[SPARK-27621][ML] Linear Regression - validate training related params such as loss only during fitting phase#24509
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ancasarb:linear_regression_params_fix
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[SPARK-27621][ML] Linear Regression - validate training related params such as loss only during fitting phase#24509ancasarb wants to merge 2 commits intoapache:masterfrom ancasarb:linear_regression_params_fix
ancasarb wants to merge 2 commits intoapache:masterfrom
ancasarb:linear_regression_params_fix
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May 1, 2019 20:04
…s called for training, ignore them during scoring
… to only be validated during fitting phase
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LGTM. This is the only class that overrides this method, and the parent method also reserves these checks for the 'fitting' context.
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Test build #4774 has finished for PR 24509 at commit
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srowen
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May 3, 2019
…s such as loss only during fitting phase ## What changes were proposed in this pull request? When transform(...) method is called on a LinearRegressionModel created directly with the coefficients and intercepts, the following exception is encountered. ``` java.util.NoSuchElementException: Failed to find a default value for loss at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.ml.param.Params$class.getOrDefault(params.scala:779) at org.apache.spark.ml.PipelineStage.getOrDefault(Pipeline.scala:42) at org.apache.spark.ml.param.Params$class.$(params.scala:786) at org.apache.spark.ml.PipelineStage.$(Pipeline.scala:42) at org.apache.spark.ml.regression.LinearRegressionParams$class.validateAndTransformSchema(LinearRegression.scala:111) at org.apache.spark.ml.regression.LinearRegressionModel.validateAndTransformSchema(LinearRegression.scala:637) at org.apache.spark.ml.PredictionModel.transformSchema(Predictor.scala:192) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186) at org.apache.spark.ml.PipelineModel.transformSchema(Pipeline.scala:311) at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74) at org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:305) ``` This is because validateAndTransformSchema() is called both during training and scoring phases, but the checks against the training related params like loss should really be performed during training phase only, I think, please correct me if I'm missing anything :) This issue was first reported for mleap (combust/mleap#455) because basically when we serialize the Spark transformers for mleap, we only serialize the params that are relevant for scoring. We do have the option to de-serialize the serialized transformers back into Spark for scoring again, but in that case, we no longer have all the training params. ## How was this patch tested? Added a unit test to check this scenario. Please let me know if there's anything additional required, this is the first PR that I've raised in this project. Closes #24509 from ancasarb/linear_regression_params_fix. Authored-by: asarb <asarb@expedia.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> (cherry picked from commit 4241a72) Signed-off-by: Sean Owen <sean.owen@databricks.com>
srowen
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May 3, 2019
…s such as loss only during fitting phase ## What changes were proposed in this pull request? When transform(...) method is called on a LinearRegressionModel created directly with the coefficients and intercepts, the following exception is encountered. ``` java.util.NoSuchElementException: Failed to find a default value for loss at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.ml.param.Params$class.getOrDefault(params.scala:779) at org.apache.spark.ml.PipelineStage.getOrDefault(Pipeline.scala:42) at org.apache.spark.ml.param.Params$class.$(params.scala:786) at org.apache.spark.ml.PipelineStage.$(Pipeline.scala:42) at org.apache.spark.ml.regression.LinearRegressionParams$class.validateAndTransformSchema(LinearRegression.scala:111) at org.apache.spark.ml.regression.LinearRegressionModel.validateAndTransformSchema(LinearRegression.scala:637) at org.apache.spark.ml.PredictionModel.transformSchema(Predictor.scala:192) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186) at org.apache.spark.ml.PipelineModel.transformSchema(Pipeline.scala:311) at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74) at org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:305) ``` This is because validateAndTransformSchema() is called both during training and scoring phases, but the checks against the training related params like loss should really be performed during training phase only, I think, please correct me if I'm missing anything :) This issue was first reported for mleap (combust/mleap#455) because basically when we serialize the Spark transformers for mleap, we only serialize the params that are relevant for scoring. We do have the option to de-serialize the serialized transformers back into Spark for scoring again, but in that case, we no longer have all the training params. ## How was this patch tested? Added a unit test to check this scenario. Please let me know if there's anything additional required, this is the first PR that I've raised in this project. Closes #24509 from ancasarb/linear_regression_params_fix. Authored-by: asarb <asarb@expedia.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> (cherry picked from commit 4241a72) Signed-off-by: Sean Owen <sean.owen@databricks.com>
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Merged to master/2.4/2.3 |
rluta
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Sep 17, 2019
