org.tribuo.classification.mnb

Class MultinomialNaiveBayesModel

java.lang.Object

org.tribuo.Model<Label>

org.tribuo.classification.mnb.MultinomialNaiveBayesModel

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable

public class MultinomialNaiveBayesModel
extends Model<Label>

A Model for multinomial Naive Bayes with Laplace smoothing.

All feature values must be non-negative, otherwise it will throw IllegalArgumentException.

See:

 Wang S, Manning CD.
 "Baselines and Bigrams: Simple, Good Sentiment and Topic Classification"
 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 2012.
 

See Also:

Serialized Form

Field Summary

Fields inherited from class org.tribuo.Model

ALL_OUTPUTS, BIAS_FEATURE, featureIDMap, generatesProbabilities, name, outputIDInfo, provenance, provenanceOutput

Method Summary

Modifier and Type	Method and Description
protected MultinomialNaiveBayesModel	copy(String newName, ModelProvenance newProvenance) Copies a model, replacing it's provenance and name with the supplied values.
Optional<Excuse<Label>>	getExcuse(Example<Label> example) Generates an excuse for an example.
Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>>	getTopFeatures(int n) Gets the top n features associated with this model.
Prediction<Label>	predict(Example<Label> example) Uses the model to predict the output for a single example.

Methods inherited from class org.tribuo.Model

copy, generatesProbabilities, getExcuses, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, innerPredict, predict, predict, setName, toString, validate

Methods inherited from class java.lang.Object

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

Method Detail

predict

public Prediction<Label> predict(Example<Label> example)

Description copied from class: Model

Uses the model to predict the output for a single example.

predict does not mutate the example.

Throws IllegalArgumentException if the example has no features or no feature overlap with the model.

Specified by:

predict in class Model<Label>

Parameters:

example - the example to predict.

Returns:

the result of the prediction.

getTopFeatures

public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)

Description copied from class: Model

Gets the top n features associated with this model.

If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.

If the model cannot describe it's top features then it returns Collections.emptyMap().

Specified by:

getTopFeatures in class Model<Label>

Parameters:

n - the number of features to return. If this value is less than 0, all features should be returned for each class, unless the model cannot score it's features.

Returns:

a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model

getExcuse

public Optional<Excuse<Label>> getExcuse(Example<Label> example)

Description copied from class: Model

Generates an excuse for an example.

This attempts to explain a classification result. Generating an excuse may be quite an expensive operation.

This excuse either contains per class information or an entry with key Model.ALL_OUTPUTS.

The optional is empty if the model does not provide excuses.

Specified by:

getExcuse in class Model<Label>

Parameters:

example - The input example.

Returns:

An optional excuse object. The optional is empty if this model does not provide excuses.

copy

protected MultinomialNaiveBayesModel copy(String newName,
                                          ModelProvenance newProvenance)

Description copied from class: Model

Copies a model, replacing it's provenance and name with the supplied values.

Used to provide the provenance removal functionality.

Specified by:

copy in class Model<Label>

Parameters:

newName - The new name.

newProvenance - The new provenance.

Returns:

A copy of the model.