/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.app.analyst.commands;
import java.io.File;
import org.encog.app.analyst.EncogAnalyst;
import org.encog.app.analyst.script.prop.ScriptProperties;
import org.encog.ml.MLMethod;
import org.encog.ml.MLResettable;
import org.encog.ml.TrainingImplementationType;
import org.encog.ml.bayesian.BayesianNetwork;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.ea.train.EvolutionaryAlgorithm;
import org.encog.ml.factory.MLTrainFactory;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.training.cross.CrossValidationKFold;
import org.encog.persist.EncogDirectoryPersistence;
import org.encog.util.logging.EncogLogging;
import org.encog.util.validate.ValidateNetwork;
/**
* This command is used to perform training on a machine learning method and
* dataset.
*
*/
public class CmdTrain extends Cmd {
/**
* The name of this command.
*/
public static final String COMMAND_NAME = "TRAIN";
/**
* Construct the train command.
* @param analyst The analyst to use.
*/
public CmdTrain(final EncogAnalyst analyst) {
super(analyst);
}
/**
* Create a trainer, use cross validation if enabled.
* @param method The method to use.
* @param trainingSet The training set to use.
* @return The trainer.
*/
private MLTrain createTrainer(final MLMethod method,
final MLDataSet trainingSet) {
final MLTrainFactory factory = new MLTrainFactory();
final String type = getProp().getPropertyString(
ScriptProperties.ML_TRAIN_TYPE);
final String args = getProp().getPropertyString(
ScriptProperties.ML_TRAIN_ARGUMENTS);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "training type:" + type);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "training args:" + args);
if( method instanceof MLResettable ) {
this.getAnalyst().setMethod(method);
}
MLTrain train = factory.create(method, trainingSet, type, args);
if ( getKfold() > 0) {
train = new CrossValidationKFold(train, getKfold() );
}
return train;
}
/**
* {@inheritDoc}
*/
@Override
public boolean executeCommand(final String args) {
setKfold( obtainCross() );
final MLDataSet trainingSet = obtainTrainingSet();
MLMethod method = obtainMethod();
final MLTrain trainer = createTrainer(method, trainingSet);
if( method instanceof BayesianNetwork ) {
final String query = getProp().getPropertyString(
ScriptProperties.ML_CONFIG_QUERY);
((BayesianNetwork)method).defineClassificationStructure(query);
}
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "Beginning training");
performTraining(trainer, method, trainingSet);
final String resourceID = getProp().getPropertyString(
ScriptProperties.ML_CONFIG_MACHINE_LEARNING_FILE);
final File resourceFile = getAnalyst().getScript().resolveFilename(
resourceID);
// reload the method
method = null;
if( trainer instanceof EvolutionaryAlgorithm ) {
EvolutionaryAlgorithm ea = (EvolutionaryAlgorithm)trainer;
method = ea.getPopulation();
}
if( method==null ) {
method = trainer.getMethod();
}
EncogDirectoryPersistence.saveObject(resourceFile, method);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "save to:" + resourceID);
trainingSet.close();
return getAnalyst().shouldStopCommand();
}
/**
* {@inheritDoc}
*/
@Override
public String getName() {
return CmdTrain.COMMAND_NAME;
}
/**
* Perform the training.
* @param train The training method.
* @param method The ML method.
* @param trainingSet The training set.
*/
private void performTraining(final MLTrain train, final MLMethod method,
final MLDataSet trainingSet) {
ValidateNetwork.validateMethodToData(method, trainingSet);
final double targetError = getProp().getPropertyDouble(
ScriptProperties.ML_TRAIN_TARGET_ERROR);
getAnalyst().reportTrainingBegin();
final int maxIteration = getAnalyst().getMaxIteration();
if (train.getImplementationType() == TrainingImplementationType.OnePass) {
train.iteration();
getAnalyst().reportTraining(train);
} else {
do {
train.iteration();
getAnalyst().reportTraining(train);
} while ((train.getError() > targetError)
&& !getAnalyst().shouldStopCommand()
&& !train.isTrainingDone()
&& ((maxIteration == -1) || (train.getIteration() < maxIteration)));
}
train.finishTraining();
getAnalyst().reportTrainingEnd();
getAnalyst().setMethod(train.getMethod());
}
}