I believe it is supposed to, at least at the high level. We don't have any 1-1
tests, so YMMV.
On Dec 17, 2011, at 8:26 PM, Lance Norskog wrote:
> Does the new approach do the same thing as the old approach?
>
> On Thu, Dec 15, 2011 at 1:56 AM, Daniele Volpi <danielevo...@gmail.com> wrote:
>> Yes Grant that was the point of my first question..
>> Now I'll take a look at the vector implementation.
>> Thanks again
>> Daniele
>>
>> On 14 December 2011 23:44, Grant Ingersoll <gsing...@apache.org> wrote:
>>> While Ted answered the Dissector question, your original issue, I believe,
>>> is that Mahout currently has two different NB implementations.
>>> trainclassifier/testclassifier use the old, word based package which
>>> requires Text as input. The new package, which TrainNaiveBayesJob uses,
>>> requires VectorWritables. For the latter case, you don't use the
>>> BayesFileFormatter at all. See the asf-email-examples for how to use the
>>> Vector based approach. I realize this is confusing, but we haven't yet
>>> made the transition fully to the new vector based approach.
>>>
>>> -Grant
>>>
>>> On Dec 14, 2011, at 3:01 AM, Daniele Volpi wrote:
>>>
>>>> The version is 0.6-SNAPSHOT
>>>> From terminal both commands trainclassifier and testclassifier work.
>>>> Actually my real purpose is to use the TrainNaiveBayesJob in order to
>>>> obtain a StandardNaiveBayesClassifier that i can use with the
>>>> ModelDissector class similiar to chapter 15 in Mahout In Action, maybe the
>>>> procedure is completely wrong.
>>>> Thank you
>>>>
>>>>
>>>> On 14 December 2011 01:24, Ted Dunning <ted.dunn...@gmail.com> wrote:
>>>>
>>>>> Which version of Mahout?
>>>>>
>>>>> And what happens when you train the classifier from the command line?
>>>>>
>>>>> On Tue, Dec 13, 2011 at 2:27 PM, Daniele Volpi <danielevo...@gmail.com
>>>>>> wrote:
>>>>>
>>>>>> First of all i've converted the train files in the format:
>>>>>> target[\t]terms
>>>>>> through the BayesFileFormatter class.
>>>>>> Then i've converted these files (one per category) in SequenceFile using
>>>>>> the seqdirectory program.
>>>>>> After that I ran this code:
>>>>>>
>>>>>> TrainNaiveBayesJob trainer = new TrainNaiveBayesJob();
>>>>>> trainer.setConf(new Configuration());
>>>>>>
>>>>>> String[] params = {"-i" + inputPath, "-o" + outputPath, "-ow", "-el"};
>>>>>> trainer.run(params);
>>>>>>
>>>>>> Here's the error message:
>>>>>>
>>>>>> java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be cast to
>>>>>> org.apache.mahout.math.VectorWritable
>>>>>> at
>>>>>>
>>>>>>
>>>>> org.apache.mahout.classifier.naivebayes.training.IndexInstancesMapper.map(IndexInstancesMapper.java:1)
>>>>>> at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:144)
>>>>>> at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:764)
>>>>>> at org.apache.hadoop.mapred.MapTask.run(MapTask.java:370)
>>>>>> at
>>>>> org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:212)
>>>>>>
>>>>>> On 13 December 2011 19:52, Grant Ingersoll <gsing...@apache.org> wrote:
>>>>>>
>>>>>>> What steps have you done?
>>>>>>>
>>>>>>> On Dec 13, 2011, at 12:29 PM, Daniele Volpi wrote:
>>>>>>>
>>>>>>>> Hi everyone,
>>>>>>>> I'm trying to implement the Naive Bayes classifier through the
>>>>>>>> TrainNaiveBayesJob class.
>>>>>>>> After convert the text files in the required sequencefile for the
>>>>> "run"
>>>>>>>> method through the seqdirectory program i get this error:
>>>>>>>>
>>>>>>>> java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be
>>>>> cast
>>>>>> to
>>>>>>>> org.apache.mahout.math.VectorWritable
>>>>>>>>
>>>>>>>> Do you have some hints on the right usage of this class?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Daniele Volpi
>>>>>>>
>>>>>>> --------------------------------------------
>>>>>>> Grant Ingersoll
>>>>>>> http://www.lucidimagination.com
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>
>>> --------------------------------------------
>>> Grant Ingersoll
>>> http://www.lucidimagination.com
>>>
>>>
>>>
>
>
>
> --
> Lance Norskog
> goks...@gmail.com
--------------------------
Grant Ingersoll
http://www.lucidimagination.com
