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https://issues.apache.org/jira/browse/SPARK-10413?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15547855#comment-15547855
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Hussein Hazimeh commented on SPARK-10413:
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As discussed in
[SPARK-16431|https://issues.apache.org/jira/browse/SPARK-16431], I think it
would useful to extend this further to support feature transformations on
single instances, which enables low-latency feature transformations on single
instances and can lead to improved code readability and testing. As a first
step, the body of the UDF of each feature transformer can be refactored into a
new low-level method (e.g. called "transformInstance") that accepts single
instances with raw data types (double, vector, etc). The new method can be
utilized later on to add full pipeline support for transforming and predicting
single instances. [~josephkb] let me know your thoughts on this.
> Model should support prediction on single instance
> --------------------------------------------------
>
> Key: SPARK-10413
> URL: https://issues.apache.org/jira/browse/SPARK-10413
> Project: Spark
> Issue Type: Umbrella
> Components: ML
> Reporter: Xiangrui Meng
> Priority: Critical
>
> Currently models in the pipeline API only implement transform(DataFrame). It
> would be quite useful to support prediction on single instance.
> UPDATE: This issue is for making predictions with single models. We can make
> methods like {{def predict(features: Vector): Double}} public.
> * This issue is *not* for single-instance prediction for full Pipelines,
> which would require making predictions on {{Row}}s.
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