/*
* DocFeatureVectors.java
*
* Yaoyong Li 22/03/2007
*
* $Id: DocFeatureVectors.java, v 1.0 2007-03-22 12:58:16 +0000 yaoyong $
*/
package gate.learning;
import gate.util.GateException;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Hashtable;
import java.util.List;
import java.util.regex.Pattern;
/**
* Convert the NLP features into a sparse feature vector for each instance of
* one document.
*/
public class DocFeatureVectors {
/** Document ID. */
public String docId = null;
/** Number of instance in the document. */
public int numInstances;
/**
* Array containing all sparse feature vectors of instances in the documents.
*/
public SparseFeatureVector[] fvs;
/** Default value of one component of feature vector. */
final static float DEFAULTVALUE = 1.0f;
/** Constructor in trival case. */
public DocFeatureVectors() {
numInstances = 0;
}
/**
* The main method for obtaining the sparse feature vector and label(s) from
* the NLP features of each instance in the documents.
*/
public void obtainFVsFromNLPFeatures(NLPFeaturesOfDoc nlpDoc,
NLPFeaturesList featList, int[] featurePosition, int maxNegPosition,
int numDocs, float ngramWeight, int valueType) {
numInstances = nlpDoc.numInstances;
docId = new String(nlpDoc.getDocId());
fvs = new SparseFeatureVector[numInstances];
// For each istance
for(int i = 0; i < numInstances; ++i) {
Hashtable indexValues = new Hashtable();
int n = 0;
String[] feat = nlpDoc.featuresInLine[i].toString().split(
ConstantParameters.ITEMSEPARATOR);
// Some variables for normalising the feature values of the same ngram
// boolean sameNgram=true;
int prevPosition = -99999;
float[] tempVals = new float[9999];
long[] tempInds = new long[9999];
int tempSize = 0;
int positionCurr = 0;
for(int j = 0; j < feat.length; ++j) {
// First get the position information for the current NLP feature
positionCurr = 0;
if(feat[j] != null && Pattern.matches((".+\\[[-0-9]+\\]$"), feat[j])) {
int ind = feat[j].lastIndexOf('[');
String positionStr = feat[j].substring(ind + 1, feat[j].length() - 1);
positionCurr = Integer.parseInt(positionStr);
feat[j] = feat[j].substring(0, ind);
}
if(prevPosition != positionCurr && tempSize > 0) {
double sum = 0.0;
for(int ii = 0; ii < tempSize; ++ii)
sum += tempVals[ii] * tempVals[ii];
sum = Math.sqrt(sum);
for(int ii = 0; ii < tempSize; ++ii) {
tempVals[ii] /= sum;
indexValues.put(new Long(tempInds[ii]), new Float(tempVals[ii]));
}
tempSize = 0;
}
String featCur = feat[j];
String featVal = null;
int kk = -3;
if(featCur.contains(NLPFeaturesList.SYMBOLNGARM)) {
kk = feat[j].lastIndexOf(NLPFeaturesList.SYMBOLNGARM);
featCur = feat[j].substring(0, kk);
featVal = feat[j].substring(kk + 2);
}
if(featCur.length() > 0) { // if there is any feature
if(featList.featuresList.containsKey(featCur)) {
if(featCur.contains(NLPFeaturesList.SYMBOLNGARM)) {
int shiftNum = 0;
if(positionCurr > 0)
shiftNum = maxNegPosition + positionCurr;
else shiftNum = -positionCurr;
long featInd = Long.parseLong(featList.featuresList.get(featCur)
.toString())
+ shiftNum * ConstantParameters.MAXIMUMFEATURES;
double val = 0.0;
switch(valueType){
case 1: // for only the presence of Ngram in the sentence
val = 1.0;
break;
case 2: // for tf representation
val = Long.parseLong(featVal);
break;
case 3: // for tf*idf representation
val = (Long.parseLong(featVal) + 1)
* Math.log((double)numDocs
/ (Long.parseLong(featList.idfFeatures.get(featCur)
.toString())));
break;
default:
try {
throw new GateException(
"The value type for ngram is not defined!");
} catch(GateException e) {
e.printStackTrace();
}
}
// indexValues.put(featInd, new Float(val));
tempInds[tempSize] = featInd;
tempVals[tempSize] = (float)val;
++tempSize;
} else {
if(positionCurr == 0)
indexValues.put(featList.featuresList.get(feat[j]), "1");
else if(positionCurr < 0)
indexValues.put(new Long((Long.parseLong(featList.featuresList
