subject:"\[jira\] \[Commented\] \(SOLR\-8542\) Integrate Learning to Rank into Solr"


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15185108#comment-15185108
 ] 

Alessandro Benedetti commented on SOLR-8542:


Maybe I still have not a clear picture, but isn't the model generated 
externally, with a training set and a training library ( that uses the feature 
vectors as well) and then fed to Solr ? ( in the Json format described ? with 
the different weights and components automatically calculated)

In that case, I don't see it as a part of the solrconfig .
Furthermore, as Diego pointed out, are we sure we want to need a core reload 
each time we add a feature/model ?
I see a better fit in there to have a managed resource ( like the synonyms for 
example), and the possibility of adding features and model at runtime, without 
any core reload or restart necessary,



> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15185083#comment-15185083
 ] 

Alessandro Benedetti commented on SOLR-8542:


Diego,
thanks for the reply!
just verified :
1) as documentation specifies, the re-rank component works on the collapsed 
results, so we can assume LTR re-rank will work as well.
2) just tried the Block Join Parent Query Parser with the re-rank query parser, 
and it is working ( the parents returned are re-ranked according to the re-rank 
parameters ) . I can assume the LTR query parser to work in that scenario as 
well.
Thanks for your help !

Cheers

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr

2016-03-08 Thread Christine Poerschke (JIRA)


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15184880#comment-15184880
 ] 

Christine Poerschke commented on SOLR-8542:
---

Thanks Steve for bringing Solr blob store into consideration. Related links:
* SOLR-8773 'Make blob store usage intuitive and robust'
* [Blob Store 
API|https://cwiki.apache.org/confluence/display/solr/Blob+Store+API] in the 
[Apache Solr Reference 
Guide|https://cwiki.apache.org/confluence/display/solr/Apache+Solr+Reference+Guide]'s
 [Configuration 
APIs|https://cwiki.apache.org/confluence/display/solr/Configuration+APIs] 
section.

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr

2016-03-08 Thread Diego Ceccarelli (JIRA)


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15184876#comment-15184876
 ] 

Diego Ceccarelli commented on SOLR-8542:


I had the same idea. My only concern is: would then be possible to update the 
{{solrconfig.xml}} without bouncing Solr? with the managed resources we would 
be able to add a feature/model at runtime and start to use it. Would be 
possible to get the same behavior with the solr config? (...and first, do we 
want it? :) ) 

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr

2016-03-08 Thread Diego Ceccarelli (JIRA)


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15184865#comment-15184865
 ] 

Diego Ceccarelli commented on SOLR-8542:


Alessandro, thanks for the questions: 

  # At the moment RankQuery (on which LTR relies) is not supported in grouping 
(but we are working on that - see SOLR-8776), I think the correct solution 
would be to perform the steps 1,2,3. Maybe we can move the discussion on 
SOLR-8776 since it affects, in general, RankQueries and grouping. The easy 
solution is to use collapsing instead of grouping, collapsing is supported by 
RankQuery and we tested that LTR works as well.  

  # Join - Parent Search.  I would if RankQuery supports block join, it should 
work, but we didn't check.

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr

2016-03-08 Thread Diego Ceccarelli (JIRA)


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15184848#comment-15184848
 ] 

Diego Ceccarelli commented on SOLR-8542:


we decided to decouple models and features because a) the general use case is 
that you use a particular model (+ relying on a set of features) to rank your 
documents, but you also want to compute (and log) new features for training a 
new model to use in the future. All the features in a feature store will be 
computed but the model will receive only the requested features (allowing also 
to update the feature store adding new features without affecting the model) b) 
two models could use the same feature, but normalize the feature values in a 
different way (see the {Normalizer} class}) 

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15184843#comment-15184843
 ] 

Alessandro Benedetti commented on SOLR-8542:


A couple of questions,  for some specific use case :

1) Grouping
How does the plugin behave for grouping ? Let's assume we have 1000 docs in 5 
groups, even if we return only 1 doc per group, i assume the plugin will :
1) first re-score the top K >>x ( so re-scoring 1000 docs )
2) group the results
3) return the 5 groups ( each one for example with top document)
Or will be possible to re-rank only the top document per group ? ( so only the 
5 top documents )

2) Join - Parent Search
Let's assume we return parents based on a query on the children .
Just wondering how to combine the block join query parser to the LTR re-rank, 
to re-rank only the parents ( without re-scoring the children) .

