def test_metric_with_eval_at(self, metric, eval_at_param, expected):
# type: (str, Dict[str, Union[int, List[int]]], str) -> None
params = {"metric": metric} # type: Dict[str, Union[str, int, List[int]]]
params.update(eval_at_param)
tuner = BaseTuner(lgbm_params=params)
assert tuner._metric_with_eval_at(metric) == expected
def test_get_booster_best_score__using_valid_names_as_list(self):
# type: () -> None
unexpected_value = 0.5
expected_value = 1.0
class DummyBooster(object):
def __init__(self):
# type: () -> None
self.best_score = {
"train": {"binary_logloss": unexpected_value},
"val": {"binary_logloss": expected_value},
}
booster = DummyBooster()
dummy_train_dataset = lgb.Dataset(None)
dummy_val_dataset = lgb.Dataset(None)
tuner = BaseTuner(
lgbm_kwargs={
"valid_names": ["train", "val"],
"valid_sets": [dummy_train_dataset, dummy_val_dataset],
}
)
val_score = tuner._get_booster_best_score(booster)
assert val_score == expected_value
def test_get_booster_best_score__using_valid_names_as_list(self):
# type: () -> None
unexpected_value = 0.5
expected_value = 1.0
class DummyBooster(object):
def __init__(self):
# type: () -> None
self.best_score = {
'train': {
'binary_logloss': unexpected_value
},
'val': {
'binary_logloss': expected_value
}
}
booster = DummyBooster()
dummy_train_dataset = lgb.Dataset(None)
dummy_val_dataset = lgb.Dataset(None)
tuner = BaseTuner(
lgbm_kwargs={
'valid_names': ['train', 'val'],
'valid_sets': [dummy_train_dataset, dummy_val_dataset],
})
val_score = tuner._get_booster_best_score(booster)
assert val_score == expected_value
def test_compare_validation_metrics(self):
# type: () -> None
for metric in [
"auc",
"ndcg",
"lambdarank",
"rank_xendcg",
"xendcg",
"xe_ndcg",
"xe_ndcg_mart",
"xendcg_mart",
"map",
"mean_average_precision",
]:
tuner = BaseTuner(lgbm_params={"metric": metric})
assert tuner.compare_validation_metrics(0.5, 0.1)
assert not tuner.compare_validation_metrics(0.5, 0.5)
assert not tuner.compare_validation_metrics(0.1, 0.5)
for metric in ["rmsle", "rmse", "binary_logloss"]:
tuner = BaseTuner(lgbm_params={"metric": metric})
assert not tuner.compare_validation_metrics(0.5, 0.1)
assert not tuner.compare_validation_metrics(0.5, 0.5)
assert tuner.compare_validation_metrics(0.1, 0.5)
def test_get_booster_best_score__using_valid_names_as_str(self):
# type: () -> None
expected_value = 1.0
class DummyBooster(object):
def __init__(self):
# type: () -> None
self.best_score = {"dev": {"binary_logloss": expected_value}}
booster = DummyBooster()
dummy_dataset = lgb.Dataset(None)
tuner = BaseTuner(lgbm_kwargs={"valid_names": "dev", "valid_sets": dummy_dataset,})
val_score = tuner._get_booster_best_score(booster)
assert val_score == expected_value
def test_get_booster_best_score(self):
# type: () -> None
expected_value = 1.0
class DummyBooster(object):
def __init__(self):
# type: () -> None
self.best_score = {"valid_0": {"binary_logloss": expected_value}}
booster = DummyBooster()
dummy_dataset = lgb.Dataset(None)
tuner = BaseTuner(lgbm_kwargs=dict(valid_sets=dummy_dataset))
val_score = tuner._get_booster_best_score(booster)
assert val_score == expected_value
def test_compare_validation_metrics(self):
# type: () -> None
for metric in ['auc', 'accuracy']:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert tuner.compare_validation_metrics(0.5, 0.1)
assert not tuner.compare_validation_metrics(0.5, 0.5)
assert not tuner.compare_validation_metrics(0.1, 0.5)
for metric in ['rmsle', 'rmse', 'binary_logloss']:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert not tuner.compare_validation_metrics(0.5, 0.1)
assert not tuner.compare_validation_metrics(0.5, 0.5)
assert tuner.compare_validation_metrics(0.1, 0.5)
def test_higher_is_better(self):
# type: () -> None
for metric in ['auc', 'accuracy']:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert tuner.higher_is_better()
for metric in ['rmsle', 'rmse', 'binary_logloss']:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert not tuner.higher_is_better()
def test_compare_validation_metrics(self):
# type: () -> None
for metric in [
'auc', 'ndcg', 'lambdarank', 'rank_xendcg', 'xendcg',
'xe_ndcg', 'xe_ndcg_mart', 'xendcg_mart', 'map',
'mean_average_precision'
]:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert tuner.compare_validation_metrics(0.5, 0.1)
assert not tuner.compare_validation_metrics(0.5, 0.5)
assert not tuner.compare_validation_metrics(0.1, 0.5)
for metric in ['rmsle', 'rmse', 'binary_logloss']:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert not tuner.compare_validation_metrics(0.5, 0.1)
assert not tuner.compare_validation_metrics(0.5, 0.5)
assert tuner.compare_validation_metrics(0.1, 0.5)
def test_higher_is_better(self):
# type: () -> None
for metric in [
'auc', 'ndcg', 'lambdarank', 'rank_xendcg', 'xendcg',
'xe_ndcg', 'xe_ndcg_mart', 'xendcg_mart', 'map',
'mean_average_precision'
]:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert tuner.higher_is_better()
for metric in ['rmsle', 'rmse', 'binary_logloss']:
tuner = BaseTuner(lgbm_params={'metric': metric})
assert not tuner.higher_is_better()
def test_higher_is_better(self):
# type: () -> None
for metric in [
"auc",
"ndcg",
"lambdarank",
"rank_xendcg",
"xendcg",
"xe_ndcg",
"xe_ndcg_mart",
"xendcg_mart",
"map",
"mean_average_precision",
]:
tuner = BaseTuner(lgbm_params={"metric": metric})
assert tuner.higher_is_better()
for metric in ["rmsle", "rmse", "binary_logloss"]:
tuner = BaseTuner(lgbm_params={"metric": metric})
assert not tuner.higher_is_better()
def test_metric_with_eval_at_error(self):
# type: () -> None
tuner = BaseTuner(lgbm_params={"metric": "ndcg", "eval_at": "1"})
with pytest.raises(ValueError):
tuner._metric_with_eval_at("ndcg")
def test_metric_with_eval_at_error(self):
# type: () -> None
tuner = BaseTuner(lgbm_params={'metric': 'ndcg', 'eval_at': '1'})
with pytest.raises(ValueError):
tuner._metric_with_eval_at('ndcg')
