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https://issues.apache.org/jira/browse/MAHOUT-1507?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13963597#comment-13963597
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Dmitriy Lyubimov commented on MAHOUT-1507:
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[~pferrel] our DRM persistence standard is hdfs is sequence file. As long as it
stays in a Mahout pipeline, or a custom SparkBindings script that passes this
around, this should be quite fine. It would seem to me now you are talking
about import and dumping, sort of what seqDirectory and clusterDump do. But as
long as we stay within pipeline, I think we'll be fine with this. For internal
needs we are kind of like Drill, quasi full table scans for anything. "Quasi"
is because vertical matrix blocks are already indexed by row numbers and/or
labels (iirc), although i don't think i do such attribution automatically
during blockification -- instead, key vector is kept explicitly in (keys,
block) pair.
I would venture to suggest that because of this automatic row key propagation,
cases of explicit id re-integration will be fairly rare (at least i can't think
of any). In case such re-integration is indeed needed to be carried out
explicitly, at this very moment one would likely have to resort to low-level
non-math distributed primitives such as join() anyway, so this would at this
point break "write once, run anywhere" notion (which is fine i guess until we
figure out logical api to deal with such concerns, but I think still not quite
desirable way of doing things).
Columns present a more compelling case, of course. Indeed, since we can't put
labels on columns, something like RSJ or any other sort of user/item
factorization can't even use input that uses item ids on column vectors.
Bummer. So as it stands, user would have to build external re-attribution logic
i guess.
But my primary concern would be grow out of POC first by adding RLike data
frames apis and grow number of available ported "black boxes" and do something
like unified column labeling dictionary and postprocessing attribution logic
later. My gut feeling is that as we start incremental progress on data frames,
we'd have a bit more specific ideas.
> Support input and output using user defined ID wherever possible
> ----------------------------------------------------------------
>
> Key: MAHOUT-1507
> URL: https://issues.apache.org/jira/browse/MAHOUT-1507
> Project: Mahout
> Issue Type: Bug
> Components: Math
> Affects Versions: 0.9
> Environment: Spark Scala, Mahout v2
> Reporter: Pat Ferrel
> Labels: spark
> Fix For: 1.0
>
>
> All users of Mahout have data which is addressed by keys or IDs of their own
> devise. In order to use much of Mahout they must translate these IDs into
> Mahout IDs, then run their jobs and translate back again when retrieving the
> output. If the ID space is very large this is a difficult problem for users
> to solve at scale.
> For many Mahout operations this would not be necessary if these external keys
> could be maintained for vectors and dimensions, or for rows and columns of a
> DRM.
> The reason I bring this up now is that much groundwork is being laid for
> Mahout's future on Spark so getting this notion in early could be
> fundamentally important and used to build on.
> If external IDs for rows and columns were maintained then RSJ, DRM Transpose
> (and other DRM ops), vector extraction, clustering, and recommenders would
> need no ID translation steps, a big user win.
> A partial solution might be to support external row IDs alone somewhat like
> the NamedVector and PropertyVector in the Mahout hadoop code.
> On Apr 3, 2014, at 11:00 AM, Pat Ferrel <[email protected]> wrote:
> Perhaps this is best phrased as a feature request.
> On Apr 2, 2014, at 2:55 PM, Dmitriy Lyubimov <[email protected]> wrote:
> PS.
> sequence file keys have also special meaning if they are Ints. .E.g. A'
> physical operator requires keys to be ints, in which case it interprets
> them as row indexes that become column indexes. This of course isn't always
> the case, e.g. (Aexpr).t %*% Aexpr doesn't require int indices because in
> reality optimizer will never choose actual transposition as a physical step
> in such pipeline. This interpretation is consistent with interpretation of
> long-existing Hadoop-side DistributedRowMatrix#transpose.
> On Wed, Apr 2, 2014 at 2:45 PM, Dmitriy Lyubimov <[email protected]> wrote:
> On Wed, Apr 2, 2014 at 1:56 PM, Pat Ferrel <[email protected]> wrote:
> On Apr 2, 2014, at 1:39 PM, Dmitriy Lyubimov <[email protected]> wrote:
> I think this duality, names and keys, is not very healthy really, and
> just
> creates addtutiinal hassle. Spark drm takes care of keys automatically
> thoughout, but propagating names from name vectors is solely algorithm
> concern as it stands.
> Not sure what you mean.
> Not what you think, it looks like.
> I mean that Mahout DRM structure is a bag of (key -> Vector) pairs. When
> persisted, key goes to the key of a sequence file. In particular, it means
> that there is a case of Bag[ key -> NamedVector]. Which means, external
> anchor could be saved to either key or name of a row. In practice it causes
> compatibility mess, e.g. we saw those numerous cases where e.g. seq2sparse
> saves external keys (file paths) into key, whereas e.g. clustering
> algorithms are not seeing them because they expect them to be the name part
> of the vector. I am just saying we have two ways to name the rows, and it
> is generally not a healthy choice for the aforementioned reason.
> In my experience Names and Properties are primarily used to store
> external keys, which are quite healthy.
> Users never have data with Mahout keys, they must constantly go back and
> forth. This is exactly what the R data frame does, no? I'm not so concerned
> with being able to address an element by the external key
> drmB["pat"]["iPad'] like a HashMap. But it would sure be nice to have the
> external ids follow the data through any calculation that makes sense.
> I am with you on this.
> This would mean clustering, recommendations, transpose, RSJ would require
> no id transforming steps. This would make dealing with Mahout much easier.
> Data frames is a little bit a different thing, right now we work just with
> matrices. Although, yes, our in-core matrices support row and column names
> (just like in R) and distributed matrices support row keys only. what i
> mean is that algebraic expression e.g.
> Aexpr %*% Bexpr will automatically propagate _keys_ from Aexpr as implied
> above, but not necessarily named vectors, because internally algorithms
> blockify things into matrix blocks, and i am far from sure that Mahout
> in-core stuff works correctly with named vectors as part of a matrix block
> in all situations. I may be wrong. I always relied on sequence file keys to
> identify data points.
> Note that sequence file keys are bigger than just a name, it is anything
> Writable. I.e. you could save a data structure there, as long as you have a
> Writable for it.
> On Apr 2, 2014 1:08 PM, "Pat Ferrel" <[email protected]> wrote:
> Are the Spark efforts supporting all Mahout Vector types? Named,
> Property
> Vectors? It occurred to me that data frames in R is a related but more
> general solution. If all rows and columns of a DRM and their
> coresponding
> Vectors (row or column vectors) were to support arbitrary properties
> attached to them in such a way that they are preserved during
> transpose,
> Vector extraction, and any other operations that make sense there
> would be
> a huge benefit for users.
> One of the constant problems with input to Mahout is translation of
> IDs.
> External to Mahout going in, Mahout to external coming out. Most of
> this
> would be unneeded if Mahout supported data frames, some would be
> avoided by
> supporting named or property vectors universally.
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