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https://issues.apache.org/jira/browse/SOLR-8492?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15090713#comment-15090713
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Joel Bernstein edited comment on SOLR-8492 at 1/9/16 5:47 PM:
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[~caomanhdat], I pulled down your latest patch, read through it and ran the
test case. It looks really good. Also the fact that the model is predicting
probabilities correctly certainly is a good sign.
There are a few small things that I need to do to prepare this to be committed,
but I'm planning on leaving your code basically as it is.
I'm sure there will be plenty of feedback on this as more people review the
code and join the conversation. We can always adjust things based on this
feedback.
We can also always have different implementations for if consensus is not
reached on one single algorithm. But I think this is a great way to kick off
the wider discussion.
Thanks for your help with this!
was (Author: joel.bernstein):
[~caomanhdat], I pulled down your latest patch, read through it and ran the
test case. It looks really good. Also the fact that the model is predicting
probabilities correctly certainly is a good sign.
There a few small things that I need to do to prepare this to be committed, but
I'm planning on leaving your code basically as it is.
I'm sure there will be plenty of feedback on this as more people review the
code and join the conversation. We can always adjust things based on this
feedback.
We can also always have different implementations for if consensus is not
reached on one single algorithm. But I think this is a great way to kick off
the wider discussion.
Thanks for your help with this!
> Add LogisticRegressionQuery and LogitStream
> -------------------------------------------
>
> Key: SOLR-8492
> URL: https://issues.apache.org/jira/browse/SOLR-8492
> Project: Solr
> Issue Type: New Feature
> Reporter: Joel Bernstein
> Attachments: SOLR-8492.patch, SOLR-8492.patch, SOLR-8492.patch
>
>
> This ticket is to add a new query called a LogisticRegressionQuery (LRQ).
> The LRQ extends AnalyticsQuery
> (http://joelsolr.blogspot.com/2015/12/understanding-solrs-analyticsquery.html)
> and returns a DelegatingCollector that implements a Stochastic Gradient
> Descent (SGD) optimizer for Logistic Regression.
> This ticket also adds the LogitStream which leverages Streaming Expressions
> to provide iteration over the shards. Each call to LogitStream.read() calls
> down to the shards and executes the LogisticRegressionQuery. The model data
> is collected from the shards and the weights are averaged and sent back to
> the shards with the next iteration. Each call to read() returns a Tuple with
> the averaged weights and error from the shards. With this approach the
> LogitStream streams the changing model back to the client after each
> iteration.
> The LogitStream will return the EOF Tuple when it reaches the defined
> maxIterations. When sent as a Streaming Expression to the Stream handler this
> provides parallel iterative behavior. This same approach can be used to
> implement other parallel iterative algorithms.
> The initial patch has a test which simply tests the mechanics of the
> iteration. More work will need to be done to ensure the SGD is properly
> implemented. The distributed approach of the SGD will also need to be
> reviewed.
> This implementation is designed for use cases with a small number of features
> because each feature is it's own discreet field.
> An implementation which supports a higher number of features would be
> possible by packing features into a byte array and storing as binary
> DocValues.
> This implementation is designed to support a large sample set. With a large
> number of shards, a sample set into the billions may be possible.
> sample Streaming Expression Syntax:
> {code}
> logit(collection1, features="a,b,c,d,e,f" outcome="x" maxIterations="80")
> {code}
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