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]/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/bofire/utils/torch_tools.py:706: UserWarning:

The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:213.)

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, 44.44it/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:=11.049]Run 0: 100%|██████████| 1/1 [00:02<00:00,  2.03s/it, Current Best:=11.049]Run 0: 100%|██████████| 1/1 [00:02<00:00,  2.03s/it, Current Best:=11.049]

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()