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Remove all use of DeviceQuantileDMatrix. (#8665)

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This commit is contained in:
Jiaming Yuan
2023-01-17 00:04:10 +08:00
committed by GitHub
parent 0ae8df9a65
commit d6018eb4b9
10 changed files with 57 additions and 53 deletions
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14
tests/python-gpu/test_from_cudf.py
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@@ -160,7 +160,7 @@ Arrow specification.'''
@pytest.mark.skipif(**tm.no_cudf())
def test_device_dmatrix_from_cudf(self):
_test_from_cudf(xgb.DeviceQuantileDMatrix)
_test_from_cudf(xgb.QuantileDMatrix)
@pytest.mark.skipif(**tm.no_cudf())
def test_cudf_training_simple_dmatrix(self):
@@ -168,7 +168,7 @@ Arrow specification.'''
@pytest.mark.skipif(**tm.no_cudf())
def test_cudf_training_device_dmatrix(self):
_test_cudf_training(xgb.DeviceQuantileDMatrix)
_test_cudf_training(xgb.QuantileDMatrix)
@pytest.mark.skipif(**tm.no_cudf())
def test_cudf_metainfo_simple_dmatrix(self):
@@ -176,7 +176,7 @@ Arrow specification.'''
@pytest.mark.skipif(**tm.no_cudf())
def test_cudf_metainfo_device_dmatrix(self):
_test_cudf_metainfo(xgb.DeviceQuantileDMatrix)
_test_cudf_metainfo(xgb.QuantileDMatrix)
@pytest.mark.skipif(**tm.no_cudf())
def test_cudf_categorical(self) -> None:
@@ -191,7 +191,7 @@ Arrow specification.'''
assert len(Xy.feature_types) == X.shape[1]
assert all(t == "c" for t in Xy.feature_types)
Xy = xgb.DeviceQuantileDMatrix(X, y, enable_categorical=True)
Xy = xgb.QuantileDMatrix(X, y, enable_categorical=True)
assert Xy.feature_types is not None
assert len(Xy.feature_types) == X.shape[1]
assert all(t == "c" for t in Xy.feature_types)
@@ -228,9 +228,9 @@ Arrow specification.'''
assert Xy.num_col() == 1
with pytest.raises(ValueError, match="enable_categorical"):
xgb.DeviceQuantileDMatrix(X, y)
xgb.QuantileDMatrix(X, y)
Xy = xgb.DeviceQuantileDMatrix(X, y, enable_categorical=True)
Xy = xgb.QuantileDMatrix(X, y, enable_categorical=True)
assert Xy.num_row() == 3
assert Xy.num_col() == 1
@@ -344,7 +344,7 @@ def test_from_cudf_iter(enable_categorical):
params = {"tree_method": "gpu_hist"}
# Use iterator
m_it = xgb.DeviceQuantileDMatrix(it, enable_categorical=enable_categorical)
m_it = xgb.QuantileDMatrix(it, enable_categorical=enable_categorical)
reg_with_it = xgb.train(params, m_it, num_boost_round=rounds)
X = it.as_array()