I will take a look on my own on these topics, but any thought would be much 
appreciated :)

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15184805#comment-15184805
 ] 

Alessandro Benedetti commented on SOLR-8542:


Really interesting stuff :) 
I think could be useful to have more details about the training phase.
I briefly reviewed both the pull request and documentation, and it seems that 
to try the demo, we already provide a trained model.
The only lines related the training seems to be :
"  A good library for training LambdaMART ( 
http://sourceforge.net/p/lemur/wiki/RankLib/ ).
 +You will need to convert the RankLib model format to the format specified 
above. " ( similar documentation for the linear SVM approach) .

It would be cool to have more documentation about the training as well, 
explaining how to train the model starting from  :
- an example point-wise training set
- a set of defined feature

A step by step tutorial would be awesome !
Anyway I will proceed in studying the plugin and try to do that on my own 
following the third party training tutorials.
Well done again,

Cheers

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr

2016-03-07 Thread Steve Rowe (JIRA)


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15184203#comment-15184203
 ] 

Steve Rowe commented on SOLR-8542:
--

Just read through the comments on the issue, but haven't looked at any code yet.

I think you're asking about using managed resources or solrconfig.xml plugins 
as configuration locations.  I think that relatively short config stuff fits 
naturally in solrconfig.xml, and managed resource infrastructure is set up to 
enable modifications to structured data in resources via API (is that enabled 
here?  probably not, just whole-resource CRUD, I'm guessing).  So I'd guess 
solrconfig.xml is a better fit here.

Note that new usages of solrconfig.xml config should consider how they can be 
addressed via the Config API.

One other consideration you didn't mention: shouldn't the Solr blob store be 
considered for storage/versioning/sharing of models?  (Skimming here makes me 
think that they are stored in Zk as files with per-collection config.)

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15183216#comment-15183216
 ] 

Christine Poerschke commented on SOLR-8542:
---

bq. ... The only question I have is regarding "'git merge' and 'git rebase' and 
'git --force push' will be avoided". Agreed about git force, but if at the end 
we're going to make a new master-ltr-plugin-rfc-march branch, and everything is 
going to be squashed and rebased, why not allow merges into the 
master-ltr-plugin-rfc to keep up to date with master changes instead of 
cherry-picking everything one by one into it?

My impression was that 'git rebase' (against master) could be run for 
master-ltr-plugin-rfc but then it would have to be followed by a 'git --force 
push' (and that is the thing to avoid). 'git merge' to pull in changes from 
master onto the master-ltr-plugin-rfc is perhaps possible without a force push, 
haven't tried that.

In terms of transition from master-ltr-plugin-rfc to 
master-ltr-plugin-rfc-march branch, for that anything can be used in my 
opinion, rebase/merge/squash/etc. since it's starting a fresh branch.

Not sure if that answered your question?

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15183199#comment-15183199
 ] 

Christine Poerschke commented on SOLR-8542:
---

[~hossman] and [~steve_rowe] - would you have any thoughts on 'managed 
resource(s)' vs. 'solrconfig.xml plugin(s)' alternatives w.r.t. feature and 
model representation/configuration? Thanks.

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15183195#comment-15183195
 ] 

Christine Poerschke commented on SOLR-8542:
---

bq. ... Question: The only reason we currently have the LTRComponent is so that 
it can register the Model and Feature stores as managed resources because it 
can be SolrCore aware. Is there a way we can do this without the use of a 
component?

Not answering directly the managed resources part of the question but having 
noticed that the features.json/model.json needs to be accompanied by various 
solrconfig.xml changes in practice - I wonder if configuring models as plugin 
part of solrconfig.xml might be something to explore?

*current (features|model).json and solrconfig.xml configuration:*
{code}
## features.json
...
## firstModel.json
...
## secondModel.json
...
## solrconfig.xml
...

...

...

...

  ...
  
ltrComponent
  

...
{code}

*potential alternative solrconfig.xml configuration:*
{code}
## solrconfig.xml
...




  
  originalScore,isBook
  org.apache.solr.ltr.feature.impl.OriginalScoreFeature
  org.apache.solr.ltr.feature.impl.SolrFeature
  {!terms f=category}book
  
  0.5
  0.1



  mySecondModelName
  ...

