def test_convergence_plot_options(options):
problems = get_benchmark_problems("example")
stop_after_10 = {
"stopping_max_criterion_evaluations": 10,
"stopping_max_iterations": 10,
}
optimizers = {
"lbfgsb": {
"algorithm": "scipy_lbfgsb",
"algo_options": stop_after_10
},
"nm": {
"algorithm": "scipy_neldermead",
"algo_options": stop_after_10
},
}
results = run_benchmark(
problems,
optimizers,
n_cores=1, # must be 1 for the test to work
logging_directory="logging",
)
convergence_plot(problems=problems,
results=results,
problem_subset=["bard_good_start"],
**options)
plt.close()
def test_run_benchmark_dict_options():
all_problems = get_benchmark_problems("more_wild")
first_two_names = list(all_problems)[:2]
first_two = {name: all_problems[name] for name in first_two_names}
optimize_options = {
"default_lbfgsb": "scipy_lbfgsb",
"tuned_lbfgsb": {
"algorithm": "scipy_lbfgsb",
"algo_options": {
"convergence.relative_criterion_tolerance": 1e-10
},
},
}
result = run_benchmark(
problems=first_two,
optimize_options=optimize_options,
)
expected_keys = {
("linear_full_rank_good_start", "default_lbfgsb"),
("linear_full_rank_bad_start", "default_lbfgsb"),
("linear_full_rank_good_start", "tuned_lbfgsb"),
("linear_full_rank_bad_start", "tuned_lbfgsb"),
}
assert set(result) == expected_keys
def test_run_benchmark_failing():
all_problems = get_benchmark_problems("more_wild")
failing_name = "jennrich_sampson"
failing = {failing_name: all_problems[failing_name]}
optimize_options = ["scipy_lbfgsb"]
with pytest.warns():
result = run_benchmark(problems=failing,
optimize_options=optimize_options)
key = (failing_name, "scipy_lbfgsb")
assert isinstance(result[key]["solution"], str)
def test_run_benchmark_list_options():
all_problems = get_benchmark_problems("example")
first_two_names = list(all_problems)[:2]
first_two = {name: all_problems[name] for name in first_two_names}
optimize_options = ["scipy_lbfgsb", "scipy_neldermead"]
result = run_benchmark(
problems=first_two,
optimize_options=optimize_options,
)
expected_keys = {
("helical_valley_good_start", "scipy_lbfgsb"),
("rosenbrock_good_start", "scipy_lbfgsb"),
("helical_valley_good_start", "scipy_neldermead"),
("rosenbrock_good_start", "scipy_neldermead"),
}
assert set(result) == expected_keys
def test_get_problems(name, additive_noise, multiplicative_noise):
is_noisy = any((additive_noise, multiplicative_noise))
problems = get_benchmark_problems(
name=name,
additive_noise=additive_noise,
multiplicative_noise=multiplicative_noise,
)
first_name = list(problems)[0]
first = problems[first_name]
func = first["inputs"]["criterion"]
params = first["inputs"]["params"]
np.random.seed()
first_eval = func(params)["value"]
second_eval = func(params)["value"]
if is_noisy:
assert first_eval != second_eval
else:
assert first_eval == second_eval
def test_run_benchmark_list_options(tmpdir):
all_problems = get_benchmark_problems("example")
first_two_names = list(all_problems)[:2]
first_two = {name: all_problems[name] for name in first_two_names}
optimize_options = ["scipy_lbfgsb", "scipy_neldermead"]
logging_directory = tmpdir / "benchmark_logs"
res = run_benchmark(
problems=first_two,
optimize_options=optimize_options,
logging_directory=logging_directory,
)
expected_keys = {
("linear_full_rank_good_start", "scipy_lbfgsb"),
("rosenbrock_good_start", "scipy_lbfgsb"),
("linear_full_rank_good_start", "scipy_neldermead"),
("rosenbrock_good_start", "scipy_neldermead"),
}
assert set(res) == expected_keys
def test_profile_plot_options(options):
problems = get_benchmark_problems("example")
stop_after_10 = {
"stopping_max_criterion_evaluations": 10,
"stopping_max_iterations": 10,
}
optimizers = {
"lbfgsb": {
"algorithm": "scipy_lbfgsb",
"algo_options": stop_after_10
},
"neldermead": {
"algorithm": "scipy_neldermead",
"algo_options": stop_after_10,
},
}
results = run_benchmark(
problems,
optimizers,
n_cores=1, # must be 1 for the test to work
)
profile_plot(problems=problems, results=results, **options)
plt.close()
