/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.classifier.naivebayes.training;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.function.Functions;
import com.google.common.base.Preconditions;
public class WeightsMapper extends Mapper<IntWritable, VectorWritable, Text, VectorWritable> {
static final String NUM_LABELS = WeightsMapper.class.getName() + ".numLabels";
private Vector weightsPerFeature;
private Vector weightsPerLabel;
@Override
protected void setup(Context ctx) throws IOException, InterruptedException {
super.setup(ctx);
int numLabels = Integer.parseInt(ctx.getConfiguration().get(NUM_LABELS));
Preconditions.checkArgument(numLabels > 0);
weightsPerLabel = new DenseVector(numLabels);
}
@Override
protected void map(IntWritable index, VectorWritable value, Context ctx) throws IOException, InterruptedException {
Vector instance = value.get();
if (weightsPerFeature == null) {
weightsPerFeature = new RandomAccessSparseVector(instance.size(), instance.getNumNondefaultElements());
}
int label = index.get();
weightsPerFeature.assign(instance, Functions.PLUS);
weightsPerLabel.set(label, weightsPerLabel.get(label) + instance.zSum());
}
@Override
protected void cleanup(Context ctx) throws IOException, InterruptedException {
if (weightsPerFeature != null) {
ctx.write(new Text(TrainNaiveBayesJob.WEIGHTS_PER_FEATURE), new VectorWritable(weightsPerFeature));
ctx.write(new Text(TrainNaiveBayesJob.WEIGHTS_PER_LABEL), new VectorWritable(weightsPerLabel));
}
super.cleanup(ctx);
}
}