def main():
for synthetic_function_cls in synthetic_functions:
meta_info = synthetic_function_cls.get_meta_information()
if "num_function_evals" in meta_info:
max_iter = meta_info["num_function_evals"]
else:
max_iter = 200
# 构造超参空间
config_space = ConfigurationSpace()
config_space.generate_all_continuous_from_bounds(
synthetic_function_cls.get_meta_information()['bounds'])
synthetic_function = synthetic_function_cls()
# 定义目标函数
def evaluation(config: dict):
config = Configuration(config_space, values=config)
return synthetic_function.objective_function(config)["function_value"] - \
synthetic_function.get_meta_information()["f_opt"]
# 对experiment_param的删除等操作放在存储后面
res = pd.DataFrame(columns=[f"trial-{i}" for i in range(10)],
index=range(max_iter))
for trial in range(10):
random_state = 50 + trial * 10
# 设置超参空间的随机种子（会影响后面的采样）
config_space.seed(random_state)
print("==========================")
print(f"= Trial -{trial:01d}- =")
print("==========================")
print('iter | loss | config origin')
print('----------------------------')
cg = SamplingSortOptimizer(config_space, [1],
min_points_in_model=25,
n_samples=2500)
loss = np.inf
for ix in range(max_iter):
config, config_info = cg.get_config(1)
cur_loss = evaluation(config)
loss = min(loss, cur_loss)
print(f" {ix:03d} {loss:.4f} {config_info.get('origin')}")
job = Job("")
job.result = {"loss": cur_loss}
job.kwargs = {
"budget": 1,
"config": config,
"config_info": config_info
}
cg.new_result(job)
res.loc[ix, f"trial-{trial}"] = cur_loss
res = raw2min(res)
m = res.mean(1)
s = res.std(1)
name = synthetic_function.get_meta_information()["name"]
final_result[name] = {"mean": m.tolist(), "std": s.tolist()}
Path("EETPE.json").write_text(json.dumps(final_result))
def main():
for synthetic_function_cls in synthetic_functions:
meta_info = synthetic_function_cls.get_meta_information()
if "num_function_evals" in meta_info:
max_iter = meta_info["num_function_evals"]
else:
max_iter = base_max_iter
# 构造超参空间
config_space = ConfigurationSpace()
config_space.generate_all_continuous_from_bounds(
synthetic_function_cls.get_meta_information()['bounds'])
synthetic_function = synthetic_function_cls()
# 定义目标函数
def evaluation(config: dict):
config = Configuration(config_space, values=config)
return synthetic_function.objective_function(config)["function_value"] - \
synthetic_function.get_meta_information()["f_opt"]
res = pd.DataFrame(columns=[f"trial-{i}" for i in range(repetitions)],
index=range(max_iter))
print(meta_info["name"])
for trial in range(repetitions):
random_state = base_random_state + 10 * trial
# Scenario object
scenario = Scenario({
"run_obj":
"quality", # we optimize quality (alternatively runtime)
"runcount-limit": max_iter,
# max. number of function evaluations; for this example set to a low number
"cs": config_space, # configuration space
"deterministic": "true"
})
smac = SMAC4HPO(scenario=scenario,
rng=np.random.RandomState(random_state),
tae_runner=evaluation,
initial_design_kwargs={"init_budget": 20})
incumbent = smac.optimize()
runhistory = smac.runhistory
configs = runhistory.get_all_configs()
losses = [runhistory.get_cost(config) for config in configs]
res[f"trial-{trial}"] = losses
print(min(losses))
res = raw2min(res)
m = res.mean(1)
s = res.std(1)
name = synthetic_function.get_meta_information()["name"]
final_result[name] = {"mean": m.tolist(), "std": s.tolist()}
Path(f"SMAC3.json").write_text(json.dumps(final_result))
def main(optimizer):
for synthetic_function_cls in synthetic_functions:
meta_info = synthetic_function_cls.get_meta_information()
if "num_function_evals" in meta_info:
max_iter = meta_info["num_function_evals"]
else:
max_iter = base_max_iter
# 构造超参空间
config_space = ConfigurationSpace()
config_space.generate_all_continuous_from_bounds(
synthetic_function_cls.get_meta_information()['bounds'])
synthetic_function = synthetic_function_cls()
# 定义目标函数
def evaluation(config: dict):
config = Configuration(config_space, values=config)
return synthetic_function.objective_function(config)["function_value"] - \
synthetic_function.get_meta_information()["f_opt"]
res = pd.DataFrame(columns=[f"trial-{i}" for i in range(repetitions)],
index=range(max_iter))
print(meta_info["name"])
for trial in range(repetitions):
