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#434 - drafted libsvm/liblinear with super module
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,19 @@ | ||
| <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> | ||
| <artifactId>dkpro-tc-io-libsvm</artifactId> | ||
| <parent> | ||
| <groupId>org.dkpro.tc</groupId> | ||
| <artifactId>dkpro-tc</artifactId> | ||
| <version>1.0.0-SNAPSHOT</version> | ||
| </parent> | ||
| <dependencies> | ||
| <dependency> | ||
| <groupId>org.dkpro.tc</groupId> | ||
| <artifactId>dkpro-tc-core</artifactId> | ||
| </dependency> | ||
| <dependency> | ||
| <groupId>org.dkpro.tc</groupId> | ||
| <artifactId>dkpro-tc-ml</artifactId> | ||
| </dependency> | ||
| </dependencies> | ||
| </project> |
34 changes: 34 additions & 0 deletions
34
dkpro-tc-io-libsvm/src/main/java/org/dkpro/tc/io/libsvm/AdapterFormat.java
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|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| /******************************************************************************* | ||
| * Copyright 2018 | ||
| * Ubiquitous Knowledge Processing (UKP) Lab | ||
| * Technische Universität Darmstadt | ||
| * | ||
| * Licensed 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 org.dkpro.tc.io.libsvm; | ||
|
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| public class AdapterFormat { | ||
|
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| public static String getOutcomeMappingFilename() { | ||
| return "outcome-mapping.txt"; | ||
| } | ||
|
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| public static String getFeatureNameMappingFilename() { | ||
| return "feature-name-mapping.txt"; | ||
| } | ||
|
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| public static String getFeatureNames() { | ||
| return "featurenames.txt"; | ||
| } | ||
|
|
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| } |
189 changes: 189 additions & 0 deletions
189
dkpro-tc-io-libsvm/src/main/java/org/dkpro/tc/io/libsvm/LibsvmDataFormatOutcomeIdReport.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,189 @@ | ||
| /******************************************************************************* | ||
| * Copyright 2018 | ||
| * Ubiquitous Knowledge Processing (UKP) Lab | ||
| * Technische Universität Darmstadt | ||
| * | ||
| * Licensed 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 org.dkpro.tc.io.libsvm; | ||
|
|
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| import java.io.File; | ||
| import java.io.IOException; | ||
| import java.io.UnsupportedEncodingException; | ||
| import java.net.URLEncoder; | ||
| import java.util.ArrayList; | ||
| import java.util.Collection; | ||
| import java.util.HashMap; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
| import java.util.Properties; | ||
|
|
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| import org.apache.commons.io.FileUtils; | ||
| import org.apache.commons.io.output.FileWriterWithEncoding; | ||
| import org.dkpro.lab.reporting.ReportBase; | ||
| import org.dkpro.lab.storage.StorageService.AccessMode; | ||
| import org.dkpro.tc.core.Constants; | ||
| import org.dkpro.tc.core.task.InitTask; | ||
| import org.dkpro.tc.ml.report.util.SortedKeyProperties; | ||
|
|
||
| /** | ||
| * Creates id 2 outcome report | ||
| */ | ||
| public class LibsvmDataFormatOutcomeIdReport extends ReportBase implements Constants { | ||
| // constant dummy value for setting as threshold which is an expected field | ||
| // in the evaluation | ||
| // module but is not needed/provided by liblinear | ||
| private static final String THRESHOLD_CONSTANT = "-1"; | ||
|
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| public LibsvmDataFormatOutcomeIdReport() { | ||
| // required by groovy | ||
| } | ||
|
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| @Override | ||
| public void execute() throws Exception { | ||
| boolean isRegression = isRegression(); | ||
|
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| boolean isUnit = getDiscriminators().get(InitTask.class.getName() + "|" + Constants.DIM_FEATURE_MODE) | ||
| .equals(Constants.FM_UNIT); | ||
|
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| Map<Integer, String> id2label = getId2LabelMapping(isRegression); | ||
| String header = buildHeader(id2label, isRegression); | ||
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| List<String> predictions = readPredictions(); | ||
| Map<String, String> index2instanceIdMap = getMapping(isUnit); | ||
|
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| Properties prop = new SortedKeyProperties(); | ||
| int lineCounter = 0; | ||
| for (String line : predictions) { | ||
| if (line.startsWith("#")) { | ||
| continue; | ||
| } | ||
| String[] split = line.split(";"); | ||
| String key = index2instanceIdMap.get(lineCounter + ""); | ||
|
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| if (isRegression) { | ||
