[
https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15304137#comment-15304137
]
Michael Nilsson commented on SOLR-8542:
---------------------------------------
Hi everyone! We just made a push with many changes that were requested by you
guys, plus a few other things.
We have also updated to the latest Solr master branch as of few days ago. Just
as a heads up, we replaced the old pull request with a [new
one|https://github.com/apache/lucene-solr/pull/40] due to some history changes
when merging with the latest master. Below you'll find a list some of the
items we changed.
- Updated our documentation about the training phase and how to train a real
model for those that are not familiar with this process. We provided a step by
step example building a rankSVM model externally, and supplied a sample script
which does this using liblinear.
- Formatted the code based on the lucene eclipse style
- Updated the hashCode and equals functions of the ModelQuery as
[~Alessandro.Benedetti] pointed out
- Renamed ModelMetadata, the class you would subclass to add a new model for
scoring docs, to LTRScoringAlgorithm
- Cleaned up the LTRScoringAlgorithm to no longer have a type parameter
- Added IntelliJ support. Thank you [~Alessandro.Benedetti] for adding it
- Renamed mstore and fstore endpoints to feature-store and model-store as per
[~Upayavira]'s suggestion
- Added support for default efi parameters using the same Solr standard in
solrconfig. When defining a feature in the config, put $\{isFromManchester:0\}
to get 0 as a default, and you won't have to specify it in the request's efi
params. Thanks for the enhancement suggestion [~Alessandro.Benedetti]
- Removed the fv=true param requirement for extracting features.
- You do not have to provide a "dummy model" first for extracting features, so
you can request the transformer without the need of an rq ranking query.
Inside the transformer you can provide a store=myFeatureStore param, and it
will extract all features from that feature store directly. You can also
provide local efi params if needed when extracting without an rq.
Please [read through the
README|https://github.com/bloomberg/lucene-solr/tree/master-ltr-plugin-release/solr/contrib/ltr]
for more information on the plugin, and how to train your own external model.
Also, we have opened up the ability to [create
issues|https://github.com/bloomberg/lucene-solr/issues] in our Github
repository where the plugin currently lives.
Please feel free to make or suggest issues, and we will keep track of them
there instead of in this long list of comments.
Thanks for the support everyone, and expect more frequent updates in the future.
> 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&q=test&wt=json&rq={!ltr%20model=svm%20reRankDocs=25%20efi.query=%27test%27}&fl=*,[features],price,score,name&fv=true
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
