public Node(string w, string T, double sc, double ps, Node prev)
{
word = w;
tag = T;
score = sc;
PathScore = ps;
PrevNode = prev;
}
static void Main(string[] args)
{
string testPath = args[0];
string BoundaryPath = args[1];
string ModelFile = args[2];
string OutFile = args[3];
double BeamSize = Convert.ToDouble(args[4]);
int TopN = Convert.ToInt32(args[5]);
int TopK = Convert.ToInt32(args[6]);
Dictionary<string, Dictionary<string, double>> ClassFeatureDict = new Dictionary<string, Dictionary<string, double>>();
List<String> TagList = new List<string>();
//Reads Model File and builds the required DS
ReadModelFile(ClassFeatureDict, ModelFile,TagList);
List<int> LineBoundaries = new List<int>();
ReadBoundaryFile(BoundaryPath, LineBoundaries);
Stopwatch stopwatch = Stopwatch.StartNew();
// we are reading test data from here on
string line, prevT="", prev2Tag="";
int docCount = 0,BoundaryValue=0,curWordNum=1;
double Correct = 0,totalLines=0;
double score = 0;
List<Node> Parent = new List<Node>();
StreamWriter Sw = new StreamWriter(OutFile);
Sw.WriteLine("%%%%% test data:");
using(StreamReader Sr = new StreamReader(testPath))
{
while ((line = Sr.ReadLine()) != null)
{
if (String.IsNullOrWhiteSpace(line))
continue;
//score refers to the prob value for this node. it has to be set to zero for every word
score = 0;
totalLines++;
List<Node> CurCandidates = new List<Node>();
if(curWordNum > BoundaryValue)
{
// this means we have moved to a new training instance
BoundaryValue = LineBoundaries[docCount++];
curWordNum = 1;
Node Root = new Node("BOS", "BOS", 1,0);
//parents from previous test instance can be cleared when we are in a new one
Parent.Clear();
Parent.Add(Root);
}
string[] words = line.Split(new string[] { " " }, StringSplitOptions.RemoveEmptyEntries);
List<String> Features = new List<string>();
string curWord = "",word="";
double maxScore = double.MinValue;
//TODO : indexed from 2 because we dont want the instance name and gold standard for now
for (int i = 2; i < words.Length; i++)
{
if (words[i].Contains("curW="))
{
curWord = words[i];
word = curWord.Substring(curWord.IndexOf("=")+1);
}
//TODO:Check if feature Value is zero
if (words[i].Trim() !="1")
Features.Add(words[i]);
}
curWord = curWord.Trim();
//this is for storign previous tags
List<string> AddFeatures = new List<string>();
List<String> ClassList = new List<string>();
foreach (var nd in Parent)
{
AddFeatures.Clear();
prevT = nd.tag;
if (nd.PrevNode != null)
prev2Tag = nd.PrevNode.tag;
else
prev2Tag = "BOS";
AddFeatures.Add("prevT=" + prevT);
AddFeatures.Add("prevTwoTags=" + prev2Tag + "+" + prevT);
//this loop handles topN condition
List<Node> CandForTopN = new List<Node>();
double normalizerPerParent = 0;
foreach (var classLabel in TagList)
{
score = 0;
var ClassLambda = ClassFeatureDict[classLabel];
//we are goign to get the value for this path
foreach (var feat in Features)
{
if (ClassLambda.ContainsKey(feat))
{
score += ClassLambda[feat];
}
}
// this is to account for prev tags
foreach (var feat in AddFeatures)
{
if (ClassLambda.ContainsKey(feat))
score += ClassLambda[feat];
}
score += ClassLambda["<default>"];
score = System.Math.Exp(score);
normalizerPerParent += score;
//we will update the pathScore later
Node Cand = new Node(word, classLabel, score,0,nd);
CandForTopN.Add(Cand);
}
CandForTopN = CandForTopN.OrderByDescending(x => x.score).Take(TopN).ToList();
foreach (var cand in CandForTopN)
{
cand.score /= normalizerPerParent;
}
foreach (var TopNNode in CandForTopN)
{
CurCandidates.Add(TopNNode);
}
}
foreach (var cd in CurCandidates)
{
//cd.score = cd.score / totalScoreAcrossAllPaths;
cd.PathScore = System.Math.Log10(cd.score) + (cd.PrevNode.PathScore);
//this will help in pruning by beam size
if (cd.PathScore > maxScore)
maxScore = cd.PathScore;
}
//update parents for the next word
Parent.Clear();
//Order by the whole path score and just take the topK candidate. We will prune further with beam.
CurCandidates = CurCandidates.OrderByDescending(x => x.PathScore).Take(TopK).ToList();
foreach (var cd in CurCandidates)
{
if(cd.PathScore == maxScore)
{
Sw.WriteLine(words[0]+" "+ words[1] +" " +cd.tag+" "+cd.score);
if (words[1] == cd.tag)
Correct++;
}
if (cd.PathScore + BeamSize >= maxScore)
Parent.Add(cd);
else
break;
}
curWordNum++;
}
}
stopwatch.Stop();
Console.WriteLine("Time elapsed: {0}", stopwatch.Elapsed);
Console.WriteLine("Accuracy : " + Convert.ToString(Correct/totalLines));
Sw.Close();
Console.ReadLine();
}