Github user akopich closed the pull request at:
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Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/1269#issuecomment-78190574
@akopich Since this is no longer an active PR, could you please close it?
It was very helpful to have this PR as a major basis for the initial LDA
PR. If you
Github user akopich commented on the pull request:
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@renchengchang
What do you mean by "topic vector"? A vector of p(t|d) \forall t? If so,
you can find these vectors in `RDD[DocumentParameters]` which is returned by
Github user renchengchang commented on the pull request:
https://github.com/apache/spark/pull/1269#issuecomment-78184357
Thanks.
I have a question:
if there is not document id ,how can I know the relation between topic
vector and raw text?
å件人: Avanesov Valeriy [
Github user akopich commented on the pull request:
https://github.com/apache/spark/pull/1269#issuecomment-78050367
@renchengchang
1. Hi.
2. Don't use code from this PR. Use either LDA (which is merged with mllib)
or https://github.com/akopich/dplsa which is a further developme
Github user renchengchang commented on the pull request:
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@akopich how to assign document id?
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how to assign document id?
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Github user jkbradley commented on the pull request:
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@IlyaKozlov Would you like your email included in the git commit for the
initial LDA PR? If so, please let me (or @mengxr ) know ASAP. Thanks!
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@akopich We'll make sure to do that. Thanks for letting us know.
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Github user akopich commented on the pull request:
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@jkbradley, @mengxr, please, include @IlyaKozlov as author too. He's helped
a lot with the implementation.
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Github user jkbradley commented on the pull request:
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@akopich I had hoped to get this into MLlib, but after more consideration,
I believe it is too complex. Currently, what would be ideal is a simple
implementation of L
Github user jkbradley commented on the pull request:
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@akopich The right way to do pseudo-randomness is to do:
```
val randomSeed = ... // if you want to pass in a seed
indx.mapPartitionsWithIndex { case (partiti
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Github user akopich commented on the pull request:
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By the way. May be it's off top, but this is related to initial
approximation generation.
Suppose, one has `indxs : RDD[Int]` and is about to create an RDD of ran
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I've fixed perplexity for robust plsa and updates perplexity value in the
comment above. Now they are almost the same.
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@akopich I've filed a JIRA to investigate that test failure, since it looks
like a flaky streaming test: https://issues.apache.org/jira/browse/SPARK-4905
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Github user akopich commented on the pull request:
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And tests fail again in obscure manner...
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Github user akopich commented on the pull request:
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I've performed sanity check on the dataset i've described above.
PLSA: tm project obtains perplexity of `2358` and this implementation ends
up with `2311`.
Github user jkbradley commented on the pull request:
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Yes, "accuracy" meant some kind of metric like perplexity. I agree
perplexity does not correlate exactly with human perception, but it's as good
as it gets (assuming n
Github user akopich commented on the pull request:
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How do you compare accuracy? Perplexity means nothing but perplexity --
topic models may be reliably compared only via application task (e.g.
classification, recommendati
Github user jkbradley commented on the pull request:
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I've been looking at the various topic modeling PRs (3 currently) to try to
get a sense of how they compare in terms of accuracy and speed. By "scaling,"
I really mean
Github user akopich closed the pull request at:
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GitHub user akopich reopened a pull request:
https://github.com/apache/spark/pull/1269
[SPARK-2199] [mllib] topic modeling
I have implemented Probabilistic Latent Semantic Analysis (PLSA) and Robust
PLSA with support of additive regularization (that actually means that I've
impleme
Github user akopich commented on the pull request:
https://github.com/apache/spark/pull/1269#issuecomment-67415235
What do you mean by scaling tests? Tests measuring the dependence of
computation time on numer of machines? Are there scaling tests for GraphX LDA
implementations? Or sho
Github user jkbradley commented on the pull request:
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@akopich I don't think you need to add the tm project to Spark, but a
comparison of perplexity would be a good sanity check. Other implementations
might be better for
Github user akopich commented on the pull request:
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``` - filter pushdown - boolean *** FAILED *** (249 milliseconds)```
I have no idea why this could happen. Should I rebase again?
