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[CI] Use latest XGBoost #572

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Jun 25, 2024
Merged

[CI] Use latest XGBoost #572

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adeaec8
[CI] Use latest XGBoost
hcho3 Jun 25, 2024
0fdb0a0
Fix tests
hcho3 Jun 25, 2024
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2 changes: 1 addition & 1 deletion ops/conda_env/dev.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -18,11 +18,11 @@ dependencies:
- llvm-openmp
- cython
- lightgbm
- xgboost
- cpplint
- pylint
- awscli
- python-build
- pip
- pip:
- cibuildwheel
- xgboost==2.1.0
12 changes: 6 additions & 6 deletions tests/python/test_xgboost_integration.py
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Expand Up @@ -112,7 +112,7 @@ def test_xgb_regressor(
xgb_model.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model(model_path)
else:
model_name = "model.model"
model_name = "model.deprecated"
model_path = pathlib.Path(tmpdir) / model_name
xgb_model.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model_legacy_binary(model_path)
Expand Down Expand Up @@ -185,7 +185,7 @@ def test_xgb_multiclass_classifier(
xgb_model.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model(model_path)
else:
model_name = "iris.model"
model_name = "iris.deprecated"
model_path = pathlib.Path(tmpdir) / model_name
xgb_model.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model_legacy_binary(model_path)
Expand Down Expand Up @@ -268,7 +268,7 @@ def test_xgb_nonlinear_objective(
if model_format == "json":
model_name = f"nonlinear_{objective_tag}.json"
else:
model_name = f"nonlinear_{objective_tag}.bin"
model_name = f"nonlinear_{objective_tag}.deprecated"
with TemporaryDirectory() as tmpdir:
model_path = pathlib.Path(tmpdir) / model_name
xgb_model.save_model(model_path)
Expand All @@ -287,7 +287,7 @@ def test_xgb_nonlinear_objective(

@given(
dataset=standard_classification_datasets(n_classes=just(2)),
model_format=sampled_from(["legacy_binary", "json"]),
model_format=sampled_from(["in_memory", "json"]),
num_boost_round=integers(min_value=5, max_value=20),
)
@settings(**standard_settings())
Expand Down Expand Up @@ -467,7 +467,7 @@ def test_xgb_multi_target_binary_classifier(
bst.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model(model_path)
else:
model_path = pathlib.Path(tmpdir) / "multi_target.model"
model_path = pathlib.Path(tmpdir) / "multi_target.deprecated"
bst.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model_legacy_binary(
model_path
Expand Down Expand Up @@ -548,7 +548,7 @@ def test_xgb_multi_target_regressor(
xgb_model.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model(model_path)
else:
model_name = "model.model"
model_name = "model.deprecated"
model_path = pathlib.Path(tmpdir) / model_name
xgb_model.save_model(model_path)
tl_model = treelite.frontend.load_xgboost_model_legacy_binary(model_path)
Expand Down
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