From issues-return-7837-archive-asf-public=cust-asf.ponee.io@systemml.apache.org Fri Feb 16 04:52:05 2018 Return-Path: X-Original-To: archive-asf-public@cust-asf.ponee.io Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by mx-eu-01.ponee.io (Postfix) with SMTP id 3D3A218064A for ; Fri, 16 Feb 2018 04:52:05 +0100 (CET) Received: (qmail 20094 invoked by uid 500); 16 Feb 2018 03:52:04 -0000 Mailing-List: contact issues-help@systemml.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@systemml.apache.org Delivered-To: mailing list issues@systemml.apache.org Received: (qmail 20083 invoked by uid 99); 16 Feb 2018 03:52:04 -0000 Received: from pnap-us-west-generic-nat.apache.org (HELO spamd1-us-west.apache.org) (209.188.14.142) by apache.org (qpsmtpd/0.29) with ESMTP; Fri, 16 Feb 2018 03:52:04 +0000 Received: from localhost (localhost [127.0.0.1]) by spamd1-us-west.apache.org (ASF Mail Server at spamd1-us-west.apache.org) with ESMTP id 90B43C10B9 for ; Fri, 16 Feb 2018 03:52:03 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd1-us-west.apache.org X-Spam-Flag: NO X-Spam-Score: -110.311 X-Spam-Level: X-Spam-Status: No, score=-110.311 tagged_above=-999 required=6.31 tests=[ENV_AND_HDR_SPF_MATCH=-0.5, RCVD_IN_DNSWL_MED=-2.3, SPF_PASS=-0.001, T_RP_MATCHES_RCVD=-0.01, USER_IN_DEF_SPF_WL=-7.5, USER_IN_WHITELIST=-100] autolearn=disabled Received: from mx1-lw-eu.apache.org ([10.40.0.8]) by localhost (spamd1-us-west.apache.org [10.40.0.7]) (amavisd-new, port 10024) with ESMTP id Ig6VTXPuSYaD for ; Fri, 16 Feb 2018 03:52:02 +0000 (UTC) Received: from mailrelay1-us-west.apache.org (mailrelay1-us-west.apache.org [209.188.14.139]) by mx1-lw-eu.apache.org (ASF Mail Server at mx1-lw-eu.apache.org) with ESMTP id C478A5F18D for ; Fri, 16 Feb 2018 03:52:01 +0000 (UTC) Received: from jira-lw-us.apache.org (unknown [207.244.88.139]) by mailrelay1-us-west.apache.org (ASF Mail Server at mailrelay1-us-west.apache.org) with ESMTP id D9114E0335 for ; Fri, 16 Feb 2018 03:52:00 +0000 (UTC) Received: from jira-lw-us.apache.org (localhost [127.0.0.1]) by jira-lw-us.apache.org (ASF Mail Server at jira-lw-us.apache.org) with ESMTP id 3C72E21E65 for ; Fri, 16 Feb 2018 03:52:00 +0000 (UTC) Date: Fri, 16 Feb 2018 03:52:00 +0000 (UTC) From: "Janardhan (JIRA)" To: issues@systemml.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Updated] (SYSTEMML-1973) Optimization of parameters, Hyperparameters, and testing. MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/SYSTEMML-1973?page=3Dcom.atlas= sian.jira.plugin.system.issuetabpanels:all-tabpanel ] Janardhan updated SYSTEMML-1973: -------------------------------- Description:=20 This Epic tracks the algorithm optimization related improvements, and their= testing. *Phase 1:* Addition of support for bayesian optimization. This procedure constructs a probabilistic model for *f\(x\)*, and then expl= oits this model to make decisions about where in input space to next evalua= te the function, while integrating out uncertainity. The essential philosop= hy is to use all of the information available from previous evaluations of = *f\(x\)*. =C2=A0 When performing Bayesian Optimization,=C2=A0 **1. one must select a prior over functions that will express assumptions a= bout the function being optimized. =E2=80=93=C2=A0*We choose Gaussian Proce= ss Prior* 2. need an acquisition function, which is used to construct a utility funct= ion from the model posterior, allowing us to determine the next point to ev= aluate. =C2=A0 *Phase 2:* Addition of Model selection & cross validation support at Engine= level or API side. Once the bayesian optimization is supported, the module is integrated into = our API as described in SYSTEMML-1962=C2=A0. By wrapping the dml functions = in the optimization algorithms and invoking them either by java or python s= cripts. =C2=A0 *Phase 3:* Addition of Optimization test functions. Testing of the training is done with the help of the well known benchmark f= unctions, SYSTEMML-1974=C2=A0, which can be imported or can be invoked with= the help of python scripts or just by importing the function into the dml = script at hand. was: This Epic tracks the algorithm optimization related improvements, and their= testing. *Phase 1:* Addition of support for bayesian optimization. This procedure constructs a probabilistic model for f(x), and then exploits= this model to make decisions about where in input space to next evaluate t= he function, while integrating out uncertainity. The essential philosophy i= s to use all of the information available from previous evaluations of f(x)= . =C2=A0 When performing Bayesian Optimization,=C2=A0 **1. one must select a prior over functions that will express assumptions a= bout the function being optimized. =E2=80=93=C2=A0*We choose Gaussian Proce= ss Prior* 2. need an acquisition function, which is used to construct a utility funct= ion from the model posterior, allowing us to determine the next point to ev= aluate. =C2=A0 *Phase 2:* Addition of Model selection & cross validation support at Engine= level or API side. Once the bayesian optimization is supported, the module is integrated into = our API as described in SYSTEMML-1962=C2=A0. By wrapping the dml functions = in the optimization algorithms and invoking them either by java or python s= cripts. =C2=A0 *Phase 3:* Addition of Optimization test functions. Testing of the training is done with the help of the well known benchmark f= unctions, SYSTEMML-1974=C2=A0, which can be imported or can be invoked with= the help of python scripts or just by importing the function into the dml = script at hand. > Optimization of parameters, Hyperparameters, and testing. > --------------------------------------------------------- > > Key: SYSTEMML-1973 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1973 > Project: SystemML > Issue Type: Epic > Components: Algorithms, APIs, Documentation, Test > Reporter: Janardhan > Assignee: Janardhan > Priority: Major > > This Epic tracks the algorithm optimization related improvements, and the= ir testing. > *Phase 1:* Addition of support for bayesian optimization. > This procedure constructs a probabilistic model for *f\(x\)*, and then ex= ploits this model to make decisions about where in input space to next eval= uate the function, while integrating out uncertainity. The essential philos= ophy is to use all of the information available from previous evaluations o= f *f\(x\)*. > =C2=A0 > When performing Bayesian Optimization,=C2=A0 > **1. one must select a prior over functions that will express assumptions= about the function being optimized. =E2=80=93=C2=A0*We choose Gaussian Pro= cess Prior* > 2. need an acquisition function, which is used to construct a utility fun= ction from the model posterior, allowing us to determine the next point to = evaluate. > =C2=A0 > *Phase 2:* Addition of Model selection & cross validation support at Engi= ne level or API side. > Once the bayesian optimization is supported, the module is integrated int= o our API as described in SYSTEMML-1962=C2=A0. By wrapping the dml function= s in the optimization algorithms and invoking them either by java or python= scripts. > =C2=A0 > *Phase 3:* Addition of Optimization test functions. > Testing of the training is done with the help of the well known benchmark= functions, SYSTEMML-1974=C2=A0, which can be imported or can be invoked wi= th the help of python scripts or just by importing the function into the dm= l script at hand. -- This message was sent by Atlassian JIRA (v7.6.3#76005)