From issues-return-4047-archive-asf-public=cust-asf.ponee.io@lucene.apache.org Fri Nov 8 04:08:03 2019 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 [207.244.88.153]) by mx-eu-01.ponee.io (Postfix) with SMTP id 4118B180668 for ; Fri, 8 Nov 2019 05:08:03 +0100 (CET) Received: (qmail 66038 invoked by uid 500); 8 Nov 2019 04:08:02 -0000 Mailing-List: contact issues-help@lucene.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@lucene.apache.org Delivered-To: mailing list issues@lucene.apache.org Received: (qmail 65939 invoked by uid 99); 8 Nov 2019 04:08:01 -0000 Received: from mailrelay1-us-west.apache.org (HELO mailrelay1-us-west.apache.org) (209.188.14.139) by apache.org (qpsmtpd/0.29) with ESMTP; Fri, 08 Nov 2019 04:08:01 +0000 Received: from jira-he-de.apache.org (static.172.67.40.188.clients.your-server.de [188.40.67.172]) by mailrelay1-us-west.apache.org (ASF Mail Server at mailrelay1-us-west.apache.org) with ESMTP id 074DDE2E0A for ; Fri, 8 Nov 2019 04:08:00 +0000 (UTC) Received: from jira-he-de.apache.org (localhost.localdomain [127.0.0.1]) by jira-he-de.apache.org (ASF Mail Server at jira-he-de.apache.org) with ESMTP id 2BBFC7804CE for ; Fri, 8 Nov 2019 04:08:00 +0000 (UTC) Date: Fri, 8 Nov 2019 04:08:00 +0000 (UTC) From: "Ahmed Adel (Jira)" To: issues@lucene.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Updated] (SOLR-13903) Classification Model Confusion Matrix Discrepancy 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/SOLR-13903?page=3Dcom.atlassia= n.jira.plugin.system.issuetabpanels:all-tabpanel ] Ahmed Adel updated SOLR-13903: ------------------------------ Description:=20 Using features and train stream sources generate a model with TP, TN, FP, F= N fields. For some reason, the summation of the values of these fields is s= ometimes less than the training set size. =C2=A0How to regenerate: 1. Create two collections:=C2=A0cellphones and cellphones-model 2. Indexing the attached dataset into cellphones 3. Run the following expression: {{commit(cellphones-model,update(cellphones-model,batchSize=3D500,}}}} {{=C2=A0 =C2=A0train(cellphones,}} {{=C2=A0 =C2=A0 =C2=A0features(cellphones, q=3D"*:*", featureSet=3D"featur= eSet",}} {{=C2=A0field=3D"title_t",}} {{=C2=A0outcome=3D"brand_i", numTerms=3D25),}} {{=C2=A0q=3D"*:*",}} {{=C2=A0name=3D"cellphones-classification-model",}} {{=C2=A0field=3D"title_t",}} {{=C2=A0outcome=3D"brand_i",}} {{=C2=A0maxIterations=3D100)))}} 4. Run the following query to retrieve confusion matrix: {{search q=3D*:*&collection=3Dcellphones-model&fl=3Dname_s,trueNegative_i,t= ruePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=3Diteration_= i%20desc&rows=3D100}} The summation of the metrics TP, TN, FP, FN is always less than the trainin= g set size by one in this instance for all iterations. was: Using features and train stream sources generate a model with TP, TN, FP, F= N fields. For some reason, the summation of the values of these fields is s= ometimes less than the training set size. =C2=A0How to regenerate: 1. Create two collections:=C2=A0cellphones and cellphones-model 2. Indexing the attached dataset into cellphones 3. Run the following expression: {{{{commit(cellphones-model,update(cellphones-model,batchSize=3D500,}}}} {{=C2=A0 =C2=A0train(cellphones,}} {{=C2=A0 =C2=A0 =C2=A0features(cellphones, q=3D"*:*", featureSet=3D"feature= Set",}} {{=C2=A0field=3D"title_t",}} {{=C2=A0outcome=3D"brand_i", numTerms=3D25),}} {{=C2=A0q=3D"*:*",}} {{=C2=A0name=3D"cellphones-classification-model",}} {{=C2=A0field=3D"title_t",}} {{=C2=A0outcome=3D"brand_i",}} {{=C2=A0maxIterations=3D100)))}} 4. Run the following query to retrieve confusion matrix: {{search q=3D*:*&collection=3Dcellphones-model&fl=3Dname_s,trueNegative_i,t= ruePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=3Diteration_= i%20desc&rows=3D100}} The summation of the metrics TP, TN, FP, FN is always less than the trainin= g set size by one in this instance for all iterations. > Classification Model Confusion Matrix Discrepancy > ------------------------------------------------- > > Key: SOLR-13903 > URL: https://issues.apache.org/jira/browse/SOLR-13903 > Project: Solr > Issue Type: Bug > Security Level: Public(Default Security Level. Issues are Public)=20 > Components: streaming expressions > Affects Versions: 8.2 > Reporter: Ahmed Adel > Priority: Major > Labels: classification > Attachments: cellphones.csv > > > Using features and train stream sources generate a model with TP, TN, FP,= FN fields. For some reason, the summation of the values of these fields is= sometimes less than the training set size. > =C2=A0How to regenerate: > 1. Create two collections:=C2=A0cellphones and cellphones-model > 2. Indexing the attached dataset into cellphones > 3. Run the following expression: > {{commit(cellphones-model,update(cellphones-model,batchSize=3D500,}}}} > {{=C2=A0 =C2=A0train(cellphones,}} > {{=C2=A0 =C2=A0 =C2=A0features(cellphones, q=3D"*:*", featureSet=3D"feat= ureSet",}} > {{=C2=A0field=3D"title_t",}} > {{=C2=A0outcome=3D"brand_i", numTerms=3D25),}} > {{=C2=A0q=3D"*:*",}} > {{=C2=A0name=3D"cellphones-classification-model",}} > {{=C2=A0field=3D"title_t",}} > {{=C2=A0outcome=3D"brand_i",}} > {{=C2=A0maxIterations=3D100)))}} > 4. Run the following query to retrieve confusion matrix: > {{search q=3D*:*&collection=3Dcellphones-model&fl=3Dname_s,trueNegative_i= ,truePositive_i,falseNegative_i,falsePositive_i,iteration_i&sort=3Diteratio= n_i%20desc&rows=3D100}} > The summation of the metrics TP, TN, FP, FN is always less than the train= ing set size by one in this instance for all iterations. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@lucene.apache.org For additional commands, e-mail: issues-help@lucene.apache.org