Add metadata to OpStandardScaler to allow for descaling #378
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Codecov Report
@@ Coverage Diff @@
## master #378 +/- ##
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+ Coverage 86.84% 86.84% +<.01%
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Files 336 336
Lines 10943 10948 +5
Branches 343 580 +237
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+ Hits 9503 9508 +5
Misses 1440 1440
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Aug 1, 2019
core/src/main/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScaler.scala
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core/src/main/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScaler.scala
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core/src/test/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScalerTest.scala
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core/src/test/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScalerTest.scala
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core/src/test/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScalerTest.scala
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August 1, 2019 15:12
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core/src/main/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScaler.scala
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Bug fixes: - Ensure correct metrics despite model failures on some CV folds [#404](#404) - Fix flaky `ModelInsight` tests [#395](#395) - Avoid creating `SparseVector`s for LOCO [#377](#377) New features / updates: - Model combiner [#385](#399) - Added new sample for HousingPrices [#365](#365) - Test to verify that custom metrics appear in model insight metrics [#387](#387) - Add `FeatureDistribution` to `SerializationFormat`s [#383](#383) - Add metadata to `OpStandadrdScaler` to allow for descaling [#378](#378) - Improve json serde error in `evalMetFromJson` [#380](#380) - Track mean & standard deviation as metrics for numeric features and for text length of text features [#354](#354) - Making model selectors robust to failing models [#372](#372) - Use compact and compressed model json by default [#375](#375) - Descale feature contribution for Linear Regression & Logistic Regression [#345](#345) Dependency updates: - Update tika version [#382](#382)
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Related issues
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Describe the proposed solution
Add metadata to OpStandardScaler to allow DescalerTransformer to descale.
Describe alternatives you've considered
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Additional context
This change is to temporarily fix the issue that optimal regression hyperparamters are data-dependent by allowing the standardizing and destandardizing of label data. A longer-term goal is to create hyperparameters that can adjust based on data.