I cannot argue about other use-cases, however MLLib doesn’t support working with text classification out of the box. There was basic support in MLI (thanks Sean for correcting me that it is MLI not MLLib), but I don’t know why it is not developed anymore.


For text classification in general, there are two major input formats: folders with text files and csv files. I can use SparkContext.textFile to load them into RDD. However in case of csv, I need to parse the loaded data, which is additional overhead. Next, I need to build dictionary of words and convert my documents into vector space using this dictionary. Currently I’m trying to implement these utilities and probably will share the code.


Best regards, Alexander


From: Debasish Das [mailto:debasish.das83@gmail.com]
Sent: Wednesday, June 25, 2014 8:08 PM
To: user@spark.apache.org
Subject: RE: Prediction using Classification with text attributes in Apache Spark MLLib


Libsvm dataset converters are data dependent since your input data can be in any serialization format and not necessarily csv...

We have flows that coverts hdfs data to libsvm/sparse vector rdd which is sent to mllib....

I am not sure if it will be easy to standardize libsvm converter on data that can be on hdfs,hbase, cassandra or solr....but of course libsvm, netflix format, csv are standard for algorithms and mllib supports all 3...

On Jun 25, 2014 6:00 AM, "Ulanov, Alexander" <alexander.ulanov@hp.com> wrote:

Hi Imk,

I am not aware of any classifier in MLLib that accept nominal type of data. They do accept RDD of LabeledPoints, which are label + vector of Double. So, you'll need to convert nominal to double.

Best regards, Alexander

-----Original Message-----
From: lmk [mailto:lakshmi.muralikrishnan@gmail.com]
Sent: Wednesday, June 25, 2014 1:27 PM
To: user@spark.incubator.apache.org
Subject: RE: Prediction using Classification with text attributes in Apache Spark MLLib

Hi Alexander,
Just one more question on a related note. Should I be following the same procedure even if my data is nominal (categorical), but having a lot of combinations? (In Weka I used to have it as nominal data)


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