package org.maltparser.ml.liblinear;
import de.bwaldvogel.liblinear.*;
import java.io.*;
import java.util.*;
import java.util.jar.JarEntry;
import java.util.regex.Pattern;
import java.util.regex.PatternSyntaxException;
import org.maltparser.core.exception.MaltChainedException;
import org.maltparser.core.feature.FeatureVector;
import org.maltparser.core.feature.function.FeatureFunction;
import org.maltparser.core.feature.value.FeatureValue;
import org.maltparser.core.feature.value.MultipleFeatureValue;
import org.maltparser.core.feature.value.SingleFeatureValue;
import org.maltparser.core.helper.NoPrintStream;
import org.maltparser.core.syntaxgraph.DependencyStructure;
import org.maltparser.ml.LearningMethod;
import org.maltparser.parser.DependencyParserConfig;
import org.maltparser.parser.guide.instance.InstanceModel;
import org.maltparser.parser.history.action.SingleDecision;
import org.maltparser.parser.history.kbest.KBestList;
import org.maltparser.parser.history.kbest.ScoredKBestList;
public class Liblinear implements LearningMethod {
public final static String LIBLINEAR_VERSION = "1.51";
public enum Verbostity {
SILENT, ERROR, ALL
}
private LinkedHashMap<String, String> liblinearOptions;
protected InstanceModel owner;
protected int learnerMode;
protected String name;
protected int numberOfInstances;
protected boolean saveInstanceFiles;
protected boolean excludeNullValues;
protected String pathExternalLiblinearTrain = null;
// private int[] cardinalities;
/**
* Instance output stream writer
*/
private BufferedWriter instanceOutput = null;
/**
* Liblinear model object, only used during classification.
*/
private Model model = null;
/**
* Parameter string
*/
private String paramString;
private ArrayList<FeatureNode> xlist = null;
private Verbostity verbosity;
private HashMap<Long, Integer> featureMap;
private int featureCounter = 1;
private boolean featurePruning = false;
private TreeSet<XNode> featureSet;
/**
* Constructs a Liblinear learner.
*
* @param owner the guide model owner
* @param learnerMode the mode of the learner TRAIN or CLASSIFY
*/
public Liblinear(InstanceModel owner, Integer learnerMode) throws MaltChainedException {
setOwner(owner);
setLearningMethodName("liblinear");
setLearnerMode(learnerMode.intValue());
setNumberOfInstances(0);
verbosity = Verbostity.SILENT;
liblinearOptions = new LinkedHashMap<String, String>();
initLiblinearOptions();
parseParameters(getConfiguration().getOptionValue("liblinear", "liblinear_options").toString());
initSpecialParameters();
if (learnerMode == BATCH) {
if (featurePruning) {
featureMap = new HashMap<Long, Integer>();
}
instanceOutput = new BufferedWriter(getInstanceOutputStreamWriter(".ins"));
}
if (featurePruning) {
featureSet = new TreeSet<XNode>();
}
}
private int addFeatureMapValue(int featurePosition, int code) {
long key = ((((long) featurePosition) << 48) | (long) code);
if (featureMap.containsKey(key)) {
return featureMap.get(key);
}
int value = featureCounter++;
featureMap.put(key, value);
return value;
}
private int getFeatureMapValue(int featurePosition, int code) {
long key = ((((long) featurePosition) << 48) | (long) code);
if (featureMap.containsKey(key)) {
return featureMap.get(key);
}
return -1;
}
private void saveFeatureMap(OutputStream os, HashMap<Long, Integer> map) throws MaltChainedException {
try {
ObjectOutputStream obj_out_stream = new ObjectOutputStream(os);
obj_out_stream.writeObject(map);
obj_out_stream.close();
} catch (IOException e) {
throw new LiblinearException("Save feature map error", e);
}
}
private HashMap<Long, Integer> loadFeatureMap(InputStream is) throws MaltChainedException {
HashMap<Long, Integer> map = new HashMap<Long, Integer>();
try {
ObjectInputStream obj_in_stream = new ObjectInputStream(is);
map = (HashMap<Long, Integer>) obj_in_stream.readObject();
obj_in_stream.close();
} catch (ClassNotFoundException e) {
throw new LiblinearException("Load feature map error", e);
} catch (IOException e) {
throw new LiblinearException("Load feature map error", e);
}
return map;
}
public void addInstance(SingleDecision decision, FeatureVector featureVector) throws MaltChainedException {
if (featureVector == null) {
throw new LiblinearException("The feature vector cannot be found");
