opennlp.tools.ml.maxent.GIS Maven / Gradle / Ivy
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package opennlp.tools.ml.maxent;
import java.io.IOException;
import opennlp.tools.ml.AbstractEventTrainer;
import opennlp.tools.ml.model.AbstractModel;
import opennlp.tools.ml.model.DataIndexer;
import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.Prior;
import opennlp.tools.ml.model.UniformPrior;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.TrainingParameters;
/**
* A Factory class which uses instances of GISTrainer to create and train
* GISModels.
* @deprecated use {@link GISTrainer}
*/
@Deprecated
public class GIS extends AbstractEventTrainer {
public static final String MAXENT_VALUE = "MAXENT";
/**
* Set this to false if you don't want messages about the progress of model
* training displayed. Alternately, you can use the overloaded version of
* trainModel() to conditionally enable progress messages.
*/
public static boolean PRINT_MESSAGES = true;
/**
* If we are using smoothing, this is used as the "number" of times we want
* the trainer to imagine that it saw a feature that it actually didn't see.
* Defaulted to 0.1.
*/
private static final double SMOOTHING_OBSERVATION = 0.1;
private static final String SMOOTHING_PARAM = "smoothing";
private static final boolean SMOOTHING_DEFAULT = false;
public GIS() {
}
public GIS(TrainingParameters parameters) {
super(parameters);
}
public boolean isValid() {
if (!super.isValid()) {
return false;
}
String algorithmName = getAlgorithm();
return !(algorithmName != null && !(MAXENT_VALUE.equals(algorithmName)));
}
public boolean isSortAndMerge() {
return true;
}
public AbstractModel doTrain(DataIndexer indexer) throws IOException {
int iterations = getIterations();
AbstractModel model;
boolean printMessages = trainingParameters.getBooleanParameter(VERBOSE_PARAM, VERBOSE_DEFAULT);
boolean smoothing = trainingParameters.getBooleanParameter(SMOOTHING_PARAM, SMOOTHING_DEFAULT);
int threads = trainingParameters.getIntParameter(TrainingParameters.THREADS_PARAM, 1);
model = trainModel(iterations, indexer, printMessages, smoothing, null, threads);
return model;
}
// << members related to AbstractEventTrainer
/**
* Train a model using the GIS algorithm, assuming 100 iterations and no
* cutoff.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(ObjectStream eventStream) throws IOException {
return trainModel(eventStream, 100, 0, false, PRINT_MESSAGES);
}
/**
* Train a model using the GIS algorithm, assuming 100 iterations and no
* cutoff.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(ObjectStream eventStream, boolean smoothing)
throws IOException {
return trainModel(eventStream, 100, 0, smoothing, PRINT_MESSAGES);
}
/**
* Train a model using the GIS algorithm.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param iterations
* The number of GIS iterations to perform.
* @param cutoff
* The number of times a feature must be seen in order to be relevant
* for training.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(ObjectStream eventStream, int iterations,
int cutoff) throws IOException {
return trainModel(eventStream, iterations, cutoff, false, PRINT_MESSAGES);
}
/**
* Train a model using the GIS algorithm.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param iterations
* The number of GIS iterations to perform.
* @param cutoff
* The number of times a feature must be seen in order to be relevant
* for training.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @param printMessagesWhileTraining
* Determines whether training status messages are written to STDOUT.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(ObjectStream eventStream, int iterations,
int cutoff, boolean smoothing, boolean printMessagesWhileTraining)
throws IOException {
GISTrainer trainer = new GISTrainer(printMessagesWhileTraining);
trainer.setSmoothing(smoothing);
trainer.setSmoothingObservation(SMOOTHING_OBSERVATION);
return trainer.trainModel(eventStream, iterations, cutoff);
}
/**
* Train a model using the GIS algorithm.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param iterations
* The number of GIS iterations to perform.
* @param cutoff
* The number of times a feature must be seen in order to be relevant
* for training.
* @param sigma
* The standard deviation for the gaussian smoother.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(ObjectStream eventStream, int iterations,
int cutoff, double sigma) throws IOException {
GISTrainer trainer = new GISTrainer(PRINT_MESSAGES);
if (sigma > 0) {
trainer.setGaussianSigma(sigma);
}
return trainer.trainModel(eventStream, iterations, cutoff);
}
/**
* Train a model using the GIS algorithm.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer, boolean smoothing) {
return trainModel(iterations, indexer, true, smoothing, null, 1);
}
/**
* Train a model using the GIS algorithm.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer) {
return trainModel(iterations, indexer, true, false, null, 1);
}
/**
* Train a model using the GIS algorithm with the specified number of
* iterations, data indexer, and prior.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @param modelPrior
* The prior distribution for the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer,
Prior modelPrior, int cutoff) {
return trainModel(iterations, indexer, true, false, modelPrior, cutoff);
}
/**
* Train a model using the GIS algorithm.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @param printMessagesWhileTraining
* Determines whether training status messages are written to STDOUT.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @param modelPrior
* The prior distribution for the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer,
boolean printMessagesWhileTraining, boolean smoothing,
Prior modelPrior) {
return trainModel(iterations, indexer, printMessagesWhileTraining, smoothing, modelPrior, 1);
}
/**
* Train a model using the GIS algorithm.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @param printMessagesWhileTraining
* Determines whether training status messages are written to STDOUT.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @param modelPrior
* The prior distribution for the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.tools.ml.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer,
boolean printMessagesWhileTraining, boolean smoothing,
Prior modelPrior, int threads) {
GISTrainer trainer = new GISTrainer(printMessagesWhileTraining);
trainer.setSmoothing(smoothing);
trainer.setSmoothingObservation(SMOOTHING_OBSERVATION);
if (modelPrior == null) {
modelPrior = new UniformPrior();
}
return trainer.trainModel(iterations, indexer, modelPrior, threads);
}
}
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