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https://issues.apache.org/jira/browse/MAHOUT-1541?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14037730#comment-14037730
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ASF GitHub Bot commented on MAHOUT-1541:
----------------------------------------
Github user dlyubimov commented on the pull request:
https://github.com/apache/mahout/pull/22#issuecomment-46606053
General note: a lot of style problems.
Code lines not to exceed 120 charactes (i think i saw some suspiciously
long).
Definitely lack of comments.
FYI Spark comment style is the following: every comment starts with a
captial letter and is formatted to cut off at 100th character. And they are
very draconian about it. And that's what i followed here as well.
Well the 100th character is questionable since it cannot be auto formatted
in IDEA, but I do believe comments do need some justification applied on the
right.
for closure stacks, i'd suggest the following comment /etc style
val b = A
// I want to map
.map { tuple =>
....
}
// I want to filter
.filter { tuple =>
....
}
So it would be useful to state in plain words what closure is to
accomplish, since they are functional units, i.e. function-grade citizens, and
as such, imo deserve some explanation.
> Create CLI Driver for Spark Cooccurrence Analysis
> -------------------------------------------------
>
> Key: MAHOUT-1541
> URL: https://issues.apache.org/jira/browse/MAHOUT-1541
> Project: Mahout
> Issue Type: Bug
> Components: CLI
> Reporter: Pat Ferrel
> Assignee: Pat Ferrel
>
> Create a CLI driver to import data in a flexible manner, create an
> IndexedDataset with BiMap ID translation dictionaries, call the Spark
> CooccurrenceAnalysis with the appropriate params, then write output with
> external IDs optionally reattached.
> Ultimately it should be able to read input as the legacy mr does but will
> support reading externally defined IDs and flexible formats. Output will be
> of the legacy format or text files of the user's specification with
> reattached Item IDs.
> Support for legacy formats is a question, users can always use the legacy
> code if they want this. Internal to the IndexedDataset is a Spark DRM so
> pipelining can be accomplished without any writing to an actual file so the
> legacy sequence file output may not be needed.
> Opinions?
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