def run_with_dask_array(DMatrixT, client):
import cupy as cp
cp.cuda.runtime.setDevice(0)
X, y = generate_array()
X = X.map_blocks(cp.asarray)
y = y.map_blocks(cp.asarray)
dtrain = DMatrixT(client, X, y)
out = dxgb.train(client, {
'tree_method': 'gpu_hist',
'debug_synchronize': True
},
dtrain=dtrain,
evals=[(dtrain, 'X')],
num_boost_round=2)
from_dmatrix = dxgb.predict(client, out, dtrain).compute()
inplace_predictions = dxgb.inplace_predict(client, out, X).compute()
single_node = out['booster'].predict(xgboost.DMatrix(X.compute()))
np.testing.assert_allclose(single_node, from_dmatrix)
device = cp.cuda.runtime.getDevice()
assert device == inplace_predictions.device.id
single_node = cp.array(single_node)
assert device == single_node.device.id
cp.testing.assert_allclose(single_node, inplace_predictions)
def test_categorical(local_cuda_cluster: LocalCUDACluster) -> None:
with Client(local_cuda_cluster) as client:
import dask_cudf
rounds = 10
X, y = make_categorical(client, 10000, 30, 13)
X = dask_cudf.from_dask_dataframe(X)
X_onehot, _ = make_categorical(client, 10000, 30, 13, True)
X_onehot = dask_cudf.from_dask_dataframe(X_onehot)
parameters = {"tree_method": "gpu_hist"}
m = dxgb.DaskDMatrix(client, X_onehot, y, enable_categorical=True)
by_etl_results = dxgb.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)["history"]
m = dxgb.DaskDMatrix(client, X, y, enable_categorical=True)
output = dxgb.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)
by_builtin_results = output["history"]
np.testing.assert_allclose(
np.array(by_etl_results["Train"]["rmse"]),
np.array(by_builtin_results["Train"]["rmse"]),
rtol=1e-3,
)
assert tm.non_increasing(by_builtin_results["Train"]["rmse"])
def check_model_output(model: dxgb.Booster) -> None:
with tempfile.TemporaryDirectory() as tempdir:
path = os.path.join(tempdir, "model.json")
model.save_model(path)
with open(path, "r") as fd:
categorical = json.load(fd)
categories_sizes = np.array(
categorical["learner"]["gradient_booster"]["model"]
["trees"][-1]["categories_sizes"])
assert categories_sizes.shape[0] != 0
np.testing.assert_allclose(categories_sizes, 1)
check_model_output(output["booster"])
reg = dxgb.DaskXGBRegressor(enable_categorical=True,
n_estimators=10,
tree_method="gpu_hist")
reg.fit(X, y)
check_model_output(reg.get_booster())
reg = dxgb.DaskXGBRegressor(enable_categorical=True, n_estimators=10)
with pytest.raises(ValueError):
reg.fit(X, y)