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from __future__ import annotations
import numpy as np
import pytest
from bayes_opt import BayesianOptimization, SequentialDomainReductionTransformer
from bayes_opt.target_space import TargetSpace
def black_box_function(x, y):
"""Function with unknown internals we wish to maximize.
This is just serving as an example, for all intents and
purposes think of the internals of this function, i.e.: the process
which generates its output values, as unknown.
"""
return -(x**2) - (y - 1) ** 2 + 1
def test_bound_x_maximize():
class Tracker:
def __init__(self):
self.start_count = 0
self.step_count = 0
self.end_count = 0
def update_start(self, event, instance):
self.start_count += 1
def update_step(self, event, instance):
self.step_count += 1
def update_end(self, event, instance):
self.end_count += 1
def reset(self):
self.__init__()
bounds_transformer = SequentialDomainReductionTransformer()
pbounds = {"x": (-10, 10), "y": (-10, 10)}
n_iter = 10
standard_optimizer = BayesianOptimization(
f=black_box_function, pbounds=pbounds, verbose=2, random_state=1
)
standard_optimizer.maximize(init_points=2, n_iter=n_iter)
mutated_optimizer = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
verbose=2,
random_state=1,
bounds_transformer=bounds_transformer,
)
mutated_optimizer.maximize(init_points=2, n_iter=n_iter)
assert len(standard_optimizer.space) == len(mutated_optimizer.space)
assert not (standard_optimizer._space.bounds == mutated_optimizer._space.bounds).any()
def test_minimum_window_is_kept():
bounds_transformer = SequentialDomainReductionTransformer(minimum_window=1.0)
pbounds = {"x": (-0.5, 0.5), "y": (-1.0, 0.0)}
mutated_optimizer = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
verbose=0,
random_state=1,
bounds_transformer=bounds_transformer,
)
mutated_optimizer.maximize(init_points=2, n_iter=10)
window_width = np.diff(bounds_transformer.bounds)
assert np.isclose(np.min(window_width), 1.0)
def test_minimum_window_array_is_kept():
window_ranges = [1.0, 0.5]
bounds_transformer = SequentialDomainReductionTransformer(minimum_window=window_ranges)
pbounds = {"x": (-0.5, 0.5), "y": (-1.0, 0.0)}
mutated_optimizer = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
verbose=0,
random_state=1,
bounds_transformer=bounds_transformer,
)
mutated_optimizer.maximize(init_points=2, n_iter=10)
window_widths = np.diff(bounds_transformer.bounds)
assert np.all(np.isclose(np.squeeze(np.min(window_widths, axis=0)), window_ranges))
def test_trimming_bounds():
"""Test if the bounds are trimmed correctly within the bounds"""
def dummy_function(x1, x2, x3, x4, x5):
return 0.0
min_window = 1.0
bounds_transformer = SequentialDomainReductionTransformer(minimum_window=min_window)
pbounds = {"x1": (-1, 0.6), "x2": (-1, 0.5), "x3": (-0.4, 0.6), "x4": (0.3, 1.3), "x5": (-1, 0.8)}
target_sp = TargetSpace(target_func=dummy_function, pbounds=pbounds)
bounds_transformer.initialize(target_sp)
new_bounds = np.concatenate((np.ones((5, 1)) * 0.1, np.ones((5, 1))), axis=1)
global_bounds = np.asarray(list(pbounds.values()))
trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)
# check that the bounds are trimmed to the minimum window
# raises ValueError if the bounds are not trimmed correctly
bounds_transformer._window_bounds_compatibility(trimmed_bounds)
def test_exceeded_bounds():
"""Raises Value Error if the bounds are exceeded."""
window_ranges = [1.01, 0.72]
bounds_transformer = SequentialDomainReductionTransformer(minimum_window=window_ranges)
pbounds = {"x": (-0.5, 0.5), "y": (-0.7, 0.0)}
with pytest.raises(ValueError):
_ = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
verbose=0,
random_state=1,
bounds_transformer=bounds_transformer,
)
def test_trim_when_both_new_bounds_exceed_global_bounds():
"""Test if the global bounds are respected when both new bounds for a given parameter
are beyond the global bounds."""
# initialize a bounds transformer
bounds_transformer = SequentialDomainReductionTransformer(minimum_window=10)
pbounds = {"x": (-10, 10), "y": (-10, 10)}
target_sp = TargetSpace(target_func=black_box_function, pbounds=pbounds)
bounds_transformer.initialize(target_sp)
global_bounds = np.asarray(list(pbounds.values()))
def verify_bounds_in_range(new_bounds, global_bounds):
"""Check if the new bounds are within the global bounds."""
test = True
for i, pbounds in enumerate(new_bounds):
if pbounds[0] < global_bounds[i, 0] or pbounds[0] > global_bounds[i, 1]:
test = False
if pbounds[1] > global_bounds[i, 1] or pbounds[1] < global_bounds[i, 0]:
test = False
return test
# test if the sorting of the bounds is correct
new_bounds = np.array([[5, -5], [-10, 10]])
trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)
assert (trimmed_bounds == np.array([[-5, 5], [-10, 10]])).all()
# test if both (upper/lower) bounds for a parameter exceed the global bounds
new_bounds = np.array([[-50, -20], [20, 50]])
with pytest.warns(UserWarning):
trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)
assert verify_bounds_in_range(trimmed_bounds, global_bounds)
# test if both (upper/lower) bounds for a parameter exceed the global bounds
# while they are out of order
new_bounds = np.array([[-20, -50], [-10, 10]])
with pytest.warns(UserWarning):
trimmed_bounds = bounds_transformer._trim(new_bounds, global_bounds)
assert verify_bounds_in_range(trimmed_bounds, global_bounds)
def test_minimum_window_dict_ordering():
"""Tests if dictionary input for minimum_window is reordered the same as pbounds"""
window_ranges = {"y": 1, "x": 3, "w": 1e5}
bounds_transformer = SequentialDomainReductionTransformer(minimum_window=window_ranges)
pbounds = {"y": (-1, 1), "w": (-1e6, 1e6), "x": (-10, 10)}
_ = BayesianOptimization(
f=None, pbounds=pbounds, verbose=0, random_state=1, bounds_transformer=bounds_transformer
)
if __name__ == "__main__":
r"""
CommandLine:
python tests/test_seq_domain_red.py
"""
pytest.main([__file__])
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