Examples of org.apache.mahout.classifier.ClassifierResult

org.apache.mahout.classifier.ClassifierResult
Result of a Document Classification. The label and the associated score(Usually probabilty)

          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++;

View Full Code Here

                  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);

View Full Code Here

          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);
      }
    }
  }

View Full Code Here

          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);
      }
    }
  }

View Full Code Here

      int actual = asfDictionary.intern(ng);
      Vector result = classifier.classifyFull(next.getSecond().get());
      int cat = result.maxValueIndex();
      double score = result.maxValue();
      double ll = classifier.logLikelihood(actual, next.getSecond().get());
      ClassifierResult cr = new ClassifierResult(asfDictionary.values().get(cat), score, ll);
      ra.addInstance(asfDictionary.values().get(actual), cr);


    }
    output.printf("%s\n\n", ra.toString());
  }

View Full Code Here

          log.info("{}", regressionAnalyzer);
        } else {
          ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
          for (double[] res : results) {
            analyzer.addInstance(dataset.getLabelString(res[0]),
              new ClassifierResult(dataset.getLabelString(res[1]), 1.0));
          }
          log.info("{}", analyzer);
        }
      }
    }

View Full Code Here

        log.info("{}", regressionAnalyzer);
      } else {
        ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
        for (double[] r : resList) {
          analyzer.addInstance(dataset.getLabelString(r[0]),
            new ClassifierResult(dataset.getLabelString(r[1]), 1.0));
        }
        log.info("{}", analyzer);
      }
    }
  }

View Full Code Here

      Vector input = helper.encodeFeatureVector(file, actual, 0, overallCounts); //no leak type ensures this is a normal vector
      Vector result = classifier.classifyFull(input);
      int cat = result.maxValueIndex();
      double score = result.maxValue();
      double ll = classifier.logLikelihood(actual, input);
      ClassifierResult cr = new ClassifierResult(newsGroups.values().get(cat), score, ll);
      ra.addInstance(newsGroups.values().get(actual), cr);


    }
    output.printf("%s\n\n", ra.toString());
  }

View Full Code Here

          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);
      }
    }
  }

View Full Code Here

0 1 2 3 4 5 6 7 8

TOP

Related Classes of org.apache.mahout.classifier.ClassifierResult

com.tamingtext.classifier.bayes.BayesUpdateRequestProcessor

com.tamingtext.classifier.maxent.TestMaxent

com.tamingtext.classifier.mlt.TestMoreLikeThis

org.apache.mahout.classifier.bayes.AbstractBayesAlgorithm

org.apache.mahout.classifier.bayes.algorithm.BayesAlgorithm

org.apache.mahout.classifier.bayes.algorithm.CBayesAlgorithm

org.apache.mahout.classifier.bayes.BayesAlgorithm

org.apache.mahout.classifier.bayes.BayesClassifier

org.apache.mahout.classifier.bayes.BayesClassifier$ClassifierResultPriorityQueue

org.apache.mahout.classifier.bayes.BayesClassifierSelfTest

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.