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support xgboost early stopping #424

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Merged
merged 3 commits into from
Aug 4, 2024
Merged

support xgboost early stopping #424

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e1abd4e
support xgboost early stopping
yiweny Aug 4, 2024
12a188c
fix test cases
yiweny Aug 4, 2024
36e913e
add comment
yiweny Aug 4, 2024
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18 changes: 12 additions & 6 deletions test/gbdt/test_gbdt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,12 +54,18 @@ def test_gbdt_with_save_load(gbdt_cls, stypes, task_type_and_metric):
with pytest.raises(RuntimeError, match="is not yet fitted"):
gbdt.save(path)

gbdt.tune(
tf_train=dataset.tensor_frame,
tf_val=dataset.tensor_frame,
num_trials=2,
num_boost_round=2,
)
if isinstance(gbdt_cls, XGBoost):
gbdt.tune(tf_train=dataset.tensor_frame,
tf_val=dataset.tensor_frame, num_trials=2,
num_boost_round=1000, early_stopping_rounds=2)
assert gbdt.model.best_iteration is not None
else:
gbdt.tune(
tf_train=dataset.tensor_frame,
tf_val=dataset.tensor_frame,
num_trials=2,
num_boost_round=2,
)
gbdt.save(path)

loaded_gbdt = gbdt_cls(
Expand Down
55 changes: 44 additions & 11 deletions torch_frame/gbdt/tuned_xgboost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,7 @@ def objective(
dtrain: Any, # xgboost.DMatrix
dvalid: Any, # xgboost.DMatrix
num_boost_round: int,
early_stopping_rounds: int,
) -> float:
r"""Objective function to be optimized.

Expand All @@ -101,6 +102,8 @@ def objective(
dtrain (xgboost.DMatrix): Train data.
dvalid (xgboost.DMatrix): Validation data.
num_boost_round (int): Number of boosting round.
early_stopping_rounds (int): Number of early stopping
rounds.

Returns:
float: Best objective value. Root mean squared error for
Expand Down Expand Up @@ -174,12 +177,26 @@ def objective(

boost = xgboost.train(self.params, dtrain,
num_boost_round=num_boost_round,
early_stopping_rounds=50, verbose_eval=False,
evals=[(dvalid, 'validation')],
callbacks=[pruning_callback])
pred = boost.predict(dvalid)
score = self.compute_metric(torch.from_numpy(dvalid.get_label()),
torch.from_numpy(pred))
early_stopping_rounds=early_stopping_rounds,
verbose_eval=False, evals=[
(dvalid, 'validation')
], callbacks=[pruning_callback])
if boost.best_iteration:
iteration_range = (0, boost.best_iteration + 1)
else:
iteration_range = None
pred = boost.predict(dvalid, iteration_range)

# If xgboost early stops on multiclass classification
# task, then the output shape would be (batch_size, num_classes).
# We need to take argmax to get the final prediction output.
if (boost.best_iteration
and self.task_type == TaskType.MULTICLASS_CLASSIFICATION):
assert pred.shape[1] == self.params["num_class"]
pred = torch.argmax(torch.from_numpy(pred), dim=1)
else:
pred = torch.from_numpy(pred)
score = self.compute_metric(torch.from_numpy(dvalid.get_label()), pred)
return score

def _tune(
Expand All @@ -188,6 +205,7 @@ def _tune(
tf_val: TensorFrame,
num_trials: int,
num_boost_round: int = 2000,
early_stopping_rounds: int = 50,
):
import optuna
import xgboost
Expand All @@ -207,13 +225,14 @@ def _tune(
feature_types=val_feat_type,
enable_categorical=True)
study.optimize(
lambda trial: self.objective(trial, dtrain, dvalid, num_boost_round
), num_trials)
lambda trial: self.objective(
trial, dtrain, dvalid, num_boost_round, early_stopping_rounds),
num_trials)
self.params.update(study.best_params)

self.model = xgboost.train(self.params, dtrain,
num_boost_round=num_boost_round,
early_stopping_rounds=50,
early_stopping_rounds=early_stopping_rounds,
verbose_eval=False,
evals=[(dvalid, 'validation')])

Expand All @@ -225,8 +244,22 @@ def _predict(self, tf_test: TensorFrame) -> Tensor:
dtest = xgboost.DMatrix(test_feat, label=test_y,
feature_types=test_feat_type,
enable_categorical=True)
pred = self.model.predict(dtest)
return torch.from_numpy(pred).to(device)
if self.model.best_iteration is not None:
iteration_range = self.model.best_iteration
else:
iteration_range = None
pred = self.model.predict(dtest, iteration_range)

# If xgboost early stops on multiclass classification
# task, then the output shape would be (batch_size, num_classes).
# We need to take argmax to get the final prediction output.
if (self.model.best_iteration
and self.task_type == TaskType.MULTICLASS_CLASSIFICATION):
assert pred.shape[1] == self._num_classes
pred = torch.argmax(torch.from_numpy(pred), dim=1)
else:
pred = torch.from_numpy(pred)
return pred.to(device)

def _load(self, path: str) -> None:
import xgboost
Expand Down
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