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https://issues.apache.org/jira/browse/MADLIB-1200?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16364602#comment-16364602
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Nandish Jayaram commented on MADLIB-1200:
-----------------------------------------
A suggestion regarding the buffer_size:
We could consider making it a factor of the page size rather than the 1GB
limit. By page size I mean the default page size that the underlying database
uses while fetching data from the disk (4 MB?). This might result in packing
fewer rows together, but that may be a good thing from user's perspective. If
we pack around 10000 rows into one, running a select query on that table, even
with limit 1 takes a long time to load. With 1GB based factoring, we might be
able to pack hundreds of thousands of rows, which makes it harder for the user
to view it I guess.
> Pre-processing helper function for mini-batching
> -------------------------------------------------
>
> Key: MADLIB-1200
> URL: https://issues.apache.org/jira/browse/MADLIB-1200
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Utilities
> Reporter: Frank McQuillan
> Priority: Major
> Fix For: v1.14
>
>
> Related to
> https://issues.apache.org/jira/browse/MADLIB-1037
> https://issues.apache.org/jira/browse/MADLIB-1048
> Story
> {{As a}}
> data scientist
> {{I want to}}
> pre-process input files for use with mini-batching
> {{so that}}
> the optimization part of MLP, SVM, etc. runs faster when I do multiple runs,
> perhaps because I am tuning parameters (i.e., pre-processing is an occasional
> operation that I don't want to re-do every time that I train a model)
> Interface
> {code:java}
> minibatch_preprocessor (
> source_table, -- Name of the table containing the input
> data.
> output_table, -- Name of the table suitable for
> mini-batching.
> dependent_varname, -- Name of the dependent variable column.
> independent_varname, -- Expression list to evaluate for the independent
> variables.
> buffer_size, -- buffer_size? Default should be to
> pack as much as possible in the 1GB limit imposed by postgres/gpdb.
> )
> {code}
>
> The main purpose of the function is to prepare the training data for
> minibatching algorithms. This will be achieved in 2 stages
> # Based on the batch size, group all the dependent and independent variables
> in a single tuple representative of the batch.
> # If the independent variables are boolean or text, perform one hot
> encoding. N/A for integer and floats. Note that if the integer vars are
> actually categorical, they must be case to ::TEXT so that they get encoded.
> Notes
> 1) Random shuffle needed for mini-batch.
> 2) Naive approach may be OK to start, not worth big investment to make run
> 10% or 20% faster.
> Acceptance
> Summary
> 1) Convert from standard to special format for mini-batching
> 2) Standardize by default for now but the user cannot opt out of it. We may
> decide to add a flag later.
> 3) Some scale testing OK (does not need to be comprehensive)
> 4) Document as a helper function user docs
> 5) Always ignore nulls in dependent variable
> 6) IC
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