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

  - ### Constructor Summary

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

  - ### Method Summary

    All Methods Instance Methods Concrete Methods
    Modifier and Type | Method and 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 Detail

    - #### 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 Detail

    - #### 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 Detail

    - #### assignClass

          public ClassificationResult<BytesRef> assignClass(String inputDocument)
                                                     throws IOException

      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

      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

      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

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