Added: opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java URL: http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java?rev=1695334&view=auto ============================================================================== --- opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java (added) +++ opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java Tue Aug 11 15:58:53 2015 @@ -0,0 +1,152 @@ +/* + * 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.naivebayes; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.List; + +import opennlp.tools.ml.model.Event; +import opennlp.tools.ml.model.MaxentModel; +import opennlp.tools.ml.model.TwoPassDataIndexer; +import opennlp.tools.util.ObjectStream; +import opennlp.tools.util.ObjectStreamUtils; + +import static org.junit.Assert.assertEquals; + +import org.junit.Test; + +/** + * Test for naive bayes classification correctness without smoothing + */ +public class NaiveBayesCorrectnessTest { + + @Test + public void testNaiveBayes1() throws IOException { + + NaiveBayesModel.setSmoothed(false); // Naive Bayes should always be run with smoothing, but I am taking it out here just for mathematical verification + + NaiveBayesModel model = + (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(createTrainingStream(), 1, false)); + + String label = "politics"; + String[] context = { "bow=united", "bow=nations" }; + Event event = new Event(label, context); + + testModel(model, event, 1.0); + + NaiveBayesModel.setSmoothed(true); // Turning smoothing back on to avoid interfering with other tests + + } + + @Test + public void testNaiveBayes2() throws IOException { + + NaiveBayesModel.setSmoothed(false); // Naive Bayes should always be run with smoothing, but I am taking it out here just for mathematical verification + + NaiveBayesModel model = + (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(createTrainingStream(), 1, false)); + + String label = "sports"; + String[] context = { "bow=manchester", "bow=united" }; + Event event = new Event(label, context); + + testModel(model, event, 1.0); + + NaiveBayesModel.setSmoothed(true); // Turning smoothing back on to avoid interfering with other tests + + } + + @Test + public void testNaiveBayes3() throws IOException { + + NaiveBayesModel.setSmoothed(false); // Naive Bayes should always be run with smoothing, but I am taking it out here just for mathematical verification + + NaiveBayesModel model = + (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(createTrainingStream(), 1, false)); + + String label = "politics"; + String[] context = { "bow=united" }; + Event event = new Event(label, context); + + testModel(model, event, 2.0/3.0); + + NaiveBayesModel.setSmoothed(true); // Turning smoothing back on to avoid interfering with other tests + + } + + @Test + public void testNaiveBayes4() throws IOException { + + NaiveBayesModel.setSmoothed(false); // Naive Bayes should always be run with smoothing, but I am taking it out here just for mathematical verification + + NaiveBayesModel model = + (NaiveBayesModel)new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(createTrainingStream(), 1, false)); + + String label = "politics"; + String[] context = { }; + Event event = new Event(label, context); + + testModel(model, event, 7.0/12.0); + + NaiveBayesModel.setSmoothed(true); // Turning smoothing back on to avoid interfering with other tests + + } + + private void testModel(MaxentModel model, Event event, double higher_probability) { + double[] outcomes = model.eval(event.getContext()); + String outcome = model.getBestOutcome(outcomes); + assertEquals(2, outcomes.length); + assertEquals(event.getOutcome(), outcome); + if (event.getOutcome().equals(model.getOutcome(0))) { + assertEquals(higher_probability, outcomes[0], 0.0001); + } + if (!event.getOutcome().equals(model.getOutcome(0))) { + assertEquals(1.0 - higher_probability, outcomes[0], 0.0001); + } + if (event.getOutcome().equals(model.getOutcome(1))) { + assertEquals(higher_probability, outcomes[1], 0.0001); + } + if (!event.getOutcome().equals(model.getOutcome(1))) { + assertEquals(1.0 - higher_probability, outcomes[1], 0.0001); + } + } + + public static ObjectStream<Event> createTrainingStream() throws IOException { + List<Event> trainingEvents = new ArrayList<Event>(); + + String label1 = "politics"; + String[] context1 = { "bow=the", "bow=united", "bow=nations" }; + trainingEvents.add(new Event(label1, context1)); + + String label2 = "politics"; + String[] context2 = { "bow=the", "bow=united", "bow=states", "bow=and" }; + trainingEvents.add(new Event(label2, context2)); + + String label3 = "sports"; + String[] context3 = { "bow=manchester", "bow=united" }; + trainingEvents.add(new Event(label3, context3)); + + String label4 = "sports"; + String[] context4 = { "bow=manchester", "bow=and", "bow=barca" }; + trainingEvents.add(new Event(label4, context4)); + + return ObjectStreamUtils.createObjectStream(trainingEvents); + } + +}
Added: opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java URL: http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java?rev=1695334&view=auto ============================================================================== --- opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java (added) +++ opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java Tue Aug 11 15:58:53 2015 @@ -0,0 +1,78 @@ +/* + * 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.naivebayes; + +import static opennlp.tools.ml.PrepAttachDataUtil.createTrainingStream; +import static opennlp.tools.ml.PrepAttachDataUtil.testModel; +import static org.junit.Assert.assertTrue; + +import java.io.IOException; +import java.util.HashMap; +import java.util.Map; + +import opennlp.tools.ml.AbstractTrainer; +import opennlp.tools.ml.model.MaxentModel; +import opennlp.tools.ml.model.TrainUtil; +import opennlp.tools.ml.model.TwoPassDataIndexer; + +import org.junit.Test; + +/** + * Test for Naive Bayes training and use with the ppa data. + */ +public class NaiveBayesPrepAttachTest { + + @Test + public void testNaiveBayesOnPrepAttachData() throws IOException { + MaxentModel model = + new NaiveBayesTrainer().trainModel(new TwoPassDataIndexer(createTrainingStream(), 1, false)); + + assertTrue(model instanceof NaiveBayesModel); + + testModel(model, 0.7897994553107205); + } + + @Test + public void testNaiveBayesOnPrepAttachDataUsingTrainUtil() throws IOException { + + Map<String, String> trainParams = new HashMap<String, String>(); + trainParams.put(AbstractTrainer.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); + trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1)); + + MaxentModel model = TrainUtil.train(createTrainingStream(), trainParams, null); + + assertTrue(model instanceof NaiveBayesModel); + + testModel(model, 0.7897994553107205); + } + + @Test + public void testNaiveBayesOnPrepAttachDataUsingTrainUtilWithCutoff5() throws IOException { + + Map<String, String> trainParams = new HashMap<String, String>(); + trainParams.put(AbstractTrainer.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); + trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(5)); + + MaxentModel model = TrainUtil.train(createTrainingStream(), trainParams, null); + + assertTrue(model instanceof NaiveBayesModel); + + testModel(model, 0.7945035899975241); + } + +}