| prop.setProperty(key, split[0] + ";" + split[1] + ";" + THRESHOLD_CONSTANT); | ||
| } else { | ||
| int pred = Double.valueOf(split[0]).intValue(); | ||
| int gold = Double.valueOf(split[1]).intValue(); | ||
| prop.setProperty(key, pred + ";" + gold + ";" + THRESHOLD_CONSTANT); | ||
| } | ||
| lineCounter++; | ||
| } | ||
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| File targetFile = getId2OutcomeFileLocation(); | ||
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| FileWriterWithEncoding fw = new FileWriterWithEncoding(targetFile, "utf-8"); | ||
| prop.store(fw, header); | ||
| fw.close(); | ||
|
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| } | ||
|
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| private boolean isRegression() { | ||
|
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| Collection<String> keys = getDiscriminators().keySet(); | ||
| for (String k : keys) { | ||
| if (k.endsWith("|" + Constants.DIM_LEARNING_MODE)) { | ||
| return getDiscriminators().get(k).equals(Constants.LM_REGRESSION); | ||
| } | ||
| } | ||
| return false; | ||
| } | ||
|
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| private Map<String, String> getMapping(boolean isUnit) throws IOException { | ||
|
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| File f; | ||
| if (isUnit) { | ||
| f = new File(getContext().getFolder(TEST_TASK_INPUT_KEY_TEST_DATA, AccessMode.READONLY), | ||
| LibsvmDataFormatWriter.INDEX2INSTANCEID); | ||
| } else { | ||
| f = new File(getContext().getFolder(TEST_TASK_INPUT_KEY_TEST_DATA, AccessMode.READONLY), | ||
| Constants.FILENAME_DOCUMENT_META_DATA_LOG); | ||
| } | ||
|
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| Map<String, String> m = new HashMap<>(); | ||
|
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| int idx = 0; | ||
| for (String l : FileUtils.readLines(f, "utf-8")) { | ||
| if (l.startsWith("#")) { | ||
| continue; | ||
| } | ||
| if (l.trim().isEmpty()) { | ||
| continue; | ||
| } | ||
| String[] split = l.split("\t"); | ||
|
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| // if (isUnit) { | ||
| m.put(idx + "", split[0]); | ||
| idx++; | ||
| // } else { | ||
| // m.put(split[0], split[1]); | ||
| // } | ||
|
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| } | ||
| return m; | ||
| } | ||
|
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| private File getId2OutcomeFileLocation() { | ||
| File evaluationFolder = getContext().getFolder("", AccessMode.READWRITE); | ||
| return new File(evaluationFolder, ID_OUTCOME_KEY); | ||
| } | ||
|
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| private List<String> readPredictions() throws IOException { | ||
| File predFolder = getContext().getFolder("", AccessMode.READWRITE); | ||
| return FileUtils.readLines(new File(predFolder, Constants.FILENAME_PREDICTIONS), "utf-8"); | ||
| } | ||
|
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| private String buildHeader(Map<Integer, String> id2label, boolean isRegression) | ||
| throws UnsupportedEncodingException { | ||
| StringBuilder header = new StringBuilder(); | ||
| header.append("ID=PREDICTION;GOLDSTANDARD;THRESHOLD" + "\n" + "labels" + " "); | ||
|
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| if (isRegression) { | ||
| // no label mapping for regression so that is all we have to do | ||
| return header.toString(); | ||
| } | ||
|
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| int numKeys = id2label.keySet().size(); | ||
| List<Integer> keys = new ArrayList<Integer>(id2label.keySet()); | ||
| for (int i = 0; i < numKeys; i++) { | ||
| Integer key = keys.get(i); | ||
| header.append(key + "=" + URLEncoder.encode(id2label.get(key), "UTF-8")); | ||
| if (i + 1 < numKeys) { | ||
| header.append(" "); | ||
| } | ||
| } | ||
| return header.toString(); | ||
| } | ||
|
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| private Map<Integer, String> getId2LabelMapping(boolean isRegression) throws Exception { | ||
| if (isRegression) { | ||
| // no map for regression; | ||
| return new HashMap<>(); | ||
| } | ||
|
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| File folder = getContext().getFolder(TEST_TASK_INPUT_KEY_TRAINING_DATA, AccessMode.READONLY); | ||
| String fileName = AdapterFormat.getOutcomeMappingFilename(); | ||
| File file = new File(folder, fileName); | ||
| Map<Integer, String> map = new HashMap<Integer, String>(); | ||
|
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| List<String> lines = FileUtils.readLines(file, "utf-8"); | ||
| for (String line : lines) { | ||
| String[] split = line.split("\t"); | ||
| map.put(Integer.valueOf(split[1]), split[0]); | ||
| } | ||
|
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| return map; | ||
| } | ||
|
|
||
| } |
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