…s such as loss only during fitting phase ## What changes were proposed in this pull request? When transform(...) method is called on a LinearRegressionModel created directly with the coefficients and intercepts, the following exception is encountered. ``` java.util.NoSuchElementException: Failed to find a default value for loss at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.ml.param.Params$class.getOrDefault(params.scala:779) at org.apache.spark.ml.PipelineStage.getOrDefault(Pipeline.scala:42) at org.apache.spark.ml.param.Params$class.$(params.scala:786) at org.apache.spark.ml.PipelineStage.$(Pipeline.scala:42) at org.apache.spark.ml.regression.LinearRegressionParams$class.validateAndTransformSchema(LinearRegression.scala:111) at org.apache.spark.ml.regression.LinearRegressionModel.validateAndTransformSchema(LinearRegression.scala:637) at org.apache.spark.ml.PredictionModel.transformSchema(Predictor.scala:192) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186) at org.apache.spark.ml.PipelineModel.transformSchema(Pipeline.scala:311) at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74) at org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:305) ``` This is because validateAndTransformSchema() is called both during training and scoring phases, but the checks against the training related params like loss should really be performed during training phase only, I think, please correct me if I'm missing anything :) This issue was first reported for mleap (combust/mleap#455) because basically when we serialize the Spark transformers for mleap, we only serialize the params that are relevant for scoring. We do have the option to de-serialize the serialized transformers back into Spark for scoring again, but in that case, we no longer have all the training params. ## How was this patch tested? Added a unit test to check this scenario. Please let me know if there's anything additional required, this is the first PR that I've raised in this project. Closes apache#24509 from ancasarb/linear_regression_params_fix. Authored-by: asarb <asarb@expedia.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> (cherry picked from commit 4241a72) Signed-off-by: Sean Owen <sean.owen@databricks.com>
kai-chi
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Sep 26, 2019
…s such as loss only during fitting phase ## What changes were proposed in this pull request? When transform(...) method is called on a LinearRegressionModel created directly with the coefficients and intercepts, the following exception is encountered. ``` java.util.NoSuchElementException: Failed to find a default value for loss at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.ml.param.Params$class.getOrDefault(params.scala:779) at org.apache.spark.ml.PipelineStage.getOrDefault(Pipeline.scala:42) at org.apache.spark.ml.param.Params$class.$(params.scala:786) at org.apache.spark.ml.PipelineStage.$(Pipeline.scala:42) at org.apache.spark.ml.regression.LinearRegressionParams$class.validateAndTransformSchema(LinearRegression.scala:111) at org.apache.spark.ml.regression.LinearRegressionModel.validateAndTransformSchema(LinearRegression.scala:637) at org.apache.spark.ml.PredictionModel.transformSchema(Predictor.scala:192) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186) at org.apache.spark.ml.PipelineModel.transformSchema(Pipeline.scala:311) at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74) at org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:305) ``` This is because validateAndTransformSchema() is called both during training and scoring phases, but the checks against the training related params like loss should really be performed during training phase only, I think, please correct me if I'm missing anything :) This issue was first reported for mleap (combust/mleap#455) because basically when we serialize the Spark transformers for mleap, we only serialize the params that are relevant for scoring. We do have the option to de-serialize the serialized transformers back into Spark for scoring again, but in that case, we no longer have all the training params. ## How was this patch tested? Added a unit test to check this scenario. Please let me know if there's anything additional required, this is the first PR that I've raised in this project. Closes apache#24509 from ancasarb/linear_regression_params_fix. Authored-by: asarb <asarb@expedia.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> (cherry picked from commit 4241a72) Signed-off-by: Sean Owen <sean.owen@databricks.com>
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I have a fix for this issue for anyone else who runs into it when re-loading a model. It seems like the default option should be saved in the params map but is not. So load your model as follows. val lrModel = LinearRegressionModel.load("/you_model_path")
lrModel.set(lrModel.loss, "squaredError")
lrModel.extractParamMap |
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What changes were proposed in this pull request?
When transform(...) method is called on a LinearRegressionModel created directly with the coefficients and intercepts, the following exception is encountered.
This is because validateAndTransformSchema() is called both during training and scoring phases, but the checks against the training related params like loss should really be performed during training phase only, I think, please correct me if I'm missing anything :)
This issue was first reported for mleap (combust/mleap#455) because basically when we serialize the Spark transformers for mleap, we only serialize the params that are relevant for scoring. We do have the option to de-serialize the serialized transformers back into Spark for scoring again, but in that case, we no longer have all the training params.
How was this patch tested?
Added a unit test to check this scenario.
Please let me know if there's anything additional required, this is the first PR that I've raised in this project.