Critical 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. Please view this list as a wish list rather than a concrete plan, because we don't have an accurate estimate of available resources. Due to limited review bandwidth, features appearing on this list will get higher priority during 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.
Instructions
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 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.
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.
- If you start reviewing a PR, please add yourself to the Shepherd field on JIRA.
- If the code looks good to you, please comment "LGTM". For non-trivial PRs, please ping a maintainer to make a final pass.
- After merging a PR, create and link JIRAs for Python, example code, and documentation if necessary.
Roadmap (WIP)
This is NOT a complete list of MLlib JIRAs for 1.5. We only include umbrella JIRAs and high-level tasks.
Algorithms and performance
- LDA improvements (
SPARK-5572) - Log-linear model for survival analysis (
SPARK-8518) -> 1.6 - Improve GLM's scalability on number of features (
SPARK-8520) - Tree and ensembles: Move + cleanup code (
SPARK-7131), provide class probabilities (SPARK-3727), feature importance (SPARK-5133) - Improve GMM scalability and stability (
SPARK-5016) - Frequent pattern mining improvements (
SPARK-6487) - R-like stats for ML models (
SPARK-7674) - Generalize classification threshold to multiclass (
SPARK-8069) - A/B testing (
SPARK-3147)
Pipeline API
- more feature transformers (
SPARK-8521) - k-means (
SPARK-7879) - naive Bayes (
SPARK-8600) - TrainValidationSplit for tuning (
SPARK-8484) - Isotonic regression (
SPARK-8671)
Model persistence
- more PMML export (
SPARK-8545) - model save/load (
SPARK-4587) - pipeline persistence (
SPARK-6725)
Python API for ML
- List of issues identified during Spark 1.4 QA: (
SPARK-7536) - Python API for streaming ML algorithms (
SPARK-3258) - Add missing model methods (
SPARK-8633)
SparkR API for ML
- MLlib + SparkR integration for 1.5 (RFormula + glm) (
SPARK-6805) - model.matrix for DataFrames (
SPARK-6823)
Documentation
Attachments
Issue Links
- is related to
-
SPARK-10324 MLlib 1.6 Roadmap
-
- Resolved
-