[jira] [Comment Edited] (OPENNLP-777) Naive Bayesian Classifier

["Cohan Sujay Carlos (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/OPENNLP-777?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14746005#comment-14746005 ] 

Cohan Sujay Carlos edited comment on OPENNLP-777 at 9/15/15 7:38 PM:
---------------------------------------------------------------------

Tommaso,

The problem in the above testcases seems to be in the use of the GenericModelWriter.

Each of the machine learning algorithms has its own set of ModelWriter and ModelReader classes which must be used to persist their models.

The Writers come in one of 2 flavours - Binary and PlainText.

So, the following testcases work for me (one thing that baffled me was that I had to use constructModel rather than getModel to make these testcases work).

I hope that answers your question.

<code>
	@Test
	  public void testBinaryModelPersistence() throws Exception {
	    NaiveBayesModel model = (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(
	        NaiveBayesCorrectnessTest.createTrainingStream(), 1, false));

	    File file = new File("test.bin");
	    NaiveBayesModelWriter modelWriter = new BinaryNaiveBayesModelWriter(model, file);
	    modelWriter.persist();

	    NaiveBayesModelReader reader = new BinaryNaiveBayesModelReader(file);
	    reader.checkModelType();
	    AbstractModel abstractModel = reader.constructModel();
	    assertNotNull(abstractModel);
	  }

	  @Test
	  public void testTextModelPersistence() throws Exception {
	    NaiveBayesModel model = (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(
	        NaiveBayesCorrectnessTest.createTrainingStream(), 1, false));

	    File file = new File("test.txt");
	    NaiveBayesModelWriter modelWriter = new PlainTextNaiveBayesModelWriter(model, file);
	    modelWriter.persist();

	    NaiveBayesModelReader reader = new PlainTextNaiveBayesModelReader(file);
	    reader.checkModelType();
	    AbstractModel abstractModel = reader.constructModel();
	    assertNotNull(abstractModel);
	  }
</code>


was (Author: cohan.sujay):
Tommaso,

The problem in the above testcases seems to be in the use of the GenericModelWriter.

Each of the machine learning algorithms has its own set of ModelWriter and ModelReader classes which must be used to persist their models.

The Writers come in one of 2 flavours - Binary and PlainText.

So, the following testcases work for me (one thing that baffled me was that I had to use constructModel rather than getModel to make these testcases work).

I hope that answers your question.

	@Test
	  public void testBinaryModelPersistence() throws Exception {
	    NaiveBayesModel model = (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(
	        NaiveBayesCorrectnessTest.createTrainingStream(), 1, false));

	    File file = new File("test.bin");
	    NaiveBayesModelWriter modelWriter = new BinaryNaiveBayesModelWriter(model, file);
	    modelWriter.persist();

	    NaiveBayesModelReader reader = new BinaryNaiveBayesModelReader(file);
	    reader.checkModelType();
	    AbstractModel abstractModel = reader.constructModel();
	    assertNotNull(abstractModel);
	  }

	  @Test
	  public void testTextModelPersistence() throws Exception {
	    NaiveBayesModel model = (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(
	        NaiveBayesCorrectnessTest.createTrainingStream(), 1, false));

	    File file = new File("test.txt");
	    NaiveBayesModelWriter modelWriter = new PlainTextNaiveBayesModelWriter(model, file);
	    modelWriter.persist();

	    NaiveBayesModelReader reader = new PlainTextNaiveBayesModelReader(file);
	    reader.checkModelType();
	    AbstractModel abstractModel = reader.constructModel();
	    assertNotNull(abstractModel);
	  }


> Naive Bayesian Classifier
> -------------------------
>
>                 Key: OPENNLP-777
>                 URL: https://issues.apache.org/jira/browse/OPENNLP-777
>             Project: OpenNLP
>          Issue Type: New Feature
>          Components: Machine Learning
>         Environment: J2SE 1.5 and above
>            Reporter: Cohan Sujay Carlos
>            Assignee: Tommaso Teofili
>            Priority: Minor
>              Labels: NBClassifier, bayes, bayesian, classifier, multinomial, naive, patch
>         Attachments: D1TopicClassifierTrainingDemoNB.java, D1TopicClassifierUsageDemoNB.java, NaiveBayesCorrectnessTest.java, naive-bayesian-classifier-for-opennlp-1.6.0-rc6-with-test-cases.patch, prep-attach-test-case-for-naive-bayesian-classifier-for-opennlp-1.6.0-rc6.patch, topics.train
>
>   Original Estimate: 504h
>  Remaining Estimate: 504h
>
> I thought it would be nice to have a Naive Bayesian classifier in OpenNLP (it lacks one at present).
> Implementation details:  We have a production-hardened piece of Java code for a multinomial Naive Bayesian classifier (with default Laplace smoothing) that we'd like to contribute.  The code is Java 1.5 compatible.  I'd have to write an adapter to make the interface compatible with the ME classifier in OpenNLP.  I expect the patch to be available 1 to 3 weeks from now.
> Below is the email trail of a discussion in the dev mailing list around this dated May 19th, 2015.
> <snip>
> Tommaso Teofili via opennlp.apache.org 
> to dev 
> Hi Cohan,
> I think that'd be a very valuable contribution, as NB is one of the
> foundation algorithms, often used as basis for comparisons.
> It would be good if you could create a Jira issue and provide more details
> about the implementation and, eventually, a patch.
> Thanks and regards,
> Tommaso
> </snip>
> 2015-05-19 9:57 GMT+02:00 Cohan Sujay Carlos 
> > I have a question for the OpenNLP project team.
> >
> > I was wondering if there is a Naive Bayesian classifier implementation in
> > OpenNLP that I've not come across, or if there are plans to implement one.
> >
> > If it is the latter, I should love to contribute an implementation.
> >
> > There is an ME classifier already available in OpenNLP, of course, but I
> > felt that there was an unmet need for a Naive Bayesian (NB) classifier
> > implementation to be offered as well.
> >
> > An NB classifier could be bootstrapped up with partially labelled training
> > data as explained in the Nigam, McCallum, et al paper of 2000 "Text
> > Classification from Labeled and Unlabeled Documents using EM".
> >
> > So, if there isn't an NB code base out there already, I'd be happy to
> > contribute a very solid implementation that we've used in production for a
> > good 5 years.
> >
> > I'd have to adapt it to load the same training data format as the ME
> > classifier, but I guess that shouldn't be very difficult to do.
> >
> > I was wondering if there was some interest in adding an NB implementation
> > and I'd love to know who could I coordinate with if there is?
> >
> > Cohan Sujay Carlos
> > CEO, Aiaioo Labs, India



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