edu.umass.cs.mallet.share.upenn

Class MaxEntShell

java.lang.Object

edu.umass.cs.mallet.share.upenn.MaxEntShell

public class MaxEntShell
extends Object

Simple wrapper for training a MALLET maxent classifier.

Version:

1.0

Author:

Fernando Pereira

Method Summary

Modifier and Type	Method and Description
static Classification[]	classify(Classifier classifier, PipeInputIterator data) Compute the maxent classifications for unlabeled instances given by an iterator.
static Classification	classify(Classifier classifier, String[] features) Compute the maxent classification of an instance.
static Classification[]	classify(Classifier classifier, String[][] features) Compute the maxent classifications of an array of instances
static Classifier	load(File modelFile) Load a classifier from a file.
static void	main(String[] args) Command-line wrapper to train, test, or run a maxent classifier.
static double	test(Classifier classifier, PipeInputIterator data) Test a maxent classifier.
static double	test(Classifier classifier, String[][] features, String[] labels) Test a maxent classifier.
static Classifier	train(PipeInputIterator data, double var, File save) Train a maxent classifier.
static Classifier	train(String[][] features, String[] labels, double var, File save) Train a maxent classifier.

Methods inherited from class java.lang.Object

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

Method Detail

train

public static Classifier train(String[][] features,
                               String[] labels,
                               double var,
                               File save)
                        throws IOException

Train a maxent classifier. Each row of features represents the features of a training instance. The label for that instance is in the corresponding position of labels.

Parameters:

features - Each row gives the on features of an instance

labels - Each position gives the label of an instance

var - Gaussian prior variance for training

save - if non-null, save the trained model to this file

Returns:

the maxent classifier

Throws:

IOException - if the trained model cannot be saved

train

public static Classifier train(PipeInputIterator data,
                               double var,
                               File save)
                        throws IOException

Train a maxent classifier. The iterator data returns training instances with a TokenSequence as data and a target object. The tokens in the instance data will be converted to features.

Parameters:

data - the iterator over training instances

var - Gaussian prior variance for training.

save - if non-null, save the trained model to this file

Returns:

the maxent classifier

Throws:

IOException - if the trained model cannot be saved

test

public static double test(Classifier classifier,
                          String[][] features,
                          String[] labels)

Test a maxent classifier. The data representation is the same as for training.

Parameters:

classifier - the classifier to test

features - an array of instances represented as arrays of features

labels - corresponding labels

Returns:

accuracy on the data

test

public static double test(Classifier classifier,
                          PipeInputIterator data)

Test a maxent classifier. The data representation is the same as for training.

Parameters:

classifier - the classifier to test

data - an iterator over labeled instances

Returns:

accuracy on the data

classify

public static Classification classify(Classifier classifier,
                                      String[] features)

Compute the maxent classification of an instance.

Parameters:

classifier - the classifier

features - the features that are on for this instance

Returns:

the classification

classify

public static Classification[] classify(Classifier classifier,
                                        String[][] features)

Compute the maxent classifications of an array of instances

Parameters:

classifier - the classifier

features - each row represents the on features for an instance

Returns:

the array of classifications for the given instances

classify

public static Classification[] classify(Classifier classifier,
                                        PipeInputIterator data)

Compute the maxent classifications for unlabeled instances given by an iterator.

Parameters:

classifier - the classifier

data - the iterator over unlabeled instances

Returns:

the array of classifications for the given instances

load

public static Classifier load(File modelFile)
                       throws IOException,
                              ClassNotFoundException

Load a classifier from a file.

Parameters:

modelFile - the file

Returns:

the classifier serialized in the file

Throws:

IOException - if the file cannot be opened or read

ClassNotFoundException - if the file does not deserialize

main

public static void main(String[] args)
                 throws Exception

Command-line wrapper to train, test, or run a maxent classifier. Instances are represented as follows:

Labeled:

label feature-1 ... feature-n

Unlabeled:

feature-1 ... feature-n

Parameters:

args - the command line arguments. Options (shell and Java quoting should be added as needed):

--help boolean

Print this command line option usage information. Give true for longer documentation. Default is false.

--prefix-code Java-code

Java code you want run before any other interpreted code. Note that the text is interpreted without modification, so unlike some other Java code options, you need to include any necessary 'new's. Default is null.

--gaussian-variance positive-number

The Gaussian prior variance used for training. Default is 1.0.

--train filenane

Train on labeled instances stored in filename. Default is no training.

--test filename

Test on the labeled instances stored in filename. Default is no testing.

--classify filename

Classify the unlabeled instances stored in filename. Default is no classification.

--model filename

The filename for reading (test/classify) or saving (train) the model. Default is no model file.

Throws:

Exception - if an error occurs