def _get_dmatrix(data: RayDMatrix, param: Dict) -> xgb.DMatrix:
if isinstance(data, RayDeviceQuantileDMatrix):
if isinstance(param["data"], list):
dm_param = {
"feature_names": data.feature_names,
"feature_types": data.feature_types,
"missing": data.missing,
}
if not isinstance(data, xgb.DeviceQuantileDMatrix):
pass
param.update(dm_param)
it = RayDataIter(**param)
matrix = xgb.DeviceQuantileDMatrix(it, **dm_param)
else:
matrix = xgb.DeviceQuantileDMatrix(**param)
else:
if isinstance(param["data"], list):
dm_param = {
"data": concat_dataframes(param["data"]),
"label": concat_dataframes(param["label"]),
"weight": concat_dataframes(param["weight"]),
"base_margin": concat_dataframes(param["base_margin"]),
"label_lower_bound":
concat_dataframes(param["label_lower_bound"]),
"label_upper_bound":
concat_dataframes(param["label_upper_bound"]),
}
param.update(dm_param)
ll = param.pop("label_lower_bound", None)
lu = param.pop("label_upper_bound", None)
matrix = xgb.DMatrix(**param)
matrix.set_info(label_lower_bound=ll, label_upper_bound=lu)
return matrix
def test_dlpack_device_dmat(self):
import cupy as cp
n = 100
X = cp.random.random((n, 2))
m = xgb.DeviceQuantileDMatrix(X.toDlpack())
with pytest.raises(xgb.core.XGBoostError):
m.slice(rindex=[0, 1, 2])
def run_invalid_category(self, tree_method: str) -> None:
rng = np.random.default_rng()
# too large
X = rng.integers(low=0, high=4, size=1000).reshape(100, 10)
y = rng.normal(loc=0, scale=1, size=100)
X[13, 7] = np.iinfo(np.int32).max + 1
# Check is performed during sketching.
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": tree_method}, Xy)
X[13, 7] = 16777216
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": tree_method}, Xy)
# mixed positive and negative values
X = rng.normal(loc=0, scale=1, size=1000).reshape(100, 10)
y = rng.normal(loc=0, scale=1, size=100)
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": tree_method}, Xy)
if tree_method == "gpu_hist":
import cupy as cp
X, y = cp.array(X), cp.array(y)
with pytest.raises(ValueError):
Xy = xgb.DeviceQuantileDMatrix(X, y, feature_types=["c"] * 10)
def test_cudf_categorical(self):
import cudf
_X, _y = tm.make_categorical(100, 30, 17, False)
X = cudf.from_pandas(_X)
y = cudf.from_pandas(_y)
Xy = xgb.DMatrix(X, y, enable_categorical=True)
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)
assert len(Xy.feature_types) == X.shape[1]
assert all(t == "c" for t in Xy.feature_types)
# test missing value
X = cudf.DataFrame({"f0": ["a", "b", np.NaN]})
X["f0"] = X["f0"].astype("category")
df, cat_codes, _, _ = xgb.data._transform_cudf_df(
X, None, None, enable_categorical=True)
for col in cat_codes:
assert col.has_nulls
y = [0, 1, 2]
with pytest.raises(ValueError):
xgb.DMatrix(X, y)
Xy = xgb.DMatrix(X, y, enable_categorical=True)
assert Xy.num_row() == 3
assert Xy.num_col() == 1
with pytest.raises(ValueError):
xgb.DeviceQuantileDMatrix(X, y)
Xy = xgb.DeviceQuantileDMatrix(X, y, enable_categorical=True)
assert Xy.num_row() == 3
assert Xy.num_col() == 1
X = X["f0"]
with pytest.raises(ValueError):
xgb.DMatrix(X, y)
Xy = xgb.DMatrix(X, y, enable_categorical=True)
assert Xy.num_row() == 3
assert Xy.num_col() == 1
def main():
# 比较适合 GPU 训练
Xy_train = IterLoadForDMatrix(train.loc[train_idx], FEATURES, "target")
dtrain = xgb.DeviceQuantileDMatrix(Xy_train, max_bin = 256)
# 比较适合 CPU 训练
it = Iterator(["file_0.svm", "file_1.svm", "file_2.svm"])
Xy = xgb.DMatrix(it)
