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https://issues.apache.org/jira/browse/SOLR-8542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15186931#comment-15186931
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ASF GitHub Bot commented on SOLR-8542:
--------------------------------------
Github user alessandrobenedetti commented on a diff in the pull request:
https://github.com/apache/lucene-solr/pull/4#discussion_r55499494
--- Diff: solr/contrib/ltr/README.txt ---
@@ -0,0 +1,330 @@
+Apache Solr Learning to Rank
+========
+
+This is the main [learning to rank integrated into
solr](http://www.slideshare.net/lucidworks/learning-to-rank-in-solr-presented-by-michael-nilsson-diego-ceccarelli-bloomberg-lp)
+repository.
+[Read up on learning to
rank](https://en.wikipedia.org/wiki/Learning_to_rank)
+
+Apache 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.
+
+
+# Changes to solrconfig.xml
+```xml
+<config>
+ ...
+
+ <!-- Query parser used to rerank top docs with a provided model -->
+ <queryParser name="ltr"
class="org.apache.solr.ltr.ranking.LTRQParserPlugin" />
+
+ <!-- Transformer that will encode the document features in the response.
+ For each document the transformer will add the features as an extra field
+ in the response. The name of the field we will be the the name of the
+ transformer enclosed between brackets (in this case [features]).
+ In order to get the feature vector you will have to
+ specify that you want the field (e.g., fl="*,[features]) -->
+ <transformer name="features"
class="org.apache.solr.ltr.ranking.LTRFeatureLoggerTransformerFactory" />
+
+
+ <!-- Component that hooks up managed resources for features and models
-->
+ <searchComponent name="ltrComponent"
class="org.apache.solr.ltr.ranking.LTRComponent"/>
+ <requestHandler name="/query" class="solr.SearchHandler">
+ <lst name="defaults">
+ <str name="echoParams">explicit</str>
+ <str name="wt">json</str>
+ <str name="indent">true</str>
+ <str name="df">id</str>
+ </lst>
+ <arr name="last-components">
+ <!-- Use the component in your requestHandler -->
+ <str>ltrComponent</str>
+ </arr>
+ </requestHandler>
+
+ <query>
+ ...
+
+ <!-- Cache for storing and fetching feature vectors -->
+ <cache name="QUERY_DOC_FV"
+ class="solr.search.LRUCache"
+ size="4096"
+ initialSize="2048"
+ autowarmCount="4096"
+ regenerator="solr.search.NoOpRegenerator" />
+ </query>
+
+</config>
+
+```
+
+
+# Build the plugin
+In the solr/contrib/ltr directory run
+`ant dist`
+
+# Install the plugin
+In your solr installation, navigate to your collection's lib directory.
+In the solr install example, it would be solr/collection1/lib.
+If lib doesn't exist you will have to make it, and then copy the plugin's
jar there.
+
+`cp lucene-solr/solr/dist/solr-ltr-X.Y.Z-SNAPSHOT.jar
mySolrInstallPath/solr/myCollection/lib`
+
+Restart your collection using the admin page and you are good to go.
+You can find more detailed instructions
[here](https://wiki.apache.org/solr/SolrPlugins).
+
+
+# Defining Features
+In the learning to rank plugin, you can define features in a feature space
+using standard Solr queries. As an example:
+
+###### features.json
+```json
+[
+{ "name": "isBook",
+ "type": "org.apache.solr.ltr.feature.impl.SolrFeature",
+ "params":{ "fq": ["{!terms f=category}book"] }
+},
+{
+ "name": "documentRecency",
+ "type": "org.apache.solr.ltr.feature.impl.SolrFeature",
+ "params": {
+ "q": "{!func}recip( ms(NOW,publish_date), 3.16e-11, 1, 1)"
+ }
+},
+{
+ "name":"originalScore",
+ "type":"org.apache.solr.ltr.feature.impl.OriginalScoreFeature",
+ "params":{}
+},
+{
+ "name" : "userTextTitleMatch",
+ "type" : "org.apache.solr.ltr.feature.impl.SolrFeature",
+ "params" : { "q" : "{!field f=title}${user_text}" }
+}
+]
+```
+
+Defines four features. Anything that is a valid Solr query can be used to
define
+a feature.
+
+### Filter Query Features
+The first feature isBook fires if the term 'book' matches the category
field
+for the given examined document. Since in this feature q was not specified,
+either the score 1 (in case of a match) or the score 0 (in case of no
match)
+will be returned.
+
+### Query Features
+In the second feature (documentRecency) q was specified using a function
query.
+In this case the score for the feature on a given document is whatever the
query
+returns (1 for docs dated now, 1/2 for docs dated 1 year ago, 1/3 for docs
dated
+2 years ago, etc..) . If both an fq and q is used, documents that don't
match
+the fq will receive a score of 0 for the documentRecency feature, all other
+documents will receive the score specified by the query for this feature.
+
+### Original Score Feature
+The third feature (originalScore) has no parameters, and uses the
+OriginalScoreFeature class instead of the SolrFeature class. Its purpose
is
+to simply return the score for the original search request against the
current
+matching document.
+
+### External Features
+Users can specify external information that can to be passed in as
+part of the query to the ltr ranking framework. In this case, the
+fourth feature (userTextPhraseMatch) will be looking for an external field
+called 'user_text' passed in through the request, and will fire if there is
+a term match for the document field 'title' from the value of the external
+field 'user_text'. See the "Run a Rerank Query" section for how
+to pass in external information.
