Esempio n. 1
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  private InstanceList readFile() throws IOException {

    String NL = System.getProperty("line.separator");
    Scanner scanner = new Scanner(new FileInputStream(fileName), encoding);

    ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
    pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}\\p{L}+")));
    pipeList.add(new TokenSequence2FeatureSequence());

    InstanceList testing = new InstanceList(new SerialPipes(pipeList));

    try {
      while (scanner.hasNextLine()) {

        String text = scanner.nextLine();
        text = text.replaceAll("\\x0d", "");

        Pattern patten = Pattern.compile("^(.*?),(.*?),(.*)$");
        Matcher matcher = patten.matcher(text);

        if (matcher.find()) {
          docIds.add(matcher.group(1));
          testing.addThruPipe(new Instance(matcher.group(3), null, "test instance", null));
        }
      }
    } finally {
      scanner.close();
    }

    return testing;
  }
Esempio n. 2
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  private InstanceList generateInstanceList() throws Exception {

    ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
    pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}\\p{L}+")));
    pipeList.add(new TokenSequence2FeatureSequence());

    Reader fileReader = new InputStreamReader(new FileInputStream(new File(fileName)), "UTF-8");
    InstanceList instances = new InstanceList(new SerialPipes(pipeList));
    instances.addThruPipe(
        new CsvIterator(
            fileReader,
            Pattern.compile("^(\\S*)[\\s,]*(\\S*)[\\s,]*(.*)$"),
            3,
            2,
            1)); // data, label, name fields

    return instances;
  }
Esempio n. 3
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  public void doInference() {

    try {

      ParallelTopicModel model = ParallelTopicModel.read(new File(inferencerFile));
      TopicInferencer inferencer = model.getInferencer();

      // TopicInferencer inferencer =
      //    TopicInferencer.read(new File(inferencerFile));

      // InstanceList testing = readFile();
      readFile();
      InstanceList testing = generateInstanceList(); // readFile();

      for (int i = 0; i < testing.size(); i++) {

        StringBuilder probabilities = new StringBuilder();
        double[] testProbabilities = inferencer.getSampledDistribution(testing.get(i), 10, 1, 5);

        ArrayList probabilityList = new ArrayList();

        for (int j = 0; j < testProbabilities.length; j++) {
          probabilityList.add(new Pair<Integer, Double>(j, testProbabilities[j]));
        }

        Collections.sort(probabilityList, new CustomComparator());

        for (int j = 0; j < testProbabilities.length && j < topN; j++) {
          if (j > 0) probabilities.append(" ");
          probabilities.append(
              ((Pair<Integer, Double>) probabilityList.get(j)).getFirst().toString()
                  + ","
                  + ((Pair<Integer, Double>) probabilityList.get(j)).getSecond().toString());
        }

        System.out.println(docIds.get(i) + "," + probabilities.toString());
      }

    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
Esempio n. 4
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  public void test() throws Exception {

    ParallelTopicModel model = ParallelTopicModel.read(new File(inferencerFile));
    TopicInferencer inferencer = model.getInferencer();

    ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
    pipeList.add(new CharSequence2TokenSequence(Pattern.compile("\\p{L}\\p{L}+")));
    pipeList.add(new TokenSequence2FeatureSequence());

    InstanceList instances = new InstanceList(new SerialPipes(pipeList));
    Reader fileReader = new InputStreamReader(new FileInputStream(new File(fileName)), "UTF-8");
    instances.addThruPipe(
        new CsvIterator(
            fileReader,
            Pattern.compile("^(\\S*)[\\s,]*(\\S*)[\\s,]*(.*)$"),
            3,
            2,
            1)); // data, label, name fields
    double[] testProbabilities = inferencer.getSampledDistribution(instances.get(1), 10, 1, 5);
    for (int i = 0; i < 1000; i++) System.out.println(i + ": " + testProbabilities[i]);
  }
  public TopicModelDiagnostics(ParallelTopicModel model, int numTopWords) {
    numTopics = model.getNumTopics();
    this.numTopWords = numTopWords;

