org.apache.ctakes.ytex.libsvm.LibSVMParser Maven / Gradle / Ivy
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* 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 org.apache.ctakes.ytex.libsvm;
import org.apache.ctakes.ytex.kernel.BaseClassifierEvaluationParser;
import org.apache.ctakes.ytex.kernel.model.ClassifierEvaluation;
import org.apache.ctakes.ytex.kernel.model.ClassifierInstanceEvaluation;
import org.apache.ctakes.ytex.kernel.model.SVMClassifierEvaluation;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
public class LibSVMParser extends BaseClassifierEvaluationParser {
public static Pattern labelsPattern = Pattern.compile("labels\\s+(.*)");
public static Pattern totalSVPattern = Pattern.compile("total_sv (\\d+)");
public static Pattern pKernel = Pattern.compile("-t\\s+(\\d)");
public static Pattern pGamma = Pattern.compile("-g\\s+([\\d\\.eE-]+)");
public static Pattern pCost = Pattern.compile("-c\\s+([\\d\\.eE-]+)");
public static Pattern pWeight = Pattern
.compile("-w-{0,1}\\d\\s+[\\d\\.]+\\b");
public static Pattern pDegree = Pattern.compile("-d\\s+(\\d+)");
/**
* parse svm-train model file to get the number of support vectors. Needed
* for model selection
*
* @param modelFile
* @return
* @throws IOException
*/
public Integer parseModel(String modelFile) throws IOException {
BufferedReader r = null;
try {
r = new BufferedReader(new FileReader(modelFile));
String line = null;
while ((line = r.readLine()) != null) {
Matcher m = totalSVPattern.matcher(line);
if (m.find()) {
return Integer.parseInt(m.group(1));
}
}
} finally {
try {
if (r != null)
r.close();
} catch (Exception e) {
System.err.println("reading model file");
e.printStackTrace(System.err);
}
}
return null;
}
// /**
// * Parse svm-predict input (instance file) and predictions (prediction
// file)
// *
// * @param predictionFile
// * @param instanceFile
// * @return
// * @throws Exception
// * @throws IOException
// */
// public ClassifierEvaluationResults parse(String predictionFile,
// String instanceFile, Properties props) throws IOException {
// ClassifierEvaluationResults results = new ClassifierEvaluationResults();
// List listResults = new
// ArrayList();
// results.setResults(listResults);
// BufferedReader instanceReader = null;
// BufferedReader predictionReader = null;
// try {
// instanceReader = new BufferedReader(new FileReader(instanceFile));
// predictionReader = new BufferedReader(
// new FileReader(predictionFile));
// String instanceLine = null;
// String predictionLine = null;
// int nLine = 0;
// // 1st line in libSVMOutputReader lists labels
//
// results.setClassIds(parseClassIds(predictionReader));
// // when working with high cutoffs resulting in mainly zero vectors
// // we sometimes have a trivial classification problem (1 class)
// // if (results.getClassIds().size() < 2)
// // throw new Exception("error parsing class ids");
// while (((instanceLine = instanceReader.readLine()) != null)
// && ((predictionLine = predictionReader.readLine()) != null)) {
// nLine++;
// ClassifierEvaluationResult result = new ClassifierEvaluationResult();
// listResults.add(result);
// String predictTokens[] = wsPattern.split(predictionLine);
// String classIdPredicted = predictTokens[0];
// String classIdTarget = extractFirstToken(instanceLine,
// wsPattern);
// result.setTargetClassId(Integer.parseInt(classIdTarget));
// result.setPredictedClassId(Integer.parseInt(classIdPredicted));
// if (predictTokens.length > 1) {
// double probabilities[] = new double[results.getClassIds()
// .size()];
// for (int i = 1; i < predictTokens.length; i++) {
// probabilities[i - 1] = Double
// .parseDouble(predictTokens[i]);
// }
// result.setProbabilities(probabilities);
// }
// }
// } finally {
// if (instanceReader != null) {
// try {
// instanceReader.close();
// } catch (Exception e) {
// System.err.println("testGramReader");
// e.printStackTrace(System.err);
// }
// }
// if (predictionReader != null) {
// try {
// predictionReader.close();
// } catch (Exception e) {
// e.printStackTrace(System.err);
// }
// }
// }
// return results;
// }
/**
* parse class ids from first line in prediction file. this correspond to
* probabilities
*
* @param predictionReader
* @return
* @throws IOException
*/
protected List parseClassIds(BufferedReader predictionReader)