} else if (decision == null) {
throw new LiblinearException("The decision cannot be found");
}
StringBuilder sb = new StringBuilder();
try {
sb.append(decision.getDecisionCode()).append("\t");
int n = featureVector.size();
for (int i = 0; i < n; i++) {
FeatureValue featureValue = featureVector.get(i).getFeatureValue();
if (excludeNullValues == true && featureValue.isNullValue()) {
sb.append("-1");
} else {
if (featureValue instanceof SingleFeatureValue) {
sb.append(((SingleFeatureValue) featureValue).getIndexCode()).append("");
} else if (featureValue instanceof MultipleFeatureValue) {
Set<Integer> values = ((MultipleFeatureValue) featureValue).getCodes();
int j = 0;
for (Integer value : values) {
sb.append(value.toString());
if (j != values.size() - 1) {
sb.append("|");
}
j++;
}
}
}
sb.append('\t');
}
sb.append('\n');
instanceOutput.write(sb.toString());
instanceOutput.flush();
increaseNumberOfInstances();
} catch (IOException e) {
throw new LiblinearException("The Liblinear learner cannot write to the instance file. ", e);
}
}
public void finalizeSentence(DependencyStructure dependencyGraph) throws MaltChainedException {
}
/*
* (non-Javadoc) @see org.maltparser.ml.LearningMethod#noMoreInstances()
*/
public void noMoreInstances() throws MaltChainedException {
closeInstanceWriter();
}
/*
* (non-Javadoc) @see
* org.maltparser.ml.LearningMethod#train(org.maltparser.parser.guide.feature.FeatureVector)
*/
public void train(FeatureVector featureVector) throws MaltChainedException {
if (featureVector == null) {
throw new LiblinearException("The feature vector cannot be found. ");
} else if (owner == null) {
throw new LiblinearException("The parent guide model cannot be found. ");
}
// cardinalities = getCardinalities(featureVector);
if (pathExternalLiblinearTrain == null) {
try {
Problem problem = null;
if (featurePruning) {
problem = readLibLinearProblemWithFeaturePruning(getInstanceInputStreamReader(".ins"));
} else {
// problem = readLibLinearProblem(getInstanceInputStreamReader(".ins"), cardinalities);
}
if (owner.getGuide().getConfiguration().getConfigLogger().isInfoEnabled()) {
owner.getGuide().getConfiguration().getConfigLogger().info("Creating Liblinear model " + getFile(".mod").getName() + "\n");
}
final PrintStream out = System.out;
final PrintStream err = System.err;
System.setOut(NoPrintStream.NO_PRINTSTREAM);
System.setErr(NoPrintStream.NO_PRINTSTREAM);
Linear.saveModel(new File(getFile(".mod").getAbsolutePath()), Linear.train(problem, getLiblinearParameters()));
System.setOut(err);
System.setOut(out);
if (!saveInstanceFiles) {
getFile(".ins").delete();
}
} catch (OutOfMemoryError e) {
throw new LiblinearException("Out of memory. Please increase the Java heap size (-Xmx<size>). ", e);
} catch (IllegalArgumentException e) {
throw new LiblinearException("The Liblinear learner was not able to redirect Standard Error stream. ", e);
} catch (SecurityException e) {
throw new LiblinearException("The Liblinear learner cannot remove the instance file. ", e);
} catch (IOException e) {
throw new LiblinearException("The Liblinear learner cannot save the model file '" + getFile(".mod").getAbsolutePath() + "'. ", e);
}
} else {
trainExternal(featureVector);
}
if (featurePruning) {
try {
saveFeatureMap(new FileOutputStream(getFile(".map").getAbsolutePath()), featureMap);
} catch (FileNotFoundException e) {
throw new LiblinearException("The Liblinear learner cannot save the feature map file '" + getFile(".map").getAbsolutePath() + "'. ", e);
}
} else {
// saveCardinalities(getInstanceOutputStreamWriter(".car"), cardinalities);
}
}
private void trainExternal(FeatureVector featureVector) throws MaltChainedException {
try {
// maltSVMFormat2OriginalSVMFormat(getInstanceInputStreamReader(".ins"), getInstanceOutputStreamWriter(".ins.tmp"), cardinalities);
owner.getGuide().getConfiguration().getConfigLogger().info("Creating Liblinear model (external) " + getFile(".mod").getName());
final String[] params = getLibLinearParamStringArray();
String[] arrayCommands = new String[params.length + 3];
int i = 0;
arrayCommands[i++] = pathExternalLiblinearTrain;
for (; i <= params.length; i++) {
arrayCommands[i] = params[i - 1];
}