.get(feat[j]).toString()) - positionCurr
* ConstantParameters.MAXIMUMFEATURES)), new Float(-1.0
/ (double)positionCurr));
else indexValues.put(
new Long((Long.parseLong(featList.featuresList.get(feat[j])
.toString()) + (positionCurr + maxNegPosition)
* ConstantParameters.MAXIMUMFEATURES)), new Float(
1.0 / (double)positionCurr));
}
++n;
}
}
prevPosition = positionCurr;
} // end of the loop on the features of one instances
// For the last ngram features
if(tempSize > 0) {
if(valueType == 3) {
double sum = 0.0;
for(int ii = 0; ii < tempSize; ++ii)
sum += tempVals[ii] * tempVals[ii];
sum = Math.sqrt(sum);
for(int ii = 0; ii < tempSize; ++ii) {
tempVals[ii] /= sum;
tempVals[ii] *= ngramWeight;
indexValues.put(new Long(tempInds[ii]), new Float(tempVals[ii]));
}
} else {
for(int ii = 0; ii < tempSize; ++ii) {
indexValues.put(new Long(tempInds[ii]), new Integer(
(int)tempVals[ii]));
}
}
tempSize = 0;
}
// if(LogService.minVerbosityLevel > 1)
// if(n != nlpDoc.featuresCounted[i]) {
// System.out.println("Error: the number of features (" + n
// + ") is not the same as the number recorded ("
// + nlpDoc.featuresCounted[i] + ")in document " + docId);
// }
// sort the indexes in ascending order
List indexes = new ArrayList(indexValues.keySet());
Collections.sort(indexes, new LongCompactor());
// Iterator iterator = indexes.iterator();
// n = 0;
// while(iterator.hasNext()) {
// Object key = iterator.next();
// fvs[i].indexes[n] = ((Long)key).longValue();
fvs[i] = new SparseFeatureVector(indexes.size());
// for(int j=0; j<n; ++j) {
for(int j = 0; j < indexes.size(); ++j) {
// System.out.println(new Integer(j) +" index=*"+
// indexes.get(j)+"*");
fvs[i].nodes[j].index = Integer.parseInt(indexes.get(j).toString());
// for the constant value 1
// fvs[i].values[j] = DEFAULTVALUE;
// for the tf or tf*idf value
fvs[i].nodes[j].value = Double.parseDouble(indexValues.get(indexes.get(j))
.toString());
}
} // end of the loop on the instances
}
/** A static class for comparing two long numbers. */
public static class LongCompactor implements java.util.Comparator {
public int compare(Object l1, Object l2) {
// return (new Long((new Long(l1.toString()).longValue()- new
// Long(l2.toString()).longValue()))).intValue();
return (int)(Long.parseLong(l1.toString()) - Long
.parseLong(l2.toString()));
}
}
/** Read the feature vectors of a document from the feature vector file. */
public void readDocFVFromFile(BufferedReader dataFile, int num,
LabelsOfFeatureVectorDoc labelsDoc) {
numInstances = num;
fvs = new SparseFeatureVector[numInstances];
labelsDoc.multiLabels = new LabelsOfFV[numInstances];
try {
String line;
for(int i = 0; i < num; ++i) {
line = dataFile.readLine();
// System.out.println("i="+i+"line="+line);
String[] items = line.split(ConstantParameters.ITEMSEPARATOR);
// get the label from the line
int iEndLabel;
// get the multilabel directly
iEndLabel = obtainMultiLabels(items, labelsDoc.multiLabels, i);
// get the feature vector
int len = items.length - iEndLabel;
if(len == 0) {
// If there is no feature vector, creat one for it
fvs[i] = new SparseFeatureVector(1);
fvs[i].nodes[0].index = 1;
fvs[i].nodes[0].value = 0;
} else {
fvs[i] = new SparseFeatureVector(len);
obtainFVs(items, iEndLabel, len, fvs[i]);
}
}
} catch(IOException e) {
e.printStackTrace();
}
return;
}
/** Get the multi label(s) from one line of feature vector. */
private int obtainMultiLabels(String[] items, LabelsOfFV[] multiLabels, int i) {
int num;
int kk = 1;
num = Integer.valueOf(items[kk++]);
multiLabels[i] = new LabelsOfFV(num);
if(num > 0) {
multiLabels[i].labels = new int[num];
for(int j = 0; j < num; ++j)
multiLabels[i].labels[j] = Integer.valueOf(items[kk++]);
}
return kk;
}
/** Get the feature vector in parse format from a String arrays. */
private void obtainFVs(String[] items, int iEndLabel, int len,
SparseFeatureVector fv) {
