TNK Benchmark

Imports

import os
import warnings

import matplotlib.pyplot as plt
import pandas as pd

import bofire.strategies.api as strategies
from bofire.benchmarks.api import TNK
from bofire.data_models.api import Domain
from bofire.data_models.strategies.api import MoboStrategy, RandomStrategy
from bofire.runners.api import run
from bofire.utils.multiobjective import compute_hypervolume


warnings.simplefilter("once")
SMOKE_TEST = os.environ.get("SMOKE_TEST")

Random Strategy

def sample(domain):
    datamodel = RandomStrategy(domain=domain)
    sampler = strategies.map(data_model=datamodel)
    sampled = sampler.ask(10)
    return sampled


def hypervolume(domain: Domain, experiments: pd.DataFrame) -> float:
    return compute_hypervolume(
        domain,
        experiments.loc[(experiments.c1 >= 0) & (experiments.c2 <= 0.5)],
        ref_point={"f1": 4, "f2": 4},
    )


random_results = run(
    TNK(),
    strategy_factory=lambda domain: strategies.map(RandomStrategy(domain=domain)),
    n_iterations=100 if not SMOKE_TEST else 1,
    metric=hypervolume,
    initial_sampler=sample,
    n_runs=1,
    n_procs=1,
)

  0%|          | 0/1 [00:00<?, ?it/s]Run 0:   0%|          | 0/1 [00:00<?, ?it/s]Run 0:   0%|          | 0/1 [00:00<?, ?it/s, Current Best:=0.000]Run 0: 100%|██████████| 1/1 [00:00<00:00, 42.63it/s, Current Best:=0.000]

MOBO Strategy

def strategy_factory(domain: Domain):
    data_model = MoboStrategy(domain=domain, ref_point={"f1": 4.0, "f2": 4.0})
    return strategies.map(data_model)


results = run(
    TNK(),
    strategy_factory=strategy_factory,
    n_iterations=100 if not SMOKE_TEST else 1,
    metric=hypervolume,
    initial_sampler=sample,
    n_runs=1,
    n_procs=1,
)

  0%|          | 0/1 [00:00<?, ?it/s]Run 0:   0%|          | 0/1 [00:02<?, ?it/s]Run 0:   0%|          | 0/1 [00:02<?, ?it/s, Current Best:=11.472]Run 0: 100%|██████████| 1/1 [00:02<00:00,  2.90s/it, Current Best:=11.472]Run 0: 100%|██████████| 1/1 [00:02<00:00,  2.90s/it, Current Best:=11.472]

if not SMOKE_TEST:
    fig, ax = plt.subplots()
    ax.plot(random_results[0][1], label="random")
    ax.plot(results[0][1], label="MOBO")
    ax.set_xlabel("iteration")
    ax.set_ylabel("hypervolume")
    ax.legend()
    plt.show()