    this.model = model;

    alphabet = model.getAlphabet();
    topicSortedWords = model.getSortedWords();

    topicTopWords = new String[numTopics][numTopWords];

    numRank1Documents = new int[numTopics];
    numNonZeroDocuments = new int[numTopics];
    numDocumentsAtProportions = new int[numTopics][DEFAULT_DOC_PROPORTIONS.length];
    sumCountTimesLogCount = new double[numTopics];

    diagnostics = new ArrayList<TopicScores>();

    for (int topic = 0; topic < numTopics; topic++) {

      int position = 0;
      TreeSet<IDSorter> sortedWords = topicSortedWords.get(topic);

      // How many words should we report? Some topics may have fewer than
      //  the default number of words with non-zero weight.
      int limit = numTopWords;
      if (sortedWords.size() < numTopWords) {
        limit = sortedWords.size();
      }

      Iterator<IDSorter> iterator = sortedWords.iterator();
      for (int i = 0; i < limit; i++) {
        IDSorter info = iterator.next();
        topicTopWords[topic][i] = (String) alphabet.lookupObject(info.getID());
      }
    }

    collectDocumentStatistics();

    diagnostics.add(getTokensPerTopic(model.tokensPerTopic));
    diagnostics.add(getDocumentEntropy(model.tokensPerTopic));
    diagnostics.add(getWordLengthScores());
    diagnostics.add(getCoherence());
    diagnostics.add(getDistanceFromUniform());
    diagnostics.add(getDistanceFromCorpus());
    diagnostics.add(getEffectiveNumberOfWords());
    diagnostics.add(getTokenDocumentDiscrepancies());
    diagnostics.add(getRank1Percent());
    diagnostics.add(getDocumentPercentRatio(FIFTY_PERCENT_INDEX, TWO_PERCENT_INDEX));
    diagnostics.add(getDocumentPercent(5));
    diagnostics.add(getExclusivity());
  }
Esempio n. 6
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  public TestCRFPipe(String trainingFilename) throws IOException {

    ArrayList<Pipe> pipes = new ArrayList<Pipe>();

    PrintWriter out = new PrintWriter("test.out");

    int[][] conjunctions = new int[3][];
    conjunctions[0] = new int[] {-1};
    conjunctions[1] = new int[] {1};
    conjunctions[2] = new int[] {-2, -1};

    pipes.add(new SimpleTaggerSentence2TokenSequence());
    // pipes.add(new FeaturesInWindow("PREV-", -1, 1));
    // pipes.add(new FeaturesInWindow("NEXT-", 1, 2));
    pipes.add(new OffsetConjunctions(conjunctions));
    pipes.add(new TokenTextCharSuffix("C1=", 1));
    pipes.add(new TokenTextCharSuffix("C2=", 2));
    pipes.add(new TokenTextCharSuffix("C3=", 3));
    pipes.add(new RegexMatches("CAPITALIZED", Pattern.compile("^\\p{Lu}.*")));
    pipes.add(new RegexMatches("STARTSNUMBER", Pattern.compile("^[0-9].*")));
    pipes.add(new RegexMatches("HYPHENATED", Pattern.compile(".*\\-.*")));
    pipes.add(new RegexMatches("DOLLARSIGN", Pattern.compile("\\$.*")));
    pipes.add(new TokenFirstPosition("FIRSTTOKEN"));
    pipes.add(new TokenSequence2FeatureVectorSequence());
    pipes.add(new SequencePrintingPipe(out));

    Pipe pipe = new SerialPipes(pipes);

    InstanceList trainingInstances = new InstanceList(pipe);

    trainingInstances.addThruPipe(
        new LineGroupIterator(
            new BufferedReader(
                new InputStreamReader(new GZIPInputStream(new FileInputStream(trainingFilename)))),
            Pattern.compile("^\\s*$"),
            true));

    out.close();
  }