opennlp.tools.ml.AbstractTrainer Maven / Gradle / Ivy
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* 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,
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*/
package opennlp.tools.ml;
import java.util.HashMap;
import java.util.Map;
import opennlp.tools.commons.Trainer;
import opennlp.tools.ml.maxent.GISTrainer;
import opennlp.tools.util.TrainingParameters;
public abstract class AbstractTrainer implements Trainer {
public static final String ALGORITHM_PARAM = "Algorithm";
public static final String TRAINER_TYPE_PARAM = "TrainerType";
public static final String CUTOFF_PARAM = "Cutoff";
public static final int CUTOFF_DEFAULT = 5;
public static final String ITERATIONS_PARAM = "Iterations";
public static final int ITERATIONS_DEFAULT = 100;
protected TrainingParameters trainingParameters;
protected Map reportMap;
public AbstractTrainer() {
}
/**
* Initializes a {@link AbstractTrainer} via {@link TrainingParameters}.
*
* @param trainParams The {@link TrainingParameters} to use.
*/
public AbstractTrainer(TrainingParameters trainParams) {
init(trainParams,new HashMap<>());
}
/**
* Initializes a {@link AbstractTrainer} via {@link TrainingParameters} and
* a {@link Map report map}.
*
* @param trainParams The {@link TrainingParameters} to use.
* @param reportMap The {@link Map} instance used as report map.
*/
@Override
public void init(TrainingParameters trainParams, Map reportMap) {
this.trainingParameters = trainParams;
if (reportMap == null) reportMap = new HashMap<>();
this.reportMap = reportMap;
}
/**
* @return Retrieves the configured {@link #ALGORITHM_PARAM} value.
*/
public String getAlgorithm() {
return trainingParameters.getStringParameter(ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE);
}
/**
* @return Retrieves the configured {@link #CUTOFF_PARAM} value.
*/
public int getCutoff() {
return trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT);
}
/**
* @return Retrieves the configured {@link #ITERATIONS_PARAM} value.
*/
public int getIterations() {
return trainingParameters.getIntParameter(ITERATIONS_PARAM, ITERATIONS_DEFAULT);
}
/**
* Checks the configured {@link TrainingParameters parameters}.
* If a subclass overrides this, it should call {@code super.validate();}.
*
* @throws IllegalArgumentException Thrown if default training parameters are invalid.
*/
public void validate() {
// TODO: Need to validate all parameters correctly ... error prone?!
// should validate if algorithm is set? What about the Parser?
try {
trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT);
trainingParameters.getIntParameter(ITERATIONS_PARAM, ITERATIONS_DEFAULT);
} catch (NumberFormatException e) {
throw new IllegalArgumentException(e);
}
}
/**
* Adds the key-value pair to the report map.
* @param key The identifying string associated with a certain training parameter.
* @param value The parameter value associated with {@code key}.
*/
protected void addToReport(String key, String value) {
reportMap.put(key, value);
}
}