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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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 <[email protected] >>>>>> 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 <[email protected]> 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 > [email protected] -------------------------- Grant Ingersoll http://www.lucidimagination.com