arrayCommands[i++] = getFile(".ins.tmp").getAbsolutePath();
arrayCommands[i++] = getFile(".mod").getAbsolutePath();
if (verbosity == Verbostity.ALL) {
owner.getGuide().getConfiguration().getConfigLogger().info('\n');
}
final Process child = Runtime.getRuntime().exec(arrayCommands);
final InputStream in = child.getInputStream();
final InputStream err = child.getErrorStream();
int c;
while ((c = in.read()) != -1) {
if (verbosity == Verbostity.ALL) {
owner.getGuide().getConfiguration().getConfigLogger().info((char) c);
}
}
while ((c = err.read()) != -1) {
if (verbosity == Verbostity.ALL || verbosity == Verbostity.ERROR) {
owner.getGuide().getConfiguration().getConfigLogger().info((char) c);
}
}
if (child.waitFor() != 0) {
owner.getGuide().getConfiguration().getConfigLogger().info(" FAILED (" + child.exitValue() + ")");
}
in.close();
err.close();
if (!saveInstanceFiles) {
getFile(".ins").delete();
getFile(".ins.tmp").delete();
}
owner.getGuide().getConfiguration().getConfigLogger().info('\n');
} catch (InterruptedException e) {
throw new LiblinearException("Liblinear is interrupted. ", e);
} catch (IllegalArgumentException e) {
throw new LiblinearException("The Liblinear learner was not able to redirect Standard Error stream. ", e);
} catch (SecurityException e) {
throw new LiblinearException("The Liblinear learner cannot remove the instance file. ", e);
} catch (IOException e) {
throw new LiblinearException("The Liblinear learner cannot save the model file '" + getFile(".mod").getAbsolutePath() + "'. ", e);
} catch (OutOfMemoryError e) {
throw new LiblinearException("Out of memory. Please increase the Java heap size (-Xmx<size>). ", e);
}
}
// private int[] getCardinalities(FeatureVector featureVector) {
// int[] cardinalities = new int[featureVector.size()];
// int i = 0;
// for (FeatureFunction feature : featureVector) {
// cardinalities[i++] = feature.getFeatureValue().getCardinality();
// }
// return cardinalities;
// }
//
// private void saveCardinalities(OutputStreamWriter osw, int[] cardinalities) throws MaltChainedException {
// final BufferedWriter out = new BufferedWriter(osw);
// try {
// for (int i = 0, n = cardinalities.length; i < n; i++) {
// out.write(Integer.toString(cardinalities[i]));
// if (i < n - 1) {
// out.write(',');
// }
// }
// out.write('\n');
// out.close();
// } catch (IOException e) {
// throw new LiblinearException("", e);
// }
// }
// private int[] loadCardinalities(InputStreamReader isr) throws MaltChainedException {
// int[] cardinalities = null;
// try {
// final BufferedReader in = new BufferedReader(isr);
// String line;
// if ((line = in.readLine()) != null) {
// String[] items = line.split(",");
// cardinalities = new int[items.length];
// for (int i = 0; i < items.length; i++) {
// cardinalities[i] = Integer.parseInt(items[i]);
// }
// }
// in.close();
// } catch (IOException e) {
// throw new LiblinearException("", e);
// } catch (NumberFormatException e) {
// throw new LiblinearException("", e);
// }
// return cardinalities;
// }
/*
* (non-Javadoc) @see
* org.maltparser.ml.LearningMethod#moveAllInstances(org.maltparser.ml.LearningMethod,
* org.maltparser.core.feature.function.FeatureFunction,
* java.util.ArrayList)
*/
public void moveAllInstances(LearningMethod method, FeatureFunction divideFeature, ArrayList<Integer> divideFeatureIndexVector) throws MaltChainedException {
if (method == null) {
throw new LiblinearException("The learning method cannot be found. ");
} else if (divideFeature == null) {
throw new LiblinearException("The divide feature cannot be found. ");
}
try {
final BufferedReader in = new BufferedReader(getInstanceInputStreamReader(".ins"));
final BufferedWriter out = method.getInstanceWriter();
final StringBuilder sb = new StringBuilder(6);
int l = in.read();
char c;
int j = 0;
while (true) {
if (l == -1) {
sb.setLength(0);
break;
}
c = (char) l;
l = in.read();
if (c == '\t') {
if (divideFeatureIndexVector.contains(j - 1)) {
out.write(Integer.toString(((SingleFeatureValue) divideFeature.getFeatureValue()).getIndexCode()));
out.write('\t');
}
out.write(sb.toString());
j++;
out.write('\t');
sb.setLength(0);
} else if (c == '\n') {
out.write(sb.toString());
if (divideFeatureIndexVector.contains(j - 1)) {
