David Causse

This is true, boosting only affects the output score of the model. This is I think what is expected when you boost a query: the query score is multiplied by...

@tmanabe thanks for digging into this! You're absolutely correct, I wrongfully assumed that the RankerQuery did not propagate the boost but it does as shown in your debugging session. Culprit...

For context I think the behavior changed with https://github.com/o19s/elasticsearch-learning-to-rank/commit/b907213a3baba02add3ae89eb3ebcbea881289de#diff-07788001c91b0b5c03be973de2a368900204bab6c6fc6d3255ec34bcf6184c09L239 where we normalized explicitly with a boost set to 1.0F (elastic 6.1.0 upgrade).

This will have to wait elastic 6.1, they added the possibility to implement our own rescorer. Without this it's impossible to access the first pass query score. A workaround is...

I haven't had time to work on this, I'd be happy to help and review some code if someone is willing to send a PR implementing this feature.

I worked on the first steps (see #204) which add a custom rescore context to the plugin. The next steps would be to build another feature type that'd be controlled...

edit: removed the comment on ranklib DataPoint, the default constructor does nothing so the float array is properly initialized with zeros which is coherent with the reset() method.

Sure, you simply have to provide the store name in the `store` param: ``` { "query": { "sltr": { "params": { "keywords": query_string }, "model": "model_name", "store": "store_name" } }...

Thanks, and sorry to have closed this issue too quickly, I agree with @ebernhardson a doc update to mention how to use feature stores is indeed the least we should...

I think it'd be very valuable to be able to request and monitor all groups when fetching the node statistics: `/_nodes/_local/stats?groups=_all`.