...
{code}

_The most obvious implication_ of having a new solrconfig.xml element instead 
of (features|model).json managed resources would be that {{solr/core}} rather 
than {{solr/contrib/ltr}} contains the code.
* From an end-user perspective this means 'Learning to Rank' support 
out-of-the-box i.e. no need to build and deploy extra jar files plus no need to 
configure LTRQParserPlugin and LTRFeatureLoggerTransformerFactory queryParser 
and transformer elements. Though note that {{}} customisation is supported if 
something other than the out-of-the-box models is required.
* One of the out-of-the-box factories could be a features-only factory similar 
to the 'dummyModel' mentioned above, e.g.
{code}

  originalScore,isBook
  org.apache.solr.ltr.feature.impl.OriginalScoreFeature
  org.apache.solr.ltr.feature.impl.SolrFeature
  {!terms f=category}book

{code}

_A concern might be_ that the reRankModelFactory element(s) would bloat 
solrconfig.xml and that the element(s) being embedded in solrconfig.xml would 
be more difficult to edit than one or two json files.
* The bloat concern can be addressed via {{xi:include}} e.g.
{code}
## solrconfig.xml
...
http://www.w3.org/2001/XInclude"/>
...
## solrconfig-reRankModelFactory-myFirstModelName.xml

  
  originalScore,isBook
  org.apache.solr.ltr.feature.impl.OriginalScoreFeature
  org.apache.solr.ltr.feature.impl.SolrFeature
  {!terms f=category}book
  
  0.5
  0.1

{code}
* xml vs. json representation is a fair point, if the feature engineering 
process usually outputs json files then perhaps a simple utility script could 
help convert that json into solrconfig.xml a reRankModelFactory xml element.

_A factory approach_ could naturally support arbitrary models including 
chaining or nesting of models. (A factory approach is of course also possible 
with json format.)
{code}

  simple,complex

  
  solr.SVMRerankModelFactory
  
  originalScore,isBook
  org.apache.solr.ltr.feature.impl.OriginalScoreFeature
  org.apache.solr.ltr.feature.impl.SolrFeature
  {!terms f=category}book
  
  0.5
  0.1

  
  mycompany.MyComplexRerankModelFactory
  
  x,y
  ...
  ...
  ...
  ...
  ...
  
  0.42
  ...

{code}

> 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, 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, 
> David Grohmann 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.

[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15183189#comment-15183189
 ] 

Christine Poerschke commented on SOLR-8542:
---

Hi Michael, thanks for the response above. Based on it, some follow-on 
questions/observations below.

bq. Typically ... you don't want to "update" an existing feature. You should 
instead add a new feature with your updates and deploy a newly trained model 
using it ... all iterations of your models will use the same feature store, 
with any new features added to the store. A model cannot use features from 
other stores. ...

If features present in a feature store aren't normally updated because existing 
models use them and if models cannot use features from other stores - I wonder 
if combining {{features.json}} and {{model.json}} content might be a viable 
option? {{models.json}} illustration shown below, please see also 
solrconfig.xml related illustration and observations that follow it.

{code}
## models.json
[
{
"type":"org.apache.solr.ltr.ranking.RankSVMModel",
"name":"myFirstModelName",
"features":[
{ "name": "originalScore",
  "type":"org.apache.solr.ltr.feature.impl.OriginalScoreFeature",
  "params":{}
},
{ "name": "isBook",
  "type": "org.apache.solr.ltr.feature.impl.SolrFeature",
  "params":{ "fq": ["{!terms f=category}book"] }
}
],
"params":{
"weights": {
"originalScore": 0.5,
"isBook": 0.1
}

}
},
{
"type":"org.apache.solr.ltr.ranking.RankSVMModel",
"name":"mySecondModelName",
...
}
]
{code}

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr

2016-03-03 Thread Michael Nilsson (JIRA)

[
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15178779#comment-15178779
]

Michael Nilsson commented on SOLR-8542:
---

Hey Christine, I've posted a response to most of your comments thus far below.

*doDeleteChild method makes no storeManagedData method call*
We have a ticket for this that we'll fix along with other improvements for our
next commit.

*ManagedFeatureStore.doGet throws an exception when the childId concerned is
not present*
We could return a response with no features if desired, we were currently using
the error response to differentiate between a feature store not existing and
one existing without any features added to it yet.

