[
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joshua Pantony updated SOLR-8542:
---------------------------------
Description:
This is a ticket to integrate learning to rank machine learning models into
Solr. Solr Learning to Rank (LTR) provides a way for you to extract features
directly inside Solr for use in training a machine learned model. You can then
deploy that model to Solr and use it to rerank your top X search results. This
concept was previously presented by the authors at Lucene/Solr Revolution 2015
(
http://www.slideshare.net/lucidworks/learning-to-rank-in-solr-presented-by-michael-nilsson-diego-ceccarelli-bloomberg-lp
).
The attached code was jointly worked on by Joshua Pantony, Michael Nilsson, and
Diego Ceccarelli.
Any chance this could make it into a 5x release? We've also attached
documentation as a github MD file, but are happy to convert to a desired format.
h3. Test the plugin with solr/example/techproducts in 6 steps
Solr provides some simple example of indices. In order to test the plugin with
the techproducts example please follow these steps
h4. 1. compile solr and the examples
cd solr
ant dist
ant example
h4. 2. run the example
./bin/solr -e techproducts
h4. 3. stop it and install the plugin:
./bin/solr stop
mkdir example/techproducts/solr/techproducts/lib
cp build/contrib/ltr/lucene-ltr-6.0.0-SNAPSHOT.jar
example/techproducts/solr/techproducts/lib/
cp contrib/ltr/example/solrconfig.xml
example/techproducts/solr/techproducts/conf/
h4. 4. run the example again
./bin/solr -e techproducts
h4. 5. index some features and a model
curl -XPUT 'http://localhost:8983/solr/techproducts/schema/fstore'
--data-binary "@./contrib/ltr/example/techproducts-features.json" -H
'Content-type:application/json'
curl -XPUT 'http://localhost:8983/solr/techproducts/schema/mstore'
--data-binary "@./contrib/ltr/example/techproducts-model.json" -H
'Content-type:application/json'
h4. 6. have fun !
*access to the default feature store*
http://localhost:8983/solr/techproducts/schema/fstore/_DEFAULT_
*access to the model store*
http://localhost:8983/solr/techproducts/schema/mstore
*perform a query using the model, and retrieve the features*
http://localhost:8983/solr/techproducts/query?indent=on&q=test&wt=json&rq={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}&fl=*,[features],price,score,name&fv=true
was:
This is a ticket to integrate learning to rank machine learning models into
Solr. Solr Learning to Rank (LTR) provides a way for you to extract features
directly inside Solr for use in training a machine learned model. You can then
deploy that model to Solr and use it to rerank your top X search results. This
concept was previously presented by the authors at Lucene/Solr Revolution 2015
(
http://www.slideshare.net/lucidworks/learning-to-rank-in-solr-presented-by-michael-nilsson-diego-ceccarelli-bloomberg-lp
).
The attached code was jointly worked on by Joshua Pantony, Michael Nilsson, and
Diego Ceccarelli.
Any chance this could make it into a 5x release? We've also attached
documentation as a github MD file, but are happy to convert to a desired format.
h3. Test the plugin with solr/example/techproducts in 6 steps
Solr provides some simple example of indices. In order to test the plugin with
the techproducts example please follow these steps
h4. 1. compile solr and the examples
cd solr
ant dist
ant example
h4. 2. run the example
./bin/solr -e techproducts
h4. 3. stop it and install the plugin:
./bin/solr stop
mkdir example/techproducts/solr/techproducts/lib
cp build/contrib/ltr/lucene-ltr-6.0.0-SNAPSHOT.jar
example/techproducts/solr/techproducts/lib/
cp contrib/ltr/example/solrconfig.xml
example/techproducts/solr/techproducts/conf/
h4. 4. run the example again
./bin/solr -e techproducts
h4. 5. index some features and a model
curl -XPUT 'http://localhost:8983/solr/techproducts/schema/fstore'
--data-binary "@./contrib/ltr/example/techproducts-features.json" -H
'Content-type:application/json'
curl -XPUT 'http://localhost:8983/solr/techproducts/schema/mstore'
--data-binary "@./contrib/ltr/example/techproducts-model.json" -H
'Content-type:application/json'
h4. 6. have fun !
*access to the default feature store*
http://localhost:8983/solr/techproducts/schema/fstore/_DEFAULT_
*access to the model store*
http://localhost:8983/solr/techproducts/schema/mstore
*perform a query using the model, and retrieve the features *
http://localhost:8983/solr/techproducts/query?indent=on&q=test&wt=json&rq={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}&fl=*,[features],price,score,name&fv=true
> Integrate Learning to Rank into Solr
> ------------------------------------
>
> Key: SOLR-8542
> URL: https://issues.apache.org/jira/browse/SOLR-8542
> Project: Solr
> Issue Type: New Feature
> Reporter: Joshua Pantony
> Assignee: Christine Poerschke
> Priority: Minor
> Attachments: README.md, SOLR-8542-branch_5x.patch,
> SOLR-8542-trunk.patch
>
>
> This is a ticket to integrate learning to rank machine learning models into
> Solr. Solr Learning to Rank (LTR) provides a way for you to extract features
> directly inside Solr for use in training a machine learned model. You can
> then deploy that model to Solr and use it to rerank your top X search
> results. This concept was previously presented by the authors at Lucene/Solr
> Revolution 2015 (
> http://www.slideshare.net/lucidworks/learning-to-rank-in-solr-presented-by-michael-nilsson-diego-ceccarelli-bloomberg-lp
> ).
> The attached code was jointly worked on by Joshua Pantony, Michael Nilsson,
> and Diego Ceccarelli.
> Any chance this could make it into a 5x release? We've also attached
> documentation as a github MD file, but are happy to convert to a desired
> format.
> h3. Test the plugin with solr/example/techproducts in 6 steps
> Solr provides some simple example of indices. In order to test the plugin
> with
> the techproducts example please follow these steps
> h4. 1. compile solr and the examples
> cd solr
> ant dist
> ant example
> h4. 2. run the example
> ./bin/solr -e techproducts
> h4. 3. stop it and install the plugin:
>
> ./bin/solr stop
> mkdir example/techproducts/solr/techproducts/lib
> cp build/contrib/ltr/lucene-ltr-6.0.0-SNAPSHOT.jar
> example/techproducts/solr/techproducts/lib/
> cp contrib/ltr/example/solrconfig.xml
> example/techproducts/solr/techproducts/conf/
> h4. 4. run the example again
>
> ./bin/solr -e techproducts
> h4. 5. index some features and a model
> curl -XPUT 'http://localhost:8983/solr/techproducts/schema/fstore'
> --data-binary "@./contrib/ltr/example/techproducts-features.json" -H
> 'Content-type:application/json'
> curl -XPUT 'http://localhost:8983/solr/techproducts/schema/mstore'
> --data-binary "@./contrib/ltr/example/techproducts-model.json" -H
> 'Content-type:application/json'
> h4. 6. have fun !
> *access to the default feature store*
> http://localhost:8983/solr/techproducts/schema/fstore/_DEFAULT_
> *access to the model store*
> http://localhost:8983/solr/techproducts/schema/mstore
> *perform a query using the model, and retrieve the features*
> http://localhost:8983/solr/techproducts/query?indent=on&q=test&wt=json&rq={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}&fl=*,[features],price,score,name&fv=true
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