+
+### Custom Features
+Custom features can be created by extending from
+org.apache.solr.ltr.ranking.Feature, however this is generally not
recommended.
+The majority of features should be possible to create using the methods
described
+above.
+
+# Defining Models
+Currently the Learning to Rank plugin supports 2 main types of
+ranking models: [Ranking
SVM](http://www.cs.cornell.edu/people/tj/publications/joachims_02c.pdf)
+and
[LambdaMART](http://research.microsoft.com/pubs/132652/MSR-TR-2010-82.pdf)
+
+### Ranking SVM
+Currently only a linear ranking svm is supported. Use LambdaMART for
+a non-linear model. If you'd like to introduce a bias set a constant
feature
+to the bias value you'd like and make a weight of 1.0 for that feature.
+
+###### model.json
+```json
+{
+ "type":"org.apache.solr.ltr.ranking.RankSVMModel",
+ "name":"myModelName",
+ "features":[
+ { "name": "userTextTitleMatch"},
+ { "name": "originalScore"},
+ { "name": "isBook"}
+ ],
+ "params":{
+ "weights": {
+ "userTextTitleMatch": 1.0,
+ "originalScore": 0.5,
+ "isBook": 0.1
+ }
+
+ }
+}
+```
+
+This is an example of a toy Ranking SVM model. Type specifies the class to
be
+using to interpret the model (RankSVMModel in the case of Ranking SVM).
+Name is the model identifier you will use when making request to the ltr
+framework. Features specifies the feature space that you want extracted
+when using this model. All features that appear in the model params will
+be used for scoring and must appear in the features list. You can add
+extra features to the features list that will be computed but not used in
the
+model for scoring, which can be useful for logging.
+Params are the Ranking SVM parameters.
+
+Good library for training SVM's
(https://www.csie.ntu.edu.tw/~cjlin/liblinear/ ,
+https://www.csie.ntu.edu.tw/~cjlin/libsvm/) . You will need to convert the
+libSVM model format to the format specified above.
+
+### LambdaMART
+
+###### model2.json
+```json
+{
+ "type":"org.apache.solr.ltr.ranking.LambdaMARTModel",
+ "name":"lambdamartmodel",
+ "features":[
+ { "name": "userTextTitleMatch"},
+ { "name": "originalScore"}
+ ],
+ "params":{
+ "trees": [
+ {
+ "weight" : 1,
+ "tree": {
+ "feature": "userTextTitleMatch",
+ "threshold": 0.5,
+ "left" : {
+ "value" : -100
+ },
+ "right": {
+ "feature" : "originalScore",
+ "threshold": 10.0,
+ "left" : {
+ "value" : 50
+ },
+ "right" : {
+ "value" : 75
+ }
+ }
+ }
+ },
+ {
+ "weight" : 2,
+ "tree": {
+ "value" : -10
+ }
+ }
+ ]
+ }
+}
+```
+This is an example of a toy LambdaMART. Type specifies the class to be
using to
+interpret the model (LambdaMARTModel in the case of LambdaMART). Name is
the
+model identifier you will use when making request to the ltr framework.
+Features specifies the feature space that you want extracted when using
this
+model. All features that appear in the model params will be used for
scoring and
+must appear in the features list. You can add extra features to the
features
+list that will be computed but not used in the model for scoring, which can
+be useful for logging. Params are the LambdaMART specific parameters. In
this
+case we have 2 trees, one with 3 leaf nodes and one with 1 leaf node.
+
+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.
+
+# Deploy Models and Features
+To send features run
+
+`curl -XPUT 'http://localhost:8983/solr/collection1/schema/fstore'
--data-binary @/path/features.json -H 'Content-type:application/json'`
+
+To send models run
+
+`curl -XPUT 'http://localhost:8983/solr/collection1/schema/mstore'
--data-binary @/path/model.json -H 'Content-type:application/json'`
+
+
+# View Models and Features
+`curl -XGET 'http://localhost:8983/solr/collection1/schema/fstore'`
+`curl -XGET 'http://localhost:8983/solr/collection1/schema/mstore'`
+
+
+# Run a Rerank Query
+Add to your original solr query
+`rq={!ltr model=myModelName reRankDocs=25}`
+
+The model name is the name of the model you sent to solr earlier.
+The number of documents you want reranked, which can be larger than the
+number you display, is reRankDocs.
+
+### Pass in external information for external features
+Add to your original solr query
+`rq={!ltr reRankDocs=3 model=externalmodel efi.field1='text1'
efi.field2='text2'}`
+
+Where "field1" specifies the name of the customized field to be used by one
+or more of your features, and text1 is the information to be pass in. As an
+example that matches the earlier shown userTextTitleMatch feature one
could do:
+
+`rq={!ltr reRankDocs=3 model=externalmodel efi.user_text='Casablanca'
efi.user_intent='movie'}`
+
+# Extract features
+To extract features you need to use the feature vector transformer + set
the
+fv parameter to true (this required parameter will be removed in the
future).
+For now you need to also use a dummy model with all the features you want
to
+extract inside the features parameter list of the model (this limitation
will
+also be changed in the future so you can extract features without a dummy
model).
+
+`fv=true&fl=*,score,[features]&rq={!ltr model=dummyModel reRankDocs=25}`
+
+## 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
--- End diff --
I think ant example is deprecated in the current master branch,
we should point that with recent releases,
ant server
is 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&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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