Examples of edu.umd.cloud9.webgraph.data.AnchorText

edu.umd.cloud9.io.pair.PairOfFloatString

This data structure represents a line of anchor text. A line of anchor text has some text, a weight, and a set of sources (targets) associated with it. Sources (targets) are the pages a line of anchor text originates from (points to) when the underlying link is an incoming (outgoing) link.

The implemented iterator makes it possible to iterate through the source or target documents for each line of anchor text.
@author Nima Asadi

    Configuration conf = IntegrationUtils.getBespinConfiguration();
    FileSystem fs = FileSystem.get(conf);


    SequenceFile.Reader reader;
    IntWritable key = new IntWritable();
    HMapSFW value = new HMapSFW();


    reader = new SequenceFile.Reader(fs.getConf(),
        SequenceFile.Reader.file(new Path(opennlpIndex + "/test_wt-term-doc-vectors/part-00000")));


    reader.next(key, value);
    System.out.println("*** top 10 terms ***");
    for (MapKF.Entry<String> entry : value.getEntriesSortedByValue(10)) {
      System.out.println(entry.getKey() + ": " + entry.getValue());
    }
    verifyTermDocVector(opennlpTermDocVector1, value);


    reader.next(key, value);
    System.out.println("*** top 10 terms ***");
    for (MapKF.Entry<String> entry : value.getEntriesSortedByValue(10)) {
      System.out.println(entry.getKey() + ": " + entry.getValue());
    }
    verifyTermDocVector(opennlpTermDocVector2, value);
    reader.close();
  }

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      sLogger = logger;
    }


    //sLogger.setLevel(Level.DEBUG);


    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(int e : tfTable.keySet()){
      // retrieve term string, tf and df
      String eTerm = eVocab.get(e);
      float tf = tfTable.get(e);
      float df = dfTable.get(e);


      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);


      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);
      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }    
      }
    }


    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }

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      sLogger = logger;
    }


    //sLogger.setLevel(Level.DEBUG);


    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(int e : tfTable.keySet()){
      // retrieve term string, tf and df
      String eTerm = eVocab.get(e);
      float tf = tfTable.get(e);
      float df = dfTable.get(eTerm);


      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);


      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);
      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }   
      }
    }


    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }

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      sLogger = logger;
    }


    //    sLogger.setLevel(Level.DEBUG);


    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(edu.umd.cloud9.util.map.MapIF.Entry entry : tfTable.entrySet()){
      // retrieve term string, tf and df
      String eTerm = eVocab.get(entry.getKey());
      float tf = entry.getValue();
      int eId = dict.getId(eTerm);
      if(eId < 1){    //OOV
        continue;
      }
      int df = dfTable.getDf(eId);
      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);
      if(df<1){
        sLogger.warn("Suspicious DF WARNING = "+eTerm+" "+tf+" "+df+" "+score);
      }


      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);


      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }    
      }
    }


    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }

View Full Code Here

      sLogger = logger;
    }


    //    sLogger.setLevel(Level.DEBUG);


    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(edu.umd.cloud9.util.map.MapKI.Entry<String> entry : tfTable.entrySet()){
      // retrieve term string, tf and df
      String eTerm = entry.getKey();
      float tf = entry.getValue();
      int eId = dict.getId(eTerm);
      if(eId < 1){    //OOV
        continue;
      }
      int df = dfTable.getDf(eId);
      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);
      if(df<1){
        sLogger.warn("Suspicious DF WARNING = "+eTerm+" "+tf+" "+df+" "+score);
      }


      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);


      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }   
      }
    }


    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }

View Full Code Here

  public static HMapSFW createTermDocVector(int docLen, HMapSIW tfTable, Vocab eVocabSrc, ScoringModel scoringModel, PrefixEncodedGlobalStats globalStats, boolean isNormalize, Logger sLogger) {
    if(sLogger == null){
      sLogger = logger;
    }


    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(edu.umd.cloud9.util.map.MapKI.Entry<String> entry : tfTable.entrySet()){
      // retrieve term string, tf and df
      String eTerm = entry.getKey();
      int tf = entry.getValue();


      int df = globalStats.getDF(eTerm);
      if(df<1){    //OOV
        continue;
      }


      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);


      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }    
      }
    }


    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }

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    Map<String,HMapSFW> scfgDist = new HashMap<String,HMapSFW>();


    // phrase2count table is a set of (source_phrase --> X) maps, where X is a set of (phrase_trans --> count) maps
    HMapSFW phraseDist = new HMapSFW();


    HMapSIW srcTokenCnt = new HMapSIW();


    Set<String> bagOfTargetTokens = new HashSet<String>();


    try {
      FSDataInputStream fis = fs.open(new Path(grammarFile));

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      return null;
    }
    PriorityQueue<PairOfFloatInt> eS = f2eProbs.get(f).getTranslationsWithProbs(lexProbThreshold);


    if (!eS.isEmpty()) {
      PairOfFloatInt entry = eS.poll();
      int e = entry.getRightElement();
      String eTerm = eVocab_f2e.get(e);
      return eTerm;
    }
    return token;
  }

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    float sumProbEF = 0;
    int numTrans = 0;
    //tf(e) = sum_f{tf(f)*prob(e|f)}
    while (numTrans < numTransPerToken && !eS.isEmpty()) {
      PairOfFloatInt entry = eS.poll();
      float probEF = entry.getLeftElement();
      int e = entry.getRightElement();
      String eTerm = eVocab_f2e.get(e);


      //      LOG.info("Pr("+eTerm+"|"+token+")="+probEF);


      if (probEF > 0 && e > 0 && !docLangTokenizer.isStopWord(eTerm) && (translateOnly == null || !translateOnly.equals("indri") || indriPuncPattern.matcher(eTerm).matches()) && (pairsInSCFG == null || pairsInSCFG.contains(new PairOfStrings(token,eTerm)))) {

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            curIndex = prevIndex;    // revert curIndex value since we're skipping this one
            skipTerm = true;
            continue;
          }
          logger.debug("Processing: "+srcTerm+" with index: "+curIndex);      
          topTrans.add(new PairOfFloatString(prob, trgTerm));
          sumOfProbs += prob;
          logger.debug("Added to queue: "+trgTerm+" with prob: "+prob+" (sum: "+sumOfProbs+")");      
        }else if(!earlyTerminate && !skipTerm && !delims.contains(srcTerm)){  //continue adding translation term,prob pairs (except if early termination is ON)
          topTrans.add(new PairOfFloatString(prob, trgTerm));
          sumOfProbs += prob;
          logger.debug("Added to queue: "+trgTerm+" with prob: "+prob+" (sum: "+sumOfProbs+")");      


          // keep top numTrans translations
          if(topTrans.size() > numTrans){
            PairOfFloatString pair = topTrans.pollFirst();
            float removedProb = pair.getLeftElement();
            sumOfProbs -= removedProb;
            logger.debug("Removed from queue: "+pair.getRightElement()+" (sum: "+sumOfProbs+")");      
          }
        }else{
          logger.debug("Skipped line: "+line);
        }
      }

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