THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
We appreciate all forms of project contributions including bug reports, providing help to new users, documentation, or code patches.
This page lists some starter projects that new contributors could work on as a way of getting more familiar with MADlib®. These starter JIRAs are tagged with the label "starter" in https://issues.apache.org/jira/browse/MADLIB/.
Please also refer to the Contribution Guidelines and Quick Start Guide for Developers.
Documentation
| No. | Item | Description | Link | Status |
|---|---|---|---|---|
| 1 | Improve module documentation | Review the latest MADlib documentation http://doc.madlib.net/latest/ and make any needed updates to content or accuracy. You can also add additional examples. | Open | |
| 2 | Improve online help | Standardize on-line help so syntax is the same for all modules. | Open | |
| 3 |
Bug Fixes and Improvements
| No. | Item | Description | Link | Open |
|---|---|---|---|---|
| 1 | Improved error message for Elastic Net predict() | When we pass the selected coefficients to elastic net's "predict()" function, it throws as ugly error message which is not indicative of the real error. | Open | |
| 2 | Confusing Error Messages while running elastic net prediction function | Fix confusing error message | Open | |
| 3 | LDA (parsed) model table and output table disagree | Investigate and determine if this is an issue. If it is, repair it. | Open | |
| 4 | PivotalR test failures indicate potential bugs in MADlib GLM | These problems may be just numerical issues with too large the condition numbers or too small of a training set. To be investigated. | Open | |
| 5 | Implement skipping of arrays-with-NULL for elastic net predict | Better NULL handling for elastic net predict. | Open | |
| 6 | Improve RF output format for variable importance | Easier way of accessing the variable importance output from random forest so that I can understand which are the most important variables. | Open | |
| 7 | Covariance matrix | Add parameter to output covariance matrix to Pierson's correlation function. | Open | |
| 8 |
New Features of Existing Modules
| No. | Item | Description | Link | Open |
|---|---|---|---|---|
| 1 | Add PMML export modules* | Support additional MADlib modules for PMML export | Open | |
| 2 |
*Some notes on PMML below...
MADlib models can be exported in PMML format for use in scoring by a PMML evaluator.
The following MADlib 1.8 algorithms can be exported in PMML format:
Linear regression
Logistic regression
GLM
Multinomial regression
Ordinal regression
Decision trees
Random forest
Your contribution here...
The Predictive Model Markup Language (PMML) is an XML-based file format that provides a way for applications to describe and exchange models produced by data mining and machine learning algorithms.
For more information, please see http://www.dmg.org/
JPMML is an open source PMML evaluator available under GPL license.
For more information, please see https://github.com/jpmml and https://github.com/jpmml/openscoring
You can only export from MADlib into PMML (no import currently)
New Non-Iterative Modules
| No. | Item | Description | Link | Open |
|---|---|---|---|---|
| 1 | k-Nearest Neighbors | Initial implementation of k-NN | Open | |
| 2 |
New Iterative Modules
| No. | Item | Description | Link | Open |
|---|---|---|---|---|
| 1 | ||||
| 2 |
PivotalR
PivotalR is a package that enables users of R, the most popular open source statistical programming language and environment, to interact with the Greenplum database, HAWQ and PostgreSQL on large data sets. It does so by providing an interface to the operations on tables/views in the database.
It would be very valuable to add to support for more MADlib modules in PivotalR. Please refer to this PivotalR wiki page for more information on how to do this.