[SPARK-15581] MLlib 2.1 Roadmap

• Type: Umbrella
• Status: Closed
• Priority: Blocker
• Resolution: Done
• Fix Version/s: 2.1.0
• Component/s: ML, MLlib
• Labels: roadmap
• Target Version/s: 2.1.0
• Assignee: Unassigned
• Reporter: Joseph K. Bradley
• Created: 26/May/16 20:36
• Updated: 11/Dec/16 19:09
• Resolved: 02/Nov/16 00:24

Description

This is a master list for MLlib improvements we are working on for the next release. Please view this as a wish list rather than a definite plan, for 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.
• Remember to add the `@Since("VERSION")` annotation to new public APIs.
• Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code review greatly helps to 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 a "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 applicable.

Roadmap (WIP)

This is NOT a complete list of MLlib JIRAs for 2.1. We only include umbrella JIRAs and high-level tasks.

Major efforts in this release:

• Feature parity for the DataFrames-based API (`spark.ml`), relative to the RDD-based API
• ML persistence
• Python API feature parity and test coverage
• R API expansion and improvements
• Note about new features: As usual, we expect to expand the feature set of MLlib. However, we will prioritize API parity, bug fixes, and improvements over new features.

Note `spark.mllib` is in maintenance mode now. We will accept bug fixes for it, but new features, APIs, and improvements will only be added to `spark.ml`.

Critical feature parity in DataFrame-based API

• Umbrella JIRA: SPARK-4591

Persistence

• Complete persistence within MLlib
  • Python tuning (SPARK-13786)
• MLlib in R format: compatibility with other languages (SPARK-15572)
• Impose backwards compatibility for persistence (SPARK-15573)

Python API

• Standardize unit tests for Scala and Python to improve and consolidate test coverage for Params, persistence, and other common functionality (SPARK-15571)
• Improve Python API handling of Params, persistence (SPARK-14771) (SPARK-14706)
  • Note: The linked JIRAs for this are incomplete. More to be created...
  • Related: Implement Python meta-algorithms in Scala (to simplify persistence) (SPARK-15574)
• Feature parity: The main goal of the Python API is to have feature parity with the Scala/Java API. You can find a complete list here. The tasks fall into two major categories:
  • Python API for missing methods (SPARK-14813)
  • Python API for new algorithms. Committers should create a JIRA for the Python API after merging a public feature in Scala/Java.

SparkR

• Improve R formula support and implementation (SPARK-15540)
• Various SparkR ML API and usability improvements
  • Note: No linked JIRA yet, but need to create an umbrella once more issues are collected.
• Wrap more MLlib algorithms (SPARK-16442)
• Release SparkR on CRAN SPARK-15799

Pipeline API

• Usability: Automatic feature preprocessing SPARK-11106
• ML attribute API improvements (SPARK-8515)
• test Kaggle datasets (SPARK-9941)
• See (SPARK-5874) for a list of other possibilities

Algorithms and performance

• Trees & ensembles scaling & speed (SPARK-14045), (SPARK-14046), (SPARK-14047)
• Locality sensitive hashing (LSH) (SPARK-5992)
• Similarity search / nearest neighbors (SPARK-2336)

Additional (may be lower priority):

• robust linear regression with Huber loss (SPARK-3181)
• vector-free L-BFGS (SPARK-10078)
• tree partition by features (SPARK-3717)
• local linear algebra (SPARK-6442)
• weighted instance support (SPARK-9610)
  • random forest (SPARK-9478)
  • GBT (SPARK-9612)
• deep learning (SPARK-5575)
  • autoencoder (SPARK-10408)
  • restricted Boltzmann machine (RBM) (SPARK-4251)
  • convolutional neural network (stretch)
• factorization machine (SPARK-7008)
• distributed LU decomposition (SPARK-8514)

Other

• Infra
  • Testing for example code (SPARK-12347)
  • Remove breeze from dependencies (SPARK-15575)
• public dataset loader (SPARK-10388)
• Documentation: improve organization of user guide (SPARK-8517)
• Python Documentation: expose default values of params in some way (SPARK-15130)

Issue Links

• is related to: SPARK-12626 MLlib 2.0 Roadmap
• relates to: SPARK-18813 MLlib 2.2 Roadmap