out.write('\t');
out.write(Integer.toString(((SingleFeatureValue) divideFeature.getFeatureValue()).getIndexCode()));
}
out.write('\n');
sb.setLength(0);
method.increaseNumberOfInstances();
this.decreaseNumberOfInstances();
j = 0;
} else {
sb.append(c);
}
}
in.close();
getFile(".ins").delete();
out.flush();
} catch (SecurityException e) {
throw new LiblinearException("The Liblinear learner cannot remove the instance file. ", e);
} catch (NullPointerException e) {
throw new LiblinearException("The instance file cannot be found. ", e);
} catch (FileNotFoundException e) {
throw new LiblinearException("The instance file cannot be found. ", e);
} catch (IOException e) {
throw new LiblinearException("The Liblinear learner read from the instance file. ", e);
}
}
/*
* (non-Javadoc) @see
* org.maltparser.ml.LearningMethod#predict(org.maltparser.parser.guide.feature.FeatureVector,
* org.maltparser.ml.KBestList)
*/
public boolean predict(FeatureVector featureVector, SingleDecision decision) throws MaltChainedException {
if (model == null) {
try {
model = Linear.loadModel(new BufferedReader(getInstanceInputStreamReaderFromConfigFile(".mod")));
} catch (IOException e) {
throw new LiblinearException("The model cannot be loaded. ", e);
}
}
if (model == null) {
throw new LiblinearException("The Liblinear learner cannot predict the next class, because the learning model cannot be found. ");
} else if (featureVector == null) {
throw new LiblinearException("The Liblinear learner cannot predict the next class, because the feature vector cannot be found. ");
}
if (featurePruning) {
return predictWithFeaturePruning(featureVector, decision);
}
// if (cardinalities == null) {
// if (getConfigFileEntry(".car") != null) {
// cardinalities = loadCardinalities(getInstanceInputStreamReaderFromConfigFile(".car"));
// } else {
// cardinalities = getCardinalities(featureVector);
// }
// }
//System.out.println("METHOD PREDICT CARDINALITIES SIZE" + cardinalities.length + " FEATURE VECTOR SIZE " +featureVector.size());
if (xlist == null) {
xlist = new ArrayList<FeatureNode>(featureVector.size());
}
// int offset = 1;
// int i = 0;
// for (FeatureFunction feature : featureVector) {
// final FeatureValue featureValue = feature.getFeatureValue();
// if (!(excludeNullValues == true && featureValue.isNullValue())) {
// if (featureValue instanceof SingleFeatureValue) {
// if (((SingleFeatureValue)featureValue).getCode() < cardinalities[i]) {
// xlist.add(new FeatureNode(((SingleFeatureValue)featureValue).getCode() + offset, 1));
// }
// } else if (featureValue instanceof MultipleFeatureValue) {
// for (Integer value : ((MultipleFeatureValue)featureValue).getCodes()) {
// if (value < cardinalities[i]) {
// xlist.add(new FeatureNode(value + offset, 1));
// }
// }
// }
// }
// offset += cardinalities[i];
// i++;
// }
FeatureNode[] xarray = new FeatureNode[xlist.size()];
for (int k = 0; k < xlist.size(); k++) {
xarray[k] = xlist.get(k);
}
if (decision.getKBestList().getK() == 1) {
decision.getKBestList().add(Linear.predict(model, xarray));
} else {
liblinear_predict_with_kbestlist(model, xarray, decision.getKBestList());
}
xlist.clear();
return true;
}
public boolean predictWithFeaturePruning(FeatureVector featureVector, SingleDecision decision) throws MaltChainedException {
if (featureMap == null) {
featureMap = loadFeatureMap(getInputStreamFromConfigFileEntry(".map"));
}
int i = 1;
for (FeatureFunction feature : featureVector) {
final FeatureValue featureValue = feature.getFeatureValue();
if (!(excludeNullValues == true && featureValue.isNullValue())) {
if (featureValue instanceof SingleFeatureValue) {
int v = getFeatureMapValue(i, ((SingleFeatureValue) featureValue).getIndexCode());
if (v != -1) {
featureSet.add(new XNode(v, 1));
}
} else if (featureValue instanceof MultipleFeatureValue) {
for (Integer value : ((MultipleFeatureValue) featureValue).getCodes()) {
int v = getFeatureMapValue(i, value);
if (v != -1) {
featureSet.add(new XNode(v, 1));
}
}
}
}
i++;
}
FeatureNode[] xarray = new FeatureNode[featureSet.size()];
int k = 0;
for (XNode x : featureSet) {
xarray[k++] = new FeatureNode(x.getIndex(), x.getValue());
}
if (decision.getKBestList().getK() == 1) {
