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Convert `DaskXGBClassifier.classes_` to an array (#8452)

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---------

Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Scott Gustafson 2023-04-26 14:23:35 -04:00 committed by GitHub
parent 0e7377ba9c
commit 353ed5339d
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2 changed files with 26 additions and 1 deletions
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8
python-package/xgboost/dask.py
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@ -73,6 +73,7 @@ from .core import (
_deprecate_positional_args,
_expect,
)
from .data import _is_cudf_ser, _is_cupy_array
from .sklearn import (
XGBClassifier,
XGBClassifierBase,
@ -1894,10 +1895,15 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierMixIn, XGBClassifierBa
)
# pylint: disable=attribute-defined-outside-init
if isinstance(y, (da.Array)):
if isinstance(y, da.Array):
self.classes_ = await self.client.compute(da.unique(y))
else:
self.classes_ = await self.client.compute(y.drop_duplicates())
if _is_cudf_ser(self.classes_):
self.classes_ = self.classes_.to_cupy()
if _is_cupy_array(self.classes_):
self.classes_ = self.classes_.get()
self.classes_ = numpy.array(self.classes_)
self.n_classes_ = len(self.classes_)
if self.n_classes_ > 2:

19
tests/test_distributed/test_with_dask/test_with_dask.py
View File

@ -192,6 +192,25 @@ def deterministic_repartition(
return X, y, m
@pytest.mark.parametrize("to_frame", [True, False])
def test_xgbclassifier_classes_type_and_value(to_frame: bool, client: "Client"):
X, y = make_classification(n_samples=1000, n_features=4, random_state=123)
if to_frame:
import pandas as pd
feats = [f"var_{i}" for i in range(4)]
df = pd.DataFrame(X, columns=feats)
df["target"] = y
df = dd.from_pandas(df, npartitions=1)
X, y = df[feats], df["target"]
else:
X = da.from_array(X)
y = da.from_array(y)
est = xgb.dask.DaskXGBClassifier(n_estimators=10).fit(X, y)
assert isinstance(est.classes_, np.ndarray)
np.testing.assert_array_equal(est.classes_, np.array([0, 1]))
def test_from_dask_dataframe() -> None:
with LocalCluster(n_workers=kWorkers, dashboard_address=":0") as cluster:
with Client(cluster) as client: