oh, I meant to say cdh5.1.3 used by Jakub's company is based on 2.3. You
can see it from the first part of the Cloudera's version number - 2.3.0-cdh
5.1.3.
Sincerely,
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, there are different strategies to do feature
scalling for linear regression
and logistic regression; as a result, we don't want to make it public
api naively without addressing
different use-case.
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DB Tsai
---
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,
DB Tsai
---
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On Mon, Sep 29, 2014 at 11:45 AM, Yanbo Liang yanboha...@gmail.com wrote:
Thank you for all your patient response.
I can conclude that if the data
You dont have to include breeze jar which is already in spark assembly jar.
For native one, its optional.
Sent from my Google Nexus 5
On Oct 3, 2014 8:04 PM, Priya Ch learnings.chitt...@gmail.com wrote:
yes. I have included breeze-0.9 in build.sbt file. I ll change this to
0.7. Apart from
.
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On Sat, Sep 13, 2014 at 2:12 AM, Yanbo Liang yanboha...@gmail.com wrote:
Hi All,
I found that LogisticRegressionWithLBFGS interface
One related question, is mllib jar independent from hadoop version (doesnt
use hadoop api directly)? Can I use mllib jar compile for one version of
hadoop and use it in another version of hadoop?
Sent from my Google Nexus 5
On Aug 6, 2014 8:29 AM, Debasish Das debasish.da...@gmail.com wrote:
Hi
After sbt gen-idea , you can open the intellji project directly without
going through pom.xml
If u want to compile inside intellji, you have to remove one of the messo
jar. This is an open issue, and u can find the detail in JIRA.
Sent from my Google Nexus 5
On Aug 6, 2014 8:54 PM, Ron Gonzalez
I'm working on it with weighted regularization. The problem is that
OWLQN doesn't work nicely with Updater now since all the L1 logic
should be in OWLQN instead of L1Updater.
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+1
Tested with my Ubuntu Linux.
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On Thu, Jul 17, 2014 at 6:36 PM, Matei Zaharia matei.zaha...@gmail.com wrote:
+1
Tested on Mac, verified
)
at java.lang.reflect.Method.invoke(Method.java:597)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:134)
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+1
On Jul 5, 2014 1:39 PM, Michael Armbrust mich...@databricks.com wrote:
+1
I tested sql/hive functionality.
On Sat, Jul 5, 2014 at 9:30 AM, Mark Hamstra m...@clearstorydata.com
wrote:
+1
On Fri, Jul 4, 2014 at 12:40 PM, Patrick Wendell pwend...@gmail.com
wrote:
I'll start
Hi Gang,
This is a bug, and I'm the one who did it :) Just add the comment to your PR.
Thanks.
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On Tue, Jun 17, 2014 at 7:13 PM, Gang Bai
Sometimes for this case, I will just standardize without centerization. I
still get good result.
Sent from my Google Nexus 5
On May 28, 2014 7:03 PM, Xiangrui Meng men...@gmail.com wrote:
RowMatrix has a method to compute column summary statistics. There is
a trade-off here because centering
the
protected method `addURL` which will not work and throw exception if the
code is wrapped in security manager.
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On Wed, May 21, 2014 at 1:13 PM, Sandy
the primary jar is not in the system
loader but custom one, so when we reference those jars added dynamically,
we can find it without reflection.
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DB Tsai
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environment.
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On Wed, May 21, 2014 at 2:57 PM, Koert Kuipers ko...@tresata.com wrote:
db tsai, i do not think userClassPathFirst is working
: Method =
classOf[URLClassLoader].getDeclaredMethod(addURL, classOf[URL])
method.setAccessible(true)
method.invoke(loader, url)
}
catch {
case t: Throwable = {
throw new IOException(Error, could not add URL to system
classloader)
}
}
}
Sincerely,
DB
The reflection actually works. But you need to get the loader by `val
loader = Thread.currentThread.getContextClassLoader` which is set by Spark
executor. Our team verified this, and uses it as workaround.
Sincerely,
DB Tsai
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The jars are included in my driver, and I can successfully use them in the
driver. I'm working on a patch, and it's almost working. Will submit a PR
soon.
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---
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the customClassloader to create a wrapped class, and in this wrapped class,
the classloader is inherited from the customClassloader so that users don't
need to do reflection in the wrapped class. I'm working on this now.
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DB Tsai
---
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will not be seen.
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_ = math.log(numerators(math.round(y - 1).toInt) / denominator)
}
(loglike, predicted)
}
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DB Tsai
---
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On Tue, May 13, 2014 at 4:08 AM, Debasish Das
+1 Would be nice that we can use different type in Vector.
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On Mon, May 5, 2014 at 2:41 PM, Debasish Das debasish.da...@gmail.comwrote:
Hi
Breeze could take any type (Int, Long, Double, and Float) in the matrix
template.
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On Mon, May 5, 2014 at 2:56 PM, Debasish Das debasish.da
You could easily achieve this by mapPartition. However, it seems that it
can not be done by using aggregate type of operation. I can see that it's a
general useful operation. For now, you could use mapPartition.
Sincerely,
DB Tsai
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straightforward, we wonder if this can
be reviewed and have this in 1.0.
Thanks.
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in Spark before we've deeper
understanding of how stochastic LBFGS works.
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On Tue, Apr 29, 2014 at 9:50 PM, David Hall d...@cs.berkeley.edu wrote
Also, how many failure of rejection will terminate the optimization
process? How is it related to numberOfImprovementFailures?
Thanks.
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DB Tsai
---
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, miniBatchFraction, lbfgs,
miniBatchSize)
val states = lbfgs.iterations(new CachedDiffFunction(costFun),
initialWeights.toBreeze.toDenseVector)
Thanks.
