Return-Path: X-Original-To: apmail-spark-issues-archive@minotaur.apache.org Delivered-To: apmail-spark-issues-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id BA97517434 for ; Thu, 7 May 2015 22:54:02 +0000 (UTC) Received: (qmail 58381 invoked by uid 500); 7 May 2015 22:54:02 -0000 Delivered-To: apmail-spark-issues-archive@spark.apache.org Received: (qmail 58352 invoked by uid 500); 7 May 2015 22:54:02 -0000 Mailing-List: contact issues-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list issues@spark.apache.org Received: (qmail 58342 invoked by uid 99); 7 May 2015 22:54:02 -0000 Received: from arcas.apache.org (HELO arcas.apache.org) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Thu, 07 May 2015 22:54:02 +0000 Date: Thu, 7 May 2015 22:54:02 +0000 (UTC) From: "Xiangrui Meng (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Updated] (SPARK-7443) MLlib 1.4 QA plan MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/SPARK-7443?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-7443: --------------------------------- Description: TODO: create JIRAs for each task and assign them accordingly. h2. API * Check API compliance using java-compliance-checker * Audit new public APIs (from the generated html doc) ** Scala (do not forget to check the object doc) ** Java compatibility ** Python API coverage * audit Pipeline APIs ** feature transformers ** tree models ** elastic-net ** ML attributes ** developer APIs * graduate spark.ml from alpha ** remove AlphaComponent annotations ** remove mima excludes for spark.ml h2. Algorithms and performance * list missing performance tests from spark-perf * LDA online/EM * ElasticNet * Bernoulli naive Bayes (SPARK-7453) * PIC * ALS.recommendAll * perf-tests in Python correctness: * PMML ** scoring using PMML evaluator vs. MLlib models * save/load h2. Documentation and example code * create JIRAs for the user guide to each new algorithm and assign them to the corresponding author * create example code for major components ** cross validation in python ** pipeline with complex feature transformations (scala/java/python) ** elastic-net (possibly with cross validation) was: TODO: create JIRAs for each task and assign them accordingly. h2. API * Check API compliance using java-compliance-checker * Audit new public APIs (from the generated html doc) ** Scala (do not forget to check the object doc) ** Java compatibility ** Python API coverage * audit Pipeline APIs ** feature transformers ** tree models ** elastic-net ** ML attributes ** developer APIs * graduate spark.ml from alpha ** remove AlphaComponent annotations ** remove mima excludes for spark.ml h2. Algorithms and performance * list missing performance tests from spark-perf * LDA online/EM * ElasticNet * Bernoulli naive Bayes * PIC * ALS.recommendAll * perf-tests in Python correctness: * PMML ** scoring using PMML evaluator vs. MLlib models * save/load h2. Documentation and example code * create JIRAs for the user guide to each new algorithm and assign them to the corresponding author * create example code for major components ** cross validation in python ** pipeline with complex feature transformations (scala/java/python) ** elastic-net (possibly with cross validation) > MLlib 1.4 QA plan > ----------------- > > Key: SPARK-7443 > URL: https://issues.apache.org/jira/browse/SPARK-7443 > Project: Spark > Issue Type: Umbrella > Components: ML, MLlib > Affects Versions: 1.4.0 > Reporter: Xiangrui Meng > Assignee: Joseph K. Bradley > Priority: Critical > > TODO: create JIRAs for each task and assign them accordingly. > h2. API > * Check API compliance using java-compliance-checker > * Audit new public APIs (from the generated html doc) > ** Scala (do not forget to check the object doc) > ** Java compatibility > ** Python API coverage > * audit Pipeline APIs > ** feature transformers > ** tree models > ** elastic-net > ** ML attributes > ** developer APIs > * graduate spark.ml from alpha > ** remove AlphaComponent annotations > ** remove mima excludes for spark.ml > h2. Algorithms and performance > * list missing performance tests from spark-perf > * LDA online/EM > * ElasticNet > * Bernoulli naive Bayes (SPARK-7453) > * PIC > * ALS.recommendAll > * perf-tests in Python > correctness: > * PMML > ** scoring using PMML evaluator vs. MLlib models > * save/load > h2. Documentation and example code > * create JIRAs for the user guide to each new algorithm and assign them to the corresponding author > * create example code for major components > ** cross validation in python > ** pipeline with complex feature transformations (scala/java/python) > ** elastic-net (possibly with cross validation) -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org For additional commands, e-mail: issues-help@spark.apache.org