fix lightgbm stuck in multiclass scenario and added stratified repartition transformer #618
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@@ Coverage Diff @@
## master #618 +/- ##
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+ Coverage 79.7% 79.8% +0.09%
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Files 224 225 +1
Lines 8965 9016 +51
Branches 473 474 +1
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+ Hits 7146 7195 +49
- Misses 1819 1821 +2
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mhamilton723
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Jul 14, 2019
src/main/scala/com/microsoft/ml/spark/lightgbm/TrainUtils.scala
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src/main/scala/com/microsoft/ml/spark/stages/StratifiedRepartition.scala
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src/main/scala/com/microsoft/ml/spark/stages/StratifiedRepartition.scala
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| labelToCount.map(lc => (lc._1, 1.0)).toMap | ||
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| val spdata = dataset.toDF().rdd.keyBy(row => row.getInt(row.schema.fieldIndex(getLabelCol))) |
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Does this have any equivalent in data frame API?
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hmm, it seems not
src/main/scala/com/microsoft/ml/spark/stages/StratifiedRepartition.scala
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Needs a fuzzer and also fails tests on build machine |
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src/test/scala/com/microsoft/ml/spark/lightgbm/split1/VerifyLightGBMClassifier.scala
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src/test/scala/com/microsoft/ml/spark/stages/StratifiedRepartitionSuite.scala
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mhamilton723
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Aug 20, 2019
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We should talk about this in person. I don't think it makes sense to add dummy rows into peoples datasets as this will change the computation and hurt the classifier. Instead consider throwing a helpful error message that points them in the direction of stratified sampling. Also this makes LightGBM the code more complex and less maintainable.
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@mhamilton723 discussed, that was a mode added for debugging user issues, it is off by default. By default we just fail if the user does not have all labels on all partitions for classification. |
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…ition transformer
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fix for issues #609 and #569