/// <summary>Builds a sigmoid model to turn the classifier outputs into probabilities.</summary>
private LinearClassifier <L, L> FitSigmoid(SVMLightClassifier <L, F> classifier, GeneralDataset <L, F> dataset)
{
RVFDataset <L, L> plattDataset = new RVFDataset <L, L>();
for (int i = 0; i < dataset.Size(); i++)
{
RVFDatum <L, F> d = dataset.GetRVFDatum(i);
ICounter <L> scores = classifier.ScoresOf((IDatum <L, F>)d);
scores.IncrementCount(null);
plattDataset.Add(new RVFDatum <L, L>(scores, d.Label()));
}
LinearClassifierFactory <L, L> factory = new LinearClassifierFactory <L, L>();
factory.SetPrior(new LogPrior(LogPrior.LogPriorType.Null));
return(factory.TrainClassifier(plattDataset));
}
public virtual SVMLightClassifier <L, F> TrainClassifierBasic(GeneralDataset <L, F> dataset)
{
IIndex <L> labelIndex = dataset.LabelIndex();
IIndex <F> featureIndex = dataset.featureIndex;
bool multiclass = (dataset.NumClasses() > 2);
try
{
// this is the file that the model will be saved to
File modelFile = File.CreateTempFile("svm-", ".model");
if (deleteTempFilesOnExit)
{
modelFile.DeleteOnExit();
}
// this is the file that the svm light formated dataset
// will be printed to
File dataFile = File.CreateTempFile("svm-", ".data");
if (deleteTempFilesOnExit)
{
dataFile.DeleteOnExit();
}
// print the dataset
PrintWriter pw = new PrintWriter(new FileWriter(dataFile));
dataset.PrintSVMLightFormat(pw);
pw.Close();
// -v 0 makes it not verbose
// -m 400 gives it a larger cache, for faster training
string cmd = (multiclass ? svmStructLearn : (useSVMPerf ? svmPerfLearn : svmLightLearn)) + " -v " + svmLightVerbosity + " -m 400 ";
// set the value of C if we have one specified
if (C > 0.0)
{
cmd = cmd + " -c " + C + " ";
}
else
{
// C value
if (useSVMPerf)
{
cmd = cmd + " -c " + 0.01 + " ";
}
}
//It's required to specify this parameter for SVM perf
// Alpha File
if (useAlphaFile)
{
File newAlphaFile = File.CreateTempFile("svm-", ".alphas");
if (deleteTempFilesOnExit)
{
newAlphaFile.DeleteOnExit();
}
cmd = cmd + " -a " + newAlphaFile.GetAbsolutePath();
if (alphaFile != null)
{
cmd = cmd + " -y " + alphaFile.GetAbsolutePath();
}
alphaFile = newAlphaFile;
}
// File and Model Data
cmd = cmd + " " + dataFile.GetAbsolutePath() + " " + modelFile.GetAbsolutePath();
if (verbose)
{
logger.Info("<< " + cmd + " >>");
}
/*Process p = Runtime.getRuntime().exec(cmd);
*
* p.waitFor();
*
* if (p.exitValue() != 0) throw new RuntimeException("Error Training SVM Light exit value: " + p.exitValue());
* p.destroy(); */
SystemUtils.Run(new ProcessBuilder(whitespacePattern.Split(cmd)), new PrintWriter(System.Console.Error), new PrintWriter(System.Console.Error));
if (doEval)
{
File predictFile = File.CreateTempFile("svm-", ".pred");
if (deleteTempFilesOnExit)
{
predictFile.DeleteOnExit();
}
string evalCmd = (multiclass ? svmStructClassify : (useSVMPerf ? svmPerfClassify : svmLightClassify)) + " " + dataFile.GetAbsolutePath() + " " + modelFile.GetAbsolutePath() + " " + predictFile.GetAbsolutePath();
if (verbose)
{
logger.Info("<< " + evalCmd + " >>");
}
SystemUtils.Run(new ProcessBuilder(whitespacePattern.Split(evalCmd)), new PrintWriter(System.Console.Error), new PrintWriter(System.Console.Error));
}
// read in the model file
Pair <double, ClassicCounter <int> > weightsAndThresh = ReadModel(modelFile, multiclass);
double threshold = weightsAndThresh.First();
ClassicCounter <Pair <F, L> > weights = ConvertWeights(weightsAndThresh.Second(), featureIndex, labelIndex, multiclass);
ClassicCounter <L> thresholds = new ClassicCounter <L>();
if (!multiclass)
{
thresholds.SetCount(labelIndex.Get(0), -threshold);
thresholds.SetCount(labelIndex.Get(1), threshold);
}
SVMLightClassifier <L, F> classifier = new SVMLightClassifier <L, F>(weights, thresholds);
if (doEval)
{
File predictFile = File.CreateTempFile("svm-", ".pred2");
if (deleteTempFilesOnExit)
{
predictFile.DeleteOnExit();
}
PrintWriter pw2 = new PrintWriter(predictFile);
NumberFormat nf = NumberFormat.GetNumberInstance();
nf.SetMaximumFractionDigits(5);
foreach (IDatum <L, F> datum in dataset)
{
ICounter <L> scores = classifier.ScoresOf(datum);
pw2.Println(Counters.ToString(scores, nf));
}
pw2.Close();
}
if (useSigmoid)
{
if (verbose)
{
System.Console.Out.Write("fitting sigmoid...");
}
classifier.SetPlatt(FitSigmoid(classifier, dataset));
if (verbose)
{
System.Console.Out.WriteLine("done");
}
}
return(classifier);
}
catch (Exception e)
{
throw new Exception(e);
}
}