Hi all,
I'm trying to write code to load and classify new data based on the existing
model.I've written some code in WEKA that does that and trying to port it to
Mahout.
Classifier cl = (Classifier) SerializationHelper.read(modelFileName);
for (int i = 0; i < unlabeled.numInstances(); i++) {
Instance curr = unlabeled.instance(i);
// predict output
double pred = cl.classifyInstance(curr); }
What would be the equivalent in Mahout? Is there a similar interface I can use?
Currently looking at a TestClassifier class that I believe tries to load a
model:
Map<String, Path> modelPaths = new HashMap<String, Path>(); String
modelBasePath = (String) cmdLine.getValue(pathOpt);
modelPaths.put("sigma_j", new Path(modelBasePath +
"/trainer-weights/Sigma_j/part-*")); modelPaths.put("sigma_k", new
Path(modelBasePath + "/trainer-weights/Sigma_k/part-*"));
modelPaths.put("sigma_kSigma_j", new Path(modelBasePath +
"/trainer-weights/Sigma_kSigma_j/part-*"));
modelPaths.put("thetaNormalizer", new Path(modelBasePath +
"/trainer-thetaNormalizer/part-*")); modelPaths.put("weight", new
Path(modelBasePath + "/trainer-tfIdf/trainer-tfIdf/part-*"));
FileSystem fs = FileSystem.get(new Path(modelBasePath).toUri(), conf);
log.info("Loading model from: {}", modelPaths);
Model model; Classifier classifier;
String classifierType = (String) cmdLine.getValue(typeOpt);
if (classifierType.equalsIgnoreCase("bayes")) { log.info("Testing
Bayes Classifier"); model = new BayesModel(); classifier = new
BayesClassifier(); } else if (classifierType.equalsIgnoreCase("cbayes")) {
log.info("Testing Complementary Bayes Classifier"); model = new
CBayesModel(); classifier = new CBayesClassifier(); } else { throw
new IllegalArgumentException("Unrecognized classifier type: " +
classifierType); }
SequenceFileModelReader.loadModel(model, fs, modelPaths, conf);
A lot of the preparations are specific to these 2 models (what
is modelPaths?!). Is there any abstraction available that would do that for me?
Thanks.
/qf
