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Upgrade sklearnex #1378
Upgrade sklearnex #1378
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Job PR-1378-1 is done. |
Job PR-1378-2 is done. |
# FIXME: DAAL OOB score is broken, returns biased predictions. Without this optimization, can't compute Efficient OOF. | ||
from daal4py.sklearn.ensemble import RandomForestClassifier, RandomForestRegressor | ||
from sklearnex.ensemble import RandomForestClassifier, RandomForestRegressor | ||
logger.log(15, '\tUsing daal4py RF backend...') |
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logger.log(15, '\tUsing daal4py RF backend...') | |
logger.log(15, '\tUsing sklernex RF backend...') |
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Good suggestion, I have updated this log for RF, KNN, and linear models.
@@ -35,9 +35,8 @@ def _get_model_type(self): | |||
if self.params_aux.get('use_daal', False): | |||
# Disabled by default because it appears to degrade performance | |||
try: | |||
# TODO: Use sklearnex instead once a suitable toggle option is provided that won't impact future models | |||
# FIXME: DAAL OOB score is broken, returns biased predictions. Without this optimization, can't compute Efficient OOF. |
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OOB score was fixed in scikit-learn-intelex=2021.5 version
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Thanks! This is very helpful. I added a new in-line comment to try the new version once it is released.
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Hi @agorshk , I tested 2021.5 and it is not yet fixed (train-time oob_score=True works, but not post-fit OOB).
Refer to uxlfoundation/scikit-learn-intelex#933 for more info
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Hi @agorshk , I tested 2021.5 and it is not yet fixed (train-time oob_score=True works, but not post-fit OOB).
Refer to intel/scikit-learn-intelex#933 for more info
Hi @Innixma, thanks for report and reproducer, we'll have a look on this problem.
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Job PR-1378-8 is done. |
…x, enable sklearnex
Job PR-1378-14 is done. |
Job PR-1378-15 is done. |
Updates finalized Note that RF does not yet use sklearnex by default due to a performance issue on KDDCup09-Upselling: uxlfoundation/scikit-learn-intelex#984 |
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LGTM!
Job PR-1378-16 is done. |
Issue #, if available:
Description of changes:
oob_score=True
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