'params': {'x0': 0.011215603413767292, 'x1': 1.0},
'means': {'branin': -14.0537528992, 'currin': -1.6379365921},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
22: {'params': {'x0': 0.03636469865528415, 'x1': 1.0},
'means': {'branin': -7.9777517319, 'currin': -2.6222782135},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
23: {'params': {'x0': 0.09102111616476667, 'x1': 0.934116582546777},
'means': {'branin': -1.8124856948999999, 'currin': -4.5170354843},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
24: {'params': {'x0': 0.01708388128533853, 'x1': 1.0},
'means': {'branin': -12.4237108231, 'currin': -1.874533534},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
25: {'params': {'x0': 0.04374628938171563, 'x1': 1.0},
'means': {'branin': -6.66355896, 'currin': -2.8913524151},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
26: {'params': {'x0': 0.07557399963886184, 'x1': 0.9604531907396057},
'means': {'branin': -2.9152641296, 'currin': -4.0221409798},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
27: {'params': {'x0': 0.10597263172039384, 'x1': 0.8925403944662526},
'means': {'branin': -0.9522657394, 'currin': -5.0136032104},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
28: {'params': {'x0': 0.02957204520974565, 'x1': 1.0},
'means': {'branin': -9.3803367615, 'currin': -2.3653521538},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
29: {'params': {'x0': 0.0055211189688683104, 'x1': 1.0},
'means': {'branin': -15.7531070709, 'currin': -1.4061617851000001},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
31: {'params': {'x0': 0.05834153023782312, 'x1': 0.9991747886097033},
'means': {'branin': -4.7280945778, 'currin': -3.3888683319},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
32: {'params': {'x0': 0.08224276040284126, 'x1': 0.942422952768897},
'means': {'branin': -2.307489872, 'currin': -4.2647199631},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
33: {'params': {'x0': 0.09650359917465445, 'x1': 0.9093742663445639},
'means': {'branin': -1.3240127563000001, 'currin': -4.7398281097},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
34: {'params': {'x0': 0.06943995566372359, 'x1': 0.9736395703099116},
'means': {'branin': -3.5054941177, 'currin': -3.8037557602},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
35: {'params': {'x0': 0.020121745427913274, 'x1': 1.0},
'means': {'branin': -11.6293401718, 'currin': -1.9957454205},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
36: {'params': {'x0': 0.11234941277601834, 'x1': 0.8699017556766556},
'means': {'branin': -0.6664524078, 'currin': -5.2371068001},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
37: {'params': {'x0': 0.04774672444463946, 'x1': 1.0},
'means': {'branin': -6.0450167656, 'currin': -3.0322315693},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
38: {'params': {'x0': 0.03996098452781553, 'x1': 1.0},
'means': {'branin': -7.3097586632, 'currin': -2.7547838688},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
39: {'params': {'x0': 0.11783575021336015, 'x1': 0.8440443364894583},
'means': {'branin': -0.4651317596, 'currin': -5.4567427635},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
40: {'params': {'x0': 0.0027455685218516056, 'x1': 1.0},
'means': {'branin': -16.6224098206, 'currin': -1.2927361727},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
41: {'params': {'x0': 0.05511253317860454, 'x1': 1.0},
