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FIX: Fix KNN predictions on X=None #2821

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FIX: Fix KNN predictions on X=None #2821

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233a9f3
fix predictions on X=None
david-cortes-intel Dec 3, 2025
ac5e52c
don't fall back on predict_proba
david-cortes-intel Dec 4, 2025
5eb1674
add comment about misleading log
david-cortes-intel Dec 4, 2025
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2 changes: 1 addition & 1 deletion onedal/neighbors/neighbors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -472,7 +472,7 @@ def predict_proba(self, X, queue=None):
_y = self._y.reshape((-1, 1))
classes_ = [self.classes_]

n_queries = _num_samples(X)
n_queries = _num_samples(X if X is not None else self._fit_X)

weights = self._get_weights(neigh_dist, self.weights)
if weights is None:
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13 changes: 13 additions & 0 deletions sklearnex/neighbors/common.py
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Expand Up @@ -151,6 +151,19 @@
patching_status = PatchingConditionsChain(
f"sklearn.neighbors.{class_name}.{method_name}"
)
# TODO: with verbosity enabled, here it would emit a log saying that it fell

Check notice on line 154 in sklearnex/neighbors/common.py

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codefactor.io / CodeFactor

sklearnex/neighbors/common.py#L154 

Unresolved comment '# TODO: with verbosity enabled, here it would emit a log saying that it fell'. (C100)
# back to sklearn, but internally, sklearn will end up calling 'kneighbors'
# which is overridden in the sklearnex classes, thus it will end up calling
# oneDAL in the end, but the log will say otherwise. Find a way to make the
# log consistent with what happens in practice.
patching_status.and_conditions(
[
(
not (data[0] is None and method_name in ["predict", "score"]),
"Predictions on 'None' data are handled by internal sklearn methods.",
)
]
)
if not patching_status.and_condition(
"radius" not in method_name, "RadiusNeighbors not implemented in sklearnex"
):
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9 changes: 6 additions & 3 deletions sklearnex/neighbors/knn_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,8 @@ def fit(self, X, y):
@wrap_output_data
def predict(self, X):
check_is_fitted(self)
check_feature_names(self, X, reset=False)
if X is not None:
check_feature_names(self, X, reset=False)
return dispatch(
self,
"predict",
Expand All @@ -93,7 +94,8 @@ def predict(self, X):
@wrap_output_data
def predict_proba(self, X):
check_is_fitted(self)
check_feature_names(self, X, reset=False)
if X is not None:
check_feature_names(self, X, reset=False)
return dispatch(
self,
"predict_proba",
Expand All @@ -107,7 +109,8 @@ def predict_proba(self, X):
@wrap_output_data
def score(self, X, y, sample_weight=None):
check_is_fitted(self)
check_feature_names(self, X, reset=False)
if X is not None:
check_feature_names(self, X, reset=False)
return dispatch(
self,
"score",
Expand Down
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