def objective(trial: optuna.trial.Trial, value: float) -> float:
trial.set_system_attr(
optuna.integration.lightgbm_tuner.optimize._STEP_NAME_KEY,
"step{:.0f}".format(value),
)
return trial.suggest_uniform("x", value, value)
def mo_objective_test_upgrade(trial: optuna.trial.Trial) -> Tuple[float, float]:
x = trial.suggest_float("x", -5, 5)
y = trial.suggest_int("y", 0, 10)
z = cast(float, trial.suggest_categorical("z", [-5, 0, 5]))
trial.set_system_attr("a", 0)
trial.set_user_attr("b", 1)
return x, x ** 2 + y ** 2 + z ** 2
def wrapper(trial: optuna.trial.Trial) -> Union[float, Sequence[float]]:
study = trial.study
self._initialize_experiment(study)
with mlflow.start_run(run_name=str(trial.number), nested=self._nest_trials) as run:
trial.set_system_attr(RUN_ID_ATTRIBUTE_KEY, run.info.run_id)
return func(trial)
def objective_test_upgrade(trial: optuna.trial.Trial) -> float:
x = trial.suggest_uniform("x", -5, 5) # optuna==0.9.0 does not have suggest_float.
y = trial.suggest_int("y", 0, 10)
z = cast(float, trial.suggest_categorical("z", [-5, 0, 5]))
trial.set_system_attr("a", 0)
trial.set_user_attr("b", 1)
trial.report(0.5, step=0)
return x ** 2 + y ** 2 + z ** 2
def f(trial: optuna.trial.Trial) -> float:
x = trial.suggest_int("x", 1, 1)
y = trial.suggest_categorical("y", (2.5, ))
assert isinstance(y, float)
trial.set_user_attr("train_loss", 3)
trial.set_system_attr("foo", "bar")
value = x + y # 3.5
# Test reported intermediate values, although it in practice is not "intermediate".
trial.report(value, step=0)
return value
def _postprocess(
self,
trial: optuna.trial.Trial,
val_score: float,
elapsed_secs: float,
average_iteration_time: float,
) -> None:
if self.pbar is not None:
self.pbar.set_description(self.pbar_fmt.format(self.step_name, self.best_score))
self.pbar.update(1)
self.report.append(
dict(
# Since v1.2.0, action was concatenation of parameter names. Currently, it is
# explicitly given to distinguish steps which tune the same parameters.
action=self.step_name,
trial=self.trial_count,
value=str(trial.params),
val_score=val_score,
elapsed_secs=elapsed_secs,
average_iteration_time=average_iteration_time,
)
)
trial.set_system_attr(_ELAPSED_SECS_KEY, elapsed_secs)
trial.set_system_attr(_AVERAGE_ITERATION_TIME_KEY, average_iteration_time)
trial.set_system_attr(_STEP_NAME_KEY, self.step_name)
trial.set_system_attr(_LGBM_PARAMS_KEY, json.dumps(self.lgbm_params))
self.trial_count += 1
def _postprocess(
self, trial: optuna.trial.Trial, elapsed_secs: float, average_iteration_time: float
) -> None:
if self.pbar is not None:
self.pbar.set_description(self.pbar_fmt.format(self.step_name, self.best_score))
self.pbar.update(1)
trial.set_system_attr(_ELAPSED_SECS_KEY, elapsed_secs)
trial.set_system_attr(_AVERAGE_ITERATION_TIME_KEY, average_iteration_time)
trial.set_system_attr(_STEP_NAME_KEY, self.step_name)
trial.set_system_attr(_LGBM_PARAMS_KEY, json.dumps(self.lgbm_params))
self.trial_count += 1
def f(trial: optuna.trial.Trial) -> float:
trial.set_system_attr("system_message", "test")
assert trial.system_attrs["system_message"] == "test"
return 0.0
