| /* |
| * 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.addons.mahout; |
| |
| import java.io.IOException; |
| import java.util.HashMap; |
| import java.util.Map; |
| |
| import opennlp.tools.ml.AbstractEventTrainer; |
| import opennlp.tools.ml.model.DataIndexer; |
| import opennlp.tools.ml.model.MaxentModel; |
| |
| import org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression; |
| import org.apache.mahout.classifier.sgd.L1; |
| import org.apache.mahout.math.RandomAccessSparseVector; |
| import org.apache.mahout.math.Vector; |
| |
| abstract class AbstractOnlineLearnerTrainer extends AbstractEventTrainer { |
| |
| protected final int iterations; |
| |
| public AbstractOnlineLearnerTrainer(Map<String, String> trainParams, |
| Map<String, String> reportMap) { |
| super(trainParams, reportMap); |
| |
| // TODO: Extract parameters here, used by all implementations, e.g. learningRate |
| |
| String iterationsValue = trainParams.get("Iterations"); |
| |
| if (iterationsValue != null) { |
| iterations = Integer.parseInt(iterationsValue); |
| } |
| else { |
| iterations = 20; |
| } |
| } |
| |
| protected void trainOnlineLearner(DataIndexer indexer, org.apache.mahout.classifier.OnlineLearner pa) { |
| int cardinality = indexer.getPredLabels().length; |
| int outcomes[] = indexer.getOutcomeList(); |
| |
| for (int i = 0; i < indexer.getContexts().length; i++) { |
| |
| Vector vector = new RandomAccessSparseVector(cardinality); |
| |
| int features[] = indexer.getContexts()[i]; |
| |
| for (int fi = 0; fi < features.length; fi++) { |
| vector.set(features[fi], indexer.getNumTimesEventsSeen()[i]); |
| } |
| |
| pa.train(outcomes[i], vector); |
| } |
| } |
| |
| protected Map<String, Integer> createPrepMap(DataIndexer indexer) { |
| Map<String, Integer> predMap = new HashMap<String, Integer>(); |
| |
| String predLabels[] = indexer.getPredLabels(); |
| for (int i = 0; i < predLabels.length; i++) { |
| predMap.put(predLabels[i], i); |
| } |
| |
| return predMap; |
| } |
| |
| @Override |
| public boolean isSortAndMerge() { |
| return true; |
| } |
| } |