[SPARK-13132] [MLlib] cache standardization param value in LogisticRegression #11027
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improve LogisticRegression training times by ~35%-45% by caching the model standardization enable parameter within the regularization closure, rather than repeatedly referencing it from the set / default maps
repeated lookup of paramter values within ParamMaps was causing a significant (35-45%) performance hit within LogisticRegression (SPARK-13132) due to the string interpolation performed by every call to hashCode. cache the stringified representation of the Param in a private instance variable, so that the string interpolation only happens once
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This looks good to test, maybe @srowen who has been active on the JIRA could whitelist the test? |
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Jenkins add to whitelist |
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Jenkins test this please |
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Test build #50636 has finished for PR 11027 at commit
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Merged to master |
asfgit
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Feb 25, 2016
…ram value ## What changes were proposed in this pull request? Like #11027 for ```LogisticRegression```, ```LinearRegression``` with L1 regularization should also cache the value of the ```standardization``` rather than re-fetching it from the ```ParamMap``` for every OWLQN iteration. cc srowen ## How was this patch tested? No extra tests are added. It should pass all existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11367 from yanboliang/spark-13490.
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cache the value of the standardization Param in LogisticRegression, rather than re-fetching it from the ParamMap for every index and every optimization step in the quasi-newton optimizer
also, fix Param#toString to cache the stringified representation, rather than re-interpolating it on every call, so any other implementations that have similar repeated access patterns will see a benefit.
this change improves training times for one of my test sets from ~7m30s to ~4m30s