[
https://issues.apache.org/jira/browse/SPARK-8445?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joseph K. Bradley updated SPARK-8445:
-------------------------------------
Description:
We expect to see many MLlib contributors for the 1.5 release. To scale out the
development, we created this master list for MLlib features we plan to have in
Spark 1.5. Due to limited review bandwidth, features appearing on this list
will get higher priority for code review. But feel free to suggest new items to
the list in comments. We are experimenting with this process. Your feedback
would be greatly appreciated.
h1. Instructions
h2. For contributors:
* Please read
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
carefully. Code style, documentation, and unit tests are important.
* If you are a first-time Spark contributor, please always start with a starter
task (TODO: add a link) rather than a medium/big feature. Based on our
experience, mixing the development process with a big feature usually causes
long delay in code review.
* Never work silently. Let everyone know on the corresponding JIRA page when
you start working on some features. This is to avoid duplicate work. For small
features, you don't need to wait to get JIRA assigned.
* For medium/big features or features with dependencies, please get assigned
first before coding and keep the ETA updated on the JIRA. If there exist no
activity on the JIRA page for a certain amount of time, the JIRA should be
released for other contributors.
* Do not claim multiple (>3) JIRAs at the same time. Try to finish them one
after another.
* Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code review
greatly helps improve others' code as well as yours.
h2. For committers:
* Try to break down big features into small and specific JIRA tasks and link
them properly.
* Add "starter" label to starter tasks.
* Put a rough estimate for medium/big features and track the progress.
h1. Roadmap (WIP)
h2. Algorithms and performance
* LDA improvements (SPARK-5572)
* Log-linear model for survival analysis (SPARK-8518)
* Improve GLM's scalability on number of features (SPARK-8520)
* Tree and ensembles: Move + cleanup code (SPARK-7131), provide class
probabilities (SPARK-3727)
* Improve GMM scalability and stability (SPARK-7206)
* Frequent itemsets improvements (SPARK-7211)
h2. Pipeline API
* more feature transformers (SPARK-8521)
* k-means (SPARK-7898)
* naive Bayes
h2. Model persistence
* more PMML export (SPARK-8545)
* model save/load (SPARK-4587)
* pipeline persistence (SPARK-6725)
h2. Python API for ML
h2. SparkR API for ML
h2. Documentation
was:
We expect to see many MLlib contributors for the 1.5 release. To scale out the
development, we created this master list for MLlib features we plan to have in
Spark 1.5. Due to limited review bandwidth, features appearing on this list
will get higher priority for code review. But feel free to suggest new items to
the list in comments. We are experimenting with this process. Your feedback
would be greatly appreciated.
h1. Instructions
h2. For contributors:
* Please read
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
carefully. Code style, documentation, and unit tests are important.
* If you are a first-time Spark contributor, please always start with a starter
task (TODO: add a link) rather than a medium/big feature. Based on our
experience, mixing the development process with a big feature usually causes
long delay in code review.
* Never work silently. Let everyone know on the corresponding JIRA page when
you start working on some features. This is to avoid duplicate work. For small
features, you don't need to wait to get JIRA assigned.
* For medium/big features or features with dependencies, please get assigned
first before coding and keep the ETA updated on the JIRA. If there exist no
activity on the JIRA page for a certain amount of time, the JIRA should be
released for other contributors.
* Do not claim multiple (>3) JIRAs at the same time. Try to finish them one
after another.
* Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code review
greatly helps improve others' code as well as yours.
h2. For committers:
* Try to break down big features into small and specific JIRA tasks and link
them properly.
* Add "starter" label to starter tasks.
* Put a rough estimate for medium/big features and track the progress.
h1. Roadmap (WIP)
h2. Algorithms and performance
* LDA improvements (SPARK-5572)
* Log-linear model for survival analysis (SPARK-8518)
* Improve GLM's scalability on number of features (SPARK-8520)
h2. Pipeline API
* more feature transformers (SPARK-8521)
* k-means (SPARK-7898)
* naive Bayes
h2. Model persistence
* more PMML export (SPARK-8545)
* model save/load (SPARK-4587)
* pipeline persistence (SPARK-6725)
h2. Python API for ML
h2. SparkR API for ML
h2. Documentation
> MLlib 1.5 Roadmap
> -----------------
>
> Key: SPARK-8445
> URL: https://issues.apache.org/jira/browse/SPARK-8445
> Project: Spark
> Issue Type: Umbrella
> Components: ML, MLlib
> Affects Versions: 1.5.0
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Priority: Critical
>
> We expect to see many MLlib contributors for the 1.5 release. To scale out
> the development, we created this master list for MLlib features we plan to
> have in Spark 1.5. Due to limited review bandwidth, features appearing on
> this list will get higher priority for code review. But feel free to suggest
> new items to the list in comments. We are experimenting with this process.
> Your feedback would be greatly appreciated.
> h1. Instructions
> h2. For contributors:
> * Please read
> https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
> carefully. Code style, documentation, and unit tests are important.
> * If you are a first-time Spark contributor, please always start with a
> starter task (TODO: add a link) rather than a medium/big feature. Based on
> our experience, mixing the development process with a big feature usually
> causes long delay in code review.
> * Never work silently. Let everyone know on the corresponding JIRA page when
> you start working on some features. This is to avoid duplicate work. For
> small features, you don't need to wait to get JIRA assigned.
> * For medium/big features or features with dependencies, please get assigned
> first before coding and keep the ETA updated on the JIRA. If there exist no
> activity on the JIRA page for a certain amount of time, the JIRA should be
> released for other contributors.
> * Do not claim multiple (>3) JIRAs at the same time. Try to finish them one
> after another.
> * Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code
> review greatly helps improve others' code as well as yours.
> h2. For committers:
> * Try to break down big features into small and specific JIRA tasks and link
> them properly.
> * Add "starter" label to starter tasks.
> * Put a rough estimate for medium/big features and track the progress.
> h1. Roadmap (WIP)
> h2. Algorithms and performance
> * LDA improvements (SPARK-5572)
> * Log-linear model for survival analysis (SPARK-8518)
> * Improve GLM's scalability on number of features (SPARK-8520)
> * Tree and ensembles: Move + cleanup code (SPARK-7131), provide class
> probabilities (SPARK-3727)
> * Improve GMM scalability and stability (SPARK-7206)
> * Frequent itemsets improvements (SPARK-7211)
> h2. Pipeline API
> * more feature transformers (SPARK-8521)
> * k-means (SPARK-7898)
> * naive Bayes
> h2. Model persistence
> * more PMML export (SPARK-8545)
> * model save/load (SPARK-4587)
> * pipeline persistence (SPARK-6725)
> h2. Python API for ML
> h2. SparkR API for ML
> h2. Documentation
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