You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/05/05 00:48:06 UTC
[jira] [Updated] (SPARK-4766) ML Estimator Params should be
distinct from Transformer Params
[ https://issues.apache.org/jira/browse/SPARK-4766?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley updated SPARK-4766:
-------------------------------------
Description:
h2. Issue
Currently, in spark.ml, both Transformers and Estimators extend the same Params classes. There should be one Params class for the Transformer and one for the Estimator. These could sometimes be the same, but for other models, we may need either (a) to make them distinct or (b) to have the Estimator params class extend the Transformer one.
E.g., it is weird to be able to do:
{code}
val model: LogisticRegressionModel = ...
model.getMaxIter()
{code}
It's also weird to be able to:
* Wrap LogisticRegressionModel (a Transformer) with CrossValidator
* Pass a set of ParamMaps to CrossValidator which includes parameter LogisticRegressionModel.maxIter
* (CrossValidator would try to set that parameter.)
* I'm not sure if this would cause a failure or just be a noop.
See the comment below about Word2Vec as well, where the Estimator and Model take different input column types.
h2. Proposal
was:
Currently, in spark.ml, both Transformers and Estimators extend the same Params classes. There should be one Params class for the Transformer and one for the Estimator. These could sometimes be the same, but for other models, we may need either (a) to make them distinct or (b) to have the Estimator params class extend the Transformer one.
E.g., it is weird to be able to do:
{code}
val model: LogisticRegressionModel = ...
model.getMaxIter()
{code}
It's also weird to be able to:
* Wrap LogisticRegressionModel (a Transformer) with CrossValidator
* Pass a set of ParamMaps to CrossValidator which includes parameter LogisticRegressionModel.maxIter
* (CrossValidator would try to set that parameter.)
* I'm not sure if this would cause a failure or just be a noop.
See the comment below about Word2Vec as well.
> ML Estimator Params should be distinct from Transformer Params
> --------------------------------------------------------------
>
> Key: SPARK-4766
> URL: https://issues.apache.org/jira/browse/SPARK-4766
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> h2. Issue
> Currently, in spark.ml, both Transformers and Estimators extend the same Params classes. There should be one Params class for the Transformer and one for the Estimator. These could sometimes be the same, but for other models, we may need either (a) to make them distinct or (b) to have the Estimator params class extend the Transformer one.
> E.g., it is weird to be able to do:
> {code}
> val model: LogisticRegressionModel = ...
> model.getMaxIter()
> {code}
> It's also weird to be able to:
> * Wrap LogisticRegressionModel (a Transformer) with CrossValidator
> * Pass a set of ParamMaps to CrossValidator which includes parameter LogisticRegressionModel.maxIter
> * (CrossValidator would try to set that parameter.)
> * I'm not sure if this would cause a failure or just be a noop.
> See the comment below about Word2Vec as well, where the Estimator and Model take different input column types.
> h2. Proposal
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org