public static void main(String[] args) {
final BayesParameters params = new BayesParameters();
params.setGramSize(1);
params.set("verbose", "true");
params.set("classifierType", "bayes");
params.set("defaultCat", "OTHER");
params.set("encoding", "UTF-8");
params.set("alpha_i", "1.0");
params.set("dataSource", "hdfs");
params.set("basePath", "/tmp/output");
try {
Path input = new Path("/tmp/input");
Path output = new Path("/tmp/output");
TrainClassifier.trainNaiveBayes(input, output, params);
Algorithm algorithm = new BayesAlgorithm();
Datastore datastore = new InMemoryBayesDatastore(params);
ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
classifier.initialize();
final BufferedReader reader = new BufferedReader(new FileReader(args[0]));
String entry = reader.readLine();
log.debug("First line: " + entry);
while (entry != null) {
log.debug("Processing line: " + entry);
List<String> document =
new NGrams(entry, Integer.parseInt(params.get("gramSize")))
.generateNGramsWithoutLabel();
ClassifierResult result =
classifier.classifyDocument(
document.toArray(new String[document.size()]), params.get("defaultCat"));
log.debug("Label: " + result.getLabel() + ", Score: " + result.getScore() + ", " + entry);
entry = reader.readLine();
}
} catch (final IOException ex) {
ex.printStackTrace();
} catch (final InvalidDatastoreException ex) {
ex.printStackTrace();
}
}
/**
* Parallel Classification
*
* @param key The label
* @param value the features (all unique) associated w/ this label
* @param output The OutputCollector to write the results to
* @param reporter Reports status back to hadoop
*/
@Override
public void map(
Text key, Text value, 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);
output.collect(outputTuple, ONE);
} catch (InvalidDatastoreException e) {
throw new IOException(e);
}
}