decision.getKBestList().add(Linear.predict(model, xarray));
} else {
liblinear_predict_with_kbestlist(model, xarray, decision.getKBestList());
}
featureSet.clear();
return true;
}
public void terminate() throws MaltChainedException {
closeInstanceWriter();
model = null;
xlist = null;
owner = null;
}
public BufferedWriter getInstanceWriter() {
return instanceOutput;
}
protected void closeInstanceWriter() throws MaltChainedException {
try {
if (instanceOutput != null) {
instanceOutput.flush();
instanceOutput.close();
instanceOutput = null;
}
} catch (IOException e) {
throw new LiblinearException("The Liblinear learner cannot close the instance file. ", e);
}
}
/**
* Returns the parameter string for used for configure Liblinear
*
* @return the parameter string for used for configure Liblinear
*/
public String getParamString() {
return paramString;
}
public InstanceModel getOwner() {
return owner;
}
protected void setOwner(InstanceModel owner) {
this.owner = owner;
}
public int getLearnerMode() {
return learnerMode;
}
public void setLearnerMode(int learnerMode) throws MaltChainedException {
this.learnerMode = learnerMode;
}
public String getLearningMethodName() {
return name;
}
/**
* Returns the current configuration
*
* @return the current configuration
* @throws MaltChainedException
*/
public DependencyParserConfig getConfiguration() throws MaltChainedException {
return owner.getGuide().getConfiguration();
}
public int getNumberOfInstances() throws MaltChainedException {
if (numberOfInstances != 0) {
return numberOfInstances;
} else {
//Do a line count of the instance file and return that
BufferedReader reader = new BufferedReader(getInstanceInputStreamReader(".ins"));
try {
while (reader.readLine() != null) {
numberOfInstances++;
owner.increaseFrequency();
}
reader.close();
} catch (IOException e) {
throw new MaltChainedException("No instances found in file", e);
}
return numberOfInstances;
}
}
public void increaseNumberOfInstances() {
numberOfInstances++;
owner.increaseFrequency();
}
public void decreaseNumberOfInstances() {
numberOfInstances--;
owner.decreaseFrequency();
}
protected void setNumberOfInstances(int numberOfInstances) {
this.numberOfInstances = 0;
}
protected void setLearningMethodName(String name) {
this.name = name;
}
protected OutputStreamWriter getInstanceOutputStreamWriter(String suffix) throws MaltChainedException {
return getConfiguration().getConfigurationDir().getAppendOutputStreamWriter(owner.getModelName() + getLearningMethodName() + suffix);
}
protected InputStreamReader getInstanceInputStreamReader(String suffix) throws MaltChainedException {
return getConfiguration().getConfigurationDir().getInputStreamReader(owner.getModelName() + getLearningMethodName() + suffix);
}
protected InputStreamReader getInstanceInputStreamReaderFromConfigFile(String suffix) throws MaltChainedException {
return getConfiguration().getConfigurationDir().getInputStreamReaderFromConfigFile(owner.getModelName() + getLearningMethodName() + suffix);
}
protected InputStream getInputStreamFromConfigFileEntry(String suffix) throws MaltChainedException {
return getConfiguration().getConfigurationDir().getInputStreamFromConfigFileEntry(owner.getModelName() + getLearningMethodName() + suffix);
}
protected File getFile(String suffix) throws MaltChainedException {
return getConfiguration().getConfigurationDir().getFile(owner.getModelName() + getLearningMethodName() + suffix);
}
protected JarEntry getConfigFileEntry(String suffix) throws MaltChainedException {
return getConfiguration().getConfigurationDir().getConfigFileEntry(owner.getModelName() + getLearningMethodName() + suffix);
}
public Problem readLibLinearProblemWithFeaturePruning(InputStreamReader isr) throws MaltChainedException {
Problem problem = new Problem();
try {
final BufferedReader fp = new BufferedReader(isr);
problem.bias = -1;
problem.l = getNumberOfInstances();
problem.x = new FeatureNode[problem.l][];
problem.y = new int[problem.l];
int i = 0;
final Pattern tabPattern = Pattern.compile("\t");
final Pattern pipePattern = Pattern.compile("\\|");
while (true) {
String line = fp.readLine();
if (line == null) {
break;
}
String[] columns = tabPattern.split(line);
if (columns.length == 0) {
continue;
}
int j = 0;
