public void Setup(bool debug)
{
var readModel = new ReadModel(InputModelFile);
var temp = new ReadModel(string.Concat(InputModelFile, ".featuresToK"));
_weightVector = new WeightVector(temp.GetFeatureToKdDictionary());
foreach (var pair in readModel.ModelIterator())
{
_weightVector.Add(pair);
}
_tags = new Tags(_tagList);
_viterbiForGlobalLinearModel = new ViterbiForGlobalLinearModel(_weightVector, _tags);
// read input file in a class and per line iterator.
var inputData = new ReadInputData(InputTestFile);
var writeModel = new WriteModel(_outputTestFile);
foreach (var line in inputData.GetSentence())
{
List<string> debugList;
var outputTags = _viterbiForGlobalLinearModel.Decode(line, debug, out debugList);
if (debug)
{
writeModel.WriteDataWithTagDebug(line, outputTags, debugList);
}
else
{
writeModel.WriteDataWithTag(line, outputTags);
}
}
writeModel.Flush();
}
public string Evalulate(string keyFile, string devFile, string dumpFile)
{
var keyModel = new ReadModel(keyFile);
var devModel = new ReadModel(devFile);
var dumpOutputModel = new WriteModel(dumpFile);
var keyIter = keyModel.ModelIterator().GetEnumerator();
var devIter = devModel.ModelIterator().GetEnumerator();
float expected = 0;
float correct = 0;
float found = 0;
float line = 0;
string dump;
while (keyIter.MoveNext() && devIter.MoveNext())
{
var key = keyIter.Current;
var dev = devIter.Current;
line++;
if (!key.Key.Equals(dev.Key))
{
dump = "line: " + line + " " + key.Key + " doesn't match " + dev.Key+"\r\n";
dumpOutputModel.WriteLine(dump);
dumpOutputModel.Flush();
return dump;
}
if (key.Value.Contains("LOCATION"))
{
expected++;
if (!dev.Value.Contains("LOCATION")) continue;
found++;
correct++;
}
else if (dev.Value.Contains("LOCATION"))
{
found++;
}
}
dump = "found: " + found + " expected: " + expected + " correct: " + correct +"\r\n";
dumpOutputModel.WriteLine(dump);
float precision = correct/found;
float recall = correct/expected;
float f1Score = (2*precision*recall)/(precision + recall);
dump += "precision\t recall \t f1score\t\r\n";
dumpOutputModel.WriteLine("precision\t recall \t f1score\t");
dump += precision.ToString(CultureInfo.InvariantCulture) + "\t" +
recall.ToString(CultureInfo.InvariantCulture) + "\t" +
f1Score.ToString(CultureInfo.InvariantCulture) +"\r\n";
dumpOutputModel.WriteLine(precision.ToString(CultureInfo.InvariantCulture)+"\t"+
recall.ToString(CultureInfo.InvariantCulture)+ "\t" +
f1Score.ToString(CultureInfo.InvariantCulture));
dumpOutputModel.Flush();
return dump;
}
public void Init()
{
var readModel = new ReadModel(InputModelFile + ".preceptron");
var temp = new ReadModel(string.Concat(InputModelFile, ".featuresToK"));
var dict = temp.GetFeatureToKdDictionary();
_weightVector = new WeightVector(dict, dict.Count);
foreach (var pair in readModel.ModelIterator())
{
_weightVector.Add(pair);
}
_tags = new Tags(_tagList);
ViterbiForGLM = new ViterbiForGlobalLinearModel(_weightVector, _tags);
}