OPENNLP-574 Moved from addons to sandbox to mature there.
diff --git a/mahout-addon/pom.xml b/mahout-addon/pom.xml
deleted file mode 100644
index d2c2d0a..0000000
--- a/mahout-addon/pom.xml
+++ /dev/null
@@ -1,86 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-
-<!--
- 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.
--->
-
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
- <modelVersion>4.0.0</modelVersion>
-
- <parent>
- <groupId>org.apache.opennlp</groupId>
- <artifactId>opennlp</artifactId>
- <version>1.6.0-SNAPSHOT</version>
- <relativePath>../opennlp/pom.xml</relativePath>
- </parent>
-
- <artifactId>mahout-addon</artifactId>
- <packaging>jar</packaging>
- <name>Apache OpenNLP Mahout Addon</name>
-
- <dependencies>
- <dependency>
- <groupId>org.apache.opennlp</groupId>
- <artifactId>opennlp-tools</artifactId>
- <version>1.6.0-SNAPSHOT</version>
- </dependency>
-
- <dependency>
- <groupId>org.apache.mahout</groupId>
- <artifactId>mahout-core</artifactId>
- <version>0.8</version>
- </dependency>
-
- <dependency>
- <groupId>junit</groupId>
- <artifactId>junit</artifactId>
- <scope>test</scope>
- </dependency>
- </dependencies>
-
- <build>
- <plugins>
- <plugin>
- <groupId>org.apache.maven.plugins</groupId>
- <artifactId>maven-dependency-plugin</artifactId>
- <version>2.1</version>
- <executions>
- <execution>
- <id>copy-dependencies</id>
- <phase>package</phase>
- <goals>
- <goal>copy-dependencies</goal>
- </goals>
- <configuration>
- <excludeScope>provided</excludeScope>
- <stripVersion>true</stripVersion>
- </configuration>
- </execution>
- </executions>
- </plugin>
- <plugin>
- <groupId>org.apache.maven.plugins</groupId>
- <artifactId>maven-surefire-plugin</artifactId>
- <configuration>
- <skipTests>true</skipTests>
- <argLine>-Xmx512m</argLine>
- </configuration>
- </plugin>
- </plugins>
- </build>
-</project>
diff --git a/mahout-addon/src/main/java/SimpleTest.java b/mahout-addon/src/main/java/SimpleTest.java
deleted file mode 100644
index cba234a..0000000
--- a/mahout-addon/src/main/java/SimpleTest.java
+++ /dev/null
@@ -1,51 +0,0 @@
-import org.apache.mahout.classifier.sgd.PassiveAggressive;
-import org.apache.mahout.math.RandomAccessSparseVector;
-import org.apache.mahout.math.Vector;
-import org.apache.mahout.vectorizer.encoders.StaticWordValueEncoder;
-
-public class SimpleTest {
-
- public static void main(String[] args) {
-
- // Prepare data in vector format ...
-
- // The basic idea is that you create a vector, typically a RandomAccessSparseVector,
- // and then you use various feature encoders to progressively add features to that vector.
- // The size of the vector should be large enough to avoid feature collisions as features are hashed.
-
- // NOTE: Looks like we need to store the cardinality of the vector in the model ?!
