/*
* 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.DataIndexer;
import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.HashSumEventStream;
import opennlp.tools.ml.model.MaxentModel;
import opennlp.tools.ml.model.OnePassDataIndexer;
import opennlp.tools.ml.model.TwoPassDataIndexer;
import opennlp.tools.util.ObjectStream;
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 AbstractEventTrainer() {
}
@Override
public boolean isValid() {
if (!super.isValid()) {
return false;
}
String dataIndexer = getStringParam(DATA_INDEXER_PARAM,
DATA_INDEXER_TWO_PASS_VALUE);
if (dataIndexer != null) {
if (!(DATA_INDEXER_ONE_PASS_VALUE.equals(dataIndexer) || DATA_INDEXER_TWO_PASS_VALUE
.equals(dataIndexer))) {
return false;
}
}
// TODO: Check data indexing ...
return true;
}
public abstract boolean isSortAndMerge();
public DataIndexer getDataIndexer(ObjectStream<Event> events) throws IOException {
String dataIndexerName = getStringParam(DATA_INDEXER_PARAM,
DATA_INDEXER_TWO_PASS_VALUE);
int cutoff = getCutoff();
boolean sortAndMerge = isSortAndMerge();
DataIndexer indexer = null;
if (DATA_INDEXER_ONE_PASS_VALUE.equals(dataIndexerName)) {
indexer = new OnePassDataIndexer(events, cutoff, sortAndMerge);
} else if (DATA_INDEXER_TWO_PASS_VALUE.equals(dataIndexerName)) {
indexer = new TwoPassDataIndexer(events, cutoff, sortAndMerge);
} else {
throw new IllegalStateException("Unexpected data indexer name: "
+ dataIndexerName);
}
return indexer;
}
public abstract MaxentModel doTrain(DataIndexer indexer) throws IOException;
public final MaxentModel train(ObjectStream<Event> events) throws IOException {
if (!isValid()) {
throw new IllegalArgumentException("trainParams are not valid!");
}
HashSumEventStream hses = new HashSumEventStream(events);
DataIndexer indexer = getDataIndexer(events);
MaxentModel model = doTrain(indexer);
addToReport("Training-Eventhash", hses.calculateHashSum().toString(16));
addToReport(AbstractTrainer.TRAINER_TYPE_PARAM, EventTrainer.EVENT_VALUE);
return model;
}
}