throws IOException {
List labels = null;
String labelLine = predictionReader.readLine();
Matcher labelMatcher = labelsPattern.matcher(labelLine);
if (labelMatcher.find()) {
String labelsA[] = wsPattern.split(labelMatcher.group(1));
if (labelsA != null && labelsA.length > 0) {
labels = new ArrayList(labelsA.length);
for (String label : labelsA)
labels.add(Integer.parseInt(label));
}
}
return labels;
}
protected SVMClassifierEvaluation initClassifierEval(String name,
String experiment, String label, String options,
String instanceIdFile) {
SVMClassifierEvaluation eval = new SVMClassifierEvaluation();
initClassifierEval(name, experiment, label, options, instanceIdFile,
eval);
return eval;
}
private void initClassifierEval(String name, String experiment,
String label, String options, String instanceIdFile,
ClassifierEvaluation eval) {
initClassifierEvaluation(instanceIdFile, eval);
eval.setName(name);
eval.setExperiment(experiment);
eval.setOptions(options);
}
/**
* parse predicted class ids, probabilities; correlate to target class ids
* and instance ids.
*
* @param predictionFile
* prediction (output)
* @param instanceFile
* input data file; contains target class ids
* @param props
* @param instanceIdFile
* instance ids corresponding to lines in input data file
* @param eval
* @throws IOException
*/
protected void parsePredictions(String predictionFile, String instanceFile,
Properties props, String instanceIdFile,
SVMClassifierEvaluation eval) throws IOException {
boolean storeProbabilities = YES.equalsIgnoreCase(props.getProperty(
ParseOption.STORE_PROBABILITIES.getOptionKey(),
ParseOption.STORE_PROBABILITIES.getDefaultValue()));
List instanceIds = null;
if (instanceIdFile != null)
instanceIds = parseInstanceIds(instanceIdFile);
BufferedReader instanceReader = null;
BufferedReader predictionReader = null;
try {
instanceReader = new BufferedReader(new FileReader(instanceFile));
predictionReader = new BufferedReader(
new FileReader(predictionFile));
String instanceLine = null;
String predictionLine = null;
int nLine = 0;
// 1st line in libSVMOutputReader lists class ids - parse them out
List classIds = parseClassIds(predictionReader);
// iterate through input data file and output predictions
// simultaneously
while (((instanceLine = instanceReader.readLine()) != null)
&& ((predictionLine = predictionReader.readLine()) != null)) {
// get instance id corresponding to this line
long instanceId = instanceIds.size() > nLine ? instanceIds
.get(nLine) : nLine;
nLine++;
// allocate instanceEval
ClassifierInstanceEvaluation instanceEval = new ClassifierInstanceEvaluation();
// parse out predicted class from output predictions
String predictTokens[] = wsPattern.split(predictionLine);
String classIdPredicted = predictTokens[0];
String classIdTarget = extractFirstToken(instanceLine,
wsPattern);
// parse out target class from input data file
instanceEval.setTargetClassId(Integer.parseInt(classIdTarget));
instanceEval.setPredictedClassId(Integer
.parseInt(classIdPredicted));
instanceEval.setInstanceId(instanceId);
instanceEval.setClassifierEvaluation(eval);
// add the instance to the map
eval.getClassifierInstanceEvaluations().put(instanceId,
instanceEval);
// parse class id probabilities
if (storeProbabilities && predictTokens.length > 1) {
for (int i = 1; i < predictTokens.length; i++) {
instanceEval.getClassifierInstanceProbabilities().put(
classIds.get(i - 1),
Double.parseDouble(predictTokens[i]));
}
}
}
} finally {
if (instanceReader != null) {
try {
instanceReader.close();
} catch (Exception e) {
e.printStackTrace(System.err);
}
}
if (predictionReader != null) {
try {
predictionReader.close();
} catch (Exception e) {
e.printStackTrace(System.err);
}
}
}
}
protected void parseOptions(SVMClassifierEvaluation eval, String options) {
// -q -b 1 -t 2 -w1 41 -g 1000 -c 1000 training_data_11_fold9_train.txt
// training_data_11_fold9_model.txt
if (options != null) {
eval.setKernel(parseIntOption(pKernel, options));
if (eval.getKernel() == null)
eval.setKernel(0);
eval.setDegree(parseIntOption(pDegree, options));
eval.setWeight(parseWeight(options));
eval.setCost(parseDoubleOption(pCost, options));
eval.setGamma(parseDoubleOption(pGamma, options));
}
}
/**
* parse the weight options out of the libsvm command line. they are of the
* form -w0 1 -w2 1.5 ...
