| /* |
| * 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; |
| |
| import java.io.IOException; |
| |
| import opennlp.tools.ml.model.AbstractDataIndexer; |
| import opennlp.tools.ml.model.ChecksumEventStream; |
| import opennlp.tools.ml.model.DataIndexer; |
| import opennlp.tools.ml.model.DataIndexerFactory; |
| import opennlp.tools.ml.model.Event; |
| import opennlp.tools.ml.model.MaxentModel; |
| import opennlp.tools.util.InsufficientTrainingDataException; |
| import opennlp.tools.util.ObjectStream; |
| import opennlp.tools.util.TrainingParameters; |
| |
| /** |
| * A basic {@link EventTrainer} implementation. |
| */ |
| public abstract class AbstractEventTrainer extends AbstractTrainer implements EventTrainer { |
| |
| public static final String DATA_INDEXER_PARAM = "DataIndexer"; |
| public static final String DATA_INDEXER_ONE_PASS_VALUE = "OnePass"; |
| public static final String DATA_INDEXER_TWO_PASS_VALUE = "TwoPass"; |
| public static final String DATA_INDEXER_ONE_PASS_REAL_VALUE = "OnePassRealValue"; |
| |
| public AbstractEventTrainer() { |
| } |
| |
| public AbstractEventTrainer(TrainingParameters parameters) { |
| super(parameters); |
| } |
| |
| @Override |
| public void validate() { |
| super.validate(); |
| } |
| |
| public abstract boolean isSortAndMerge(); |
| |
| public DataIndexer getDataIndexer(ObjectStream<Event> events) throws IOException { |
| |
| trainingParameters.put(AbstractDataIndexer.SORT_PARAM, isSortAndMerge()); |
| // If the cutoff was set, don't overwrite the value. |
| if (trainingParameters.getIntParameter(CUTOFF_PARAM, -1) == -1) { |
| trainingParameters.put(CUTOFF_PARAM, 5); |
| } |
| |
| DataIndexer indexer = DataIndexerFactory.getDataIndexer(trainingParameters, reportMap); |
| indexer.index(events); |
| return indexer; |
| } |
| |
| public abstract MaxentModel doTrain(DataIndexer indexer) throws IOException; |
| |
| @Override |
| public final MaxentModel train(DataIndexer indexer) throws IOException { |
| validate(); |
| |
| if (indexer.getOutcomeLabels().length <= 1) { |
| throw new InsufficientTrainingDataException("Training data must contain more than one outcome"); |
| } |
| |
| MaxentModel model = doTrain(indexer); |
| addToReport(AbstractTrainer.TRAINER_TYPE_PARAM, EventTrainer.EVENT_VALUE); |
| return model; |
| } |
| |
| @Override |
| public final MaxentModel train(ObjectStream<Event> events) throws IOException { |
| validate(); |
| |
| ChecksumEventStream hses = new ChecksumEventStream(events); |
| DataIndexer indexer = getDataIndexer(hses); |
| |
| addToReport("Training-Eventhash", String.valueOf(hses.calculateChecksum())); |
| return train(indexer); |
| } |
| } |