| /* |
| * 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.classifier.sgd.OnlineLogisticRegression; |
| import org.apache.mahout.classifier.sgd.PassiveAggressive; |
| import org.apache.mahout.math.RandomAccessSparseVector; |
| import org.apache.mahout.math.Vector; |
| |
| public class LogisticRegressionTrainer extends AbstractOnlineLearnerTrainer { |
| |
| public LogisticRegressionTrainer(Map<String, String> trainParams, |
| Map<String, String> reportMap) { |
| } |
| |
| @Override |
| public MaxentModel doTrain(DataIndexer indexer) throws IOException { |
| |
| // TODO: Lets use the predMap here as well for encoding |
| |
| int outcomes[] = indexer.getOutcomeList(); |
| |
| int cardinality = indexer.getPredLabels().length; |
| |
| |
| AdaptiveLogisticRegression pa = new AdaptiveLogisticRegression(indexer.getOutcomeLabels().length, |
| cardinality, new L1()); |
| |
| pa.setInterval(800); |
| pa.setAveragingWindow(500); |
| |
| // PassiveAggressive pa = new PassiveAggressive(indexer.getOutcomeLabels().length, cardinality); |
| // pa.learningRate(10000); |
| |
| // OnlineLogisticRegression pa = new OnlineLogisticRegression(indexer.getOutcomeLabels().length, cardinality, |
| // new L1()); |
| // |
| // pa.alpha(1).stepOffset(250) |
| // .decayExponent(0.9) |
| // .lambda(3.0e-5) |
| // .learningRate(3000); |
| |
| // TODO: Should we do both ?! AdaptiveLogisticRegression ?! |
| |
| for (int k = 0; k < iterations; k++) { |
| trainOnlineLearner(indexer, pa); |
| |
| // What should be reported at the end of every iteration ?! |
| System.out.println("Iteration " + (k + 1)); |
| } |
| |
| pa.close(); |
| |
| 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 new VectorClassifierModel(pa.getBest().getPayload().getLearner(), indexer.getOutcomeLabels(), predMap); |
| |
| // return new VectorClassifierModel(pa, indexer.getOutcomeLabels(), predMap); |
| } |
| |
| @Override |
| public boolean isSortAndMerge() { |
| return true; |
| } |
| } |