[jira] [Updated] (SPARK-9568) Spark MLlib 1.5.0 testing umbrella
[ https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-9568: - Description: h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.5 (towards end of QA) (SPARK-9671) was: h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) Spark MLlib 1.5.0 testing umbrella -- Key: SPARK-9568 URL: https://issues.apache.org/jira/browse/SPARK-9568 Project: Spark Issue Type: Umbrella Components: MLlib Reporter: Reynold Xin Assignee: Xiangrui Meng h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.5 (towards end of QA) (SPARK-9671) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9568) Spark MLlib 1.5.0 testing umbrella
[ https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-9568: - Description: h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.5 (towards end of QA) (SPARK-9671) * Update website (SPARK-10120) was: h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.5 (towards end of QA) (SPARK-9671) Spark MLlib 1.5.0 testing umbrella -- Key: SPARK-9568 URL: https://issues.apache.org/jira/browse/SPARK-9568 Project: Spark Issue Type: Umbrella Components: MLlib Reporter: Reynold Xin Assignee: Xiangrui Meng h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.5 (towards end of QA) (SPARK-9671) * Update website (SPARK-10120) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9568) Spark MLlib 1.5.0 testing umbrella
[ https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-9568: - Description: h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) was: h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) Spark MLlib 1.5.0 testing umbrella -- Key: SPARK-9568 URL: https://issues.apache.org/jira/browse/SPARK-9568 Project: Spark Issue Type: Umbrella Components: MLlib Reporter: Reynold Xin Assignee: Xiangrui Meng h2. API * Check binary API compatibility (SPARK-9658) * Audit new public APIs (from the generated html doc) ** Scala (SPARK-9660) ** Java compatibility (SPARK-9661) ** Python coverage (SPARK-9662) * Check Experimental, DeveloperApi tags (SPARK-9665) h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9568) Spark MLlib 1.5.0 testing umbrella
[ https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-9568: - Description: h2. API * Check API compliance (SPARK-7458) * Audit new public APIs (from the generated html doc) ** Scala (do not forget to check the object doc) (SPARK-7537) ** Java compatibility (SPARK-7529) ** Python API coverage (SPARK-7536) * audit Pipeline APIs (SPARK-7535) * graduate spark.ml from alpha (SPARK-7748) ** remove AlphaComponent annotations ** remove mima excludes for spark.ml ** mark concrete classes final wherever reasonable h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * Bernoulli naive Bayes (SPARK-7453) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * PMML ** scoring using PMML evaluator vs. MLlib models (SPARK-7540) * model save/load (SPARK-7541) h2. Documentation and example code * Create JIRAs for the user guide to each new algorithm and assign them to the corresponding author. Link here as requires ** Now that we have algorithms in spark.ml which are not in spark.mllib, we should start making subsections for the spark.ml API as needed. We can follow the structure of the spark.mllib user guide. *** The spark.ml user guide can provide: (a) code examples and (b) info on algorithms which do not exist in spark.mllib. *** We should not duplicate info in the spark.ml guides. Since spark.mllib is still the primary API, we should provide links to the corresponding algorithms in the spark.mllib user guide for more info. * Create example code for major components. Link here as requires ** cross validation in python (SPARK-7387) ** pipeline with complex feature transformations (scala/java/python) (SPARK-7546) ** elastic-net (possibly with cross validation) (SPARK-7547) ** kernel density (SPARK-7707) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-7715) Spark MLlib 1.5.0 testing umbrella -- Key: SPARK-9568 URL: https://issues.apache.org/jira/browse/SPARK-9568 Project: Spark Issue Type: Umbrella Components: MLlib Reporter: Reynold Xin Assignee: Xiangrui Meng h2. API * Check API compliance (SPARK-7458) * Audit new public APIs (from the generated html doc) ** Scala (do not forget to check the object doc) (SPARK-7537) ** Java compatibility (SPARK-7529) ** Python API coverage (SPARK-7536) * audit Pipeline APIs (SPARK-7535) * graduate spark.ml from alpha (SPARK-7748) ** remove AlphaComponent annotations ** remove mima excludes for spark.ml ** mark concrete classes final wherever reasonable h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * Bernoulli naive Bayes (SPARK-7453) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * PMML ** scoring using PMML evaluator vs. MLlib models (SPARK-7540) * model save/load (SPARK-7541) h2. Documentation and example code * Create JIRAs for the user guide to each new algorithm and assign them