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https://issues.apache.org/jira/browse/SPARK-13132?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-13132:
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Assignee: Apache Spark
> LogisticRegression spends 35% of its time fetching the standardization
> parameter
> --------------------------------------------------------------------------------
>
> Key: SPARK-13132
> URL: https://issues.apache.org/jira/browse/SPARK-13132
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 1.6.0
> Reporter: Gary King
> Assignee: Apache Spark
>
> when L1 regularization is used, the inner functor passed to the quasi-newton
> optimizer in {{org.apache.spark.ml.classification.LogisticRegression#train}}
> makes repeated calls to {{$(standardization)}}. because this ultimately
> involves repeated string interpolation triggered by
> {{org.apache.spark.ml.param.Param#hashCode}}, this line of code consumes
> 35%-45% of the entire training time in my application.
> the range depends on whether the application sets an explicit value for the
> standardization parameter or relies on the default value (which needs an
> extra map lookup, resulting in an extra string interpolation, compared to the
> explicitly set case)
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