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https://issues.apache.org/jira/browse/SYSTEMML-700?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15533640#comment-15533640
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Niketan Pansare edited comment on SYSTEMML-700 at 9/29/16 6:37 PM:
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[~JeremyNixon] As an FYI, the algorithm wrappers performs the label
transformations that you suggested:
http://apache.github.io/incubator-systemml/algorithms-classification.html#examples
The links to the code where this transformation is performed:
https://github.com/apache/incubator-systemml/blob/master/src/main/scala/org/apache/sysml/api/ml/PredictionUtils.scala#L52
was (Author: niketanpansare):
As an FYI, the algorithm wrappers performs the label transformations that you
suggested:
http://apache.github.io/incubator-systemml/algorithms-classification.html#examples
The links to the code where this transformation is performed:
https://github.com/apache/incubator-systemml/blob/master/src/main/scala/org/apache/sysml/api/ml/PredictionUtils.scala#L52
> Inflexible category labels for Multinomial Logistic Regression
> --------------------------------------------------------------
>
> Key: SYSTEMML-700
> URL: https://issues.apache.org/jira/browse/SYSTEMML-700
> Project: SystemML
> Issue Type: Bug
> Components: Algorithms
> Reporter: Jeremy
> Priority: Minor
> Original Estimate: 4h
> Remaining Estimate: 4h
>
> The Logistic Regression algorithm requires that category labels be labeled as
> 0 up to the number of classes-1. It should be able to handle any set of
> category labels provided by the user. B_out should have the appropriate size
> regardless of the values of the labels given, and the algorithm should also
> preserve the original labeling for the user.
> Added detail:
> The solution I'm currently using is to transform the labels from whatever
> values they are to 0, 1, 2,... before hand, and then transform them back to
> their original labels after the algorithm runs.
> Currently the algorithm doesn't handle class values that don't start at 0 or
> 1, and doesn't handle non-contiguous integers, both of which can come up. For
> example, the result for class labels 4,5,6 will return 5 sets of coefficients
> (correct number should be 2), and class labels -1, 0, 1 returns just one set
> of coefficients (correct number should be 2).
> Handling frames with strings would be a really great user experience - that
> could look like R's coercion internally. Both glmnet and scikit-learn handle
> string label arguments, but both apis are weakly typed as well.
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