# Other tree methods including ``hist`` and ``gpu_hist`` also work,
# but has some caveats as noted in following sections.
booster = xgb.train({"tree_method": "approx"}, Xy)
def test_large_input():
available_bytes, _ = cp.cuda.runtime.memGetInfo()
# 15 GB
required_bytes = 1.5e+10
if available_bytes < required_bytes:
pytest.skip("Not enough memory on this device")
n = 1000
m = ((1 << 31) + n - 1) // n
assert (np.log2(m * n) > 31)
X = cp.ones((m, n), dtype=np.float32)
y = cp.ones(m)
dmat = xgb.DeviceQuantileDMatrix(X, y)
xgb.train({"tree_method": "gpu_hist", "max_depth": 1}, dmat, 1)
def test_categorical(self):
import cudf
_X, _y = tm.make_categorical(100, 30, 17, False)
X = cudf.from_pandas(_X)
y = cudf.from_pandas(_y)
Xy = xgb.DMatrix(X, y, enable_categorical=True)
assert len(Xy.feature_types) == X.shape[1]
assert all(t == "categorical" for t in Xy.feature_types)
Xy = xgb.DeviceQuantileDMatrix(X, y, enable_categorical=True)
assert len(Xy.feature_types) == X.shape[1]
assert all(t == "categorical" for t in Xy.feature_types)
def test_invalid_categorical(self):
import cupy as cp
rng = np.random.default_rng()
X = rng.normal(loc=0, scale=1, size=1000).reshape(100, 10)
y = rng.normal(loc=0, scale=1, size=100)
# Check is performe during sketching.
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": "gpu_hist"}, Xy)
X, y = cp.array(X), cp.array(y)
with pytest.raises(ValueError):
Xy = xgb.DeviceQuantileDMatrix(X, y, feature_types=["c"] * 10)
def test_metainfo(self) -> None:
import cupy as cp
rng = cp.random.RandomState(1994)
rows = 10
cols = 3
data = rng.randn(rows, cols)
labels = rng.randn(rows)
fw = rng.randn(rows)
fw -= fw.min()
m = xgb.DeviceQuantileDMatrix(data=data, label=labels, feature_weights=fw)
got_fw = m.get_float_info("feature_weights")
got_labels = m.get_label()
cp.testing.assert_allclose(fw, got_fw)
cp.testing.assert_allclose(labels, got_labels)
def test_from_cudf_iter():
rounds = 100
it = IterForDMatrixTest()
# Use iterator
m_it = xgb.DeviceQuantileDMatrix(it)
reg_with_it = xgb.train({'tree_method': 'gpu_hist'},
m_it,
num_boost_round=rounds)
predict_with_it = reg_with_it.predict(m_it)
# Without using iterator
m = xgb.DMatrix(it.as_array(), it.as_array_labels())
assert m_it.num_col() == m.num_col()
assert m_it.num_row() == m.num_row()
reg = xgb.train({'tree_method': 'gpu_hist'}, m, num_boost_round=rounds)
predict = reg.predict(m)
np.testing.assert_allclose(predict_with_it, predict)
def test_from_cudf_iter(enable_categorical):
rounds = 100
it = IterForDMatrixTest(enable_categorical)
params = {"tree_method": "gpu_hist"}
# Use iterator
m_it = xgb.DeviceQuantileDMatrix(it, enable_categorical=enable_categorical)
reg_with_it = xgb.train(params, m_it, num_boost_round=rounds)
X = it.as_array()
y = it.as_array_labels()
m = xgb.DMatrix(X, y, enable_categorical=enable_categorical)
assert m_it.num_col() == m.num_col()
assert m_it.num_row() == m.num_row()
reg = xgb.train(params, m, num_boost_round=rounds)
predict = reg.predict(m)
predict_with_it = reg_with_it.predict(m_it)
np.testing.assert_allclose(predict_with_it, predict)
def main():
rounds = 100
it = IterForDMatrixDemo()
# Use iterator, must be `DeviceQuantileDMatrix`
m_with_it = xgboost.DeviceQuantileDMatrix(it)
# Use regular DMatrix.
m = xgboost.DMatrix(it.as_array(),
it.as_array_labels(),
weight=it.as_array_weights())
assert m_with_it.num_col() == m.num_col()
assert m_with_it.num_row() == m.num_row()
reg_with_it = xgboost.train({'tree_method': 'gpu_hist'},
m_with_it,
num_boost_round=rounds)
predict_with_it = reg_with_it.predict(m_with_it)
reg = xgboost.train({'tree_method': 'gpu_hist'}, m, num_boost_round=rounds)
predict = reg.predict(m)
numpy.testing.assert_allclose(predict_with_it, predict, rtol=1e6)
def test_dmatrix_cupy_init(self):
import cupy as cp
data = cp.random.randn(5, 5)
dm = xgb.DeviceQuantileDMatrix(data, cp.ones(5, dtype=np.float64))
def test_dmatrix_numpy_init(self):
data = np.random.randn(5, 5)
with pytest.raises(AssertionError,
match='is not supported for DeviceQuantileDMatrix'):
dm = xgb.DeviceQuantileDMatrix(data, np.ones(5, dtype=np.float64))
def test_dlpack_device_dmat(self):
import cupy as cp
n = 100
X = cp.random.random((n, 2))
xgb.DeviceQuantileDMatrix(X.toDlpack())
def get_device_dmat(self):
w = None if self.w is None else cp.array(self.w)
X = cp.array(self.X, dtype=np.float32)
y = cp.array(self.y, dtype=np.float32)
return xgb.DeviceQuantileDMatrix(X, y, w, base_margin=self.margin)
