/*
* 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.perceptron;
import static opennlp.PrepAttachDataUtil.createTrainingStream;
import static opennlp.PrepAttachDataUtil.testModel;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import opennlp.model.AbstractModel;
import opennlp.model.TrainUtil;
import opennlp.model.TwoPassDataIndexer;
import org.junit.Test;
/**
* Test for perceptron training and use with the ppa data.
*/
public class PerceptronPrepAttachTest {
@Test
public void testPerceptronOnPrepAttachData() throws IOException {
AbstractModel model =
new PerceptronTrainer().trainModel(400,
new TwoPassDataIndexer(createTrainingStream(), 1, false), 1);
testModel(model, 0.7650408516959644);
}
@Test
public void testPerceptronOnPrepAttachDataWithSkippedAveraging() throws IOException {
Map<String, String> trainParams = new HashMap<String, String>();
trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.PERCEPTRON_VALUE);
trainParams.put(TrainUtil.CUTOFF_PARAM, Integer.toString(1));
trainParams.put("UseSkippedAveraging", Boolean.toString(true));
AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
testModel(model, 0.773706362961129);
}
@Test
public void testPerceptronOnPrepAttachDataWithTolerance() throws IOException {
Map<String, String> trainParams = new HashMap<String, String>();
trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.PERCEPTRON_VALUE);
trainParams.put(TrainUtil.CUTOFF_PARAM, Integer.toString(1));
trainParams.put(TrainUtil.ITERATIONS_PARAM, Integer.toString(500));
trainParams.put("Tolerance", Double.toString(0.0001d));
AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
testModel(model, 0.7677642980935875);
}
@Test
public void testPerceptronOnPrepAttachDataWithStepSizeDecrease() throws IOException {
Map<String, String> trainParams = new HashMap<String, String>();
trainParams.put(TrainUtil.ALGORITHM_PARAM, TrainUtil.PERCEPTRON_VALUE);
trainParams.put(TrainUtil.CUTOFF_PARAM, Integer.toString(1));
trainParams.put(TrainUtil.ITERATIONS_PARAM, Integer.toString(500));
trainParams.put("StepSizeDecrease", Double.toString(0.06d));
AbstractModel model = TrainUtil.train(createTrainingStream(), trainParams, null);
testModel(model, 0.7756870512503095);
}
}