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/*
* 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.lang.reflect.Constructor;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;
import opennlp.tools.ml.maxent.GISTrainer;
import opennlp.tools.ml.maxent.quasinewton.QNTrainer;
import opennlp.tools.ml.naivebayes.NaiveBayesTrainer;
import opennlp.tools.ml.perceptron.PerceptronTrainer;
import opennlp.tools.ml.perceptron.SimplePerceptronSequenceTrainer;
import opennlp.tools.util.TrainingParameters;
import opennlp.tools.util.ext.ExtensionLoader;
import opennlp.tools.util.ext.ExtensionNotLoadedException;
public class TrainerFactory {
public enum TrainerType {
EVENT_MODEL_TRAINER,
EVENT_MODEL_SEQUENCE_TRAINER,
SEQUENCE_TRAINER
}
// built-in trainers
private static final Map BUILTIN_TRAINERS;
static {
Map _trainers = new HashMap<>();
_trainers.put(GISTrainer.MAXENT_VALUE, GISTrainer.class);
_trainers.put(QNTrainer.MAXENT_QN_VALUE, QNTrainer.class);
_trainers.put(PerceptronTrainer.PERCEPTRON_VALUE, PerceptronTrainer.class);
_trainers.put(SimplePerceptronSequenceTrainer.PERCEPTRON_SEQUENCE_VALUE,
SimplePerceptronSequenceTrainer.class);
_trainers.put(NaiveBayesTrainer.NAIVE_BAYES_VALUE, NaiveBayesTrainer.class);
BUILTIN_TRAINERS = Collections.unmodifiableMap(_trainers);
}
/**
* Determines the trainer type based on the ALGORITHM_PARAM value.
*
* @param trainParams - Map of training parameters
* @return the trainer type or null if type couldn't be determined.
*/
public static TrainerType getTrainerType(TrainingParameters trainParams) {
String algorithmValue = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null);
// Check if it is defaulting to the MAXENT trainer
if (algorithmValue == null) {
return TrainerType.EVENT_MODEL_TRAINER;
}
Class trainerClass = BUILTIN_TRAINERS.get(algorithmValue);
if (trainerClass != null) {
if (EventTrainer.class.isAssignableFrom(trainerClass)) {
return TrainerType.EVENT_MODEL_TRAINER;
}
else if (EventModelSequenceTrainer.class.isAssignableFrom(trainerClass)) {
return TrainerType.EVENT_MODEL_SEQUENCE_TRAINER;
}
else if (SequenceTrainer.class.isAssignableFrom(trainerClass)) {
return TrainerType.SEQUENCE_TRAINER;
}
}
// Try to load the different trainers, and return the type on success
try {
ExtensionLoader.instantiateExtension(EventTrainer.class, algorithmValue);
return TrainerType.EVENT_MODEL_TRAINER;
}
catch (ExtensionNotLoadedException ignored) {
// this is ignored
}
try {
ExtensionLoader.instantiateExtension(EventModelSequenceTrainer.class, algorithmValue);
return TrainerType.EVENT_MODEL_SEQUENCE_TRAINER;
}
catch (ExtensionNotLoadedException ignored) {
// this is ignored
}
try {
ExtensionLoader.instantiateExtension(SequenceTrainer.class, algorithmValue);
return TrainerType.SEQUENCE_TRAINER;
}
catch (ExtensionNotLoadedException ignored) {
// this is ignored
}
return null;
}
public static SequenceTrainer getSequenceModelTrainer(TrainingParameters trainParams,
Map reportMap) {
String trainerType = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null);
if (trainerType != null) {
if (BUILTIN_TRAINERS.containsKey(trainerType)) {
SequenceTrainer trainer = TrainerFactory.createBuiltinTrainer(
BUILTIN_TRAINERS.get(trainerType));
trainer.init(trainParams, reportMap);
return trainer;
} else {
SequenceTrainer trainer =
ExtensionLoader.instantiateExtension(SequenceTrainer.class, trainerType);
trainer.init(trainParams, reportMap);
return trainer;
}
}
else {
throw new IllegalArgumentException("Trainer type couldn't be determined!");
}
}
public static EventModelSequenceTrainer getEventModelSequenceTrainer(TrainingParameters trainParams,
Map reportMap) {
String trainerType = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null);
if (trainerType != null) {
if (BUILTIN_TRAINERS.containsKey(trainerType)) {
EventModelSequenceTrainer trainer = TrainerFactory.createBuiltinTrainer(
BUILTIN_TRAINERS.get(trainerType));
trainer.init(trainParams, reportMap);
return trainer;
} else {
EventModelSequenceTrainer trainer =
ExtensionLoader.instantiateExtension(EventModelSequenceTrainer.class, trainerType);
trainer.init(trainParams, reportMap);
return trainer;
}
}
else {
throw new IllegalArgumentException("Trainer type couldn't be determined!");
}
}
public static EventTrainer getEventTrainer(TrainingParameters trainParams,
Map reportMap) {
// if the trainerType is not defined -- use the GISTrainer.
String trainerType =
trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE);
if (BUILTIN_TRAINERS.containsKey(trainerType)) {
EventTrainer trainer = TrainerFactory.createBuiltinTrainer(
BUILTIN_TRAINERS.get(trainerType));
trainer.init(trainParams, reportMap);
return trainer;
} else {
EventTrainer trainer = ExtensionLoader.instantiateExtension(EventTrainer.class, trainerType);
trainer.init(trainParams, reportMap);
return trainer;
}
}
public static boolean isValid(TrainingParameters trainParams) {
// TODO: Need to validate all parameters correctly ... error prone?!
String algorithmName = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null);
// If a trainer type can be determined, then the trainer is valid!
if (algorithmName != null &&
!(BUILTIN_TRAINERS.containsKey(algorithmName) || getTrainerType(trainParams) != null)) {
return false;
}
try {
// require that the Cutoff and the number of iterations be an integer.
// if they are not set, the default values will be ok.
trainParams.getIntParameter(AbstractTrainer.CUTOFF_PARAM, 0);
trainParams.getIntParameter(AbstractTrainer.ITERATIONS_PARAM, 0);
}
catch (NumberFormatException e) {
return false;
}
// no reason to require that the dataIndexer be a 1-pass or 2-pass dataindexer.
trainParams.getStringParameter(AbstractEventTrainer.DATA_INDEXER_PARAM, null);
// TODO: Check data indexing ...
return true;
}
private static T createBuiltinTrainer(Class trainerClass) {
T theTrainer = null;
if (trainerClass != null) {
try {
Constructor contructor = trainerClass.getConstructor();
theTrainer = contructor.newInstance();
} catch (Exception e) {
String msg = "Could not instantiate the "
+ trainerClass.getCanonicalName()
+ ". The initialization throw an exception.";
System.err.println(msg);
e.printStackTrace();
throw new IllegalArgumentException(msg, e);
}
}
return theTrainer;
}
}
