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https://issues.apache.org/jira/browse/SPARK-20047?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
DB Tsai updated SPARK-20047:
----------------------------
Description:
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.
One solution could be to modify these implementations and do a Projected
Gradient Descent in the primal by zeroing the negative weights at each step.
But this process is inconvenient because the nice convergence properties are
then lost.
was:
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.
One solution could be to modify these implementations and do a Projected
Gradient Descent in the primal by zeroing the negative weights at each step.
But this process is inconvenient because the nice convergence properties are
then lost.
> 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: 2.1.0
> Reporter: DB Tsai
> Assignee: Yanbo Liang
>
> 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.
>
> One solution could be to modify these implementations and do a Projected
> Gradient Descent in the primal by zeroing the negative weights at each step.
> But this process is inconvenient because the nice convergence properties are
> then lost.
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