*ManagedResource.doPut addFeature could throw an exception when a name being
updated/added already exists. Should repeats of the same name simply replace
the existing entry for that name?*
Typically when you have models deployed using some features, you don't want to
"update" an existing feature. You should instead add a new feature with your
updates and deploy a newly trained model using it, because you don't want the
meaning/value of the original feature used by historical models to change.
This is to ensure reproducible results when testing an old model that used the
old version of the feature. We use this error to prevent this from happening.

*LTRComponent state + use of state separation. Would feature store and model
store changes still propagate through to ltr_ms*
If you deploy new features to your feature store, you would want to start
extracting those features, which means we should propagate them down. We could
make feature stores write-once, and any new features would require a new
feature store with all the old ones copied over to avoid this, but that might
be cumbersome to the user and leave lots of old feature stores around until the
user cleans them up.
Question: The only reason we currently have the LTRComponent is so that it can
register the Model and Feature stores as managed resources because it can be
SolrCore aware. Is there a way we can do this without the use of a component?

*Branch/commit process*
Everything you said sounds do-able. The only question I have is regarding
"'git merge' and 'git rebase' and 'git --force push' will be avoided". Agreed
about git force, but if at the end we're going to make a new
master-ltr-plugin-rfc-march branch, and everything is going to be squashed and
rebased, why not allow merges into the master-ltr-plugin-rfc to keep up to date
with master changes instead of cherry-picking everything one by one into it?

*Feature engineering dummy model replacement*
Currently you have to use a dummy model to reference what features you want
extracted like you said.
{code}fv=true=*,score,[features]={!ltr model=dummyModel
reRankDocs=25}{code}
The only reason you need the model is because it has a FeatureStore, which has
all the features you are looking to extract. Instead, we are planning on
allowing you to specify which FeatureStore you want to use for feature
extraction directly in the features Document Transformer. We will also remove
the superfluous fv=true parameter, since the document transformer already
identifies the fact that you want to extract features. The new expected sample
request for feature extraction would probably look something like this instead
{code}fl=*,score,[features featureStore=MyFeatures]{code}

*would the efi. parameters move out of the rq*
We will probably also move efi out as well, since you need them for both
feature extraction and reranking with a model

*might it be useful to have optional version and/or comment string elements in
the feature*
I think the comment section would be a good idea. The version touches on the
what I mentioned earlier about updates vs adds. We'll have to think about the
best way to handle this since you don't want to lose/replace versions 1 and 2
when you deploy version 3 of a feature.

*Could you clarify/outline when/how the "store" element would be used?*
A FeatureStore is a list of features that you want to extract (and use for
training, logging, or in a model for reranking). In the majority of the cases,
you will probably just have 1 feature store, and all iterations of your models
will use the same feature store, with any new features added to the store. A
model cannot use features from other stores. It may be the case that a single
collection services many different applications. If each of those applications
wants to rerank its results differently and only cares about a subset of
features, then they could each make their own FeatureStores with their say 100
features for extraction instead of pulling out the thousands of other features
that all the other teams made for that same collection.

*Are feature and model stores local to each solr config or can they be shared
across

[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15163109#comment-15163109
 ] 

Christine Poerschke commented on SOLR-8542:
---

Question related to the optional {{"store"}} element in the features and model 
JSON.

Could you clarify/outline when/how the "store" element would be used? 
Illustration:
{code}
## features.json
[
{
  "name":"isBook",
  # absence of "store" element means default store
  "type":"org.apache.solr.ltr.feature.impl.OriginalScoreFeature",
  "params":{}
},
{
  "name": "isBook", # same feature name but different store (and different type 
and/or params)
  "store": "someStore",
  "type": "org.apache.solr.ltr.feature.impl.SolrFeature",
  "params":{ "fq": ["{!terms f=category}book"] }
}
]
...
## model.json
{
"type":"org.apache.solr.ltr.ranking.RankSVMModel",
"name":"myModelName",
"name":"myStore", # can this model reference features from another store 
(in this example assume the myStore store has no isBook feature)?
"features":[
{ "name": "userTextTitleMatch"},
{ "name": "originalScore"},
{ "name": "isBook"}
],
"params":{
"weights": {
"userTextTitleMatch": 1.0,
"originalScore": 0.5,
"isBook": 0.1
}

