[jira] [Created] (SYSTEMML-1937) Vector Free L-BFGS implementation
Janardhan created SYSTEMML-1937: --- Summary: Vector Free L-BFGS implementation Key: SYSTEMML-1937 URL: https://issues.apache.org/jira/browse/SYSTEMML-1937 Project: SystemML Issue Type: New Feature Components: Algorithms, ParFor Reporter: Janardhan This is for the implementation of vector free L-BFGS, as in the paper http://papers.nips.cc/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf , to avoid the parameter server. Example implementation for spark-ml lib : @ https://github.com/yanboliang/spark-vlbfgs -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1938) Regularized Greedy Forest (RGF) Implementation
[ https://issues.apache.org/jira/browse/SYSTEMML-1938?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Janardhan updated SYSTEMML-1938: Description: RGF is a machine learning method for building decision forests. Based on Learning Nonlinear Functions Using Regularized Greedy Forest https://arxiv.org/pdf/1109.0887.pdf A C++ implementation is at https://github.com/baidu/fast_rgf was: RGF is a machine learning method for building decision forests. A C++ implementation is at https://github.com/baidu/fast_rgf > Regularized Greedy Forest (RGF) Implementation > -- > > Key: SYSTEMML-1938 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1938 > Project: SystemML > Issue Type: New Feature > Components: Algorithms >Reporter: Janardhan >Priority: Minor > > RGF is a machine learning method for building decision forests. Based on > Learning Nonlinear Functions Using > Regularized Greedy Forest https://arxiv.org/pdf/1109.0887.pdf > A C++ implementation is at https://github.com/baidu/fast_rgf -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (SYSTEMML-1938) Regularized Greedy Forest (RGF) Implementation
Janardhan created SYSTEMML-1938: --- Summary: Regularized Greedy Forest (RGF) Implementation Key: SYSTEMML-1938 URL: https://issues.apache.org/jira/browse/SYSTEMML-1938 Project: SystemML Issue Type: New Feature Components: Algorithms Reporter: Janardhan Priority: Minor RGF is a machine learning method for building decision forests. A C++ implementation is at https://github.com/baidu/fast_rgf -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1938) Regularized Greedy Forest (RGF) Implementation
[ https://issues.apache.org/jira/browse/SYSTEMML-1938?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Janardhan updated SYSTEMML-1938: Description: RGF is a machine learning method for building decision forests. Based on 1. Learning Nonlinear Functions UsingRegularized Greedy Forest - https://arxiv.org/pdf/1109.0887.pdf 2. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost - https://arxiv.org/ftp/arxiv/papers/1203/1203.3491.pdf 3. A general boosting method and its application to learning ranking functions for web search - https://papers.nips.cc/paper/3305-a-general-boosting-method-and-its-application-to-learning-ranking-functions-for-web-search.pdf A C++ implementation is at https://github.com/baidu/fast_rgf was: RGF is a machine learning method for building decision forests. Based on Learning Nonlinear Functions Using Regularized Greedy Forest https://arxiv.org/pdf/1109.0887.pdf A C++ implementation is at https://github.com/baidu/fast_rgf > Regularized Greedy Forest (RGF) Implementation > -- > > Key: SYSTEMML-1938 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1938 > Project: SystemML > Issue Type: New Feature > Components: Algorithms >Reporter: Janardhan >Priority: Minor > > RGF is a machine learning method for building decision forests. Based on > 1. Learning Nonlinear Functions UsingRegularized Greedy Forest - > https://arxiv.org/pdf/1109.0887.pdf > 2. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost - > https://arxiv.org/ftp/arxiv/papers/1203/1203.3491.pdf > 3. A general boosting method and its application to learning ranking > functions for web search - > https://papers.nips.cc/paper/3305-a-general-boosting-method-and-its-application-to-learning-ranking-functions-for-web-search.pdf > A C++ implementation is at https://github.com/baidu/fast_rgf -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Commented] (SYSTEMML-1938) Regularized Greedy Forest (RGF) Implementation
[ https://issues.apache.org/jira/browse/SYSTEMML-1938?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16180790#comment-16180790 ] Janardhan commented on SYSTEMML-1938: - This ( if needed) can be implemented after adding the gradient boost support. > Regularized Greedy Forest (RGF) Implementation > -- > > Key: SYSTEMML-1938 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1938 > Project: SystemML > Issue Type: New Feature > Components: Algorithms >Reporter: Janardhan >Priority: Minor > > RGF is a machine learning method for building decision forests. Based on > 1. Learning Nonlinear Functions UsingRegularized Greedy Forest - > https://arxiv.org/pdf/1109.0887.pdf > 2. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost - > https://arxiv.org/ftp/arxiv/papers/1203/1203.3491.pdf > 3. A general boosting method and its application to learning ranking > functions for web search - > https://papers.nips.cc/paper/3305-a-general-boosting-method-and-its-application-to-learning-ranking-functions-for-web-search.pdf > A C++ implementation is at https://github.com/baidu/fast_rgf -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-822) Gradient Boosted Trees
