public static NaiveBayesModel materialize(Path output, Configuration conf) throws IOException {
FileSystem fs = output.getFileSystem(conf);
Vector weightsPerLabel = null;
Vector perLabelThetaNormalizer = null;
Vector weightsPerFeature = null;
Matrix weightsPerLabelAndFeature;
float alphaI;
FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"));
try {
alphaI = in.readFloat();
weightsPerFeature = VectorWritable.readVector(in);
weightsPerLabel = VectorWritable.readVector(in);
perLabelThetaNormalizer = VectorWritable.readVector(in);
weightsPerLabelAndFeature =
new SparseMatrix(weightsPerLabel.size(), weightsPerFeature.size());
for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
}
} finally {
Closeables.closeQuietly(in);
}
NaiveBayesModel model =
new NaiveBayesModel(
weightsPerLabelAndFeature,
weightsPerFeature,
weightsPerLabel,
perLabelThetaNormalizer,
alphaI);
model.validate();
return model;
}
@Override
public int run(String[] args) throws Exception {
String path = System.getProperty("user.dir");
addInputOption();
addOutputOption();
addOption(ALPHA_I, "a", "smoothing parameter", String.valueOf(1.0f));
addOption(
buildOption(
TRAIN_COMPLEMENTARY, "c", "train complementary?", false, false, String.valueOf(false)));
addOption(LABEL_INDEX, "li", "The path to store the label index in", false);
addOption(DefaultOptionCreator.overwriteOption().create());
Path labPath = new Path(path + "/../out/labelindex/");
long labelSize = createLabelIndex(labPath);
float alphaI = 1.0F;
boolean trainComplementary = true;
HadoopUtil.setSerializations(getConf());
HadoopUtil.cacheFiles(labPath, getConf());
HadoopUtil.delete(getConf(), new Path("/tmp/summedObservations"));
HadoopUtil.delete(getConf(), new Path("/tmp/weights"));
HadoopUtil.delete(getConf(), new Path("/tmp/thetas"));
// Add up all the vectors with the same labels, while mapping the labels into our index
Job indexInstances =
prepareJob(
new Path(path + "/../out/training"),
new Path("/tmp/summedObservations"),
SequenceFileInputFormat.class,
IndexInstancesMapper.class,
IntWritable.class,
VectorWritable.class,
VectorSumReducer.class,
IntWritable.class,
VectorWritable.class,
SequenceFileOutputFormat.class);
indexInstances.setCombinerClass(VectorSumReducer.class);
boolean succeeded = indexInstances.waitForCompletion(true);
if (!succeeded) {
return -1;
}
// Sum up all the weights from the previous step, per label and per feature
Job weightSummer =
prepareJob(
new Path("/tmp/summedObservations"),
new Path("/tmp/weights"),
SequenceFileInputFormat.class,
WeightsMapper.class,
Text.class,
VectorWritable.class,
VectorSumReducer.class,
Text.class,
VectorWritable.class,
SequenceFileOutputFormat.class);
weightSummer.getConfiguration().set(WeightsMapper.NUM_LABELS, String.valueOf(labelSize));
weightSummer.setCombinerClass(VectorSumReducer.class);
succeeded = weightSummer.waitForCompletion(true);
if (!succeeded) {
return -1;
}
// Put the per label and per feature vectors into the cache
HadoopUtil.cacheFiles(new Path("/tmp/weights"), getConf());
if (trainComplementary) {
// Calculate the per label theta normalizers, write out to LABEL_THETA_NORMALIZER vector
// see http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - Section 3.2, Weight
// Magnitude Errors
Job thetaSummer =
prepareJob(
new Path("/tmp/summedObservations"),
new Path("/tmp/thetas"),
SequenceFileInputFormat.class,
ThetaMapper.class,
Text.class,
VectorWritable.class,
VectorSumReducer.class,
Text.class,
VectorWritable.class,
SequenceFileOutputFormat.class);
thetaSummer.setCombinerClass(VectorSumReducer.class);
thetaSummer.getConfiguration().setFloat(ThetaMapper.ALPHA_I, alphaI);
thetaSummer
.getConfiguration()
.setBoolean(ThetaMapper.TRAIN_COMPLEMENTARY, trainComplementary);
succeeded = thetaSummer.waitForCompletion(true);
if (!succeeded) {
return -1;
}
}
// Put the per label theta normalizers into the cache
HadoopUtil.cacheFiles(new Path("/tmp/thetas"), getConf());
// Validate our model and then write it out to the official output
getConf().setFloat(ThetaMapper.ALPHA_I, alphaI);
getConf().setBoolean(NaiveBayesModel.COMPLEMENTARY_MODEL, trainComplementary);
NaiveBayesModel naiveBayesModel = BayesUtils.readModelFromDir(new Path("/tmp/"), getConf());
naiveBayesModel.validate();
naiveBayesModel.serialize(new Path(path + "/../out/model"), getConf());
return 0;
}