Java tutorial
/* * avenir: Predictive analytic based on Hadoop Map Reduce * Author: Pranab Ghosh * * Licensed 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.avenir.knn; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.FileSplit; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; import org.chombo.util.SecondarySort; import org.chombo.util.Tuple; import org.chombo.util.Utility; /** * Joins training vector feature class conditional probability with training vector nearest * neighbours * @author pranab * */ public class FeatureCondProbJoiner extends Configured implements Tool { private static final int GR_PROBABILITY = 0; private static final int GR_NEIGHBOUR = 1; @Override public int run(String[] args) throws Exception { Job job = new Job(getConf()); String jobName = "Training vector feature cond probability joiner MR"; job.setJobName(jobName); job.setJarByClass(FeatureCondProbJoiner.class); FileInputFormat.addInputPaths(job, args[0]); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.setMapperClass(FeatureCondProbJoiner.JoinerMapper.class); job.setReducerClass(FeatureCondProbJoiner.JoinerReducer.class); job.setMapOutputKeyClass(Tuple.class); job.setMapOutputValueClass(Tuple.class); job.setOutputKeyClass(NullWritable.class); job.setOutputValueClass(Text.class); job.setGroupingComparatorClass(SecondarySort.TuplePairGroupComprator.class); job.setPartitionerClass(SecondarySort.TuplePairPartitioner.class); Utility.setConfiguration(job.getConfiguration()); job.setNumReduceTasks(job.getConfiguration().getInt("num.reducer", 1)); int status = job.waitForCompletion(true) ? 0 : 1; return status; } /** * @author pranab * */ public static class JoinerMapper extends Mapper<LongWritable, Text, Tuple, Tuple> { private Tuple outKey = new Tuple(); private Tuple outVal = new Tuple(); private String fieldDelimRegex; private boolean isFeatureCondProbSplit; private String[] items; /* (non-Javadoc) * @see org.apache.hadoop.mapreduce.Mapper#setup(org.apache.hadoop.mapreduce.Mapper.Context) */ protected void setup(Context context) throws IOException, InterruptedException { fieldDelimRegex = context.getConfiguration().get("field.delim.regex", ","); String splitPrefix = context.getConfiguration().get("fcb.feature.cond.prob.split.prefix", "condProb"); isFeatureCondProbSplit = ((FileSplit) context.getInputSplit()).getPath().getName() .startsWith(splitPrefix); } /* (non-Javadoc) * @see org.apache.hadoop.mapreduce.Mapper#map(KEYIN, VALUEIN, org.apache.hadoop.mapreduce.Mapper.Context) */ @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { items = value.toString().split(fieldDelimRegex); outKey.initialize(); outVal.initialize(); if (isFeatureCondProbSplit) { //training itemID outKey.add(items[0], GR_PROBABILITY); //all class conditional probabilities ending with class value and skip feature prior probability for (int i = 2; i < items.length; ++i) { outVal.add(items[i]); } } else { //nearest neighbor split outKey.add(items[0], GR_NEIGHBOUR); //test vector neighbor itemdID, distance, class outVal.add(items[1], items[2], items[4]); } context.write(outKey, outVal); } } /** * @author pranab * */ public static class JoinerReducer extends Reducer<Tuple, Tuple, NullWritable, Text> { private Text outVal = new Text(); private StringBuilder stBld = new StringBuilder();; private String fieldDelim; private String trainingClassValProb; private String trainITemID; private String classVal; /* (non-Javadoc) * @see org.apache.hadoop.mapreduce.Reducer#setup(org.apache.hadoop.mapreduce.Reducer.Context) */ protected void setup(Context context) throws IOException, InterruptedException { Configuration config = context.getConfiguration(); fieldDelim = config.get("field.delim.out", ","); } /* (non-Javadoc) * @see org.apache.hadoop.mapreduce.Reducer#reduce(KEYIN, java.lang.Iterable, org.apache.hadoop.mapreduce.Reducer.Context) */ protected void reduce(Tuple key, Iterable<Tuple> values, Context context) throws IOException, InterruptedException { boolean first = true; trainITemID = key.getString(0); for (Tuple val : values) { if (first) { //class value and corresponding posterior probability for training vector String classVal = val.getString(val.getSize() - 1); for (int i = 0; i < val.getSize() - 1; i += 2) { if (val.getString(i).equals(classVal)) { trainingClassValProb = classVal + fieldDelim + val.getString(i + 1); break; } } first = false; } else { //0.test ItemID, 1.test Item class value, 2.trainingItemID, 3.distance, 4.traingItem class value, //5.trainingItem feature posterior probability stBld.delete(0, stBld.length()); stBld.append(val.getString(0)).append(fieldDelim).append(val.getString(2)).append(fieldDelim) .append(trainITemID).append(fieldDelim).append(val.getString(1)).append(fieldDelim) .append(trainingClassValProb); outVal.set(stBld.toString()); context.write(NullWritable.get(), outVal); } } } } public static void main(String[] args) throws Exception { int exitCode = ToolRunner.run(new FeatureCondProbJoiner(), args); System.exit(exitCode); } }