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FIX sklearn sample_weights checks for cupy and numpy<2 #102

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FIX sklearn sample_weights checks for cupy and numpy<2 #102

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6 changes: 4 additions & 2 deletions himalaya/kernel_ridge/_sklearn_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -244,7 +244,8 @@ def fit(self, X, y=None, sample_weight=None):
# Apply sample weight scaling to dual coefficients (sklearn compatibility)
if sample_weight is not None:
# Ensure sw is on the same device as dual_coef_
sw = backend.asarray(sw, device=self.dual_coef_.device if hasattr(self.dual_coef_, 'device') else None)
if not backend.is_in_gpu(self.dual_coef_):
sw = backend.to_cpu(sw)
Comment on lines +247 to +248

@gemini-code-assist gemini-code-assist Bot Jun 16, 2026

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medium

Instead of checking is_in_gpu and calling to_cpu, we can use getattr(self.dual_coef_, "device", "cpu") to get the device of self.dual_coef_ (defaulting to "cpu" for NumPy < 2 which lacks the .device attribute). This is cleaner, avoids branching, and preserves the explicit device-matching behavior of backend.asarray on multi-GPU setups.

Suggested change
if not backend.is_in_gpu(self.dual_coef_):
sw = backend.to_cpu(sw)
device = getattr(self.dual_coef_, "device", "cpu")
sw = backend.asarray(sw, device=device)

@kroq-gar78 kroq-gar78 Jun 16, 2026

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Since this only ever moves to the CPU, and there are no device IDs for 'cpu', I think the current check is actually safer.

self.dual_coef_ = self.dual_coef_ * sw

if ravel:
Expand Down Expand Up @@ -1173,7 +1174,8 @@ def fit(self, X, y=None, sample_weight=None):
# Apply sample weight scaling to dual coefficients (sklearn compatibility)
if sample_weight is not None:
# Ensure sw is on the same device as dual_coef_
sw = backend.asarray(sw, device=self.dual_coef_.device if hasattr(self.dual_coef_, 'device') else None)
if not backend.is_in_gpu(self.dual_coef_):
sw = backend.to_cpu(sw)
Comment on lines +1177 to +1178

@gemini-code-assist gemini-code-assist Bot Jun 16, 2026

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medium

Instead of checking is_in_gpu and calling to_cpu, we can use getattr(self.dual_coef_, "device", "cpu") to get the device of self.dual_coef_ (defaulting to "cpu" for NumPy < 2 which lacks the .device attribute). This is cleaner, avoids branching, and preserves the explicit device-matching behavior of backend.asarray on multi-GPU setups.

Suggested change
if not backend.is_in_gpu(self.dual_coef_):
sw = backend.to_cpu(sw)
device = getattr(self.dual_coef_, "device", "cpu")
sw = backend.asarray(sw, device=device)

@kroq-gar78 kroq-gar78 Jun 16, 2026

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See previous comment.

self.dual_coef_ = self.dual_coef_ * sw

if ravel or self.deltas_.shape[1] != self.dual_coef_.shape[1]:
Expand Down