hat you can add code in
> it.
> >
> >
> > Thanks,
> >
> > Ashutosh
> >
> >
> > From: slcclimber [via Apache Spark Developers List] <
> > [hidden email] <http://user/SendEmail.jtp?type=node&node=9467&i=1>>
> > Sent: Thurs
add code in it.
>
>
> Thanks,
>
> Ashutosh
>
>
> From: slcclimber [via Apache Spark Developers List] <
> ml-node+s1001551n9441...@n3.nabble.com>
> Sent: Thursday, November 20, 2014 7:49 AM
> To: Ashutosh Trivedi (MT2013030)
>
Algorithm for Outlier Detection
You could also use rdd.zipWithIndex() to create indexes.
Anant
If you reply to this email, your message will be added to the discussion below:
http://apache-spark-developers-list.1001551.n3.nabble.com/MLlib-Contributing-Algorithm-for
You could also use rdd.zipWithIndex() to create indexes.
Anant
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View this message in context:
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Sent from the Apache Spark Developers List mailing list archive at Nabble
List]
Sent: Monday, November 17, 2014 10:45 AM
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
Ashutosh,
The counter will certainly be an parellization issue when multiple nodes are
used specially over massive datasets.
A better approach would
Ashutosh,
The counter will certainly be an parellization issue when multiple nodes are
used specially over massive datasets.
A better approach would be to use some thing along these lines:
val index = sc.parallelize(Range.Long(0, rdd.count, 1),
rdd.partitions.size)
val rddWithIndex = rdd.z
}
From: Meethu Mathew-2 [via Apache Spark Developers List]
Sent: Friday, November 14, 2014 11:42 AM
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
Hi,
I have a doubt regarding the input to your algorithm.
PM
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
Mayur,
Libsvm format sounds good to me. I could work on writing the tests if that
helps you?
Anant
On Nov 11, 2014 11:06 AM, "Ashutosh [via Apache Spark Developers List]" <[hid
opers List]
Sent: Tuesday, November 11, 2014 11:46 PM
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
Mayur,
Libsvm format sounds good to me. I could work on writing the tests if that
helps you?
Anant
On Nov 11, 2014 11:06 AM, "Ashut
M
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
Mayur,
Libsvm format sounds good to me. I could work on writing the tests if that
helps you?
Anant
On Nov 11, 2014 11:06 AM, "Ashutosh [via Apache Spark Developers List]"
<[hidden e
=node&node=9286&i=0>>
Sent: Saturday, November 8, 2014 12:52 PM
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
>
> We should take a vector instead giving the user flexibility to decide
> data source/ type
What do you mean
rom:* Mayur Rustagi [via Apache Spark Developers List] email] <http://user/SendEmail.jtp?type=node&node=9286&i=0>>
> *Sent:* Saturday, November 8, 2014 12:52 PM
> *To:* Ashutosh Trivedi (MT2013030)
> *Subject:* Re: [MLlib] Contributing Algorithm for Outlier Detectio
Sent: Saturday, November 8, 2014 12:52 PM
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
>
> We should take a vector instead giving the user flexibility to decide
> data source/ type
What do you mean by vector datatype exactly?
Mayur
p;i=0>> wrote:
> >
> >> Okay. I'll try it and post it soon with test case. After that I think
> >> we can go ahead with the PR.
> >> --
> >> *From:* slcclimber [via Apache Spark Developers List] >> email] &
> *From:* slcclimber [via Apache Spark Developers List] email] <http://user/SendEmail.jtp?type=node&node=9083&i=0>>
> *Sent:* Friday, October 31, 2014 10:09 AM
> *To:* Ashutosh Trivedi (MT2013030)
> *Subject:* Re: [MLlib] Contributing Algorithm for Outlier Detection
>
>
>
Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
You should create a jira ticket to go with it as well.
Thanks
On Oct 30, 2014 10:38 PM, "Ashutosh [via Apache Spark Developers List]"
<[hidden email]> wrote:
?Okay. I'll try it a
Ashutosh,
A vector would be a good idea vectors are used very frequently.
Test data is usually stored in the spark/data/mllib folder
On Oct 30, 2014 10:31 PM, "Ashutosh [via Apache Spark Developers List]" <
ml-node+s1001551n9034...@n3.nabble.com> wrote:
> Hi Anant,
> sorry for my late reply. Than
go ahead with the PR.
From: slcclimber [via Apache Spark Developers List] http://user/SendEmail.jtp?type=node&node=9036&i=0>>
Sent: Friday, October 31, 2014 10:03 AM
To: Ashutosh Trivedi (MT2013030)
Subject: Re: [MLlib] Contributing Algorithm for Outlier Detection
Ashu
uting Algorithm for Outlier Detection
Ashutosh,
A vector would be a good idea vectors are used very frequently.
Test data is usually stored in the spark/data/mllib folder
On Oct 30, 2014 10:31 PM, "Ashutosh [via Apache Spark Developers List]"
<[hidden email]> wrote:
Hi Anant,
so
Hi Anant,
sorry for my late reply. Thank you for taking time and reviewing it.
I have few comments on first issue.
You are correct on the string (csv) part. But we can not take input of type
you mentioned. We calculate frequency in our function. Otherwise user has to
do all this computation. I r
Ashu,
There is one main issue and a few stylistic/ grammatical things I noticed.
1> You take and rdd or type String which you expect to be comma separated.
This limits usability since the user will have to convert their RDD to that
format only for you to split it on string.
It would make more sens
Hi Anant,
Thank you for reviewing and helping us out. Please find the following link
where you can see the initial code.
https://github.com/codeAshu/Outlier-Detection-with-AVF-Spark/blob/master/OutlierWithAVFModel.scala
The input file for the code should be in csv format. We have provided a
data
Hi,
We are ready with the initial code. Where can I submit it for review ? I
want to get it reviewed before testing
it at scale.
Also, I see that most of the algorithms take data as RDD[LabeledPoint] . How
should we take input for this since there are no labels.
Can any body help me out with thes
Hi Xiangrui,
Thanks for the reply. AVF is not so difficult to implement in parallel. It
just calculate the frequency of each attribute and calculate the overall
'score' of the datapoint. Low score points are considered outlier. One
advantage of it is that it does not calculate distance, so in that
Hi Ashutosh,
The process you described is correct, with details documented in
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
. There is no outlier detection algorithm in MLlib. Before you start
coding, please open an JIRA and let's discuss which algorithms are
appropriate
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