The classify package provides facilities for training classifiers. In this package, data points are viewed as single instances, not sequences. The most commonly used classifier is the log-linear classifier with binary features. More classifiers, such as SVM and Naive Bayes, are also available in this package.

The Classifier contract only guarantees routines for getting a classification for an example, and the scores assigned to each class for that example. Note that training is dependent upon the individual classifier.

Classifiers operate over Datum objects. A Datum is a list of descriptive features and a class label; features and labels can be any object, but usually Strings are used. Datum objects are grouped using Dataset objects. Some classifiers use Dataset objects as a way of grouping inputs.

Following is a set of examples outlining how to create, train, and use each of the different classifier types.

Linear Classifiers

To build a classifier, one first creates a GeneralDataset, which is a list to Datum objects. A Datum is a list of descriptive features, along with a label; features and labels can be any object, though we usually use strings.

GeneralDataset dataSet=new Dataset();
while (more datums to make) {
  ... make featureList: e.g., ["PrevWord=at","CurrentTag=NNP","isUpperCase"]
  ... make label: e.g., ["PLACE"];
  Datum d = new BasicDatum(featureList, label);
  dataSet.add(d);
}

There are some useful methods in GeneralDataset such as:

dataSet.applyFeatureCountThreshold(int cutoff);
dataSet.summaryStatistics(); // dumps the number of features and datums

Next, one makes a LinearClassifierFactory and calls its trainClassifier(GeneralDataset dataSet) method:

  LinearClassifierFactory lcFactory = new LinearClassifierFactory();
  LinearClassifier c = lcFactory.trainClassifier(dataSet);

LinearClassifierFactory has options for different optimizers (default: QNminimizer), the converge threshold for minimization, etc. Check the class description for detailed information.

A classifier, once built, can be used to classify new Datum instances:

Object label = c.classOf(mysteryDatum);

Counter scores = c.scoresOf(mysteryDatum);

The scores which are returned by the log-linear classifiers are the feature-weight dot products, not the normalized probabilities.

There are some other useful methods like justificationOf(Datum d), and logProbabilityOf(Datum d), also various methods for visualizing the weights and the most highly weighted features. This concludes the log-linear classifiers with binary features.

We can also train log-linear classifiers with real-valued features. In this case, RVFDatum should be used.

Real Valued Classifiers

Real Valued Classifiers (RVF) operate over RVFDatum objects. A RVFDatum is composed of a set of feature and real-value pairs. RVFDatums are grouped using a RVFDataset.

To assemble an RVFDatum by using a Counter and assigning an Object label to it.

  Counter features = new Counter();
  features.incrementCount("FEATURE_A", 1.2);
  features.incrementCount("FEATURE_B", 2.3);
  features.incrementCount("FEATURE_C", 0.5);
  RVFDatum rvfDatum = new RVFDatum(features, "DATUM_LABEL");

RVFDataset objects are representations of RVFDatum objects that efficiently store the data with which to train the classifier. This type of dataset only accepts RVFDatum objects via its add method (other Datum objects that are not instances of RVFDatum will be ignored), and is equivalent to a Dataset if all RVFDatum objects have only features with value 1.0. Since it is a subclass of GeneralDataset, the methods shown above as applied to the GeneralDataset can also be applied to the RVFDataset.

(TODO) An example for LinearType2Classifier.

(TODO) Saving Classifier out to file (from LearningExperiment)

 private static void saveClassifierToFile(LinearClassifier classifier, String serializePath) {
    System.err.print("Serializing classifier to " + serializePath + "...");

    try {
      ObjectOutputStream oos;
      if (serializePath.endsWith(".gz")) {
        oos = new ObjectOutputStream(new BufferedOutputStream(new GZIPOutputStream(new FileOutputStream(serializePath))));
      } else {
        oos = new ObjectOutputStream(new BufferedOutputStream(new FileOutputStream(serializePath)));
      }

      oos.writeObject(classifier);

      oos.close();
      System.err.println("done.");

    } catch (Exception e) {
      e.printStackTrace();
      throw new RuntimeException("Serialization failed: "+e.getMessage());
    }

  }

Alternately, if your features are Strings, and you wish to serialize to a human readable text file, you can use saveToFilename in LinearClassifier and reconstitute using loadFromFilename in LinearClassifierFactory. Though the format is not as compact as a serialized object, and implicitly presumes the features are Strings, this is useful for debugging purposes. @author Dan Klein @author Eric Yeh