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ID3.java
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ID3.java
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import java.io.BufferedReader;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.*;
class Node {
Node parent;
Node children[];
/**
* The test feature for internal node and it has value from 0-14 [0==>'Age',1==>'Occupation' etc.]
*/
int testFts;
int numOfFts;
List<Instance> instances;
int predictedLabel = -1;
Node(Node parent, List<Instance> instances) {
this.parent = parent;
children = new Node[50]; //Maximum number of features for any attribute
this.instances = instances;
numOfFts = Instance.ATTRIBUTES;
testFts = -1;
int count[] = { 0, 0 };
for (Instance t : this.instances)
count[t.label]++;
predictedLabel = count[0] > count[1] ? 0 : 1;
}
/*Classifies the test examples after bulding the tree*/
public int classify(Instance t) {
if (testFts == -1) {
return predictedLabel;
} else {
if (children[t.fts[testFts]] != null) {
return children[t.fts[testFts]].classify(t);
} else {
return -1;// cannot decide; return parent label
}
}
}
}
/*Encapsulates the data of each instance*/
class Instance {
static int ATTRIBUTES = 14;
int label;
Mapper map=new Mapper();
int fts[]=new int[ATTRIBUTES];
int uniqueId;
/* Class constructor takes in a example from file and processes it*/
public Instance(String line, int id) {
int i=0;
this.uniqueId = id;
line=line.replaceAll("\\s","");
StringTokenizer st=new StringTokenizer(line,",");
while(st.hasMoreTokens())
{
if(i!=0 && i!=2 && i!=4 && i!=10 && i!=11 && i!=12)
{
String token=st.nextToken();
if(!(token.equals("<=50K")||token.equals(">50K")))
fts[i]=(int)map.list.get(i).get(token);
else
{
if(token.equals("<=50K"))
label=0;
else
label=1;
}
}
else
{
if(i==0)
{
String token=st.nextToken();
if(Integer.parseInt(token)<=Split.split[0])
{
fts[i]=0;
}
else
fts[i]=1;
}
if(i==2)
{
String token=st.nextToken();
if(Integer.parseInt(token)<=Split.split[1])
{
fts[i]=0;
}
else
fts[i]=1;
}
if(i==4)
{
String token=st.nextToken();
if(Integer.parseInt(token)<=Split.split[2])
{
fts[i]=0;
}
else
fts[i]=1;
}
if(i==10)
{
String token=st.nextToken();
if(Integer.parseInt(token)<=Split.split[3])
{
fts[i]=0;
}
else
fts[i]=1;
}
if(i==11)
{
String token=st.nextToken();
if(Integer.parseInt(token)<=Split.split[4])
{
fts[i]=0;
}
else
fts[i]=1;
}
if(i==12)
{
String token=st.nextToken();
if(Integer.parseInt(token)<=Split.split[5])
{
fts[i]=0;
}
else
fts[i]=1;
}
}
i++;
}
}
}
class Split{
static double split[]=new double[6];
static int a[];
static int b[];
Split()
{
DataSet data=new DataSet();
int i;
int k=0;
a=new int[32561];
b=new int[32561];
for(i=0;i<14;i++)
{
if(i==0 || i==2 || i==4 || i==10 || i==11 || i==12)
{
for(int j=0;j<data.arr.size();j++)
{
if(data.arr.get(j).size()!=0)
{
a[j]=Integer.parseInt(data.arr.get(j).get(i));
if(data.arr.get(j).get(14).equals("<=50K"))
b[j]=0;
else
b[j]=1;
}
}
Gini gn = new Gini();
split[k++]=gn.gini(a,b);
}
}
}
}
public class ID3 {
Node root;
static int features[]={2,8,2,16,2,7,14,6,5,2,2,2,2,41};
public abstract static class ImpurityFunction {
public abstract double calc(int a, int b);
}
public static ImpurityFunction impurity_entropy = new ImpurityFunction() {
public double calc(int a, int b) {
double pa = a / ((double) a + (double) b);
double pb = b / ((double) a + (double) b);
double res = 0;
if (a > 0)
res += -pa * Math.log(pa);
if (b > 0)
res += -pb * Math.log(pb);
return res / Math.log(2);
}
};
public Node generate(List<Instance> instances, ImpurityFunction f) {
Node root = new Node(null, instances);
expand(root, f, 0);
return root;
}
void expand(Node node, ImpurityFunction impurityFunction, int depth) {
double maxGain = -100000;
int maxGainDecision = -1;
int num = node.instances.size();
int ftsNum = Instance.ATTRIBUTES;
int mcount[][] = new int[50][2];
int parentPos = 0, parentNeg = 0;
for (int i = 0; i < node.instances.size(); i++) {
