[
https://issues.apache.org/jira/browse/SPARK-6323?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Debasish Das updated SPARK-6323:
--------------------------------
Description:
Currently ml.recommendation.ALS is optimized for gram matrix generation which
only scales to modest ranks. The problems that we can solve are in the normal
equation/quadratic form: 0.5x'Hx + c'x + g(z)
g(z) can be one of the constraints from Breeze proximal library:
https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/Proximal.scala
In this PR we will re-use ml.recommendation.ALS design and come up with
ml.recommendation.ALM (Alternating Minimization). Thanks to [~mengxr] recent
changes, it's straightforward to do it now !
ALM will be capable of solving the following problems: min f(x) + g(z)
1. Loss function f(x) can be LeastSquareLoss, LoglikelihoodLoss and HingeLoss.
Most likely we will re-use the Gradient interfaces already defined and
implement LoglikelihoodLoss
2. Constraints g(z) supported are same as above except that we don't support
affine + bounds yet Aeq x = beq , lb <= x <= ub yet. Most likely we don't need
that for ML applications
3. For solver we will use breeze.optimize.proximal.NonlinearMinimizer which in
turn uses projection based solver (SPG) or proximal solvers (ADMM) based on
convergence speed.
https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/NonlinearMinimizer.scala
4. The factors will be SparseVector so that we keep shuffle size in check. For
example we will run with 10K ranks but we will force factors to be 100-sparse.
This is closely related to Sparse LDA
https://issues.apache.org/jira/browse/SPARK-5564 with the difference that we
are not using graph representation here.
As we do scaling experiments, we will understand which flow is more suited as
ratings get denser (my understanding is that since we already scaled ALS to 2
billion ratings and since we will keep sparsity in check, the same 2 billion
flow will scale to 10K ranks as well)...
This JIRA is intended to extend the capabilities of ml recommendation to
generalized loss function.
was:
Currently ml.recommendation.ALS is optimized for gram matrix generation which
only scales to modest ranks. The problems that we can solve are in the normal
equation/quadratic form: 0.5x'Hx + c'x + g(z)
g(z) can be one of the constraints from Breeze proximal library:
https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/Proximal.scala
In this PR we will re-use ml.recommendation.ALS design and come up with
ml.recommendation.ALM (Alternating Minimization). Thanks to [~mengxr] recent
changes, it's straightforward to do it now !
ALM will be capable of solving the following problems: min f(x) + g(z)
1. Loss function f(x) can be LeastSquareLoss, LoglikelihoodLoss and HingeLoss.
Most likely we will re-use the Gradient interfaces already defined and
implement LoglikelihoodLoss
2. Constraints g(z) supported are same as above except that we don't support
affine + bounds yet Aeq x = beq , lb <= x <= ub yet. Most likely we don't need
that for ML applications
3. For solver we will use breeze.optimize.proximal.NonlinearMinimizer which in
turn uses projection based solver (SPG) or proximal solvers (ADMM) based on
convergence speed.
https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/NonlinearMinimizer.scala
4. The factors will be SparseVector so that we keep shuffle size in check. For
example we will run with 10K ranks but we will force factors to be 100-sparse.
This is closely related to Sparse LDA
https://issues.apache.org/jira/browse/SPARK-5564 with the difference that we
are not using graph representation here.
As we do scaling experiments, we will understand which flow is more suited as
ratings get denser (my understanding is that since we already scaled ALS to 2
billion ratings and since we will keep sparsity in check, the same 2 billion
flow will scale to 10K ranks as well)...
This JIRA is intended to extend the capabilities of Spark's collaborative
filtering toolkit to generalized loss function.
> Large rank matrix factorization with Nonlinear loss and constraints
> -------------------------------------------------------------------
>
> Key: SPARK-6323
> URL: https://issues.apache.org/jira/browse/SPARK-6323
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Affects Versions: 1.4.0
> Reporter: Debasish Das
> Fix For: 1.4.0
>
> Original Estimate: 672h
> Remaining Estimate: 672h
>
> Currently ml.recommendation.ALS is optimized for gram matrix generation which
> only scales to modest ranks. The problems that we can solve are in the normal
> equation/quadratic form: 0.5x'Hx + c'x + g(z)
> g(z) can be one of the constraints from Breeze proximal library:
> https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/Proximal.scala
> In this PR we will re-use ml.recommendation.ALS design and come up with
> ml.recommendation.ALM (Alternating Minimization). Thanks to [~mengxr] recent
> changes, it's straightforward to do it now !
> ALM will be capable of solving the following problems: min f(x) + g(z)
> 1. Loss function f(x) can be LeastSquareLoss, LoglikelihoodLoss and
> HingeLoss. Most likely we will re-use the Gradient interfaces already defined
> and implement LoglikelihoodLoss
> 2. Constraints g(z) supported are same as above except that we don't support
> affine + bounds yet Aeq x = beq , lb <= x <= ub yet. Most likely we don't
> need that for ML applications
> 3. For solver we will use breeze.optimize.proximal.NonlinearMinimizer which
> in turn uses projection based solver (SPG) or proximal solvers (ADMM) based
> on convergence speed.
> https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/proximal/NonlinearMinimizer.scala
> 4. The factors will be SparseVector so that we keep shuffle size in check.
> For example we will run with 10K ranks but we will force factors to be
> 100-sparse.
> This is closely related to Sparse LDA
> https://issues.apache.org/jira/browse/SPARK-5564 with the difference that we
> are not using graph representation here.
> As we do scaling experiments, we will understand which flow is more suited as
> ratings get denser (my understanding is that since we already scaled ALS to 2
> billion ratings and since we will keep sparsity in check, the same 2 billion
> flow will scale to 10K ranks as well)...
> This JIRA is intended to extend the capabilities of ml recommendation to
> generalized loss function.
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