/*
* 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.explore;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
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.Reducer.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.avenir.util.RuleExpression;
import org.chombo.util.AttributeFilter;
import org.chombo.util.BasicUtils;
import org.chombo.util.Tuple;
import org.chombo.util.Utility;
/**
* @author pranab
*
*/
public class RuleEvaluator extends Configured implements Tool {
private static String CONF_ENTROPY = "confEntropy";
private static String CONF_ACCURACY = "confAccuracy";
@Override
public int run(String[] args) throws Exception {
Job job = new Job(getConf());
String jobName = "Rule evaluator MR";
job.setJobName(jobName);
job.setJarByClass(RuleEvaluator.class);
FileInputFormat.addInputPaths(job, args[0]);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
Utility.setConfiguration(job.getConfiguration());
job.setMapperClass(RuleEvaluator.EvaluatorMapper.class);
job.setReducerClass(RuleEvaluator.EvaluatorReducer.class);
job.setCombinerClass(RuleEvaluator.EvaluatorCombiner.class);
job.setMapOutputKeyClass(Tuple.class);
job.setMapOutputValueClass(Tuple.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(Text.class);
int numReducer = job.getConfiguration().getInt("rue.num.reducer", -1);
numReducer = -1 == numReducer ? job.getConfiguration().getInt("num.reducer", 1) : numReducer;
job.setNumReduceTasks(numReducer);
int status = job.waitForCompletion(true) ? 0 : 1;
return status;
}
/**
* @author pranab
*
*/
public static class EvaluatorMapper extends Mapper<LongWritable, Text, Tuple, Tuple> {
private Tuple outKey = new Tuple();
private Tuple outVal = new Tuple();
private String fieldDelimRegex;
private String[] items;
private Map<String, RuleExpression> ruleExpressions = new HashMap<String, RuleExpression>();
private int classAttrOrdinal;
/* (non-Javadoc)
* @see org.apache.hadoop.mapreduce.Mapper#setup(org.apache.hadoop.mapreduce.Mapper.Context)
*/
protected void setup(Context context) throws IOException, InterruptedException {
Configuration config = context.getConfiguration();
fieldDelimRegex = config.get("field.delim.regex", ",");
String condDelim = config.get("rue.cond.delim");
if (null != condDelim) {
AttributeFilter.setCondSeparator(condDelim);
}
String[] ruleNames = Utility.assertStringArrayConfigParam(config, "rue.rule.names", Utility.configDelim,
"missing rule list");
for (String ruleName : ruleNames) {
String key = "rue.rule." + ruleName;
String rule = Utility.assertStringConfigParam(config, key, "missing rule definition");
RuleExpression ruleExp = RuleExpression.createRule(rule);
ruleExpressions.put(ruleName, ruleExp);
}
classAttrOrdinal = Utility.assertIntConfigParam(config, "rue.class.attr.ord",
"missing class attribute ordinal");
}
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
items = value.toString().split(fieldDelimRegex, -1);
//all rules
for (String ruleName : ruleExpressions.keySet()) {
RuleExpression ruleExp = ruleExpressions.get(ruleName);
if (ruleExp.evaluate(items)) {
outKey.initialize();
outKey.add(ruleName);
outVal.initialize();
outVal.add(items[classAttrOrdinal], 1);
context.write(outKey, outVal);
}
}
}
}
/**
* @author pranab
*
*/
public static class EvaluatorCombiner extends Reducer<Tuple, Tuple, Tuple, Tuple> {
private Tuple outVal = new Tuple();
private Map<String, Integer> classCounts = new HashMap<String, Integer>();
private String classVal;
private int classCount;
/* (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 {
classCounts.clear();
for (Tuple value : values){
int i = 0;
classVal = value.getString(i++);
classCount = value.getInt(i++);
addToConsCount();
if (value.getSize() > 2) {
classVal = value.getString(i++);
