[jira] [Assigned] (SPARK-30641) Project Matrix: Linear Models revisit and refactor

["zhengruifeng (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-30641?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

zhengruifeng reassigned SPARK-30641:
------------------------------------

    Assignee:     (was: zhengruifeng)

> 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] , 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.



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