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https://issues.apache.org/jira/browse/SPARK-30641?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhengruifeng updated SPARK-30641:
---------------------------------
Description:
We had been refactoring linear models for a long time, and there still are some
works in the future. After some discuss among [~huaxingao] [~srowen]
[~weichenxu123] [~mengxr] [~podongfeng] , we decide to gather related works
under a sub-project Matrix, it includes:
# *Blockification (vectorization of vectors)*
** vectors are stacked into matrices, so that high-level BLAS can be used for
better performance. (about ~3x faster on sparse datasets, up to ~18x faster on
dense datasets, see SPARK-31783 for details).
** Since 3.1.1, LoR/SVC/LiR/AFT supports blockification, and we need to
blockify KMeans in the future.
# *Standardization (virutal centering)*
** Existing impl of standardization in linear models does NOT center the
vectors by removing the means, for the purpose of keeping dataset _*sparsity*_.
However, this will cause feature values with small var be scaled to large
values, and underlying solver like LBFGS can not efficiently handle this case.
see SPARK-34448 for details.
** If internal vectors are centers (like other famous impl, i.e.
GLMNET/Scikit-Learn), the convergence ratio will be better. In the case in
SPARK-34448, the number of iteration to convergence will be reduced from 93 to
6. Moreover, the final solution is much more close to the one in GLMNET.
** Luckily, we find a new way to _*virtually*_ center the vectors without
densifying the dataset. Good results had been observed in LoR, we will take it
into account in other linear models.
# _*Initialization (To be discussed)*_
** Initializing model coef with a given model, should be beneficial to: 1,
convergence ratio (should reduce number of iterations); 2, model stability (may
obtain a new solution more close to the previous one);
# _*Early Stopping* *(To be discussed)*_
** we can compute the test error in the procedure (like tree models), and stop
the training procedure if test error begin to increase;
If you want to add other features in these models, please comment in the
ticket.
was:
We had been refactoring linear models for a long time, and there still are some
works in the future. After some discuss among [~huaxingao] [~srowen]
[~weichenxu123] , we decide to gather related works under a sub-project Matrix,
it include:
# *Blockification (vectorization of vectors)*
** vectors are stacked into matrices, so that high-level BLAS can be used for
better performance. (about ~3x faster on sparse datasets, up to ~18x faster on
dense datasets, see SPARK-31783 for details).
** Since 3.1.1, LoR/SVC/LiR/AFT supports blockification, and we need to
blockify KMeans in the future.
# *Standardization (virutal centering)*
** Existing impl of standardization in linear models does NOT center the
vectors by removing the means, for the purpose of keeping dataset _*sparsity*_.
However, this will cause feature values with small var be scaled to large
values, and underlying solver like LBFGS can not efficiently handle this case.
see SPARK-34448 for details.
** If internal vectors are centers (like other famous impl, i.e.
GLMNET/Scikit-Learn), the convergence ratio will be better. In the case in
SPARK-34448, the number of iteration to convergence will be reduced from 93 to
6. Moreover, the final solution is much more close to the one in GLMNET.
** Luckily, we find a new way to _*virtually*_ center the vectors without
densifying the dataset. Good results had been observed in LoR, we will take it
into account in other linear models.
# _*Initialization (To be discussed)*_
** Initializing model coef with a given model, should be beneficial to: 1,
convergence ratio (should reduce number of iterations); 2, model stability (may
obtain a new solution more close to the previous one);
# _*Early Stopping* *(To be discussed)*_
** we can compute the test error in the procedure (like tree models), and stop
the training procedure if test error begin to increase;
If you want to add other features in these models, please comment in the
ticket.
> Project Matrix: Linear Models revisit and refactor
> --------------------------------------------------
>
> Key: SPARK-30641
> URL: https://issues.apache.org/jira/browse/SPARK-30641
> Project: Spark
> Issue Type: New Feature
> Components: ML, PySpark
> Affects Versions: 3.1.0, 3.2.0
> Reporter: zhengruifeng
> Priority: Major
>
> We had been refactoring linear models for a long time, and there still are
> some works in the future. After some discuss among [~huaxingao] [~srowen]
> [~weichenxu123] [~mengxr] [~podongfeng] , we decide to gather related works
> under a sub-project Matrix, it includes:
> # *Blockification (vectorization of vectors)*
> ** vectors are stacked into matrices, so that high-level BLAS can be used
> for better performance. (about ~3x faster on sparse datasets, up to ~18x
> faster on dense datasets, see SPARK-31783 for details).
> ** Since 3.1.1, LoR/SVC/LiR/AFT supports blockification, and we need to
> blockify KMeans in the future.
> # *Standardization (virutal centering)*
> ** Existing impl of standardization in linear models does NOT center the
> vectors by removing the means, for the purpose of keeping dataset
> _*sparsity*_. However, this will cause feature values with small var be
> scaled to large values, and underlying solver like LBFGS can not efficiently
> handle this case. see SPARK-34448 for details.
> ** If internal vectors are centers (like other famous impl, i.e.
> GLMNET/Scikit-Learn), the convergence ratio will be better. In the case in
> SPARK-34448, the number of iteration to convergence will be reduced from 93
> to 6. Moreover, the final solution is much more close to the one in GLMNET.
> ** Luckily, we find a new way to _*virtually*_ center the vectors without
> densifying the dataset. Good results had been observed in LoR, we will take
> it into account in other linear models.
> # _*Initialization (To be discussed)*_
> ** Initializing model coef with a given model, should be beneficial to: 1,
> convergence ratio (should reduce number of iterations); 2, model stability
> (may obtain a new solution more close to the previous one);
> # _*Early Stopping* *(To be discussed)*_
> ** we can compute the test error in the procedure (like tree models), and
> stop the training procedure if test error begin to increase;
>
> If you want to add other features in these models, please comment in
> the ticket.
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