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https://issues.apache.org/jira/browse/SPARK-20047?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai resolved SPARK-20047.
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Resolution: Fixed
Fix Version/s: 2.2.1
Target Version/s: 2.2.1 (was: 2.3.0)
> Constrained Logistic Regression
> -------------------------------
>
> Key: SPARK-20047
> URL: https://issues.apache.org/jira/browse/SPARK-20047
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 2.2.0
> Reporter: DB Tsai
> Assignee: Yanbo Liang
> Fix For: 2.2.1
>
>
> For certain applications, such as stacked regressions, it is important to put
> non-negative constraints on the regression coefficients. Also, if the ranges
> of coefficients are known, it makes sense to constrain the coefficient search
> space.
> Fitting generalized constrained regression models object to Cβ ≤ b, where C ∈
> R^\{m×p\} and b ∈ R^\{m\} are predefined matrices and vectors which places a
> set of m linear constraints on the coefficients is very challenging as
> discussed in many literatures.
> However, for box constraints on the coefficients, the optimization is well
> solved. For gradient descent, people can projected gradient descent in the
> primal by zeroing the negative weights at each step. For LBFGS, an extended
> version of it, LBFGS-B can handle large scale box optimization efficiently.
> Unfortunately, for OWLQN, there is no good efficient way to do optimization
> with box constrains.
> As a result, in this work, we only implement constrained LR with box
> constrains without L1 regularization.
> Note that since we standardize the data in training phase, so the
> coefficients seen in the optimization subroutine are in the scaled space; as
> a result, we need to convert the box constrains into scaled space.
> Users will be able to set the lower / upper bounds of each coefficients and
> intercepts.
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