*
* @param options
* @return null if no weight options, else weight options
*/
private String parseWeight(String options) {
StringBuilder bWeight = new StringBuilder();
Matcher m = pWeight.matcher(options);
boolean bWeightParam = false;
while (m.find()) {
bWeightParam = true;
bWeight.append(m.group()).append(" ");
}
if (bWeightParam)
return bWeight.toString();
else
return null;
}
/**
* parse directory. Expect following files:
*
* - model.txt - libsvm model file
*
- options.properties - properties file with needed parameter settings
* (see ParseOption)
*
- predict.txt - predictions on test set
*
*/
@Override
public void parseDirectory(File dataDir, File outputDir) throws IOException {
String model = outputDir.getPath() + File.separator + "model.txt";
String predict = outputDir.getPath() + File.separator + "predict.txt";
String optionsFile = outputDir.getPath() + File.separator
+ "options.properties";
if (checkFileRead(model) && checkFileRead(predict)
&& checkFileRead(optionsFile)) {
// read options.properties
Properties props = this.loadProps(outputDir);
SVMClassifierEvaluation eval = new SVMClassifierEvaluation();
// set algorithm
eval.setAlgorithm("libsvm");
// parse results
parseResults(dataDir, outputDir, model, predict, eval, props);
// store results
storeResults(dataDir, props, eval);
}
}
/**
* store the parsed classifier evaluation
*
* @param props
* @param eval
* @throws IOException
*/
protected void storeResults(File dataDir, Properties props,
SVMClassifierEvaluation eval) throws IOException {
// store the classifier evaluation
getClassifierEvaluationDao().saveClassifierEvaluation(
eval,
this.loadClassIdMap(dataDir, eval.getLabel()),
YES.equalsIgnoreCase(props.getProperty(
ParseOption.STORE_INSTANCE_EVAL.getOptionKey(),
ParseOption.STORE_INSTANCE_EVAL.getDefaultValue())));
}
/**
* parse the results in the specified output dir. use reference data from
* dataDir.
*
* @param dataDir
* @param outputDir
* @param model
* @param predict
* @param eval
* @param props
* @throws IOException
*/
protected void parseResults(File dataDir, File outputDir, String model,
String predict, SVMClassifierEvaluation eval, Properties props)
throws IOException {
// initialize common properties
initClassifierEvaluationFromProperties(props, eval);
// parse number of support vectors from model
eval.setSupportVectors(this.parseModel(model));
// parse options from command line
parseOptions(eval,
props.getProperty(ParseOption.EVAL_LINE.getOptionKey()));
// parse fold, run, label from file base name
String fileBaseName = this.getFileBaseName(props);
initClassifierEvaluation(fileBaseName, eval);
// parse predictions
String instanceIdFile = dataDir + File.separator + fileBaseName
+ "test_id.txt";
String instanceFile = dataDir + File.separator + fileBaseName
+ "test_data.txt";
this.parsePredictions(predict, instanceFile, props, instanceIdFile,
eval);
}
}
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