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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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16
tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
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@@ -135,7 +135,7 @@ def run_with_dask_array(DMatrixT: Type, client: Client) -> None:
def to_cp(x: Any, DMatrixT: Type) -> Any:
import cupy
if isinstance(x, np.ndarray) and DMatrixT is dxgb.DaskDeviceQuantileDMatrix:
if isinstance(x, np.ndarray) and DMatrixT is dxgb.DaskQuantileDMatrix:
X = cupy.array(x)
else:
X = x
@@ -169,7 +169,7 @@ def run_gpu_hist(
else:
w = None
if DMatrixT is dxgb.DaskDeviceQuantileDMatrix:
if DMatrixT is dxgb.DaskQuantileDMatrix:
m = DMatrixT(
client, data=X, label=y, weight=w, max_bin=params.get("max_bin", 256)
)
@@ -227,7 +227,7 @@ class TestDistributedGPU:
@pytest.mark.skipif(**tm.no_dask_cudf())
def test_dask_dataframe(self, local_cuda_client: Client) -> None:
run_with_dask_dataframe(dxgb.DaskDMatrix, local_cuda_client)
run_with_dask_dataframe(dxgb.DaskDeviceQuantileDMatrix, local_cuda_client)
run_with_dask_dataframe(dxgb.DaskQuantileDMatrix, local_cuda_client)
@pytest.mark.skipif(**tm.no_dask_cudf())
def test_categorical(self, local_cuda_client: Client) -> None:
@@ -245,7 +245,7 @@ class TestDistributedGPU:
num_rounds=strategies.integers(1, 20),
dataset=tm.dataset_strategy,
dmatrix_type=strategies.sampled_from(
[dxgb.DaskDMatrix, dxgb.DaskDeviceQuantileDMatrix]
[dxgb.DaskDMatrix, dxgb.DaskQuantileDMatrix]
),
)
@settings(
@@ -268,7 +268,7 @@ class TestDistributedGPU:
@pytest.mark.skipif(**tm.no_cupy())
def test_dask_array(self, local_cuda_client: Client) -> None:
run_with_dask_array(dxgb.DaskDMatrix, local_cuda_client)
run_with_dask_array(dxgb.DaskDeviceQuantileDMatrix, local_cuda_client)
run_with_dask_array(dxgb.DaskQuantileDMatrix, local_cuda_client)
@pytest.mark.skipif(**tm.no_cupy())
def test_early_stopping(self, local_cuda_client: Client) -> None:
@@ -357,7 +357,7 @@ class TestDistributedGPU:
)
X = ddf[ddf.columns.difference(["y"])]
y = ddf[["y"]]
dtrain = dxgb.DaskDeviceQuantileDMatrix(local_cuda_client, X, y)
dtrain = dxgb.DaskQuantileDMatrix(local_cuda_client, X, y)
bst_empty = xgb.dask.train(
local_cuda_client, parameters, dtrain, evals=[(dtrain, "train")]
)
@@ -369,7 +369,7 @@ class TestDistributedGPU:
)
X = ddf[ddf.columns.difference(["y"])]
y = ddf[["y"]]
dtrain = dxgb.DaskDeviceQuantileDMatrix(local_cuda_client, X, y)
dtrain = dxgb.DaskQuantileDMatrix(local_cuda_client, X, y)
bst = xgb.dask.train(
local_cuda_client, parameters, dtrain, evals=[(dtrain, "train")]
)
@@ -546,7 +546,7 @@ async def run_from_dask_array_asyncio(scheduler_address: str) -> dxgb.TrainRetur
X = X.map_blocks(cp.array)
y = y.map_blocks(cp.array)
m = await xgb.dask.DaskDeviceQuantileDMatrix(client, X, y)
m = await xgb.dask.DaskQuantileDMatrix(client, X, y)
output = await xgb.dask.train(client, {"tree_method": "gpu_hist"}, dtrain=m)
with_m = await xgb.dask.predict(client, output, m)