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https://issues.apache.org/jira/browse/MADLIB-1200?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16339681#comment-16339681
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Nikhil edited comment on MADLIB-1200 at 1/25/18 7:26 PM:
---------------------------------------------------------
Suggenstions:-
1. Naming the function in a way that is reflective of it's purpose
So something like
{code}
minibatch_preprocessing
{code}
2. Change n_elements to batch_size to be consistent with the function name
was (Author: nikhilkak):
I would suggest naming the function in a way that is reflective of it's purpose
So something like
{code}
minibatch_preprocessing
{code}
> 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 a 1-time
> operation)
> Interface
> This function is kind of the inverse of:
> array_unnest_2d_to_1d()
>
> [http://madlib.apache.org/docs/latest/array__ops_8sql__in.html#af057b589f2a2cb1095caa99feaeb3d70]
> but the difference is we want to persist an output table for the packed 2d
> array.
> Suggested interface:
> matrix_nest_1d_to_2d (
> source_table,
> output_table,
> independent_varname,
> dependent_varname,
> n_elements — Number of elements to pack,
> encode -- one hot encoding if set to true
> );
> where dependent_varname is a column of 1d arrays
> Or call it
> array_nest_1d_to_2d ()
> ?
> The main purpose of the function is to prepare the training data for
> minibatching algorithms. This will be achieved in 2 stages
> 1. Based on the batch size, group all the dependent and independent variables
> in a single tuple representative of the batch.
> 2. If the encode parameter is True, perform one hot encoding for the
> dependent variable. Users will need to set encode to true for multi class
> SVM/MLP and false for single class SVM.
> 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
> 1) Convert from standard to special format for mini-batching
> 2) Some scale testing OK (does not need to be comprehensive)
> 3) Document as a helper function user docs
> 4) IC
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