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@jkbradley Thank you for explanation about setters.
tm implementation was tested (it was succesfully used in one of my project)
but it was tested with scala 2.11,
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Github user srowen commented on a diff in the pull request:
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--- Diff: mllib/pom.xml ---
@@ -112,6 +112,11 @@
test-jar
test
+
+colt
--- End diff --
Use comm
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--- Diff: mllib/pom.xml ---
@@ -112,6 +112,11 @@
test-jar
test
+
+colt
--- End diff --
In Diri
Github user jkbradley commented on a diff in the pull request:
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--- Diff: mllib/pom.xml ---
@@ -112,6 +112,11 @@
test-jar
test
+
+colt
--- End diff --
Where
Github user jkbradley commented on the pull request:
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@akopich Thanks for the updates! (Much easier to see the diff now)
The decision about setters vs. constructor arguments was from [this JIRA
(design doc linke
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@jkbradley
I moved Dirichlet to mllib/stats and added setters to `TokenEnumerator`.
BTW, why was it decided to use setter instead of constructors? We can
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@akopich Thanks for the updates. It looks like rebasing did not work
correctly (looking at the 10K+ lines in this PR!). It should be possible to
fix with rebase + co
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(5) Enumerator
BTW, names `TokenIndexer` and `TokenIndex` look confusive (though, these
classes rely on `breeze.util.Index`).
So I renamed it to `TokenE
Github user akopich commented on the pull request:
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Succeeded at the third attempt.
(5) Enumerator
@jkbradley, as you can see, I moved `Enumerator` to `mllib/features` folder
and renamed it to `TokenIndexer`.
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Github user akopich commented on the pull request:
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@chazchandler, thank you very much for your help. I shouldn't have rebase
on master. Rebase on 1.2 was successful.
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Github user akopich commented on the pull request:
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@karlhigley, yes I've heard something about abstract classes. Though, I see
no way to employ this concept here.
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Github user chazchandler commented on the pull request:
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@akopich , rebasing can be tricky, especially if you've been off on a
branch for a while. `git reflog` can be helpful in getting back to previous
states if you end
Github user karlhigley commented on the pull request:
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Re:
> (2) Regular and Robust in the same class
>It's possible to implement, but I don't want to turn class hierarchy
inside out. It just violates OOP principles
Github user akopich commented on the pull request:
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It seems like something went wrong. I've got multiple compilation errors
like
```
[error]
/home/valerij/contribute/spark/core/src/main/scala/org/apache/spar
Github user akopich commented on the pull request:
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@chazchandler, thank you very much for your quick reply! It did the trick.
Now I'm a bit confused about ml/ folder. What's it for?
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Github user chazchandler commented on the pull request:
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re: (5)
you can add a remote:
`git remote add upstream https://github.com/apache/spark.git`
fetch the latest state:
`git fetch upstream`
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@jkbradley
Tests fail again...
Stab in the dark: looks like something is changed in the testing
environment.
(2) Regular and Robust in the same cl
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@akopich
The test failure seems unrelated (from a Python SQL test). I'll re-run the
tests.
(2) Regular and Robust in the same class
Would i
Github user akopich commented on the pull request:
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@jkbradley, could you please have a look at logs -- a have no idea why
PySpark tests failed.
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Github user akopich commented on the pull request:
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(1) Users implementing their own regularizers
OK. I'd prefer to set all the methods private[mllib] for regularizers.
(2) Regular and Robust in the same c
Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/1269#issuecomment-65682412
@akopich Thanks for the responses! Follow-ups:
(1) Users implementing their own regularizers
You're right that this would be nice to have. If we incl
Github user akopich commented on the pull request:
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@jkbradley, thank you for you comments!
It seems like we should discuss API for this set of models first.
As far as I can understand, you are not about to pro
Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/1269#issuecomment-62070537
@akopich This would be a great set of models to add to MLlib! I wanted to
follow up about some comments I saw before, and see if we can get this PR
moving again.
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