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---
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.
The difference you saw is actually from dense feature or sparse feature
vector. For LBFGS and GD dense feature, you can see the first iteration
takes the same time. It's true for GD.
I'm going to run rcv1.binary which only has 0.15% non-zero elements to
verify the hypothesis.
Sincerely,
DB Tsai
, Vectors.fromBreeze(gradientSum / miniBatchSize), stepSize,
i, regParam)
weights = update._1
regVal = update._2
timeStamp.append(System.nanoTime() - startTime)
}
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rcv1.binary is too sparse (0.15% non-zero elements), so dense format will
not run due to out of memory. But sparse format runs really well.
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/dbtsai/.rbenv/versions/2.1.1/lib/ruby/gems/2.1.0/gems/commander-4.1.6/lib/commander/import.rb:10:in
`block in top (required)'
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---
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On Tue, Apr 22
But what doesSKIP_SCALADOC=1 mean? export SKIP_SCALADOC=1?
Thanks.
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DB Tsai
---
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Matei, thanks. It works with kramdown.
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On Tue, Apr 22, 2014 at 11:38 PM, Matei Zaharia matei.zaha...@gmail.comwrote:
Try doing “gem install
result.
Thanks.
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DB Tsai
---
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a 1% (or so) sparse dataset to experiment with?
On Apr 23, 2014, at 9:21 PM, DB Tsai dbt...@stanford.edu wrote:
Hi all,
I'm benchmarking Logistic Regression in MLlib using the newly added
optimizer LBFGS and GD. I'm using the same dataset and the same methodology
in this paper, http
The figure showing the Log-Likelihood vs Time can be found here.
https://github.com/dbtsai/spark-lbfgs-benchmark/raw/fd703303fb1c16ef5714901739154728550becf4/result/a9a11M.pdf
Let me know if you can not open it.
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---
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ps, it doesn't make sense to have weight and gradient sparse unless
with strong L1 penalty.
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On Wed, Apr 23, 2014 at 10:17 PM, DB Tsai dbt
Not yet since it's running in the cluster. Will run locally with
profiler. Thanks for help.
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---
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On Wed, Apr 23, 2014 at 10:22 PM, David Hall d
The figure showing the Log-Likelihood vs Time can be found here.
https://github.com/dbtsai/spark-lbfgs-benchmark/raw/fd703303fb1c16ef5714901739154728550becf4/result/a9a11M.pdf
Let me know if you can not open it. Thanks.
Sincerely,
DB Tsai
stepSize: Double,
var numIterations: Int,
var regParam: Double,
var miniBatchFraction: Double
Xiangrui, what do you think?
For now, you can use my L-BFGS solver by copying and pasting the
LogisticRegressionWithSGD code, and changing the optimizer to L-BFGS.
Sincerely,
DB Tsai
computation per RDD is done on each of the workers...
This miniBatchFraction is also a heuristic which I don't think makes sense
for LogisticRegressionWithBFGS...does it ?
On Tue, Apr 8, 2014 at 3:44 PM, DB Tsai dbt...@stanford.edu wrote:
Hi Debasish,
The L-BFGS solver will be in the master like
I don't experiment it. That's the use-case in theory I could think of. ^^
However, from what I saw, BFGS converges really fast so that I only
need 20~30 iterations in general.
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---
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Hi guys,
The latest PR uses Breeze's L-BFGS implement which is introduced by
Xiangrui's sparse input format work in SPARK-1212.
https://github.com/apache/spark/pull/353
Now, it works with the new sparse framework!
Any feedback would be greatly appreciated.
Thanks.
Sincerely,
DB Tsai
Hi Xiangrui,
I think it doesn't matter whether we use Fortran/Breeze/RISO for
optimizers since optimization only takes 1% of time. Most of the
time is in gradientSum and lossSum parallel computation.
Sincerely,
DB Tsai
Machine Learning Engineer
Alpine Data Labs
Iteration 29: loss 0.30788249908237314, diff 0.23885980452569502
Sincerely,
DB Tsai
Machine Learning Engineer
Alpine Data Labs
--
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On Wed, Mar 5, 2014 at 2:00 PM, David Hall d...@cs.berkeley.edu wrote:
On Wed, Mar 5, 2014 at 1:57 PM
for you to
investigate the issue? Or do I need to make it as a standalone test?
Will send you the test later today.
Thanks.
Sincerely,
DB Tsai
Machine Learning Engineer
Alpine Data Labs
--
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, let's have a design
discussion around this. It may be more effective since we can design a
architecture that have to work for both cases in the codebase, and will
be easier to think about the edge case for it.
Thanks.
Sincerely,
DB Tsai
Machine Learning Engineer
Alpine Data Labs
this?
Thanks.
Sincerely,
DB Tsai
Machine Learning Engineer
Alpine Data Labs
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On Sun, Mar 2, 2014 at 10:23 AM, Debasish Das debasish.da...@gmail.com wrote:
Hi DB,
1. Could you point to the BFGS repositories used to publish artifacts
.
Is this getting merged to the master or there will be revisions on it ?
https://github.com/apache/spark/pull/53
Thanks.
Deb
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Machine Learning Engineer
Alpine Data Labs
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to fix the L-BFGS in breeze, and we can get OWL-QN and
L-BFGS-B.
What do you think?
Thanks.
Sincerely,
DB Tsai
Machine Learning Engineer
Alpine Data Labs
--
Web: http://alpinenow.com/
On Mon, Mar 3, 2014 at 3:52 PM, DB Tsai dbt...@alpinenow.com wrote:
Hi Deb
.
Thanks.
Sincerely,
DB Tsai
Machine Learning Engineer
Alpine Data Labs
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