-
- StaticWordValueEncoder encoder = new StaticWordValueEncoder("word-encoder");
-
- RandomAccessSparseVector vector1 = new RandomAccessSparseVector(3);
- vector1.set(0, 1);
- vector1.set(1, 0);
- vector1.set(2, 1);
-
-// encoder.addToVector("f1", vector1);
-// encoder.addToVector("f", vector1);
-
- RandomAccessSparseVector vector2 = new RandomAccessSparseVector(3);
-
- vector2.set(0, 0);
- vector2.set(1, 1);
- vector2.set(2, 1);
-
-// encoder.addToVector("f2", vector2);
-// encoder.addToVector("f", vector2);
-
- // do the training
- PassiveAggressive pa = new PassiveAggressive(2, 3);
- pa.train(0, vector1);
- pa.train(1, vector2);
-
- RandomAccessSparseVector vector = new RandomAccessSparseVector(pa.numFeatures());
- vector.set(0, 1);
- vector.set(1, 0);
- vector.set(2, 1);
-
- Vector result = pa.classifyFull(vector);
-
- System.out.println(result);
- }
-}
diff --git a/mahout-addon/src/main/java/opennlp/addons/mahout/AbstractOnlineLearnerTrainer.java b/mahout-addon/src/main/java/opennlp/addons/mahout/AbstractOnlineLearnerTrainer.java
deleted file mode 100644
index 471e862..0000000
--- a/mahout-addon/src/main/java/opennlp/addons/mahout/AbstractOnlineLearnerTrainer.java
+++ /dev/null
@@ -1,88 +0,0 @@
-/*
- * 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;
- }
-}
diff --git a/mahout-addon/src/main/java/opennlp/addons/mahout/AdaptiveLogisticRegressionTrainer.java b/mahout-addon/src/main/java/opennlp/addons/mahout/AdaptiveLogisticRegressionTrainer.java
deleted file mode 100644
index c3c2e43..0000000
--- a/mahout-addon/src/main/java/opennlp/addons/mahout/AdaptiveLogisticRegressionTrainer.java
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- * 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.Map;
-
-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;
-
-public class AdaptiveLogisticRegressionTrainer extends AbstractOnlineLearnerTrainer {
-
- public AdaptiveLogisticRegressionTrainer(Map<String, String> trainParams,
- Map<String, String> reportMap) {
- super(trainParams, reportMap);
- }
-
- @Override
- public MaxentModel doTrain(DataIndexer indexer) throws IOException {
-
- // TODO: Lets use the predMap here as well for encoding
- int numberOfOutcomes = indexer.getOutcomeLabels().length;
- int numberOfFeatures = indexer.getPredLabels().length;
-
- AdaptiveLogisticRegression pa = new AdaptiveLogisticRegression(numberOfOutcomes,
- numberOfFeatures, new L1());
-
- // TODO: Make these parameters configurable ...
- // what are good values ?!
- pa.setInterval(800);
- pa.setAveragingWindow(500);
-
- 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();
-
- return new VectorClassifierModel(pa.getBest().getPayload().getLearner(),
- indexer.getOutcomeLabels(), createPrepMap(indexer));
- }
-
- @Override
- public boolean isSortAndMerge() {
- return true;
- }
-}
diff --git a/mahout-addon/src/main/java/opennlp/addons/mahout/LogisticRegressionTrainer.java b/mahout-addon/src/main/java/opennlp/addons/mahout/LogisticRegressionTrainer.java
deleted file mode 100644
index dbd2396..0000000
--- a/mahout-addon/src/main/java/opennlp/addons/mahout/LogisticRegressionTrainer.java
+++ /dev/null
@@ -1,98 +0,0 @@
-/*
- * 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) {
- super(trainParams, 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;
- }
-}
diff --git a/mahout-addon/src/main/java/opennlp/addons/mahout/OnlineLogisticRegressionTrainer.java b/mahout-addon/src/main/java/opennlp/addons/mahout/OnlineLogisticRegressionTrainer.java
deleted file mode 100644
index c015c60..0000000
--- a/mahout-addon/src/main/java/opennlp/addons/mahout/OnlineLogisticRegressionTrainer.java
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * 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.Map;
-
-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;
-
-public class OnlineLogisticRegressionTrainer extends AbstractOnlineLearnerTrainer {
-
- public OnlineLogisticRegressionTrainer(Map<String, String> trainParams,
- Map<String, String> reportMap) {
- super(trainParams, reportMap);
- }
-
- @Override
- public MaxentModel doTrain(DataIndexer indexer) throws IOException {
-
- // TODO: Lets use the predMap here as well for encoding
- int numberOfOutcomes = indexer.getOutcomeLabels().length;
- int numberOfFeatures = indexer.getPredLabels().length;
-
- // TODO: Make these parameters configurable ...
