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Make XGBoostClassifier compatible with any class labels #11727

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Make XGBoostClassifier compatible with any class labels #11727

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23 changes: 17 additions & 6 deletions python-package/xgboost/sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1730,6 +1730,7 @@ def fit(
) -> "XGBClassifier":
# pylint: disable = attribute-defined-outside-init,too-many-statements
with config_context(verbosity=self.verbosity):
encoded_classes = None
# We keep the n_classes_ as a simple member instead of loading it from
# booster in a Python property. This way we can have efficient and
# thread-safe prediction.
Expand All @@ -1744,22 +1745,28 @@ def fit(
elif _is_cupy_alike(y):
cp = import_cupy()

classes = cp.unique(y)
classes, encoded_classes = cp.unique(y, return_inverse=True)
self.n_classes_ = len(classes)
expected_classes = cp.array(self.classes_)
else:
classes = np.unique(np.asarray(y))
classes, encoded_classes = np.unique(np.asarray(y), return_inverse=True)
self.n_classes_ = len(classes)
expected_classes = self.classes_
if (
classes.shape != expected_classes.shape
or not (classes == expected_classes).all()
):
if classes.shape != expected_classes.shape:
raise ValueError(
f"Invalid classes inferred from unique values of `y`. "
f"Expected: {expected_classes}, got {classes}"
)

self.classes_labels: ArrayLike | None = None
# if classes are not label encoded as [0, 1, ..., n_classes - 1], then
# we need to transform them.
if not (classes == expected_classes).all():
self.classes_labels = classes
if encoded_classes is None:
encoded_classes = np.unique(np.asarray(y), return_inverse=True)[1]
y = encoded_classes

params = self.get_xgb_params()

if callable(self.objective):
Expand Down Expand Up @@ -1861,6 +1868,10 @@ def predict(
column_indexes = np.repeat(0, class_probs.shape[0])
column_indexes[class_probs > 0.5] = 1

# map back to original class labels
if self.classes_labels is not None:
return self.classes_labels[column_indexes]

return column_indexes

def predict_proba(
Expand Down