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* 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
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* KIND, either express or implied. See the License for the
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package org.apache.uima.ruta.textruler.learner.trabal;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import org.apache.uima.ruta.textruler.extension.TextRulerLearner;
import org.apache.uima.ruta.textruler.extension.TextRulerLearnerDelegate;
import org.apache.uima.ruta.textruler.extension.TextRulerLearnerFactory;
import org.apache.uima.ruta.textruler.extension.TextRulerLearnerParameter;
import org.apache.uima.ruta.textruler.extension.TextRulerLearnerParameter.MLAlgorithmParamType;
public class TrabalFactory implements TextRulerLearnerFactory {
public TrabalFactory() {
}
public Map<String, Object> getAlgorithmParameterStandardValues() {
Map<String, Object> values = new HashMap<String, Object>();
values.put(TrabalLearner.ALGORITHM_ITERATIONS_KEY, TrabalLearner.ALGORITHM_ITERATIONS);
values.put(TrabalLearner.MAX_NUMBER_OF_BASIC_RULES_KEY, TrabalLearner.MAX_NUMBER_OF_BASIC_RULES);
values.put(TrabalLearner.MAX_NUMBER_OF_RULES_KEY, TrabalLearner.MAX_NUMBER_OF_RULES);
values.put(TrabalLearner.MAX_NUMBER_OF_ITERATIONS_KEY, TrabalLearner.MAX_NUMBER_OF_ITERATIONS);
values.put(TrabalLearner.MAX_ERROR_RATE_KEY, TrabalLearner.MAX_ERROR_RATE);
values.put(TrabalLearner.ENABLE_FEATURES_KEY, TrabalLearner.ENABLE_FEATURES);
return values;
}
public TextRulerLearner createAlgorithm(String inputFolderPath, String additionalFolderPath,
String preprocessorRutaFile, String tempFolderPath, String[] fullSlotTypeNames,
Set<String> filterSet, boolean skip, TextRulerLearnerDelegate delegate) {
return new TrabalLearner(inputFolderPath, additionalFolderPath, preprocessorRutaFile,
tempFolderPath, fullSlotTypeNames, filterSet, skip, delegate);
}
public TextRulerLearnerParameter[] getAlgorithmParameters() {
TextRulerLearnerParameter[] result = new TextRulerLearnerParameter[6];
result[0] = new TextRulerLearnerParameter(TrabalLearner.ALGORITHM_ITERATIONS_KEY,
"Number of times, the algorithm iterates.", MLAlgorithmParamType.ML_INT_PARAM);
result[1] = new TextRulerLearnerParameter(TrabalLearner.MAX_NUMBER_OF_BASIC_RULES_KEY,
"Number of basic rules to be created for one example.",
MLAlgorithmParamType.ML_INT_PARAM);
result[2] = new TextRulerLearnerParameter(TrabalLearner.MAX_NUMBER_OF_RULES_KEY,
"Number of optimized rules to be created for one example.",
MLAlgorithmParamType.ML_INT_PARAM);
result[3] = new TextRulerLearnerParameter(TrabalLearner.MAX_NUMBER_OF_ITERATIONS_KEY,
"Maximum number of iterations, when optimizing rules.",
MLAlgorithmParamType.ML_INT_PARAM);
result[4] = new TextRulerLearnerParameter(TrabalLearner.MAX_ERROR_RATE_KEY,
"Maximum allowed error rate.", MLAlgorithmParamType.ML_DOUBLE_PARAM);
result[5] = new TextRulerLearnerParameter(TrabalLearner.ENABLE_FEATURES_KEY,
"Correct features in rules and conditions.", MLAlgorithmParamType.ML_BOOL_PARAM);
return result;
}
}