Github user Yunni commented on the issue:
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Thanks for the discussion, everyone! I will take a look at the JIRA.
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Github user jkbradley commented on the issue:
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Phew, done! https://issues.apache.org/jira/browse/SPARK-18392
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Github user jkbradley commented on the issue:
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Good points: Array of Vectors sounds good to me.
There has been a lot of discussion. I'm going to try to summarize things
in a follow-up JIRA, which I'll link here shortly. LSH turned
Github user sethah commented on the issue:
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If we were to use a matrix for the output, then when we do
`approxSimilarityJoin` we would want to explode the output column by matrix
rows, assuming the matrix structure was:
|
Github user MLnick commented on the issue:
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> This is very common in academic research and literature, but it may not
be in industry. I'm fine with not considering it for now.
Ok makes sense - for the `transform` case if users are
Github user jkbradley commented on the issue:
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@MLnick I agree with most of your comments. A few responses:
> In terms of transform - I disagree somewhat that the main use case is
"dimensionality reduction". Perhaps there are common
Github user MLnick commented on the issue:
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Oh and for naming - I'm ok with the current ones actually. However we could
think about changing to `ScalarRandomProjectionLSH` (a term mentioned in
@karlhigley's package), as later we will have
Github user MLnick commented on the issue:
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I tend to agree that the terminology used here is a little confusing, and
doesn't seem to match up with the "general" terminology (I use that term
loosely however).
Terminology
Github user jkbradley commented on the issue:
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@sethah
> What is the intended use of the output column generated by transform? As
an alternative set of features with decreased dimensionality?
I agree it's mainly for
Github user Yunni commented on the issue:
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@jkbradley I agree with most of your comments above. And I would like to
suggest the following:
- I would recommend a more intuitive name like `HyperplaneProjection`
instead of `PStableHashing`
Github user karlhigley commented on the issue:
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@jkbradley: "Multi-probe" seems like a standard term, and I think this is
the [original paper](http://www.cs.princeton.edu/cass/papers/mplsh_vldb07.pdf)
that coined it.
> Terminology:
Github user karlhigley commented on the issue:
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@sethah: Your description of the combination of AND and OR amplification
from the literature matches my understanding, and the combination of the two is
what I was aiming for in spark-neighbors.
Github user sethah commented on the issue:
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I was using L to refer to the number of compound hash functions, but you're
right that in my explanation L was the "OR" parameter and d was the "AND"
parameter.
Thinking more about it, this
Github user jkbradley commented on the issue:
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> The current implementation is equivalent to the L = 1 case always, and
outputDim corresponds to d.
That is true if you're talking about comparing hash values. But for approx
Github user sethah commented on the issue:
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So I'll try to summarize the AND/OR amplification and how I think it fits
into the current API right now. LSH relies on a single hashing function `h(x)`
which is (R, cR, p1, p2)-sensitive which just
Github user jkbradley commented on the issue:
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It sounds like discussions are converging, but I want to confirm a few
things + make a few additions.
### Amplification
Is this agreed?
* Approx neighbors and similarity are
Github user Yunni commented on the issue:
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@sethah Yes, that's why `outputDim` is introduced for users to trade off
between false negative rate and running time.
During my tests, LSH without amplification can be (0.5, 0.5)-sensitive or
Github user sethah commented on the issue:
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Ok, I'm looking more closely at this algorithm versus the literature. I
agree that there is a lot of inconsistent terminology which is probably leading
to some of the confusion here.
Most
Github user Yunni commented on the issue:
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@sethah I think you are right. OR-amplification is only applied inside NN
search and similarity join through `hashDistance` and `explode`. `transform`
itself does not apply amplifications.
Github user sethah commented on the issue:
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@karlhigley Thanks for your detailed response. From the amplification
section on
[Wikipedia](https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Amplification),
it is pretty clear to me that
Github user karlhigley commented on the issue:
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@sethah: I think you're right that there's a discrepancy here, and I'm
embarrassed that I didn't see it when I first reviewed the PR. On a reread of
the source and your comment above, it looks
Github user sethah commented on the issue:
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I apologize for coming late to this, but I am taking a look at some of the
documentation now. For `RandomProjection` class there are two links: one to
wikipedia entry on stable distributions and one
Github user Yunni commented on the issue:
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Awesome! Thanks Joseph and thanks everyone else for reviewing this! ð
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This LGTM now. Any other comments from other reviewers? I'll merge this,
but we can follow up as needed.
Thanks very much @Yunni for the PR and everyone else for helping to review!
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Only 2 comments remain, I believe:
* I'd still like to remove the default outputCol value
* Discussion about approxNearestNeighbors internals (in comments above)
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Thanks @jkbradley . I have made several changes to unit tests. Please let
me know if I missed any.
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Thanks @jkbradley. I have removed BitSampling and SignRandomProjection for
a follow-up PR.
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jenkins retest this please
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Github user Yunni commented on the issue:
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Have no idea to solve this MiMa test. Could anyone give some clue?
```
java.lang.ArrayIndexOutOfBoundsException: 1660
at
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/15148
**[Test build #66659 has
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/66659/consoleFull)**
for PR 15148 at commit
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