String[] indexValue;
for(int i = 0; i < len; ++i) {
indexValue = items[i + iEndLabel]
.split(ConstantParameters.INDEXVALUESEPARATOR);
if(indexValue.length <= 1) {
System.out.println("i=" + i + " item=" + items[i + iEndLabel]);
}
fv.nodes[i].index = (new Integer(indexValue[0])).intValue();
fv.nodes[i].value = (new Float(indexValue[1])).floatValue();
}
return;
}
/** Get number of instances in the document. */
public int getNumInstances() {
return numInstances;
}
/** Get the fv array. */
public SparseFeatureVector[] getFvs() {
return fvs;
}
/** Delete the fv array. */
public void deleteFvs() {
for(int i=0; i<fvs.length; ++i)
fvs[i] = null;
//fvs= null;
}
/** Set the DocID. */
public void setDocID(String docI) {
this.docId = docI;
}
/** Expand the feature vector to including the context tokens. */
public void expandFV(int winSizeLeft, int winSizeRight) {
SparseFeatureVector[] fvsExpand = new SparseFeatureVector[fvs.length];
for(int i = 0; i < fvs.length; ++i) {
int lenT0 = fvs[i].len;
for(int j = -1; j >= -winSizeLeft; --j) {
if(j + i >= 0) lenT0 += fvs[j + i].len;
}
for(int j = 1; j <= winSizeRight; ++j)
if(j + i < fvs.length) lenT0 += fvs[j + i].len;
fvsExpand[i] = new SparseFeatureVector(lenT0);
// System.out.println("lent0="+lenT0);
for(int j1 = 0; j1 < fvs[i].len; ++j1) {
fvsExpand[i].nodes[j1].index = fvs[i].nodes[j1].index;
fvsExpand[i].nodes[j1].value = fvs[i].nodes[j1].value;
}
int lenTotal = fvs[i].len;
for(int j = -1; j >= -winSizeLeft; --j) {
int kk = j + i;
if(kk >= 0) {
int gapLen = -j * (int)ConstantParameters.MAXIMUMFEATURES;
for(int j1 = 0; j1 < fvs[kk].len; ++j1) {
if(j1 + lenTotal >= lenT0)
System.out.println("i=" + i + ", j=" + j + ",j1=" + j1
+ ", newlen=" + lenTotal);
fvsExpand[i].nodes[j1 + lenTotal].index = fvs[kk].nodes[j1].index + gapLen;
fvsExpand[i].nodes[j1 + lenTotal].value = fvs[kk].nodes[j1].value / (-j);
}
lenTotal += fvs[kk].len;
}
}
for(int j = 1; j <= winSizeRight; ++j) {
int kk = j + i;
if(kk < fvs.length) {
int gapLen = (j + winSizeLeft)
* (int)ConstantParameters.MAXIMUMFEATURES;
for(int j1 = 0; j1 < fvs[kk].len; ++j1) {
fvsExpand[i].nodes[j1 + lenTotal].index = fvs[kk].nodes[j1].index + gapLen;
fvsExpand[i].nodes[j1 + lenTotal].value = fvs[kk].nodes[j1].value / j;
}
lenTotal += fvs[kk].len;
}
}
}// end of the loop for each fv
fvs = fvsExpand;
}
/** Write the FVs of one document into file. */
public void addDocFVsToFile(int index, BufferedWriter out, int[] labels) {
try {
out.write(new Integer(index) + ConstantParameters.ITEMSEPARATOR
+ new Integer(numInstances) + ConstantParameters.ITEMSEPARATOR
+ docId);
out.newLine();
for(int i = 0; i < numInstances; ++i) {
StringBuffer line = new StringBuffer();
line.append(new Integer(i + 1) + ConstantParameters.ITEMSEPARATOR
+ new Integer(labels[i]));
for(int j = 0; j < fvs[i].len; ++j)
line.append(ConstantParameters.ITEMSEPARATOR
+ fvs[i].nodes[j].index + ConstantParameters.INDEXVALUESEPARATOR
+ fvs[i].nodes[j].value);
out.write(line.toString());
out.newLine();
}
} catch(IOException e) {
}
}
/** Write the FVs with labels of one document into file. */
public void addDocFVsMultiLabelToFile(int index, BufferedWriter out,
LabelsOfFV[] multiLabels) {
try {
out.write(new Integer(index) + ConstantParameters.ITEMSEPARATOR
+ new Integer(numInstances) + ConstantParameters.ITEMSEPARATOR
+ docId);
out.newLine();
for(int i = 0; i < numInstances; ++i) {
StringBuffer line = new StringBuffer();
line.append(new Integer(i + 1) + ConstantParameters.ITEMSEPARATOR
+ multiLabels[i].num);
for(int j = 0; j < multiLabels[i].num; ++j)
line.append(ConstantParameters.ITEMSEPARATOR
+ multiLabels[i].labels[j]);
for(int j = 0; j < fvs[i].len; ++j)
line.append(ConstantParameters.ITEMSEPARATOR
+ fvs[i].nodes[j].index + ConstantParameters.INDEXVALUESEPARATOR
+ fvs[i].nodes[j].value);
out.write(line.toString());
out.newLine();
}
} catch(IOException e) {
}
}
}
