[jira] [Resolved] (MADLIB-1375) ParamSearch: Add utility function for generating model selection table

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

     [ https://issues.apache.org/jira/browse/MADLIB-1375?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Frank McQuillan resolved MADLIB-1375.
-------------------------------------
    Resolution: Fixed

see PR for tests

> ParamSearch: Add utility function for generating model selection table
> ----------------------------------------------------------------------
>
>                 Key: MADLIB-1375
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1375
>             Project: Apache MADlib
>          Issue Type: New Feature
>          Components: Deep Learning
>            Reporter: Yuhao Zhang
>            Priority: Major
>             Fix For: v1.17
>
>
> As part of this JIRA, we create a utility function to generate the model selection table.
> The user, inputs the model_arch_table, sets of model_arch_ids, compile_params, and fit_params. The utility function creates a table with a combination of these three inputs, each with a distinct `MST_KEY`.
> *API* 
> {code}
> load_model_selection_table(
> model_arch_table, 
> model_selection_table, -- output table name
> ARRAY[] model_arch_id, -- Assuming there is only 1 model_arch_table and all model_arch_id are from that table
> ARRAY[] compile_params,
> ARRAY[] fit_params
> )
> {code}
> *Output Model Selection Table
> *
> |MST_Key (sequence)| model_arch | model_arch_id |compile_params |fit_params| 
> |----|----|----|----|----|
> |1|model_arch_library|1|C1|F1|
> |2|model_arch_library|2|C2|F2|
> |3|model_arch_library|3|C3|F3|
> *Acceptance*
> 1. Validate model_arch_table exists and model_selection_table does not exist
> 2. The utility function should error if the user inputs an invalid model_arch_id
> 3. The utility function should error if user inputs invalid compile_param/fit_param
> 4. The utility function should deduplicate model_arch_id, compile_param, fit_param if passed in
> *Notes/open questions
> 1. We may want to offer other options later for automatically generating the `model_selection_table` (e.g., log, random)
> 2. How to reconcile with Keras and other libraries that have param selection capability? We are working at a higher level here, but can we take advantage nonetheless?



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