[Ansor][AutoTVM v2.0] Phase 1: XGBoost Cost Model #6270
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comaniac
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Aug 13, 2020
| gradient = x - y | ||
| hessian = np.ones_like(gradient) | ||
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| if len(weight) == 0: |
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| if len(weight) == 0: | |
| if not weight: |
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weight is an np.ndarray. We can not use if not weight for an np.ndarray
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tqchen
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Aug 18, 2020
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Aug 26, 2020
* port xgb cost model * add xgboost cost model * fix lint * address comments * address comments * Fix
trevor-m
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Aug 26, 2020
* port xgb cost model * add xgboost cost model * fix lint * address comments * address comments * Fix
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Aug 26, 2020
* port xgb cost model * add xgboost cost model * fix lint * address comments * address comments * Fix
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Sep 2, 2020
* port xgb cost model * add xgboost cost model * fix lint * address comments * address comments * Fix
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* port xgb cost model * add xgboost cost model * fix lint * address comments * address comments * Fix
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For the full upstream plan, see Ansor RFC.
This PR adds a xgboost-based cost model.
It is similar to the existing xgboost model in autotvm but works on the more general feature representation introduced by #6190 .
RMSE is used as the loss function, but the general feature representation needs slight modification to the loss function.
To support the new loss function, this PR implements a custom xgboost loss function "pack-sum-RMSE".
It is called "pack-sum" because we combine several samples into a "pack" and sum up their predictions.