[ https://issues.apache.org/jira/browse/SYSTEMML-822?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Janardhan updated SYSTEMML-822: --- Description: It would be great to have an implementation of gradient boosted trees in SystemML, similar to scikit-learn's gradient boosting machine [1] or DMLC's XGBoost [2]. [1] http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html [2] https://github.com/dmlc/xgboost/ [3] http://homes.cs.washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html For some inspiration, implementation for MLlib - https://github.com/apache/spark/pull/2607/files was: It would be great to have an implementation of gradient boosted trees in SystemML, similar to scikit-learn's gradient boosting machine [1] or DMLC's XGBoost [2]. [1] http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html [2] https://github.com/dmlc/xgboost/ [3] http://homes.cs.washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html > Gradient Boosted Trees > -- > > Key: SYSTEMML-822 > URL: https://issues.apache.org/jira/browse/SYSTEMML-822 > Project: SystemML > Issue Type: New Feature > Components: Algorithms >Affects Versions: SystemML 0.11 >Reporter: Abhinav Maurya > Labels: Hacktoberfest, features > > It would be great to have an implementation of gradient boosted trees in > SystemML, similar to scikit-learn's gradient boosting machine [1] or DMLC's > XGBoost [2]. > [1] > http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html > [2] https://github.com/dmlc/xgboost/ > [3] > http://homes.cs.washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html > For some inspiration, implementation for MLlib - > https://github.com/apache/spark/pull/2607/files -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Assigned] (SYSTEMML-1426) Rename builtin function ceil to ceiling
[ https://issues.apache.org/jira/browse/SYSTEMML-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Glenn Weidner reassigned SYSTEMML-1426: --- Assignee: Glenn Weidner > Rename builtin function ceil to ceiling > --- > > Key: SYSTEMML-1426 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1426 > Project: SystemML > Issue Type: Sub-task > Components: APIs, Compiler, Runtime >Reporter: Matthias Boehm >Assignee: Glenn Weidner > Labels: beginner > Fix For: SystemML 1.0 > > > The builtin function ceil unnecessarily differs from R's ceiling, which might > cause confusion. Hence, this task aims to rename ceil to ceiling. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Commented] (SYSTEMML-1929) Update deploy-mode in sparkDML.sh and docs
[ https://issues.apache.org/jira/browse/SYSTEMML-1929?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16181492#comment-16181492 ] Glenn Weidner commented on SYSTEMML-1929: - Submitted [PR 670|https://github.com/apache/systemml/pull/670]. > Update deploy-mode in sparkDML.sh and docs > -- > > Key: SYSTEMML-1929 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1929 > Project: SystemML > Issue Type: Improvement >Reporter: Glenn Weidner >Assignee: Glenn Weidner >Priority: Minor > > Update sparkDML.sh to use --deploy-mode instead of deprecated parameters. > Also update references in documentation (e.g., spark-batch-mode). -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1426) Rename builtin function ceil to ceiling
[ https://issues.apache.org/jira/browse/SYSTEMML-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Glenn Weidner updated SYSTEMML-1426: Issue Type: Task (was: Sub-task) Parent: (was: SYSTEMML-1299) > Rename builtin function ceil to ceiling > --- > > Key: SYSTEMML-1426 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1426 > Project: SystemML > Issue Type: Task > Components: APIs, Compiler, Runtime >Reporter: Matthias Boehm >Assignee: Glenn Weidner > Labels: beginner > Fix For: SystemML 1.0 > > > The builtin function ceil unnecessarily differs from R's ceiling, which might > cause confusion. Hence, this task aims to rename ceil to ceiling. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1426) Rename builtin function ceil to ceiling
[ https://issues.apache.org/jira/browse/SYSTEMML-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Glenn Weidner updated SYSTEMML-1426: Sprint: Sprint 7 > Rename builtin function ceil to ceiling > --- > > Key: SYSTEMML-1426 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1426 > Project: SystemML > Issue Type: Task > Components: APIs, Compiler, Runtime >Reporter: Matthias Boehm >Assignee: Glenn Weidner > Labels: beginner > Fix For: SystemML 1.0 > > > The builtin function ceil unnecessarily differs from R's ceiling, which might > cause confusion. Hence, this task aims to rename ceil to ceiling. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Commented] (SYSTEMML-1649) Verify whether GLM scripts work with MLContext
[ https://issues.apache.org/jira/browse/SYSTEMML-1649?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16181684#comment-16181684 ] Jerome commented on SYSTEMML-1649: -- Hi Janardhan: Yes, I will look at it this week. Cheers, J -- Jerome Nilmeier, PhD Cell: 510-325-8695 Home: 925-292-5321 > Verify whether GLM scripts work with MLContext > -- > > Key: SYSTEMML-1649 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1649 > Project: SystemML > Issue Type: Improvement > Components: Algorithms >Reporter: Imran Younus >Assignee: Janardhan > > This jira will verify whether GLM scripts work properly with new MLContext. > These scripts include GLM.dml and GLM-predict.dml. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (SYSTEMML-1939) IPA repetitions until fixpoint
Matthias Boehm created SYSTEMML-1939: Summary: IPA repetitions until fixpoint Key: SYSTEMML-1939 URL: https://issues.apache.org/jira/browse/SYSTEMML-1939 Project: SystemML Issue Type: Task Reporter: Matthias Boehm This task aims to increase the number of IPA repetitions from 2 to 3 with an explicit check for fixpoint conditions where the size information for function calls does not change anymore in order to reduce overhead for scenarios with simple function call patterns. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
