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From "Joshua Pantony (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SOLR-8542) Integrate Learning to Rank into Solr
Date Fri, 15 Jan 2016 20:54:40 GMT

     [ 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.


## 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

1. compile solr and the examples 

    cd solr
    ant dist
    ant example

2. run the example

   ./bin/solr -e techproducts 

3. stop it and install the plugin:
   
   ./bin/solr stop
   #create the lib folder 
   mkdir example/techproducts/solr/techproducts/lib
   # install the plugin in the lib folder
   cp build/contrib/ltr/lucene-ltr-6.0.0-SNAPSHOT.jar example/techproducts/solr/techproducts/lib/
   # replace the original solrconfig with one importing all the ltr componenet
   cp contrib/ltr/example/solrconfig.xml example/techproducts/solr/techproducts/conf/

4. run the example again
    
   ./bin/solr -e techproducts

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'

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.



> 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.
> ## 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
> 1. compile solr and the examples 
>     cd solr
>     ant dist
>     ant example
> 2. run the example
>    ./bin/solr -e techproducts 
> 3. stop it and install the plugin:
>    
>    ./bin/solr stop
>    #create the lib folder 
>    mkdir example/techproducts/solr/techproducts/lib
>    # install the plugin in the lib folder
>    cp build/contrib/ltr/lucene-ltr-6.0.0-SNAPSHOT.jar example/techproducts/solr/techproducts/lib/
>    # replace the original solrconfig with one importing all the ltr componenet
>    cp contrib/ltr/example/solrconfig.xml example/techproducts/solr/techproducts/conf/
> 4. run the example again
>     
>    ./bin/solr -e techproducts
> 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'
> 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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