}
}
{code}


Are feature and model stores local to each solr config or can they be shared 
across configs? Illustration:
{code}
## extract from zookeeper data:
/collections 
 /collections/collection1
 DATA:
 {"configName":"configA"}
 /collections/collection2
 DATA:
 {"configName":"configB"}

/configs
 /configs/configA
  /configs/configA/solrconfig.xml
  /configs/configA/schema.xml
 /configs/configB
  /configs/configB/solrconfig.xml
  /configs/configB/schema.xml

???/features.json
???/model.json
{code}

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15163105#comment-15163105
 ] 

Christine Poerschke commented on SOLR-8542:
---

Question related to [Feature 
Engineering|https://en.wikipedia.org/wiki/Feature_engineering] - is that the 
right term? - and feature extraction.

[LTRQParserPlugin.java#L117|https://github.com/bloomberg/lucene-solr/blob/master-ltr-plugin-rfc/solr/contrib/ltr/src/java/org/apache/solr/ltr/ranking/LTRQParserPlugin.java#L117]
 mentions

bq. For training a new model offline you need feature vectors, but dont yet 
have a model.

and 
[README.txt#L280|https://github.com/bloomberg/lucene-solr/blob/master-ltr-plugin-rfc/solr/contrib/ltr/README.txt#L280]
 mentions about for now using a dummy model e.g.

bq. fv=true=*,score,\[features\]={!ltr model=dummyModel reRankDocs=25}

to extract features.

If it is known already, could you outline what the replacement for the above 
fv/fl/dummyModel combination is likely to look like?

Semi-related to that:
* would the {{efi.*}} parameters move out of the {{rq}} then since candidate 
features to be returned in the response might reference external feature info?
* might it be useful to have optional version and/or comment string elements in 
the feature and model JSON format? Illustration:
{code}
{
  "type": "org.apache.solr.ltr.feature.impl.SolrFeature",
  "name":  "documentRecency",
  "comment": "Initial version, we may have to tweak the recip function 
arguments later.",
  "params": {
  "q": "{!func}recip( ms(NOW,publish_date), 3.16e-11, 1, 1)"
  }
}
...
{
"type":"org.apache.solr.ltr.ranking.RankSVMModel",
"name":"myModelName",
"version": "1.0",
"comment": "features and parameters determined using XYZ with ABC data, 
ticket reference: 12345",
"features":[
...
],
"params":{
...
}
}
{code}

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr


[ 
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15163098#comment-15163098
 ] 

Christine Poerschke commented on SOLR-8542:
---

The branch behind the https://github.com/apache/lucene-solr/pull/4 above is 
[master-ltr-plugin-rfc|https://github.com/bloomberg/lucene-solr/tree/master-ltr-plugin-rfc]
 and i've just created 
[master-ltr-plugin-rfc-cpoerschke-comments|https://github.com/bloomberg/lucene-solr/tree/master-ltr-plugin-rfc-cpoerschke-comments]
 branch off that.

In (unrelated) SOLR-8621 we had an in-progress branch also and its usage and 
intentions emerged and were clarified over time, and so based on that perhaps 
it's helpful to suggest usage up-front here:
* master-ltr-plugin-rfc branches off (Jan 29th) master
* master-ltr-plugin-rfc-cpoerschke-comments branches off (Feb 24th) 
master-ltr-plugin-rfc
* 'git merge' and 'git rebase' and 'git --force push' will be avoided
* further commits to master-ltr-plugin-rfc* are anticipated
* 'git cherry-pick' of changes from master to master-ltr-plugin-rfc* will be 
done where helpful (e.g. SOLR-8600 was cherry-picked from master to 
master-ltr-plugin-rfc-cpoerschke-comments)
* cherry-picking between master-ltr-plugin-rfc* branches welcome and will be 
done where helpful
* at some point in the future activity on master-ltr-plugin-rfc* branches will 
cease and if required a new (say) master-ltr-plugin-rfc-march branch off (Mar 
1?th) master will be created
* at the very end everything will be squashed and rebased onto latest master 
and then committed as a single commit

Does that sound workable or too complicated? Alternatives, comments, etc. 
welcome as usual. (And to clarify, suggested usage here is specific for this 
SOLR-8542 ticket only, any general recommended usage type discussions would be 
for elsewhere.)

> 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, 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, 
> David Grohmann 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=test=json={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}=*,[features],price,score,name=true



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[jira] [Commented] (SOLR-8542) Integrate Learning to Rank into Solr