Remove experimental SmoothedHingeLoss training task #455
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ashelkovnykov
commented
Feb 14, 2020
ashelkovnykov
commented
- Experimental results when comparing Smoothed Hinge Loss to Logistic Regression were substantially worse and also increased training time significantly due to slower convergence
- Removed GLMLossFunction and derived classes, since all training tasks in Photon ML are GLM tasks
- Experimental results when comparing Smoothed Hinge Loss to Logistic Regression were substantially worse and also reduced training time significantly due to slower convergence - Removed GLMLossFunction and derived classes, since all training tasks in Photon ML are GLM tasks
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|
@cmjiang @yunboouyang @lguo |
yunboouyang
approved these changes
May 19, 2020
| classOf[GeneralizedLinearModel], | ||
| classOf[GeneralizedLinearOptimizationProblem[_]], | ||
| classOf[GLMOptimizationConfiguration], | ||
| classOf[HessianDiagonalAggregator], |
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Why do we need to add this class?
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I noticed that we had the ValueAndGradientAggregator and HessianVectorAggregator being registered with Kryo, but not the HessianDiagonalAggregator or the HessianMatrixAggregator. Since all four function the same way, it stands to reason that they should either all be registered or none of them be registered. Since objects of these four class types will for sure be distributed, therefore they should all be registered.
Apparently, Kryo was able to figure this out on its own, but I think it best to make it explicit.
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