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https://issues.apache.org/jira/browse/SOLR-8492?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15146074#comment-15146074
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Joel Bernstein edited comment on SOLR-8492 at 2/13/16 5:29 PM:
---------------------------------------------------------------
[~caomanhdat], I've been testing out the algorithm by comparing it to R. I've
attached the data set I'm using to the ticket (logit.csv). I'm attempting make
sense of the results but they don't seem to match up.
I ran the following LogitStream call with all data in *1 shard*:
{code}
logit(collection3, q="*:*", features="married_i", outcome="buy_i",
maxIterations=100)
{code}
And got this for the final weights:
{code}
{"features":["married_i"],"weights":[-0.5048080969966502],"error":0.3808560947044004}
{code}
In R, here is the output for the same data set. *Note* that *Is.Married* is the
same field as *married_i* in Solr and same for *Buy* and *buy_i*
{code}
Call:
glm(formula = Buy ~ Is.Married, family = "binomial", data = mydata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.9687 -0.4201 -0.4201 -0.4201 2.2232
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.3830 0.1718 -13.870 <2e-16 ***
Is.Married 1.8699 0.2184 8.563 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 646.05 on 672 degrees of freedom
Residual deviance: 564.47 on 671 degrees of freedom
AIC: 568.47
Number of Fisher Scoring iterations: 5
{code}
It appears that they are giving different results. Am I performing the test
correctly?
was (Author: joel.bernstein):
[~caomanhdat], I've been testing out the algorithm by comparing it to R. I've
attached the data set I'm using to the ticket. I'm attempting make sense of the
results but they don't seem to match up.
I ran the following LogitStream call with all data in *1 shard*:
{code}
logit(collection3, q="*:*", features="married_i", outcome="buy_i",
maxIterations=100)
{code}
And got this for the final weights:
{code}
{"features":["married_i"],"weights":[-0.5048080969966502],"error":0.3808560947044004}
{code}
In R, here is the output for the same data set. *Note* that *Is.Married* is the
same field as *married_i* in Solr and same for *Buy* and *buy_i*
{code}
Call:
glm(formula = Buy ~ Is.Married, family = "binomial", data = mydata)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.9687 -0.4201 -0.4201 -0.4201 2.2232
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.3830 0.1718 -13.870 <2e-16 ***
Is.Married 1.8699 0.2184 8.563 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 646.05 on 672 degrees of freedom
Residual deviance: 564.47 on 671 degrees of freedom
AIC: 568.47
Number of Fisher Scoring iterations: 5
{code}
It appears that they are giving different results. Am I performing the test
correctly?
> 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,
> SOLR-8492.patch, SOLR-8492.patch, SOLR-8492.patch, logit.csv
>
>
> 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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