try {
problem.y[i] = Integer.parseInt(columns[j]);
for (j = 1; j < columns.length; j++) {
final String[] items = pipePattern.split(columns[j]);
for (int k = 0; k < items.length; k++) {
try {
int colon = items[k].indexOf(':');
if (colon == -1) {
if (Integer.parseInt(items[k]) != -1) {
int v = addFeatureMapValue(j, Integer.parseInt(items[k]));
if (v != -1) {
featureSet.add(new XNode(v, 1));
}
}
} else {
int index = addFeatureMapValue(j, Integer.parseInt(items[k].substring(0, colon)));
double value;
if (items[k].substring(colon + 1).indexOf('.') != -1) {
value = Double.parseDouble(items[k].substring(colon + 1));
} else {
value = Integer.parseInt(items[k].substring(colon + 1));
}
featureSet.add(new XNode(index, value));
}
} catch (NumberFormatException e) {
throw new LiblinearException("The instance file contain a non-integer value '" + items[k] + "'", e);
}
}
}
problem.x[i] = new FeatureNode[featureSet.size()];
int p = 0;
for (XNode x : featureSet) {
problem.x[i][p++] = new FeatureNode(x.getIndex(), x.getValue());
}
featureSet.clear();
i++;
} catch (ArrayIndexOutOfBoundsException e) {
throw new LiblinearException("Cannot read from the instance file. ", e);
}
}
fp.close();
featureSet = null;
problem.n = featureMap.size();
System.out.println("Number of features: " + problem.n);
} catch (IOException e) {
throw new LiblinearException("Cannot read from the instance file. ", e);
}
return problem;
}
/**
* Reads an instance file into a problem object according to the Malt-SVM
* format, which is column fixed format (tab-separated).
*
* @param isr the instance stream reader for the instance file
* @param cardinalities a array containing the number of distinct values for
* a particular column.
* @throws LiblinearException
*/
public Problem readLibLinearProblem(InputStreamReader isr, int[] cardinalities) throws MaltChainedException {
Problem problem = new Problem();
try {
final BufferedReader fp = new BufferedReader(isr);
int max_index = 0;
if (xlist == null) {
xlist = new ArrayList<FeatureNode>();
}
problem.bias = -1; //getBias();
problem.l = getNumberOfInstances();
problem.x = new FeatureNode[problem.l][];
problem.y = new int[problem.l];
int i = 0;
final Pattern tabPattern = Pattern.compile("\t");
final Pattern pipePattern = Pattern.compile("\\|");
while (true) {
String line = fp.readLine();
if (line == null) {
break;
}
String[] columns = tabPattern.split(line);
if (columns.length == 0) {
continue;
}
int offset = 1;
int j = 0;
try {
problem.y[i] =
Integer.parseInt(columns[j]);
int p = 0;
for (j = 1; j < columns.length; j++) {
final String[] items = pipePattern.split(columns[j]);
for (int k = 0; k < items.length; k++) {
try {
if (Integer.parseInt(items[k]) != -1) {
xlist.add(p, new FeatureNode(Integer.parseInt(items[k]) + offset, 1));
p++;
}
} catch (NumberFormatException e) {
throw new LiblinearException("The instance file contain a non-integer value '" + items[k] + "'", e);
}
}
offset += cardinalities[j - 1];
}
problem.x[i] = xlist.subList(0, p).toArray(new FeatureNode[0]);
if (columns.length > 1) {
max_index = Math.max(max_index, problem.x[i][p - 1].index);
}
i++;
xlist.clear();
} catch (ArrayIndexOutOfBoundsException e) {
throw new LiblinearException("Cannot read from the instance file. ", e);
}
}
fp.close();
problem.n = max_index;
System.out.println("Number of features: " + problem.n);
// if ( problem.bias >= 0 ) {
// problem.n++;
// }
xlist = null;
} catch (IOException e) {
throw new LiblinearException("Cannot read from the instance file. ", e);
}
return problem;
}
protected void initSpecialParameters() throws MaltChainedException {
if (getConfiguration().getOptionValue("singlemalt", "null_value") != null && getConfiguration().getOptionValue("singlemalt", "null_value").toString().equalsIgnoreCase("none")) {
excludeNullValues = true;
} else {
excludeNullValues = false;
}
saveInstanceFiles = ((Boolean) getConfiguration().getOptionValue("liblinear", "save_instance_files")).booleanValue();
// featurePruning = ((Boolean)getConfiguration().getOptionValue("liblinear", "feature_pruning")).booleanValue();
featurePruning = true;
if (!getConfiguration().getOptionValue("liblinear", "liblinear_external").toString().equals("")) {
try {
