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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-7443) MLlib 1.4 QA plan
Date Thu, 07 May 2015 22:58:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-7443?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Xiangrui Meng updated SPARK-7443:
---------------------------------
    Description: 
TODO: create JIRAs for each task and assign them accordingly.

h2. API

* Check API compliance using java-compliance-checker (SPARK-7458)

* Audit new public APIs (from the generated html doc)
** Scala (do not forget to check the object doc)
** Java compatibility
** Python API coverage

* audit Pipeline APIs
** feature transformers
** tree models
** elastic-net
** ML attributes
** developer APIs

* graduate spark.ml from alpha
** remove AlphaComponent annotations
** remove mima excludes for spark.ml

h2. Algorithms and performance

* list missing performance tests from spark-perf
* LDA online/EM (SPARK-7455)
* ElasticNet (SPARK-7456)
* Bernoulli naive Bayes (SPARK-7453)
* PIC (SPARK-7454)
* ALS.recommendAll (SPARK-7457)
* perf-tests in Python

correctness:
* PMML
** scoring using PMML evaluator vs. MLlib models
* save/load

h2. Documentation and example code

* create JIRAs for the user guide to each new algorithm and assign them to the corresponding
author

* create example code for major components
** cross validation in python
** pipeline with complex feature transformations (scala/java/python)
** elastic-net (possibly with cross validation)

  was:
TODO: create JIRAs for each task and assign them accordingly.

h2. API

* Check API compliance using java-compliance-checker

* Audit new public APIs (from the generated html doc)
** Scala (do not forget to check the object doc)
** Java compatibility
** Python API coverage

* audit Pipeline APIs
** feature transformers
** tree models
** elastic-net
** ML attributes
** developer APIs

* graduate spark.ml from alpha
** remove AlphaComponent annotations
** remove mima excludes for spark.ml

h2. Algorithms and performance

* list missing performance tests from spark-perf
* LDA online/EM (SPARK-7455)
* ElasticNet (SPARK-7456)
* Bernoulli naive Bayes (SPARK-7453)
* PIC (SPARK-7454)
* ALS.recommendAll (SPARK-7457)
* perf-tests in Python

correctness:
* PMML
** scoring using PMML evaluator vs. MLlib models
* save/load

h2. Documentation and example code

* create JIRAs for the user guide to each new algorithm and assign them to the corresponding
author

* create example code for major components
** cross validation in python
** pipeline with complex feature transformations (scala/java/python)
** elastic-net (possibly with cross validation)


> MLlib 1.4 QA plan
> -----------------
>
>                 Key: SPARK-7443
>                 URL: https://issues.apache.org/jira/browse/SPARK-7443
>             Project: Spark
>          Issue Type: Umbrella
>          Components: ML, MLlib
>    Affects Versions: 1.4.0
>            Reporter: Xiangrui Meng
>            Assignee: Joseph K. Bradley
>            Priority: Critical
>
> TODO: create JIRAs for each task and assign them accordingly.
> h2. API
> * Check API compliance using java-compliance-checker (SPARK-7458)
> * Audit new public APIs (from the generated html doc)
> ** Scala (do not forget to check the object doc)
> ** Java compatibility
> ** Python API coverage
> * audit Pipeline APIs
> ** feature transformers
> ** tree models
> ** elastic-net
> ** ML attributes
> ** developer APIs
> * graduate spark.ml from alpha
> ** remove AlphaComponent annotations
> ** remove mima excludes for spark.ml
> h2. Algorithms and performance
> * list missing performance tests from spark-perf
> * LDA online/EM (SPARK-7455)
> * ElasticNet (SPARK-7456)
> * Bernoulli naive Bayes (SPARK-7453)
> * PIC (SPARK-7454)
> * ALS.recommendAll (SPARK-7457)
> * perf-tests in Python
> correctness:
> * PMML
> ** scoring using PMML evaluator vs. MLlib models
> * save/load
> h2. Documentation and example code
> * create JIRAs for the user guide to each new algorithm and assign them to the corresponding
author
> * create example code for major components
> ** cross validation in python
> ** pipeline with complex feature transformations (scala/java/python)
> ** elastic-net (possibly with cross validation)



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