Hi Niketan, The jira for SystemML seems to be open now: https://issues.apache.org/jira/browse/SYSTEMML
Do you mind creating issue for the tasks? We can avoid conflicts of assignee by using JIRA. Thanks, - Tsuyoshi -----Original Message----- From: Glenn Weidner [mailto:gweid...@us.ibm.com] Sent: Tuesday, December 15, 2015 5:28 AM To: dev@systemml.incubator.apache.org Cc: npan...@us.ibm.com Subject: Re: Open tasks: Integration with MLPipeline Hi, I'm interested in working on item 4: 4. Add MLPipeline wrappers for existing scripts. - Refer to https://github.com/apache/incubator-systemml/tree/master/scripts/algorithms to pick the algorithm and http://apache.github.io/incubator-systemml/algorithms-reference.html to understand the assumptions as well as parameters to the given algorithm. - A good algorithm to start with is L2SVM: http://apache.github.io/incubator-systemml/algorithms-classification.html#bi nary-class-support-vector-machines https://github.com/apache/incubator-systemml/blob/master/scripts/algorithms/ l2-svm.dml https://github.com/apache/incubator-systemml/blob/master/scripts/algorithms/ l2-svm-predict.dml Thanks, Glenn Niketan Pansare---12/03/2015 02:32:50 PM---Hi all, In this email, I list the open tasks related to integration with From: Niketan Pansare/Almaden/IBM@IBMUS To: dev@systemml.incubator.apache.org Cc: "Tatsuya Nishiyama" <nishiyama.tats...@lab.ntt.co.jp> Date: 12/03/2015 02:32 PM Subject: Open tasks: Integration with MLPipeline ________________________________ Hi all, In this email, I list the open tasks related to integration with MLPipeline. This allows external developers to contribute to the SystemML project until our JIRA server is up and running. 1. Make the existing Logistic regression wrapper more robust: - Extend the wrapper or the DML script to handle zero-based labels (either throw an error or support zero-based labels). 2. Improve the performance of the Logistic regression wrapper: - Profile the wrapper to find potential bottlenecks. The candidates for bottlenecks are RDDConverterUtilsExt.vectorDataFrameToBinaryBlock and line 153-158 in LogisticRegressionModel. 3. Perform detailed performance analysis of the converter utils. - Also explore the usability aspect of these utils. 4. Add MLPipeline wrappers for existing scripts. - Refer to https://github.com/apache/incubator-systemml/tree/master/scripts/algorithms to pick the algorithm and http://apache.github.io/incubator-systemml/algorithms-reference.html to understand the assumptions as well as parameters to the given algorithm. - A good algorithm to start with is L2SVM: http://apache.github.io/incubator-systemml/algorithms-classification.html#bi nary-class-support-vector-machines https://github.com/apache/incubator-systemml/blob/master/scripts/algorithms/ l2-svm.dml https://github.com/apache/incubator-systemml/blob/master/scripts/algorithms/ l2-svm-predict.dml 5. Add the documentation for MLPipeline wrappers to http://apache.github.io/incubator-systemml/index.html References: 1. Existing Logistic regression wrappers: https://github.com/apache/incubator-systemml/blob/master/src/main/java/org/a pache/sysml/api/ml/LogisticRegression.java https://github.com/apache/incubator-systemml/blob/master/src/main/java/org/a pache/sysml/api/ml/LogisticRegressionModel.java 2. Converter utils: https://github.com/apache/incubator-systemml/blob/master/src/main/java/org/a pache/sysml/runtime/instructions/spark/utils/RDDConverterUtilsExt.java Thanks, Niketan Pansare IBM Almaden Research Center E-mail: npansar At us.ibm.com http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