to the corresponding author. Link here as requires ** Now that we have algorithms in spark.ml which are not in spark.mllib, we should start making subsections for the spark.ml API as needed. We can follow the structure of the spark.mllib user guide. *** The spark.ml user guide can provide: (a) code examples and (b) info on algorithms which do not exist in spark.mllib. *** We should not duplicate info in the spark.ml guides. Since spark.mllib is still the primary API, we should provide links to the corresponding algorithms in the spark.mllib user guide for more info. * Create example code for major components. Link here as requires ** cross validation in python (SPARK-7387) ** pipeline with complex feature transformations (scala/java/python) (SPARK-7546) ** elastic-net (possibly with cross validation) (SPARK-7547) ** kernel density (SPARK-7707) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-7715) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9568) Spark MLlib 1.5.0 testing umbrella
[ https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-9568: - Description: h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) was: h2. API * Check API compliance (SPARK-7458) * Audit new public APIs (from the generated html doc) ** Scala (do not forget to check the object doc) (SPARK-7537) ** Java compatibility (SPARK-7529) ** Python API coverage (SPARK-7536) * audit Pipeline APIs (SPARK-7535) * graduate spark.ml from alpha (SPARK-7748) ** remove AlphaComponent annotations ** remove mima excludes for spark.ml ** mark concrete classes final wherever reasonable h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * Bernoulli naive Bayes (SPARK-7453) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * PMML ** scoring using PMML evaluator vs. MLlib models (SPARK-7540) * model save/load (SPARK-7541) h2. Documentation and example code * Create JIRAs for the user guide to each new algorithm and assign them to the corresponding author. Link here as requires ** Now that we have algorithms in spark.ml which are not in spark.mllib, we should start making subsections for the spark.ml API as needed. We can follow the structure of the spark.mllib user guide. *** The spark.ml user guide can provide: (a) code examples and (b) info on algorithms which do not exist in spark.mllib. *** We should not duplicate info in the spark.ml guides. Since spark.mllib is still the primary API, we should provide links to the corresponding algorithms in the spark.mllib user guide for more info. * Create example code for major components. Link here as requires ** cross validation in python (SPARK-7387) ** pipeline with complex feature transformations (scala/java/python) (SPARK-7546) ** elastic-net (possibly with cross validation) (SPARK-7547) ** kernel density (SPARK-7707) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-7715) Spark MLlib 1.5.0 testing umbrella -- Key: SPARK-9568 URL: https://issues.apache.org/jira/browse/SPARK-9568 Project: Spark Issue Type: Umbrella Components: MLlib Reporter: Reynold Xin Assignee: Xiangrui Meng h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9568) Spark MLlib 1.5.0 testing umbrella
[ https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-9568: - Description: h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) was: h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) Spark MLlib 1.5.0 testing umbrella -- Key: SPARK-9568 URL: https://issues.apache.org/jira/browse/SPARK-9568 Project: Spark Issue Type: Umbrella Components: MLlib Reporter: Reynold Xin Assignee: Xiangrui Meng h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9568) Spark MLlib 1.5.0 testing umbrella
[ https://issues.apache.org/jira/browse/SPARK-9568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-9568: - Description: h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) was: h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) Spark MLlib 1.5.0 testing umbrella -- Key: SPARK-9568 URL: https://issues.apache.org/jira/browse/SPARK-9568 Project: Spark Issue Type: Umbrella Components: MLlib Reporter: Reynold Xin Assignee: Xiangrui Meng h2. API * Check binary API compatibility * Audit new public APIs (from the generated html doc) ** Scala ** Java compatibility ** Python coverage * Check Experimental, DeveloperApi tags h2. Algorithms and performance *Performance* * _List any other missing performance tests from spark-perf here_ * LDA online/EM (SPARK-7455) * ElasticNet for linear regression and logistic regression (SPARK-7456) * PIC (SPARK-7454) * ALS.recommendAll (SPARK-7457) * perf-tests in Python (SPARK-7539) *Correctness* * model save/load (SPARK-9666) h2. Documentation and example code * For new algorithms, create JIRAs for updating the user guide (SPARK-9668) * For major components, create JIRAs for example code (SPARK-9670) * Update Programming Guide for 1.4 (towards end of QA) (SPARK-9671) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