if (!new File(getConfiguration().getOptionValue("liblinear", "liblinear_external").toString()).exists()) {
throw new LiblinearException("The path to the external Liblinear trainer 'svm-train' is wrong.");
}
if (new File(getConfiguration().getOptionValue("liblinear", "liblinear_external").toString()).isDirectory()) {
throw new LiblinearException("The option --liblinear-liblinear_external points to a directory, the path should point at the 'train' file or the 'train.exe' file");
}
if (!(getConfiguration().getOptionValue("liblinear", "liblinear_external").toString().endsWith("train") || getConfiguration().getOptionValue("liblinear", "liblinear_external").toString().endsWith("train.exe"))) {
throw new LiblinearException("The option --liblinear-liblinear_external does not specify the path to 'train' file or the 'train.exe' file. ");
}
pathExternalLiblinearTrain = getConfiguration().getOptionValue("liblinear", "liblinear_external").toString();
} catch (SecurityException e) {
throw new LiblinearException("Access denied to the file specified by the option --liblinear-liblinear_external. ", e);
}
}
if (getConfiguration().getOptionValue("liblinear", "verbosity") != null) {
verbosity = Verbostity.valueOf(getConfiguration().getOptionValue("liblinear", "verbosity").toString().toUpperCase());
}
}
public String getLibLinearOptions() {
StringBuilder sb = new StringBuilder();
for (String key : liblinearOptions.keySet()) {
sb.append('-');
sb.append(key);
sb.append(' ');
sb.append(liblinearOptions.get(key));
sb.append(' ');
}
return sb.toString();
}
public void parseParameters(String paramstring) throws MaltChainedException {
if (paramstring == null) {
return;
}
final String[] argv;
String allowedFlags = "sceB";
try {
argv = paramstring.split("[_\\p{Blank}]");
} catch (PatternSyntaxException e) {
throw new LiblinearException("Could not split the liblinear-parameter string '" + paramstring + "'. ", e);
}
for (int i = 0; i < argv.length - 1; i++) {
if (argv[i].charAt(0) != '-') {
throw new LiblinearException("The argument flag should start with the following character '-', not with " + argv[i].charAt(0));
}
if (++i >= argv.length) {
throw new LiblinearException("The last argument does not have any value. ");
}
try {
int index = allowedFlags.indexOf(argv[i - 1].charAt(1));
if (index != -1) {
liblinearOptions.put(Character.toString(argv[i - 1].charAt(1)), argv[i]);
} else {
throw new LiblinearException("Unknown liblinear parameter: '" + argv[i - 1] + "' with value '" + argv[i] + "'. ");
}
} catch (ArrayIndexOutOfBoundsException e) {
throw new LiblinearException("The liblinear parameter '" + argv[i - 1] + "' could not convert the string value '" + argv[i] + "' into a correct numeric value. ", e);
} catch (NumberFormatException e) {
throw new LiblinearException("The liblinear parameter '" + argv[i - 1] + "' could not convert the string value '" + argv[i] + "' into a correct numeric value. ", e);
} catch (NullPointerException e) {
throw new LiblinearException("The liblinear parameter '" + argv[i - 1] + "' could not convert the string value '" + argv[i] + "' into a correct numeric value. ", e);
}
}
}
public double getBias() throws MaltChainedException {
try {
return Double.valueOf(liblinearOptions.get("B")).doubleValue();
} catch (NumberFormatException e) {
throw new LiblinearException("The liblinear bias value is not numerical value. ", e);
}
}
public Parameter getLiblinearParameters() throws MaltChainedException {
Parameter param = new Parameter(SolverType.MCSVM_CS, 0.1, 0.1);
String type = liblinearOptions.get("s");
if (type.equals("0")) {
param.setSolverType(SolverType.L2R_LR);
} else if (type.equals("1")) {
param.setSolverType(SolverType.L2R_L2LOSS_SVC_DUAL);
} else if (type.equals("2")) {
param.setSolverType(SolverType.L2R_L2LOSS_SVC);
} else if (type.equals("3")) {
param.setSolverType(SolverType.L2R_L1LOSS_SVC_DUAL);
} else if (type.equals("4")) {
param.setSolverType(SolverType.MCSVM_CS);
} else if (type.equals("5")) {
param.setSolverType(SolverType.L1R_L2LOSS_SVC);
} else if (type.equals("6")) {
param.setSolverType(SolverType.L1R_LR);
} else {
throw new LiblinearException("The liblinear type (-s) is not an integer value between 0 and 4. ");
}
try {
param.setC(Double.valueOf(liblinearOptions.get("c")).doubleValue());
