Package org.apache.mahout.classifier

Examples of org.apache.mahout.classifier.ClassifierResult

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  }
 
  public void test() throws InvalidDatastoreException {
    ClassifierContext classifier = new ClassifierContext(algorithm, store);
    String[] document = {"aa", "ff"};
    ClassifierResult result = classifier.classifyDocument(document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to e", "e", result.getLabel());
   
    document = new String[] {"dd"};
    result = classifier.classifyDocument(document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to d", "d", result.getLabel());
   
    document = new String[] {"cc"};
    result = classifier.classifyDocument(document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to d", "d", result.getLabel());
  }
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  }
 
  public void testResults() throws Exception {
    ClassifierContext classifier = new ClassifierContext(algorithm, store);
    String[] document = {"aa", "ff"};
    ClassifierResult result = classifier.classifyDocument(document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    System.out.println("Result: " + result);
  }
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    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], Integer.parseInt(params.get("gramSize")))
          .generateNGramsWithoutLabel();
      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
        params.get("defaultCat"), 100).length);
      ClassifierResult result = classifier.classifyDocument(document.toArray(new String[document.size()]), params
          .get("defaultCat"));
      assertEquals(entry[0], result.getLabel());
      resultAnalyzer.addInstance(entry[0], result);
    }
    int[][] matrix = resultAnalyzer.getConfusionMatrix().getConfusionMatrix();
    for (int i = 0; i < 3; i++) {
      for (int j = 0; j < 3; j++) {
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    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], Integer.parseInt(params.get("gramSize")))
          .generateNGramsWithoutLabel();
      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
        params.get("defaultCat"), 100).length);
      ClassifierResult result = classifier.classifyDocument(document.toArray(new String[document.size()]), params
          .get("defaultCat"));
      assertEquals(entry[0], result.getLabel());
      resultAnalyzer.addInstance(entry[0], result);
    }
    int[][] matrix = resultAnalyzer.getConfusionMatrix().getConfusionMatrix();
    for (int i = 0; i < 3; i++) {
      for (int j = 0; j < 3; j++) {
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    String label = key.toString();
   
    List<String> ngrams = new NGrams(value.toString(), gramSize).generateNGramsWithoutLabel();
   
    try {
      ClassifierResult result = classifier.classifyDocument(ngrams.toArray(new String[ngrams.size()]),
        defaultCategory);
     
      String correctLabel = label;
      String classifiedLabel = result.getLabel();
     
      StringTuple outputTuple = new StringTuple(BayesConstants.CLASSIFIER_TUPLE);
      outputTuple.add(correctLabel);
      outputTuple.add(classifiedLabel);
     
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          bestScore = element.get();
          bestIdx = element.index();
        }
      }
      if (bestIdx != Integer.MIN_VALUE) {
        ClassifierResult classifierResult = new ClassifierResult(labelMap.get(bestIdx), bestScore);
        analyzer.addInstance(pair.getFirst().toString(), classifierResult);
      }
    }
  }
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          for (Map.Entry<String,List<String>> stringListEntry : document.entrySet()) {
            String correctLabel = stringListEntry.getKey();
            List<String> strings = stringListEntry.getValue();
            TimingStatistics.Call call = operationStats.newCall();
            TimingStatistics.Call outercall = totalStatistics.newCall();
            ClassifierResult classifiedLabel = classifier.classifyDocument(strings.toArray(new String[strings
                .size()]), params.get("defaultCat"));
            call.end();
            outercall.end();
            boolean correct = resultAnalyzer.addInstance(correctLabel, classifiedLabel);
            if (verbose) {
              // We have one document per line
              log.info("Line Number: {} Line(30): {} Expected Label: {} Classified Label: {} Correct: {}",
                new Object[] {lineNum, line.length() > 30 ? line.substring(0, 30) : line, correctLabel,
                              classifiedLabel.getLabel(), correct,});
            }
            // log.info("{} {}", correctLabel, classifiedLabel);
           
          }
          lineNum++;
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  @Override
  public ClassifierResult classifyDocument(String[] document,
                                           Datastore datastore,
                                           String defaultCategory) throws InvalidDatastoreException {
    ClassifierResult result = new ClassifierResult(defaultCategory);
    double max = Double.MIN_VALUE;
    Collection<String> categories = datastore.getKeys("labelWeight");
   
    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (max < prob) {
        max = prob;
        result.setLabel(category);
      }
    }
    result.setScore(max);
    return result;
  }
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                  OutputCollector<StringTuple,DoubleWritable> output,
                  Reporter reporter) throws IOException {
    List<String> ngrams = new NGrams(value.toString(), gramSize).generateNGramsWithoutLabel();
   
    try {
      ClassifierResult result = classifier.classifyDocument(ngrams.toArray(new String[ngrams.size()]),
        defaultCategory);
     
      String correctLabel = key.toString();
      String classifiedLabel = result.getLabel();
     
      StringTuple outputTuple = new StringTuple(BayesConstants.CLASSIFIER_TUPLE);
      outputTuple.add(correctLabel);
      outputTuple.add(classifiedLabel);
     
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  @Override
  public ClassifierResult classifyDocument(String[] document,
                                           Datastore datastore,
                                           String defaultCategory) throws InvalidDatastoreException {
    ClassifierResult result = new ClassifierResult(defaultCategory);
    double max = Double.MAX_VALUE;
    Collection<String> categories = datastore.getKeys("labelWeight");
   
    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (prob < max) {
        max = prob;
        result.setLabel(category);
      }
    }
    result.setScore(max);
    return result;
  }
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