'means': {'branin': -5.082482338, 'currin': -3.2816679478},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
42: {'params': {'x0': 0.0263546645956135, 'x1': 1.0},
'means': {'branin': -10.1077156067, 'currin': -2.240945816},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
43: {'params': {'x0': 0.032901205897832915, 'x1': 1.0},
'means': {'branin': -8.6701259613, 'currin': -2.4923026562},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
44: {'params': {'x0': 0.09168115288618003, 'x1': 0.9140534069660875},
'means': {'branin': -1.5569028854, 'currin': -4.6077036858},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
45: {'params': {'x0': 0.014118986676679964, 'x1': 1.0},
'means': {'branin': -13.2316837311, 'currin': -1.7553508282},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
46: {'params': {'x0': 0.0854193006653092, 'x1': 0.9307528158024131},
'means': {'branin': -2.0187549591, 'currin': -4.3889622688},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
47: {'params': {'x0': 0.06662741330447378, 'x1': 0.980596996858974},
'means': {'branin': -3.8031392097000003, 'currin': -3.6988897324},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
48: {'params': {'x0': 0.008342318515166378, 'x1': 1.0},
'means': {'branin': -14.8969230652, 'currin': -1.5211905241},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}},
49: {'params': {'x0': 0.07858564508878803, 'x1': 0.9495088262544781},
'means': {'branin': -2.6068229675, 'currin': -4.1417350769},
'cov_matrix': {'branin': {'branin': 0.0, 'currin': 0.0},
'currin': {'branin': 0.0, 'currin': 0.0}}}}
1boa.scheduler_to_df(scheduler)
| trial_index | arm_name | trial_status | generation_method | branin | currin | is_feasible | x0 | x1 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0_0 | COMPLETED | Sobol | -2.193543 | -9.131011 | False | 0.940524 | 0.224564 |
| 1 | 1 | 1_0 | COMPLETED | Sobol | -63.826210 | -12.030728 | False | 0.157166 | 0.214496 |
| 2 | 2 | 2_0 | COMPLETED | Sobol | -100.926003 | -7.237036 | False | 0.064131 | 0.312555 |
| 3 | 3 | 3_0 | COMPLETED | Sobol | -58.000885 | -6.389218 | False | 0.824799 | 0.525802 |
| 4 | 4 | 4_0 | COMPLETED | Sobol | -26.762484 | -7.304705 | False | 0.308640 | 0.626811 |
| 5 | 5 | 5_0 | COMPLETED | MOO | -17.288162 | -8.091130 | False | 0.461521 | 0.444864 |
| 6 | 6 | 6_0 | COMPLETED | MOO | -8.931918 | -10.190450 | False | 0.986451 | 0.000000 |
| 7 | 7 | 7_0 | COMPLETED | MOO | -93.663002 | -5.238615 | False | 0.329519 | 0.979883 |
| 8 | 8 | 8_0 | COMPLETED | MOO | -9.932772 | -7.367684 | False | 1.000000 | 0.388637 |
| 9 | 9 | 9_0 | COMPLETED | MOO | -145.872208 | -4.005316 | False | 1.000000 | 1.000000 |
| 10 | 10 | 10_0 | COMPLETED | MOO | -30.126415 | -12.322750 | False | 0.418845 | 0.000000 |
| 11 | 11 | 11_0 | COMPLETED | MOO | -18.562027 | -10.723989 | False | 0.704596 | 0.000000 |
| 12 | 12 | 12_0 | COMPLETED | MOO | -52.582935 | -6.222844 | False | 0.482649 | 0.670020 |
| 13 | 13 | 13_0 | COMPLETED | MOO | -17.508297 | -1.180408 | True | 0.000000 | 1.000000 |
| 14 | 14 | 14_0 | COMPLETED | MOO | -31.839594 | -1.329088 | False | 0.000000 | 0.854346 |
| 15 | 15 | 15_0 | COMPLETED | MOO | -23.409067 | -1.249744 | False | 0.000000 | 0.927901 |
| 16 | 16 | 16_0 | COMPLETED | MOO | -43.511116 | -1.425069 | False | 0.000000 | 0.775915 |
| 17 | 17 | 17_0 | COMPLETED | MOO | -5.456632 | -3.179057 | True | 0.052035 | 1.000000 |