} catch (NumberFormatException e) {
throw new LiblinearException("The liblinear cost (-c) value is not numerical value. ", e);
}
try {
param.setEps(Double.valueOf(liblinearOptions.get("e")).doubleValue());
} catch (NumberFormatException e) {
throw new LiblinearException("The liblinear epsilon (-e) value is not numerical value. ", e);
}
return param;
}
public void initLiblinearOptions() {
liblinearOptions.put("s", "4"); // type = SolverType.L2LOSS_SVM_DUAL (default)
liblinearOptions.put("c", "0.1"); // cost = 1 (default)
liblinearOptions.put("e", "0.1"); // epsilon = 0.1 (default)
liblinearOptions.put("B", "-1"); // bias = -1 (default)
}
public String[] getLibLinearParamStringArray() {
final ArrayList<String> params = new ArrayList<String>();
for (String key : liblinearOptions.keySet()) {
params.add("-" + key);
params.add(liblinearOptions.get(key));
}
return params.toArray(new String[params.size()]);
}
public void liblinear_predict_with_kbestlist(Model model, FeatureNode[] x, KBestList kBestList) throws MaltChainedException {
int i;
final int nr_class = model.getNrClass();
final double[] dec_values = new double[nr_class];
Linear.predictValues(model, x, dec_values);
final int[] labels = model.getLabels();
int[] predictionList = new int[nr_class];
for (i = 0; i < nr_class; i++) {
predictionList[i] = labels[i];
}
double tmpDec;
int tmpObj;
int lagest;
for (i = 0; i < nr_class - 1; i++) {
lagest = i;
for (int j = i; j < nr_class; j++) {
if (dec_values[j] > dec_values[lagest]) {
lagest = j;
}
}
tmpDec = dec_values[lagest];
dec_values[lagest] = dec_values[i];
dec_values[i] = tmpDec;
tmpObj = predictionList[lagest];
predictionList[lagest] = predictionList[i];
predictionList[i] = tmpObj;
}
int k = nr_class - 1;
if (kBestList.getK() != -1) {
k = kBestList.getK() - 1;
}
for (i = 0; i < nr_class && k >= 0; i++, k--) {
if (kBestList instanceof ScoredKBestList) {
((ScoredKBestList) kBestList).add(predictionList[i], (float) dec_values[i]);
} else {
kBestList.add(predictionList[i]);
}
}
}
/**
* Converts the instance file (Malt's own SVM format) into the Liblinear
* (SVMLight) format. The input instance file is removed (replaced) by the
* instance file in the Liblinear (SVMLight) format. If a column contains
* -1, the value will be removed in destination file.
*
* @param isr the input stream reader for the source instance file
* @param osw the output stream writer for the destination instance file
* @param cardinalities a vector containing the number of distinct values
* for a particular column
* @throws LiblinearException
*/
public static void maltSVMFormat2OriginalSVMFormat(InputStreamReader isr, OutputStreamWriter osw, int[] cardinalities) throws MaltChainedException {
try {
final BufferedReader in = new BufferedReader(isr);
final BufferedWriter out = new BufferedWriter(osw);
int c;
int j = 0;
int offset = 1;
int code = 0;
while (true) {
c = in.read();
if (c == -1) {
break;
}
if (c == '\t' || c == '|') {
if (j == 0) {
out.write(Integer.toString(code));
j++;
} else {
if (code != -1) {
out.write(' ');
out.write(Integer.toString(code + offset));
out.write(":1");
}
if (c == '\t') {
offset += cardinalities[j - 1];
j++;
}
}
code = 0;
} else if (c == '\n') {
j = 0;
offset = 1;
out.write('\n');
code = 0;
} else if (c == '-') {
code = -1;
} else if (code != -1) {
if (c > 47 && c < 58) {
code = code * 10 + (c - 48);
} else {
throw new LiblinearException("The instance file contain a non-integer value, when converting the Malt SVM format into Liblinear format.");
}
}
}
in.close();
out.close();
} catch (IOException e) {
throw new LiblinearException("Cannot read from the instance file, when converting the Malt SVM format into Liblinear format. ", e);
}
}
@Override
protected void finalize() throws Throwable {
try {
closeInstanceWriter();
} finally {
super.finalize();
}
}
/*
* (non-Javadoc) @see java.lang.Object#toString()
*/
@Override
public String toString() {
final StringBuffer sb = new StringBuffer();
sb.append("\nLiblinear INTERFACE\n");
sb.append(" Liblinear version: ").append(LIBLINEAR_VERSION).append("\n");
sb.append(" Liblinear string: ").append(paramString).append("\n");
sb.append(getLibLinearOptions());
return sb.toString();
}
}