if (node.instances.get(i).label == 1) {
parentPos++;
} else {
parentNeg++;
}
}
/* Iterate over all attributes, find the best attribute */
for (int s = 0; s < node.numOfFts; ++s) {
int count[][] = new int[ID3.features[s]+1][2];
for (Instance t : node.instances) {
if (t.label == 1)
{
if(t.fts[s]==0)
{
for(int j=1;j<=ID3.features[s];j++)
{
count[j][1]++;
}
}
else
count[t.fts[s]][1]++;
}
else
{
if(t.fts[s]==0)
{
for(int j=1;j<=ID3.features[s];j++)
{
count[j][0]++;
}
}
else{
count[t.fts[s]][0]++;
}
}
}
double gain = impurityFunction.calc(parentPos, parentNeg);
for (int i = 1; i <= ID3.features[s]; i++) {
gain -= 1.0 * (count[i][0] + count[i][1])
/ (parentPos + parentNeg)
* impurityFunction.calc(count[i][0], count[i][1]);
}
if (gain > maxGain) { /*Finding attribute with maximum gain*/
maxGain = gain;
maxGainDecision = s;
for (int i = 0; i <= ID3.features[s]; i++) {
mcount[i][0] = count[i][0];
mcount[i][1] = count[i][1];
}
}
}
if (maxGain > 1e-10) {
node.testFts = maxGainDecision;
ArrayList<ArrayList<Instance>> ts = new ArrayList<ArrayList<Instance>>();
for (int i = 0; i <= ID3.features[maxGainDecision]; ++i) {
ts.add(new ArrayList<Instance>());
}
for (Instance t : node.instances)
{
if(t.fts[maxGainDecision]==0)
{
for(int i=1;i <= ID3.features[maxGainDecision];i++)
ts.get(i).add(t);
}
else {
ts.get(t.fts[maxGainDecision]).add(t);}
}
/* Grow the tree recursively */
for (int i = 1; i <= ID3.features[maxGainDecision]; i++) {
if (maxGainDecision == 16 && i == 2) {
int x = 0;
}
if (ts.get(i).size() > 0) {
node.children[i] = new Node(node, ts.get(i));
expand(node.children[i], impurityFunction, depth + 1);
}
}
}
}
public void learn(List<Instance> instances) {
this.root = generate(instances, ID3.impurity_entropy);
}
public List<Integer> classify(List<Instance> testInstances) {
List<Integer> predictions = new ArrayList<Integer>();
for (Instance t : testInstances) {
int predictedCategory = root.classify(t);
predictions.add(predictedCategory);
}
return predictions;
}
public static void load(String trainfile, String testfile,
List<Instance> trainInstances, List<Instance> testInstances) {
int UNIQEID = 0;
try {
BufferedReader br = new BufferedReader(new FileReader(trainfile));
String line;
while ((line = br.readLine()) != null) {
Instance ins = new Instance(line, UNIQEID++);
trainInstances.add(ins);
}
br.close();
} catch (Exception e) {
e.printStackTrace();
}
try {
BufferedReader br = new BufferedReader(new FileReader(testfile));
String line;
while ((line = br.readLine()) != null) {
Instance ins = new Instance(line, UNIQEID++);
testInstances.add(ins);
}
br.close();
} catch (Exception e) {
e.printStackTrace();
}
}
public static double computeAccuracy(List<Integer> predictions,
List<Instance> testInstances) {
if (predictions.size() != testInstances.size()) {
return 0;
} else {
int right = 0, wrong = 0;
for (int i = 0; i < predictions.size(); i++) {
if (predictions.get(i) == null) {
wrong++;
} else if (predictions.get(i) == testInstances.get(i).label) {
right++;
} else {
wrong++;
}
}
return right * 1.0 / (right + wrong);
}
}
/**Usage:
* javac ID3
* java ID3*/
public static void main(String[] args) {
long startTime = System.nanoTime();
Split sp=new Split();
ArrayList<Instance> trainInstances = new ArrayList<Instance>();
ArrayList<Instance> testInstances = new ArrayList<Instance>();
load("src/adult.txt", "src/test.txt", trainInstances,
testInstances);
{
ID3 id3 = new ID3();
id3.learn(trainInstances);
long endTime = System.nanoTime();
System.out.println("Learning time taken in seconds is\t"+ (endTime-startTime)/1000000000);
List<Integer> trainpredictions = id3.classify(trainInstances);
System.out.println("ID3 with full tree on training\t"
+ id3.computeAccuracy(trainpredictions, trainInstances)*100+" %");
List<Integer> predictions = id3.classify(testInstances);
System.out.println("Accuracy using ID3 on testdata is\t"
+ id3.computeAccuracy(predictions, testInstances)*100+" %");
}
}
}