classCount = value.getInt(i++);
addToConsCount();
}
}
outVal.initialize();
for (String clVal : classCounts.keySet()) {
outVal.add(clVal, classCounts.get(clVal));
}
context.write(key, outVal);
}
/**
*
*/
private void addToConsCount() {
Integer count = classCounts.get(classVal);
if (count == null) {
classCounts.put(classVal, classCount);
} else {
classCounts.put(classVal, count + classCount);
}
}
}
/**
* @author pranab
*
*/
public static class EvaluatorReducer extends Reducer<Tuple, Tuple, NullWritable, Text> {
private Text outVal = new Text();
private String fieldDelimOut;
private Map<String, Integer> classCounts = new HashMap<String, Integer>();
private Map<String, String> ruleConsequents = new HashMap<String, String>();
private String confStrategy;
private int dataSize;
private int totalCount;
private String classVal;
private int classCount;
private double confidence;
private double support;
private String[] classValues;
/* (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();
fieldDelimOut = config.get("field.delim", ",");
confStrategy = Utility.assertStringConfigParam(config, "rue.conf.strategy",
"missing confidence strategy list");
dataSize = Utility.assertIntConfigParam(config, "rue.data.size", "missing data size");
String[] ruleNames = Utility.assertStringArrayConfigParam(config, "rue.rule.names", Utility.configDelim,
"missing rule list");
for (String ruleName : ruleNames) {
String key = "rue.rule." + ruleName;
String rule = Utility.assertStringConfigParam(config, key, "missing rule definition");
ruleConsequents.put(ruleName, RuleExpression.extractConsequent(rule));
}
classValues = Utility.assertStringArrayConfigParam(config, "rue.class.values", Utility.configDelim,
"missing class values");
}
/* (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 {
classCounts.clear();
totalCount = 0;
for (Tuple value : values){
int i = 0;
classVal = value.getString(i++);
classCount = value.getInt(i++);
addToConsCount();
totalCount += classCount;
if (value.getSize() > 2) {
classVal = value.getString(i++);
classCount = value.getInt(i++);
addToConsCount();
totalCount += classCount;
}
}
//confidence and support
String ruleName = key.getString(0);
classVal = ruleConsequents.get(ruleName);
if (confStrategy.equals(CONF_ACCURACY)) {
confidence = ((double)getConsCount(classVal)) / totalCount;
} else if (confStrategy.equals(CONF_ENTROPY)) {
String otherClassVal = getOtherClassValue(classVal);
double prThisClass = ((double)getConsCount(classVal)) / totalCount;
double prOtherClass = ((double)getConsCount(otherClassVal)) / totalCount;
confidence = (prThisClass * Math.log(prThisClass) + prOtherClass * Math.log(prOtherClass)) / Math.log(2);
confidence += 1.0;
} else {
throw new IllegalStateException("invalid confidence strategy");
}
support = ((double)totalCount) / dataSize;
outVal.set(ruleName + fieldDelimOut + BasicUtils.formatDouble(confidence, 3) +
fieldDelimOut + BasicUtils.formatDouble(support, 3));
context.write(NullWritable.get(), outVal);
}
/**
* @param clVal
* @return
*/
private int getConsCount(String clVal) {
Integer count = classCounts.get(clVal);
count = count != null ? count : 0;
return count;
}
/**
*
*/
private void addToConsCount() {
Integer count = classCounts.get(classVal);
if (count == null) {
classCounts.put(classVal, classCount);
} else {
classCounts.put(classVal, count + classCount);
}
}
/**
* @param classVal
* @return
*/
private String getOtherClassValue(String classVal) {
String otherClassVal = null;
for (int i = 0; i < classValues.length; ++i) {
if (classValues[i].equals(classVal)) {
otherClassVal = classValues[i ^ 1];
break;
}
}
return otherClassVal;
}
}
/**
* @param args
*/
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new RuleEvaluator(), args);
System.exit(exitCode);
}
}