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[experiment] Verify diverging parameters do not impact SVM fit/predict quality. #2213

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[experiment] Verify diverging parameters do not impact SVM fit/predict quality. #2213

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864d810
Update neighbors.py
icfaust Jan 16, 2025
7978eb1
Update knn_classification.py
icfaust Jan 16, 2025
3d55f90
Update knn_classification.py
icfaust Jan 16, 2025
b5a60f5
Update knn_classification.py
icfaust Jan 17, 2025
84c87a5
Update knn_classification.py
icfaust Jan 17, 2025
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16 changes: 0 additions & 16 deletions onedal/neighbors/neighbors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -415,17 +415,6 @@ def _get_daal_params(self, data):
return params

def _onedal_fit(self, X, y, queue):
gpu_device = queue is not None and queue.sycl_device.is_gpu
if self.effective_metric_ == "euclidean" and not gpu_device:
params = self._get_daal_params(X)
if self._fit_method == "brute":
train_alg = bf_knn_classification_training

else:
train_alg = kdtree_knn_classification_training

return train_alg(**params).compute(X, y).model

policy = self._get_policy(queue, X, y)
X_table, y_table = to_table(X, y, queue=queue)
params = self._get_onedal_params(X_table, y)
Expand All @@ -436,11 +425,6 @@ def _onedal_fit(self, X, y, queue):
return train_alg.model

def _onedal_predict(self, model, X, params, queue):
if type(self._onedal_model) is kdtree_knn_classification_model:
return kdtree_knn_classification_prediction(**params).compute(X, model)
elif type(self._onedal_model) is bf_knn_classification_model:
return bf_knn_classification_prediction(**params).compute(X, model)

policy = self._get_policy(queue, X)
X = to_table(X, queue=queue)
if hasattr(self, "_onedal_model"):
Expand Down
15 changes: 6 additions & 9 deletions sklearnex/neighbors/knn_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,8 @@
# limitations under the License.
# ===============================================================================

import numpy as np

from sklearn.metrics import accuracy_score
from sklearn.neighbors._classification import (
KNeighborsClassifier as _sklearn_KNeighborsClassifier,
Expand Down Expand Up @@ -113,8 +115,6 @@ def fit(self, X, y):
@wrap_output_data
def predict(self, X):
check_is_fitted(self)
if sklearn_check_version("1.0"):
self._check_feature_names(X, reset=False)
return dispatch(
self,
"predict",
Expand All @@ -128,8 +128,6 @@ def predict(self, X):
@wrap_output_data
def predict_proba(self, X):
check_is_fitted(self)
if sklearn_check_version("1.0"):
self._check_feature_names(X, reset=False)
return dispatch(
self,
"predict_proba",
Expand All @@ -143,8 +141,6 @@ def predict_proba(self, X):
@wrap_output_data
def score(self, X, y, sample_weight=None):
check_is_fitted(self)
if sklearn_check_version("1.0"):
self._check_feature_names(X, reset=False)
return dispatch(
self,
"score",
Expand All @@ -160,8 +156,6 @@ def score(self, X, y, sample_weight=None):
@wrap_output_data
def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
check_is_fitted(self)
if sklearn_check_version("1.0") and X is not None:
self._check_feature_names(X, reset=False)
return dispatch(
self,
"kneighbors",
Expand All @@ -175,6 +169,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
)

def _onedal_fit(self, X, y, queue=None):
X, y = validate_data(self, X, y, dtype=[np.float64, np.float32], accept_sparse="csr")
onedal_params = {
"n_neighbors": self.n_neighbors,
"weights": self.weights,
Expand All @@ -187,7 +182,6 @@ def _onedal_fit(self, X, y, queue=None):
requires_y = self._get_tags()["requires_y"]
except KeyError:
requires_y = False

self._onedal_estimator = onedal_KNeighborsClassifier(**onedal_params)
self._onedal_estimator.requires_y = requires_y
self._onedal_estimator.effective_metric_ = self.effective_metric_
Expand All @@ -197,14 +191,17 @@ def _onedal_fit(self, X, y, queue=None):
self._save_attributes()

def _onedal_predict(self, X, queue=None):
X = validate_data(self, X, dtype=[np.float64, np.float32], accept_sparse="csr", reset=False)
return self._onedal_estimator.predict(X, queue=queue)

def _onedal_predict_proba(self, X, queue=None):
X = validate_data(self, X, dtype=[np.float64, np.float32], accept_sparse="csr", reset=False)
return self._onedal_estimator.predict_proba(X, queue=queue)

def _onedal_kneighbors(
self, X=None, n_neighbors=None, return_distance=True, queue=None
):
X = validate_data(self, X, dtype=[np.float64, np.float32], accept_sparse="csr", reset=False)
return self._onedal_estimator.kneighbors(
X, n_neighbors, return_distance, queue=queue
)
Expand Down
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