- OnlineLogisticRegression pa = new OnlineLogisticRegression(
- numberOfOutcomes, numberOfFeatures, new L1());
-
- pa.alpha(1).stepOffset(250).decayExponent(0.9).lambda(3.0e-5)
- .learningRate(3000);
-
- 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();
-
- return new VectorClassifierModel(pa, indexer.getOutcomeLabels(), createPrepMap(indexer));
- }
-
- @Override
- public boolean isSortAndMerge() {
- return true;
- }
-}
diff --git a/mahout-addon/src/main/java/opennlp/addons/mahout/PassiveAggressiveTrainer.java b/mahout-addon/src/main/java/opennlp/addons/mahout/PassiveAggressiveTrainer.java
deleted file mode 100644
index 8365fd9..0000000
--- a/mahout-addon/src/main/java/opennlp/addons/mahout/PassiveAggressiveTrainer.java
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * 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.Map;
-
-import opennlp.tools.ml.model.DataIndexer;
-import opennlp.tools.ml.model.MaxentModel;
-
-import org.apache.mahout.classifier.sgd.PassiveAggressive;
-
-public class PassiveAggressiveTrainer extends AbstractOnlineLearnerTrainer {
-
- public PassiveAggressiveTrainer(Map<String, String> trainParams,
- Map<String, String> reportMap) {
- super(trainParams, reportMap);
- }
-
- @Override
- public MaxentModel doTrain(DataIndexer indexer) throws IOException {
-
- // TODO: Lets use the predMap here as well for encoding
- int numberOfOutcomes = indexer.getOutcomeLabels().length;
- int numberOfFeatures = indexer.getPredLabels().length;
-
- PassiveAggressive pa = new PassiveAggressive(numberOfOutcomes, numberOfFeatures);
-
- 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();
-
- return new VectorClassifierModel(pa, indexer.getOutcomeLabels(), createPrepMap(indexer));
- }
-
- @Override
- public boolean isSortAndMerge() {
- return true;
- }
-}
diff --git a/mahout-addon/src/main/java/opennlp/addons/mahout/VectorClassifierModel.java b/mahout-addon/src/main/java/opennlp/addons/mahout/VectorClassifierModel.java
deleted file mode 100644
index 046ed2a..0000000
--- a/mahout-addon/src/main/java/opennlp/addons/mahout/VectorClassifierModel.java
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * 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.util.Map;
-
-import opennlp.tools.ml.model.MaxentModel;
-
-import org.apache.mahout.classifier.AbstractVectorClassifier;
-import org.apache.mahout.math.RandomAccessSparseVector;
-import org.apache.mahout.math.Vector;
-
-// TODO: Would be nice to have an abstract maxent model impl ..
-
-public class VectorClassifierModel implements MaxentModel {
-
- private final AbstractVectorClassifier classifier;
- private final String[] outcomeLabels;
- private final Map<String, Integer> predMap;
-
- public VectorClassifierModel(AbstractVectorClassifier pa, String outcomeLabels[],
- Map<String, Integer> predMap) {
- this.classifier = pa;
- // TODO: We should make a copy, so the model is immutable ...
- this.outcomeLabels = outcomeLabels;
- this.predMap = predMap;
- }
-
- public double[] eval(String[] features) {
- Vector vector = new RandomAccessSparseVector(predMap.size());
-
- for (String feature : features) {
- Integer featureId = predMap.get(feature);
-
- if (featureId != null) {
- vector.set(featureId, vector.get(featureId) + 1);
- }
- }
-
- Vector resultVector = classifier.classifyFull(vector);
-
- double outcomes[] = new double[classifier.numCategories()];
-
- for (int i = 0; i < outcomes.length; i++) {
- outcomes[i] = resultVector.get(i);
- }
-
- return outcomes;
- }
-
- public double[] eval(String[] context, double[] probs) {
- return eval(context);
- }
-
- public double[] eval(String[] context, float[] values) {
- return eval(context);
- }
-
- @Override
- public String getBestOutcome(double[] ocs) {
- int best = 0;
- for (int i = 1; i < ocs.length; i++)
- if (ocs[i] > ocs[best]) best = i;
- return outcomeLabels[best];
- }
-
- @Override
- public String getAllOutcomes(double[] outcomes) {
- return null;
- }
-
- @Override
- public String getOutcome(int i) {
- return outcomeLabels[i];
- }
-
- @Override
- public int getIndex(String outcome) {
- for (int i = 0; i < outcomeLabels.length; i++) {
- if (outcomeLabels[i].equals(outcome)) {
- return i;
- }
- }
-
- return -1;
- }
-
- @Override
- public int getNumOutcomes() {
- return outcomeLabels.length;
- }
-}