random_state = base_random_state + trial * 10
ret = fmin(evaluation,
config_space,
optimizer=optimizer,
random_state=random_state,
n_iterations=max_iter)
print(ret)
losses = ret["budget2obvs"][1]["losses"]
print(ret["best_loss"])
res[f"trial-{trial}"] = losses
res = raw2min(res)
m = res.mean(1)
s = res.std(1)
name = synthetic_function.get_meta_information()["name"]
final_result[name] = {
"mean": m.tolist(),
"std": s.tolist(),
"q25": res.quantile(0.25, 1).tolist(),
"q75": res.quantile(0.75, 1).tolist(),
"q90": res.quantile(0.90, 1).tolist()
}
Path(f"ultraopt_{optimizer}.json").write_text(json.dumps(final_result))
def main():
for synthetic_function_cls in synthetic_functions:
meta_info = synthetic_function_cls.get_meta_information()
if "num_function_evals" in meta_info:
max_iter = meta_info["num_function_evals"]
else:
max_iter = base_max_iter
# 构造超参空间
config_space = ConfigurationSpace()
config_space.generate_all_continuous_from_bounds(
synthetic_function_cls.get_meta_information()['bounds'])
synthetic_function = synthetic_function_cls()
space = CS2HyperoptSpace(config_space)
# 定义目标函数
def evaluation(config: dict):
config = Configuration(config_space, values=config)
return synthetic_function.objective_function(config)["function_value"] - \
synthetic_function.get_meta_information()["f_opt"]
# 对experiment_param的删除等操作放在存储后面
res = pd.DataFrame(columns=[f"trial-{i}" for i in range(repetitions)],
index=range(max_iter))
for trial in range(repetitions):
random_state = base_random_state + trial * 10
# 设置超参空间的随机种子（会影响后面的采样）
config_space.seed(random_state)
trials = Trials()
best = fmin(
evaluation,
space,
algo=partial(tpe.suggest, n_startup_jobs=20),
max_evals=max_iter,
rstate=np.random.RandomState(random_state),
trials=trials,
)
losses = trials.losses()
res[f"trial-{trial}"] = losses
res = raw2min(res)
m = res.mean(1)
s = res.std(1)
name = synthetic_function.get_meta_information()["name"]
final_result[name] = {"mean": m.tolist(), "std": s.tolist()}
Path("hyperopt.json").write_text(json.dumps(final_result))
def main():
for synthetic_function_cls in synthetic_functions:
meta_info = synthetic_function_cls.get_meta_information()
if "num_function_evals" in meta_info:
max_iter = meta_info["num_function_evals"]
else:
max_iter = base_max_iter
# 构造超参空间
config_space = ConfigurationSpace()
config_space.generate_all_continuous_from_bounds(
synthetic_function_cls.get_meta_information()['bounds'])
synthetic_function = synthetic_function_cls()
# 定义目标函数
def evaluation(config: dict):
config = Configuration(config_space, values=config)
return synthetic_function.objective_function(config)["function_value"] - \
synthetic_function.get_meta_information()["f_opt"]
# 对experiment_param的删除等操作放在存储后面
res = pd.DataFrame(columns=[f"trial-{i}" for i in range(repetitions)],
index=range(max_iter))
print(meta_info["name"])
for trial in range(repetitions):
random_state = base_random_state + trial * 10
# 设置超参空间的随机种子（会影响后面的采样）
ret = fmin(evaluation,
config_space,
optimizer=ETPEOptimizer(gamma2=0.95),
random_state=random_state,
n_iterations=max_iter)
print(ret)
losses = ret["budget2obvs"][1]["losses"]
# print('iter | loss | config origin')
# print('----------------------------')
print(ret["best_loss"])
res[f"trial-{trial}"] = losses
res = raw2min(res)
m = res.mean(1)
s = res.std(1)
name = synthetic_function.get_meta_information()["name"]
final_result[name] = {"mean": m.tolist(), "std": s.tolist()}
Path("ultraopt.json").write_text(json.dumps(final_result))
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Author : qichun tang # @Date : 2020-12-20 # @Contact : [email protected] from ConfigSpace import ConfigurationSpace, Configuration __all__ = ["config_space", "evaluate"] from ultraopt.benchmarks.synthetic_functions import MultiFidelityRosenbrock2D synthetic_function_cls = MultiFidelityRosenbrock2D config_space = ConfigurationSpace() config_space.generate_all_continuous_from_bounds( synthetic_function_cls.get_meta_information()['bounds']) synthetic_function = synthetic_function_cls() # 定义目标函数 def evaluate(config: dict, budget=100): config = Configuration(config_space, values=config) return synthetic_function.objective_function(config, budget=budget)["function_value"] - \ synthetic_function.get_meta_information()["f_opt"]
