Package org.apache.lucene.classification

# Class SimpleNaiveBayesClassifier

java.lang.Object
org.apache.lucene.classification.SimpleNaiveBayesClassifier

All Implemented Interfaces:
  `Classifier<BytesRef>`

Direct Known Subclasses:
  `CachingNaiveBayesClassifier`, `SimpleNaiveBayesDocumentClassifier`

public class SimpleNaiveBayesClassifier extends Object implements Classifier<BytesRef>
A simplistic Lucene based NaiveBayes classifier, see `http://en.wikipedia.org/wiki/Naive_Bayes_classifier`

WARNING: This API is experimental and might change in incompatible ways in the next release.

- ## Field Summary

  Fields
  Modifier and Type
  Field
  Description
  `protected final Analyzer`
  `analyzer`
  `Analyzer` to be used for tokenizing unseen input text
  `protected final String`
  `classFieldName`
  name of the field to be used as a class / category output
  `protected final IndexReader`
  `indexReader`
  `IndexReader` used to access the `Classifier`'s index
  `protected final IndexSearcher`
  `indexSearcher`
  `IndexSearcher` to run searches on the index for retrieving frequencies
  `protected final Query`
  `query`
  `Query` used to eventually filter the document set to be used to classify
  `protected final String[]`
  `textFieldNames`
  names of the fields to be used as input text

- ## Constructor Summary

  Constructors
  Constructor
  Description
  `SimpleNaiveBayesClassifier(IndexReader indexReader, Analyzer analyzer, Query query, String classFieldName, String... textFieldNames)`
  Creates a new NaiveBayes classifier.

- ## Method Summary

  Modifier and Type
  Method
  Description
  `ClassificationResult<BytesRef>`
  `assignClass(String inputDocument)`
  Assign a class (with score) to the given text String
  `protected List<ClassificationResult<BytesRef>>`
  `assignClassNormalizedList(String inputDocument)`
  Calculate probabilities for all classes for a given input text
  `protected int`
  `countDocsWithClass()`
  count the number of documents in the index having at least a value for the 'class' field
  `List<ClassificationResult<BytesRef>>`
  `getClasses(String text)`
  Get all the classes (sorted by score, descending) assigned to the given text String.
  `List<ClassificationResult<BytesRef>>`
  `getClasses(String text, int max)`
  Get the first `max` classes (sorted by score, descending) assigned to the given text String.
  `protected ArrayList<ClassificationResult<BytesRef>>`
  `normClassificationResults(List<ClassificationResult<BytesRef>> assignedClasses)`
  Normalize the classification results based on the max score available
  `protected String[]`
  `tokenize(String text)`
  tokenize a `String` on this classifier's text fields and analyzer

  ### Methods inherited from class java.lang.Object

  `clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- ## Field Details

  - ### indexReader

    protected final IndexReader indexReader
    `IndexReader` used to access the `Classifier`'s index

  - ### textFieldNames

    protected final String[] textFieldNames
    names of the fields to be used as input text

  - ### classFieldName

    protected final String classFieldName
    name of the field to be used as a class / category output

  - ### analyzer

    protected final Analyzer analyzer
    `Analyzer` to be used for tokenizing unseen input text

  - ### indexSearcher

    protected final IndexSearcher indexSearcher
    `IndexSearcher` to run searches on the index for retrieving frequencies

  - ### query

    protected final Query query
    `Query` used to eventually filter the document set to be used to classify

- ## Constructor Details

  - ### SimpleNaiveBayesClassifier

    public SimpleNaiveBayesClassifier(IndexReader indexReader, Analyzer analyzer, Query query, String classFieldName, String... textFieldNames)
    Creates a new NaiveBayes classifier.
    Parameters:
      `indexReader` - the reader on the index to be used for classification
      `analyzer` - an `Analyzer` used to analyze unseen text
      `query` - a `Query` to eventually filter the docs used for training the classifier, or `null` if all the indexed docs should be used
      `classFieldName` - the name of the field used as the output for the classifier NOTE: must not be havely analyzed as the returned class will be a token indexed for this field
      `textFieldNames` - the name of the fields used as the inputs for the classifier, NO boosting supported per field

- ## Method Details

  - ### assignClass

    public ClassificationResult<BytesRef> assignClass(String inputDocument) throws IOException
    Description copied from interface: `Classifier`
    Assign a class (with score) to the given text String
    Specified by:
      `assignClass` in interface `Classifier<BytesRef>`
    Parameters:
      `inputDocument` - a String containing text to be classified
    Returns:
      a `ClassificationResult` holding assigned class of type `T` and score
    Throws:
      `IOException` - If there is a low-level I/O error.

  - ### getClasses

    public List<ClassificationResult<BytesRef>> getClasses(String text) throws IOException
    Description copied from interface: `Classifier`
    Get all the classes (sorted by score, descending) assigned to the given text String.
    Specified by:
      `getClasses` in interface `Classifier<BytesRef>`
    Parameters:
      `text` - a String containing text to be classified
    Returns:
      the whole list of `ClassificationResult`, the classes and scores. Returns `null` if the classifier can't make lists.
    Throws:
      `IOException` - If there is a low-level I/O error.

  - ### getClasses

    public List<ClassificationResult<BytesRef>> getClasses(String text, int max) throws IOException
    Description copied from interface: `Classifier`
    Get the first `max` classes (sorted by score, descending) assigned to the given text String.
    Specified by:
      `getClasses` in interface `Classifier<BytesRef>`
    Parameters:
      `text` - a String containing text to be classified
      `max` - the number of return list elements
    Returns:
      the whole list of `ClassificationResult`, the classes and scores. Cut for "max" number of elements. Returns `null` if the classifier can't make lists.
    Throws:
      `IOException` - If there is a low-level I/O error.

  - ### assignClassNormalizedList

    protected List<ClassificationResult<BytesRef>> assignClassNormalizedList(String inputDocument) throws IOException
    Calculate probabilities for all classes for a given input text
    Parameters:
      `inputDocument` - the input text as a `String`
    Returns:
      a `List` of `ClassificationResult`, one for each existing class
    Throws:
      `IOException` - if assigning probabilities fails

  - ### countDocsWithClass

    protected int countDocsWithClass() throws IOException
    count the number of documents in the index having at least a value for the 'class' field
    Returns:
      the no. of documents having a value for the 'class' field
    Throws:
      `IOException` - if accessing to term vectors or search fails

  - ### tokenize

    protected String[] tokenize(String text) throws IOException
    tokenize a `String` on this classifier's text fields and analyzer
    Parameters:
      `text` - the `String` representing an input text (to be classified)
    Returns:
      a `String` array of the resulting tokens
    Throws:
      `IOException` - if tokenization fails

  - ### normClassificationResults

    protected ArrayList<ClassificationResult<BytesRef>> normClassificationResults(List<ClassificationResult<BytesRef>> assignedClasses)
    Normalize the classification results based on the max score available
    Parameters:
      `assignedClasses` - the list of assigned classes
    Returns:
      the normalized results