def test_lightgbm_pruning_callback_call(cv):
# type: (bool) -> None
callback_env = partial(
lgb.callback.CallbackEnv,
model='test',
params={},
begin_iteration=0,
end_iteration=1,
iteration=1)
if cv:
env = callback_env(evaluation_result_list=[(('cv_agg', 'binary_error', 1., False, 1.))])
else:
env = callback_env(evaluation_result_list=[('validation', 'binary_error', 1., False)])
# The pruner is deactivated.
study = optuna.create_study(pruner=DeterministicPruner(False))
trial = create_running_trial(study, 1.0)
pruning_callback = LightGBMPruningCallback(trial, 'binary_error', valid_name='validation')
pruning_callback(env)
# The pruner is activated.
study = optuna.create_study(pruner=DeterministicPruner(True))
trial = create_running_trial(study, 1.0)
pruning_callback = LightGBMPruningCallback(trial, 'binary_error', valid_name='validation')
with pytest.raises(optuna.structs.TrialPruned):
pruning_callback(env)
def test_lightgbm_pruning_callback_call(cv: bool) -> None:
callback_env = partial(
lgb.callback.CallbackEnv,
model="test",
params={},
begin_iteration=0,
end_iteration=1,
iteration=1,
)
if cv:
env = callback_env(evaluation_result_list=[(("cv_agg", "binary_error", 1.0, False, 1.0))])
else:
env = callback_env(evaluation_result_list=[("validation", "binary_error", 1.0, False)])
# The pruner is deactivated.
study = optuna.create_study(pruner=DeterministicPruner(False))
trial = create_running_trial(study, 1.0)
pruning_callback = LightGBMPruningCallback(trial, "binary_error", valid_name="validation")
pruning_callback(env)
# The pruner is activated.
study = optuna.create_study(pruner=DeterministicPruner(True))
trial = create_running_trial(study, 1.0)
pruning_callback = LightGBMPruningCallback(trial, "binary_error", valid_name="validation")
with pytest.raises(optuna.TrialPruned):
pruning_callback(env)
def test_lightgbm_pruning_callback_call():
# type: () -> None
env = lgb.callback.CallbackEnv(model='test',
params={},
begin_iteration=0,
end_iteration=1,
iteration=1,
evaluation_result_list=[
('validation', 'binary_error', 1.,
False)
])
# The pruner is deactivated.
study = optuna.create_study(pruner=DeterministicPruner(False))
trial = study._run_trial(func=lambda _: 1.0, catch=(Exception, ))
pruning_callback = LightGBMPruningCallback(trial,
'binary_error',
valid_name='validation')
pruning_callback(env)
# The pruner is activated.
study = optuna.create_study(pruner=DeterministicPruner(True))
trial = study._run_trial(func=lambda _: 1.0, catch=(Exception, ))
pruning_callback = LightGBMPruningCallback(trial,
'binary_error',
valid_name='validation')
with pytest.raises(optuna.structs.TrialPruned):
pruning_callback(env)
def objective(trial,
metric='binary_error',
valid_name='valid_0',
force_default_valid_names=False):
# type: (optuna.trial.Trial, str, str, bool) -> float
dtrain = lgb.Dataset([[1.]], label=[1.])
dtest = lgb.Dataset([[1.]], label=[1.])
if force_default_valid_names:
valid_names = None
else:
valid_names = [valid_name]
pruning_callback = LightGBMPruningCallback(trial,
metric,
valid_name=valid_name)
lgb.train({
'objective': 'binary',
'metric': ['auc', 'binary_error']
},
dtrain,
1,
valid_sets=[dtest],
valid_names=valid_names,
verbose_eval=False,
callbacks=[pruning_callback])
return 1.0
def objective(
trial: optuna.trial.Trial,
metric: str = "binary_error",
valid_name: str = "valid_0",
interval: int = 1,
num_boost_round: int = 1,
force_default_valid_names: bool = False,
cv: bool = False,
) -> float:
dtrain = lgb.Dataset(np.asarray([[1.0], [2.0], [3.0]]),
label=[1.0, 0.0, 1.0])
dtest = lgb.Dataset(np.asarray([[1.0]]), label=[1.0])
if force_default_valid_names:
valid_names = None
else:
valid_names = [valid_name]
pruning_callback = LightGBMPruningCallback(trial,
metric,
valid_name=valid_name,
report_interval=interval)
if cv:
lgb.cv(
{
"objective": "binary",
"metric": ["auc", "binary_error"]
},
dtrain,
num_boost_round,
verbose_eval=False,
nfold=2,
callbacks=[pruning_callback],
)
else:
lgb.train(
{
"objective": "binary",
"metric": ["auc", "binary_error"]
},
dtrain,
num_boost_round,
valid_sets=[dtest],
valid_names=valid_names,
verbose_eval=False,
callbacks=[pruning_callback],
)
return 1.0
def objective(trial,
metric="binary_error",
valid_name="valid_0",
force_default_valid_names=False,
cv=False):
# type: (optuna.trial.Trial, str, str, bool, bool) -> float
dtrain = lgb.Dataset([[1.0], [2.0], [3.0]], label=[1.0, 0.0, 1.0])
dtest = lgb.Dataset([[1.0]], label=[1.0])
if force_default_valid_names:
valid_names = None
else:
valid_names = [valid_name]
pruning_callback = LightGBMPruningCallback(trial,
metric,
valid_name=valid_name)
if cv:
lgb.cv(
{
"objective": "binary",
"metric": ["auc", "binary_error"]
},
dtrain,
1,
verbose_eval=False,
nfold=2,
callbacks=[pruning_callback],
)
else:
lgb.train(
{
"objective": "binary",
"metric": ["auc", "binary_error"]
},
dtrain,
1,
valid_sets=[dtest],
valid_names=valid_names,
verbose_eval=False,
callbacks=[pruning_callback],
)
return 1.0