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https://issues.apache.org/jira/browse/MADLIB-1223?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16427567#comment-16427567
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ASF GitHub Bot commented on MADLIB-1223:
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GitHub user njayaram2 opened a pull request:
https://github.com/apache/madlib/pull/255
MLP: Remove source table dependency for predicting regression models
JIRA: MADLIB-1223
We were getting the type of the dependent variable from the
input table to know if it was an array or not. With this commit, we now get
this information from the model summary table.
We also had to add a column in the minibatch preprocessor summary table
called `dependent_vartype` in order to get the dependent type in the mlp
code for minibatching.
Co-authored-by: Nikhil Kak <n...@pivotal.io>
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/madlib/madlib
bugfix/mlp/remove-training-data-dependency
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/madlib/pull/255.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #255
----
commit a17a54a4bf4ce0f9b256ccaf32a8a2f61b2c4f18
Author: Nandish Jayaram <njayaram@...>
Date: 2018-04-03T21:32:03Z
MLP: Remove source table dependency for predicting regression models
JIRA: MADLIB-1223
We were getting the type of the dependent variable from the
input table to know if it was an array or not. With this commit, we now get
this information from the model summary table.
We also had to add a column in the minibatch preprocessor summary table
called `dependent_vartype` in order to get the dependent type in the mlp
code for minibatching.
Co-authored-by: Nikhil Kak <n...@pivotal.io>
commit 16a06924dcee1ba273c5e4a49f81ee48cda778cd
Author: Nandish Jayaram <njayaram@...>
Date: 2018-04-04T21:42:38Z
MLP: Fix bug in array dep var for regression
MLP training for regression fails if the dependent var type is an array.
This is because the variable dependent_varname was not updated to
reflect the new column created in the standardized table. This commit
fixes that issue.
Co-authored-by: Nikhil Kak <n...@pivotal.io>
----
> MLP regression predict fails if input table does not exist
> ----------------------------------------------------------
>
> Key: MADLIB-1223
> URL: https://issues.apache.org/jira/browse/MADLIB-1223
> Project: Apache MADlib
> Issue Type: Bug
> Components: Module: Neural Networks
> Reporter: Nikhil
> Priority: Major
> Fix For: v1.14
>
>
> If a model is trained with mlp regression and then the input table is
> dropped, mlp predict fails for that model.
> Ideally the predict function should not depend on the existence of the
> training data.
> The predict code for regression only needs to know if the dependent varname
> type is an array or not. This information can be potentially stored in the
> model's summary table.
> We also need to make sure that the predict function is backwards compatible.
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