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Update FIL tests to use XGBoost UBJSON instead of binary #6153

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merged 6 commits into from
Dec 4, 2024
Merged

Update FIL tests to use XGBoost UBJSON instead of binary #6153

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3e84150
Update FIL tests to use XGBoost UBJSON instead of binary
hcho3 Dec 3, 2024
9f4f2de
Remove deprecated method
hcho3 Dec 3, 2024
4883dbc
Reshape
hcho3 Dec 3, 2024
505183a
Install XGBoost for testing
hcho3 Dec 3, 2024
a7dad45
Use xgboost>=2.1.3
hcho3 Dec 3, 2024
ba2cb4b
Use xgboost>=2.1.0 bound
hcho3 Dec 3, 2024
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1 change: 1 addition & 0 deletions conda/environments/all_cuda-118_arch-x86_64.yaml
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Expand Up @@ -77,4 +77,5 @@ dependencies:
- sysroot_linux-64==2.17
- treelite==4.3.0
- umap-learn==0.5.6
- xgboost>=2.1.3
name: all_cuda-118_arch-x86_64
1 change: 1 addition & 0 deletions conda/environments/all_cuda-125_arch-x86_64.yaml
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Expand Up @@ -73,4 +73,5 @@ dependencies:
- sysroot_linux-64==2.17
- treelite==4.3.0
- umap-learn==0.5.6
- xgboost>=2.1.3
name: all_cuda-125_arch-x86_64
3 changes: 2 additions & 1 deletion dependencies.yaml
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Expand Up @@ -523,6 +523,7 @@ dependencies:
- pytest-xdist
- seaborn
- *scikit_learn
- &xgboost xgboost>=2.1.3
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- statsmodels
- umap-learn==0.5.6
- pynndescent
Expand All @@ -537,4 +538,4 @@ dependencies:
- pandas
- *scikit_learn
- seaborn
- xgboost
- *xgboost
20 changes: 10 additions & 10 deletions python/cuml/cuml/tests/experimental/test_filex.py
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Expand Up @@ -163,7 +163,7 @@ def test_fil_classification(
X, y, train_size=0.8, random_state=0
)

model_path = os.path.join(tmp_path, "xgb_class.model")
model_path = os.path.join(tmp_path, "xgb_class.ubj")

bst = _build_and_save_xgboost(
model_path,
Expand Down Expand Up @@ -258,7 +258,7 @@ def test_fil_regression(
X, y, train_size=train_size, random_state=0
)

model_path = os.path.join(tmp_path, "xgb_reg.model")
model_path = os.path.join(tmp_path, "xgb_reg.ubj")
bst = _build_and_save_xgboost(
model_path,
X_train,
Expand Down Expand Up @@ -490,12 +490,12 @@ def test_fil_skl_regression(
np.testing.assert_allclose(fil_preds_opt, fil_preds, atol=1.2e-3)


@pytest.fixture(scope="session", params=["binary", "json"])
@pytest.fixture(scope="session", params=["ubjson", "json"])
def small_classifier_and_preds(tmpdir_factory, request):
X, y = simulate_data(500, 10, random_state=43210, classification=True)

ext = "json" if request.param == "json" else "model"
model_type = "xgboost_json" if request.param == "json" else "xgboost"
ext = "json" if request.param == "json" else "ubj"
model_type = "xgboost_json" if request.param == "json" else "xgboost_ubj"
model_path = str(
tmpdir_factory.mktemp("models").join(f"small_class.{ext}")
)
Expand Down Expand Up @@ -738,7 +738,7 @@ def test_predict_per_tree(
classification=True,
)

model_path = os.path.join(tmp_path, "xgb_class.model")
model_path = os.path.join(tmp_path, "xgb_class.ubj")

xgboost_params = {"base_score": (0.5 if n_classes == 2 else 0.0)}
bst = _build_and_save_xgboost(
Expand All @@ -751,7 +751,7 @@ def test_predict_per_tree(
xgboost_params=xgboost_params,
)
fm = ForestInference.load(model_path, output_class=True)
tl_model = treelite.Model.from_xgboost(bst)
tl_model = treelite.frontend.from_xgboost(bst)
pred_per_tree_tl = treelite.gtil.predict_per_tree(tl_model, X)

with using_device_type(infer_device):
Expand All @@ -773,7 +773,7 @@ def test_predict_per_tree(
assert pred_per_tree.shape == expected_shape
np.testing.assert_almost_equal(sum_by_class, margin_pred, decimal=3)
np.testing.assert_almost_equal(
pred_per_tree, pred_per_tree_tl, decimal=3
pred_per_tree.reshape((n_rows, -1, 1)), pred_per_tree_tl, decimal=3
)
np.testing.assert_almost_equal(
pred_per_tree_opt, pred_per_tree, decimal=3
Expand Down Expand Up @@ -844,7 +844,7 @@ def test_apply(train_device, infer_device, n_classes, tmp_path):
classification=True,
)

model_path = os.path.join(tmp_path, "xgb_class.model")
model_path = os.path.join(tmp_path, "xgb_class.ubj")

xgboost_params = {"base_score": (0.5 if n_classes == 2 else 0.0)}
bst = _build_and_save_xgboost(
Expand All @@ -858,7 +858,7 @@ def test_apply(train_device, infer_device, n_classes, tmp_path):
)

fm = ForestInference.load(
model_path, output_class=True, model_type="xgboost"
model_path, output_class=True, model_type="xgboost_ubj"
)

with using_device_type(infer_device):
Expand Down
12 changes: 6 additions & 6 deletions python/cuml/cuml/tests/test_fil.py
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@@ -1,4 +1,4 @@
# Copyright (c) 2019-2023, NVIDIA CORPORATION.
# Copyright (c) 2019-2024, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -155,7 +155,7 @@ def test_fil_classification(
X, y, train_size=train_size, random_state=0
)

model_path = os.path.join(tmp_path, "xgb_class.model")
model_path = os.path.join(tmp_path, "xgb_class.ubj")

bst = _build_and_save_xgboost(
model_path,
Expand Down Expand Up @@ -226,7 +226,7 @@ def test_fil_regression(n_rows, n_columns, num_rounds, tmp_path, max_depth):
X, y, train_size=train_size, random_state=0
)

model_path = os.path.join(tmp_path, "xgb_reg.model")
model_path = os.path.join(tmp_path, "xgb_reg.ubj")
bst = _build_and_save_xgboost(
model_path,
X_train,
Expand Down Expand Up @@ -447,12 +447,12 @@ def test_fil_skl_regression(
assert np.allclose(fil_preds, skl_preds, 1.2e-2)


@pytest.fixture(scope="session", params=["binary", "json"])
@pytest.fixture(scope="session", params=["ubjson", "json"])
def small_classifier_and_preds(tmpdir_factory, request):
X, y = simulate_data(500, 10, random_state=43210, classification=True)

ext = "json" if request.param == "json" else "model"
model_type = "xgboost_json" if request.param == "json" else "xgboost"
ext = "json" if request.param == "json" else "ubj"
model_type = "xgboost_json" if request.param == "json" else "xgboost_ubj"
model_path = str(
tmpdir_factory.mktemp("models").join(f"small_class.{ext}")
)
Expand Down
1 change: 1 addition & 0 deletions python/cuml/pyproject.toml
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Expand Up @@ -137,6 +137,7 @@ test = [
"seaborn",
"statsmodels",
"umap-learn==0.5.6",
"xgboost>=2.1.3",
] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.

[project.urls]
Expand Down
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