| 18 | 18 | 18_0 | COMPLETED | MOO | -10.850421 | -2.118943 | True | 0.023237 | 1.000000 |
| 19 | 19 | 19_0 | COMPLETED | MOO | -3.690049 | -4.211072 | True | 0.087733 | 1.000000 |
| 20 | 20 | 20_0 | COMPLETED | MOO | -4.101331 | -3.619261 | True | 0.065816 | 1.000000 |
| 21 | 21 | 21_0 | COMPLETED | MOO | -14.053753 | -1.637937 | True | 0.011216 | 1.000000 |
| 22 | 22 | 22_0 | COMPLETED | MOO | -7.977752 | -2.622278 | True | 0.036365 | 1.000000 |
| 23 | 23 | 23_0 | COMPLETED | MOO | -1.812486 | -4.517035 | True | 0.091021 | 0.934117 |
| 24 | 24 | 24_0 | COMPLETED | MOO | -12.423711 | -1.874534 | True | 0.017084 | 1.000000 |
| 25 | 25 | 25_0 | COMPLETED | MOO | -6.663559 | -2.891352 | True | 0.043746 | 1.000000 |
| 26 | 26 | 26_0 | COMPLETED | MOO | -2.915264 | -4.022141 | True | 0.075574 | 0.960453 |
| 27 | 27 | 27_0 | COMPLETED | MOO | -0.952266 | -5.013603 | True | 0.105973 | 0.892540 |
| 28 | 28 | 28_0 | COMPLETED | MOO | -9.380337 | -2.365352 | True | 0.029572 | 1.000000 |
| 29 | 29 | 29_0 | COMPLETED | MOO | -15.753107 | -1.406162 | True | 0.005521 | 1.000000 |
| 30 | 30 | 30_0 | COMPLETED | MOO | -0.987803 | -5.322257 | True | 0.119465 | 0.879292 |
| 31 | 31 | 31_0 | COMPLETED | MOO | -4.728095 | -3.388868 | True | 0.058342 | 0.999175 |
| 32 | 32 | 32_0 | COMPLETED | MOO | -2.307490 | -4.264720 | True | 0.082243 | 0.942423 |
| 33 | 33 | 33_0 | COMPLETED | MOO | -1.324013 | -4.739828 | True | 0.096504 | 0.909374 |
| 34 | 34 | 34_0 | COMPLETED | MOO | -3.505494 | -3.803756 | True | 0.069440 | 0.973640 |
| 35 | 35 | 35_0 | COMPLETED | MOO | -11.629340 | -1.995745 | True | 0.020122 | 1.000000 |
| 36 | 36 | 36_0 | COMPLETED | MOO | -0.666452 | -5.237107 | True | 0.112349 | 0.869902 |
| 37 | 37 | 37_0 | COMPLETED | MOO | -6.045017 | -3.032232 | True | 0.047747 | 1.000000 |
| 38 | 38 | 38_0 | COMPLETED | MOO | -7.309759 | -2.754784 | True | 0.039961 | 1.000000 |
| 39 | 39 | 39_0 | COMPLETED | MOO | -0.465132 | -5.456743 | True | 0.117836 | 0.844044 |
| 40 | 40 | 40_0 | COMPLETED | MOO | -16.622410 | -1.292736 | True | 0.002746 | 1.000000 |
| 41 | 41 | 41_0 | COMPLETED | MOO | -5.082482 | -3.281668 | True | 0.055113 | 1.000000 |
| 42 | 42 | 42_0 | COMPLETED | MOO | -10.107716 | -2.240946 | True | 0.026355 | 1.000000 |
| 43 | 43 | 43_0 | COMPLETED | MOO | -8.670126 | -2.492303 | True | 0.032901 | 1.000000 |
| 44 | 44 | 44_0 | COMPLETED | MOO | -1.556903 | -4.607704 | True | 0.091681 | 0.914053 |
| 45 | 45 | 45_0 | COMPLETED | MOO | -13.231684 | -1.755351 | True | 0.014119 | 1.000000 |
| 46 | 46 | 46_0 | COMPLETED | MOO | -2.018755 | -4.388962 | True | 0.085419 | 0.930753 |
| 47 | 47 | 47_0 | COMPLETED | MOO | -3.803139 | -3.698890 | True | 0.066627 | 0.980597 |
| 48 | 48 | 48_0 | COMPLETED | MOO | -14.896923 | -1.521191 | True | 0.008342 | 1.000000 |
| 49 | 49 | 49_0 | COMPLETED | MOO | -2.606823 | -4.141735 | True | 0.078586 | 0.949509 |
EDA Plots with Pareto#
Because we ran a multi objective optimization, we can plot our pareto frontiers.
1boa.plot_pareto_frontier(scheduler)
The rest of our plots are the same as for a single objective optimization. Trace plots, contour plots, and slice plots.
1boa.plot_metrics_trace(scheduler)
1boa.plot_contours(scheduler)
1boa.plot_slice(scheduler)
We can also directly pass in our scheduler file path instead of having to reload it ourselves
1boa.plot_metrics_trace(scheduler_fp)